Search results for: accumulated degree days

Authors: Merrick Lopez, Aashish Abraham, Melissa Egge, Marissa Hood, Jui Shah

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A 3 year old male with unknown medical history presented initially with encephalopathy, intubated for respiratory failure, and admitted to the pediatric intensive care unit (PICU) with refractory shock. During resuscitation in the emergency department, he was found to be in severe metabolic acidosis with a pH of 7.03 and escalated on vasopressor drips for hypotension. His initial sodium was 174. He was noted to have burn injuries to his scalp, forehead, right axilla, bilateral arm creases and lower legs. He had rhabdomyolysis (initial creatinine kinase 5,430 U/L with peak levels of 62,340 normal <335 U/L), cardiac injury (initial troponin 88 ng/L with peak at 145 ng/L, normal <15ng/L), hypernatremia (peak 174, normal 140), hypocalcemia, liver injury, acute kidney injury, and neuronal loss on magnetic resonance imaging (MRI). Soft restraints and a shock collar were found in the home. He was critically ill for 8 days, but was gradually weaned off drips, extubated, and started on feeds. Discussion Electrical injury, specifically lightning injury is an uncommon but devastating cause of injury in pediatric patients. This patient with suspected abusive use of a dog shock collar presented similar to a lightning strike. Common entrance points include the hands and head, similar to our patient with linear wounds on his forehead. When current enters, it passes through tissues with the least resistance. Nerves, blood vessels, and muscles, have high fluid and electrolyte content and are commonly affected. Exit points are extremities: our child who had circumferential burns around his arm creases and ankles. Linear burns preferentially follow areas of high sweat concentration, and are thought to be due to vaporization of water on the skin’s surface. The most common cause of death from a lightning strike is due to cardiopulmonary arrest. The massive depolarization of the myocardium can result in arrhythmias and myocardial necrosis. The patient presented in cardiogenic shock with evident cardiac damage. Electricity going through vessels can lead to vaporization of intravascular water. This can explain his severe hypernatremia. He also sustained other internal organ injuries (adrenal glands, pancreas, liver, and kidney). Electrical discharge also leads to direct skeletal muscle injury in addition to prolonged muscular spasm. Rhabdomyolysis, the acute damage of muscle, leads to release of potentially toxic components into the circulation which could lead to acute renal failure. The patient had severe rhabdomyolysis and renal injury. Early hypocalcemia has been consistently demonstrated in patients with rhabdomyolysis. This was present in the patient and led to increased vasopressor needs. Central nervous system injuries are also common which can include encephalopathy, hypoxic injury, and cerebral infarction. The patient had evidence of brain injury as seen on MRI. Conclusion Electrical injuries due to lightning strikes and abusive use of a dog shock collar are rare, but can both present in similar ways with respiratory failure, shock, hypernatremia, rhabdomyolysis, brain injury, and multiorgan damage. Although rare, it is essential for early identification and prompt management for acute and chronic complications in these children.

Keywords: cardiogenic shock, dog shock collar, lightning strike, rhabdomyolysis

Procedia PDF Downloads 86

Authors: Aastha Arora, Vikas Bhuria, Saurabh Singh, Uma Pathak, Shweta Mathur, Puja P. Hazari, Rajat Sandhir, Ravi Soni, Anant N. Bhatt, Bilikere S. Dwarakanath

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Radiotherapy is an effective curative and palliative option for patients with thoracic malignancies. However, lung injury, comprising of pneumonitis and fibrosis, remains a significant clin¬ical complication of thoracic radiation, thus making it a dose-limiting factor. Also, injury to the lung is often reported as part of multi-organ failure in victims of accidental radiation exposures. Radiation induced inflammatory response in the lung, characterized by leukocyte infiltration and vascular changes, is an important contributing factor for the injury. Therefore, countermeasure agents to attenuate radiation induced inflammatory response are considered as an important approach to prevent chronic lung damage. Although Amifostine, the widely used, FDA approved radio-protector, has been found to reduce the radiation induced pneumonitis during radiation therapy of non-small cell lung carcinoma, its application during mass and field exposure is limited due to associated toxicity and ineffectiveness with the oral administration. The amifostine analogue (DRDE-30) overcomes this limitation as it is orally effective in reducing the mortality of whole body irradiated mice. The current study was undertaken to investigate the potential of DRDE-30 to ameliorate radiation induced lung damage. DRDE-30 was administered intra-peritoneally, 30 minutes prior to 13.5 Gy thoracic (60Co-gamma) radiation in C57BL/6 mice. Broncheo- alveolar lavage fluid (BALF) and lung tissues were harvested at 12 and 24 weeks post irradiation for studying inflammatory and fibrotic markers. Lactate dehydrogenase (LDH) leakage, leukocyte count and protein content in BALF were used as parameters to evaluate lung vascular permeability. Inflammatory cell signaling (p38 phosphorylation) and anti-oxidant status (MnSOD and Catalase level) was assessed by Western blot, while X-ray CT scan, H & E staining and trichrome staining were done to study the lung architecture and collagen deposition. Irradiation of the lung increased the total protein content, LDH leakage and total leukocyte count in the BALF, reflecting endothelial barrier dysfunction. These disruptive effects were significantly abolished by DRDE-30, which appear to be linked to the DRDE-30 mediated abrogation of activation of the redox-sensitive pro- inflammatory signaling cascade, the MAPK pathway. Concurrent administration of DRDE-30 with radiation inhibited radiation-induced oxidative stress by strengthening the anti-oxidant defense system and abrogated p38 mitogen-activated protein kinase activation, which was associated with reduced vascular leak and macrophage recruitment to the lungs. Histopathological examination (by H & E staining) of the lung showed radiation-induced inflammation of the lungs, characterized by cellular infiltration, interstitial oedema, alveolar wall thickening, perivascular fibrosis and obstruction of alveolar spaces, which were all reduced by pre-administration of DRDE-30. Structural analysis with X-ray CT indicated lung architecture (linked to the degree of opacity) comparable to un-irradiated mice that correlated well with the lung morphology and reduced collagen deposition. Reduction in the radiation-induced inflammation and fibrosis brought about by DRDE-30 resulted in a profound increase in animal survival (72 % in the combination vs 24% with radiation) observed at the end of 24 weeks following irradiation. These findings establish the potential of the Amifostine analogue, DRDE-30, in reducing radiation induced pulmonary injury by attenuating the inflammatory and fibrotic responses.

Keywords: amifostine, fibrosis, inflammation, lung injury radiation

Procedia PDF Downloads 508

Authors: Elsie M. Nolte, Annie M. Joubert, Roy Lakier, Maryke Etsebeth, Jolene M. Helena, Marcel Verwey, Laurence Lafanechere, Anne E. Theron

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Treatment regimens for breast- and lung cancers may include both radiation- and chemotherapy. Ideally, a pharmaceutical agent which selectively sensitizes cancer cells to gamma (γ)-radiation would allow administration of lower doses of each modality, yielding synergistic anti-cancer benefits and lower metastasis occurrence, in addition to decreasing the side-effect profiles. A range of 2-methoxyestradiol (2-ME) analogues, namely 2-ethyl-3-O-sulphamoyl-estra-1,3,5 (10) 15-tetraene-3-ol-17one (ESE-15-one), 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10),15-tetraen-17-ol (ESE-15-ol) and 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10)16-tetraene (ESE-16) were in silico-designed by our laboratory, with the aim of improving the parent compound’s bioavailability in vivo. The main effect of these compounds is the disruption of microtubule dynamics with a resultant mitotic accumulation and induction of programmed cell death in various cancer cell lines. This in vitro study aimed to determine the cellular responses involved in the radiation sensitization effects of these analogues at low doses in breast- and lung cancer cell lines. The oestrogen receptor positive MCF-7-, oestrogen receptor negative MDA-MB-231- and triple negative BT-20 breast cancer cell lines as well as the A549 lung cancer cell line were used. The minimal compound- and radiation doses able to induce apoptosis were determined using annexin-V and cell cycle progression markers. These doses (cell line dependent) were used to pre-sensitize the cancer cells 24 hours prior to 6 gray (Gy) radiation. Experiments were conducted on samples exposed to the individual- as well as the combination treatment conditions in order to determine whether the combination treatment yielded an additive cell death response. Morphological studies included light-, fluorescence- and transmission electron microscopy. Apoptosis induction was determined by flow cytometry employing annexin V, cell cycle analysis, B-cell lymphoma 2 (Bcl-2) signalling, as well as reactive oxygen species (ROS) production. Clonogenic studies were performed by allowing colony formation for 10 days post radiation. Deoxyribonucleic acid (DNA) damage was quantified via γ-H2AX foci and micronuclei quantification. Amplification of the p53 signalling pathway was determined by western blot. Results indicated that exposing breast- and lung cancer cells to nanomolar concentrations of these analogues 24 hours prior to γ-radiation induced more cell death than the compound- and radiation treatments alone. Hypercondensed chromatin, decreased cell density, a damaged cytoskeleton and an increase in apoptotic body formation were observed in cells exposed to the combination treatment condition. An increased number of cells present in the sub-G1 phase as well as increased annexin-V staining, elevation of ROS formation and decreased Bcl-2 signalling confirmed the additive effect of the combination treatment. In addition, colony formation decreased significantly. p53 signalling pathways were significantly amplified in cells exposed to the analogues 24 hours prior to radiation, as was the amount of DNA damage. In conclusion, our results indicated that pre-treatment of breast- and lung cancer cells with low doses of 2-ME analogues sensitized breast- and lung cancer cells to γ-radiation and induced apoptosis more so than the individual treatments alone. Future studies will focus on the effect of the combination treatment on non-malignant cellular counterparts.

Keywords: cancer, microtubule dynamics, radiation therapy, radiosensitization

Procedia PDF Downloads 205

Authors: Sadaf Abbas, HimGauri Sabnis

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Introduction: Peripartum cardiomyopathy is a rare but potentially life-threatening condition that presents as heart failure during the last month of pregnancy or within five months postpartum. The incidence of postpartum cardiomyopathy ranges from 1 in 1300 to 1 in 15,000 pregnancies. Risk factors include multiparty, advanced maternal age, multiple pregnancies, pre-eclampsia, and chronic hypertension. Study: A 30-year-old Para3+0 presented to the Emergency Department of St’Marry Hospital, Isle of Wight, on the seventh day postpartum, with acute shortness of breath (SOB), chest pain, cough, and a temperature of 38 degrees. The risk factors were smoking and class II obesity (BMI of 40.62). The patient had mild pre-eclampsia in the last pregnancy and was on labetalol and aspirin during an antenatal period, which was stopped postnatally. There was also a history of pre-eclampsia and haemolysis, elevated liver enzymes, low platelets (HELLP syndrome) in previous pregnancies, which led to preterm delivery at 35 weeks in the second pregnancy, and the first baby was stillborn at 24 weeks. On assessment, there was a national early warning score (NEWS score) of 3, persistent tachycardia, and mild crepitation in the lungs. Initial investigations revealed an enlarged heart on chest X-ray, and a CT pulmonary angiogram indicated bilateral basal pulmonary congestion without pulmonary embolism, suggesting fluid overload. Laboratory results showed elevated CRP and normal troponin levels initially, which later increased, indicating myocardial involvement. Echocardiography revealed a severely dilated left ventricle with an ejection fraction (EF) of 31%, consistent with severely impaired systolic function. The cardiology team reviewed the patient and admitted to the Coronary Care Unit. As sign and symptoms were suggestive of fluid overload and congestive cardiac failure, management was done with diuretics, beta-blockers, angiotensin-converting enzyme inhibitors (ACE inhibitors), proton pump inhibitors, and supportive care. During admission, there was complications such as acute kidney injury, but then recovered well. Chest pain had resolved following the treatment. After being admitted for eight days, there was an improvement in the symptoms, and the patient was discharged home with a further plan of cardiac MRI and genetic testing due to a family history of sudden cardiac death. Regular appointment has been made with the Cardiology team to follow-up on the symptoms. Since discharge, the patient made a good recovery. A cardiac MRI was done, which showed severely impaired left ventricular function, ejection fraction (EF) of 38% with mild left ventricular dilatation, and no evidence of previous infarction. Overall appearance is of non-ischemic dilated cardiomyopathy. The main challenge at the time of admission was the non-availability of a cardiac radiology team, so the definitive diagnosis was delayed. The long-term implications include risk of recurrence, chronic heart failure, and, consequently, an effect on quality of life. Therefore, regular follow-up is critical in patient’s management. Conclusions: Peripartum cardiomyopathy is one of the cardiovascular diseases whose causes are still unknown yet and, in some cases, are uncontrolled. By raising awareness about the symptoms and management of this complication it will reduce morbidity and mortality rates and also the length of stay in the hospital.

Keywords: cardiomyopathy, cardiomegaly, pregnancy, puerperium

Procedia PDF Downloads 28

Authors: Abbas Ahmadi, Nasser Aliasgharzad, Ali Asghar Jafarzadeh

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Nowadays, drying of the Urmia Lake as a largest saline lake in the world and emerging its saline bed from water has caused the risk of salty dune storms, which threats the health of human society and also plants and animal communities living in the region. Biological soil crusts (BSCs) as a dust stabilizer attracted the attention of Soil conservation experts in recent years. Although the presence of water by the impenetrable lake bed and endorheic basin can be an advantage to create BSCs, but the extraordinary of the lake bed salinity is a factor for prevention of its establishment in the region. Therefore, the present research work has been carried out to investigate the effects of inoculating the Cyanobacteria, algae and their combination to create BSCs for dust control. In this study, an algae attributed to Chlamydomonas sp and a cyanobacteria attributed to Anabaena sp isolated from the soils of Urmia Lake margin were used to create BSC in four soil samples which collected from 0-10 cm of the current margin (A), the previous bed (B), affected lands by lake (C) and Quomtappe sand dune (D). The main characteristics of the A, B and C soil samples are their highly salinity (their ECe are 108, 140 and 118 dS/m, respectively) and sodicity. Also, texture class of the soil A was loamy sand, and other two soils had clay textures. Soil D was Non-saline, but it was sodic with a sandy texture class. This study was conducted separately in each soil in a completely randomized design under four inoculation treatments of non-inoculated (T0), Algae (T1), cyanobacteria (T2) and equal mixture of algae and cyanobacteria (T3) with three replications. In the experiment, the soil was placed into wind tunnel trays, and a suspension containing microorganisms mixed with the trays surface soil. During the experiment, water was sprayed to the trays at the morning and evening of every day. After passing the incubation period (30 days), some characteristics of samples such as pH, EC, cold water extractable carbohydrate (CWEC), hot water extractable carbohydrate (HWEC), sulfuric acid extractable carbohydrate (SAEC), organic matter, crust thickness, penetration resistance, wind erosion threshold velocity and soil loss in the wind tunnel were measured, and Correlation between the measured characteristics was obtained through the SPSS software. Analysis of variance and so comparison between the means of treatments were analyzed with MSTATC software. In this research, Chlorophyll, an amount, was used as an indicator of the microorganism's population in the samples. Based on obtained results, the amount of Chlorophyll a in the T2 treatment of soil A and all treatments of soil D was significantly increased in comparison to the control and crust thickness showed increase in all treatments by microorganism’s inoculation. But effect of the treatments was significant in soils A and D. At all treatment’s inoculation of microorganisms in soil A caused to increase %46, %34 and %55 of the wind erosion threshold velocity in T1, T2 and T3 treatments in comparison to the control, respectively, and in soil D all treatments caused wind erosion threshold velocity became two times more than control. However, soil loss in the wind tunnel experiments was significant in T2 and T3 treatments of these soils and T1 treatment had no effect in reducing soil loss. Correlation between Chlorophyll a and salinity shows the important role of salinity in microbial growth prevention and formation of BSCs in the studied samples. In general, according to the obtained results, it can be concluded that salinity reduces the growth of microorganisms in saline soils of the region, and in soils with fine textures, salinity role in prevention of the microbial growth is clear. Also, using the mix of algae and cyanobacteria together caused the synergistic growth of them and consequently, better protection of the soil against wind erosion was provided.

Keywords: wind erosion, algae, cyanobacteria, carbohydrate

Procedia PDF Downloads 61

Authors: Christina Kirsch, Adam Hatzigiannis

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Rail operators and maintainers worldwide are increasingly replacing walking patrols in the rail corridor with mechanized track patrols -essentially data capture on trains- and on-screen reviews of track infrastructure in centralized review facilities. The benefit is that infrastructure workers are less exposed to the dangers of the rail corridor. The impact is a significant change in work design from walking track sections and direct observation in the real world to sedentary jobs in the review facility reviewing captured data on screens. Defects in rail infrastructure can have catastrophic consequences. Reviewer performance regarding accuracy and efficiency of reviews within the available time frame is essential to ensure safety and operational performance. Rail operators must optimize workload and resource loading to transition to on-screen reviews successfully. Therefore, they need to know what workload assessment methodologies will provide reliable and valid data to optimize resourcing for on-screen reviews. This paper compares objective workload measures, including track difficulty ratings and review distance covered per hour, and subjective workload assessments (NASA TLX) and analyses the link between workload and reviewer performance, including sensitivity, precision, and overall accuracy. An experimental study was completed with eight on-screen reviewers, including infrastructure workers and engineers, reviewing track sections with different levels of track difficulty over nine days. Each day the reviewers completed four 90-minute sessions of on-screen inspection of the track infrastructure. Data regarding the speed of review (km/ hour), detected defects, false negatives, and false positives were collected. Additionally, all reviewers completed a subjective workload assessment (NASA TLX) after each 90-minute session and a short employee engagement survey at the end of the study period that captured impacts on job satisfaction and motivation. The results showed that objective measures for tracking difficulty align with subjective mental demand, temporal demand, effort, and frustration in the NASA TLX. Interestingly, review speed correlated with subjective assessments of physical and temporal demand, but to mental demand. Subjective performance ratings correlated with all accuracy measures and review speed. The results showed that subjective NASA TLX workload assessments accurately reflect objective workload. The analysis of the impact of workload on performance showed that subjective mental demand correlated with high precision -accurately detected defects, not false positives. Conversely, high temporal demand was negatively correlated with sensitivity and the percentage of detected existing defects. Review speed was significantly correlated with false negatives. With an increase in review speed, accuracy declined. On the other hand, review speed correlated with subjective performance assessments. Reviewers thought their performance was higher when they reviewed the track sections faster, despite the decline in accuracy. The study results were used to optimize resourcing and ensure that reviewers had enough time to review the allocated track sections to improve defect detection rates in accordance with the efficiency-thoroughness trade-off. Overall, the study showed the importance of a multi-method approach to workload assessment and optimization, combining subjective workload assessments with objective workload and performance measures to ensure that recommendations for work system optimization are evidence-based and reliable.

Keywords: automation, efficiency-thoroughness trade-off, human factors, job design, NASA TLX, performance optimization, subjective workload assessment, workload analysis

Procedia PDF Downloads 118

Authors: Sajeli A. Begum, Ameer Basha, Kirti Hira, Rukaiyya Khan

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Cyclic dipeptides are simple diketopiperazine derivatives being investigated by several scientists for their biological effects which include anticancer, antimicrobial, haematological, anticonvulsant, immunomodulatory effect, etc. They are potentially active microbial metabolites having been synthesized too, for developing into drug candidates. Cultures of Pseudomonas species have earlier been reported to produce cyclic dipeptides, helping in quorum sensing signals and bacterial–host colonization phenomena during infections, causing cell anti-proliferation and immunosuppression. Fluorescing Pseudomonas species have been identified to secrete lipid derivatives, peptides, pyrroles, phenazines, indoles, aminoacids, pterines, pseudomonic acids and some antibiotics. In the present work, results of investigation on the cyclic dipeptide metabolites secreted by the culture broth of Pseudomonas species as potent pro-inflammatory cytokine inhibitors are discussed. The bacterial strain was isolated from the rhizospheric soil of groundnut crop and identified as Pseudomonas aeruginosa by 16S rDNA sequence (GenBank Accession No. KT625586). Culture broth of this strain was prepared by inoculating into King’s B broth and incubating at 30 ºC for 7 days. The ethyl acetate extract of culture broth was prepared and lyophilized to get a dry residue (EEPA). Lipopolysaccharide (LPS)-induced ELISA assay proved the inhibition of tumor necrosis factor-alpha (TNF-α) secretion in culture supernatant of RAW 264.7 cells by EEPA (IC50 38.8 μg/mL). The effect of oral administration of EEPA on plasma TNF-α level in rats was tested by ELISA kit. The LPS mediated plasma TNF-α level was reduced to 45% with 125 mg/kg dose of EEPA. Isolation of the chemical constituents of EEPA through column chromatography yielded ten cyclic dipeptides, which were characterized using nuclear magnetic resonance and mass spectroscopic techniques. These cyclic dipeptides are biosynthesized in microorganisms by multifunctional assembly of non-ribosomal peptide synthases and cyclic dipeptide synthase. Cyclo (Gly-L-Pro) was found to be more potentially (IC50 value 4.5 μg/mL) inhibiting TNF-α production followed by cyclo (trans-4-hydroxy-L-Pro-L-Phe) (IC50 value 14.2 μg/mL) and the effect was equal to that of standard immunosuppressant drug, prednisolone. Further, the effect was analyzed by determining mRNA expression of TNF-α in LPS-stimulated RAW 264.7 macrophages using quantitative real-time reverse transcription polymerase chain reaction. EEPA and isolated cyclic dipeptides demonstrated diminution of TNF-α mRNA expression levels in a dose-dependent manner under the tested conditions. Also, they were found to control the expression of other pro-inflammatory cytokines like IL-1β and IL-6, when tested through their mRNA expression levels in LPS-stimulated RAW 264.7 macrophages under LPS-stimulated conditions. In addition, significant inhibition effect was found on Nitric oxide production. Further all the compounds exhibited weak toxicity to LPS-induced RAW 264.7 cells. Thus the outcome of the study disclosed the effectiveness of EEPA and the isolated cyclic dipeptides in down-regulating key cytokines involved in pathophysiology of autoimmune diseases.In another study led by the investigators, microbial cyclic dipeptides were found to exhibit excellent antimicrobial effect against Fusarium moniliforme which is an important causative agent of Sorghum grain mold disease. Thus, cyclic dipeptides are emerging small molecular drug candidates for various autoimmune diseases.

Keywords: cyclic dipeptides, cytokines, Fusarium moniliforme, Pseudomonas, TNF-alpha

Procedia PDF Downloads 210

Authors: Aisha Musaazi Sebunya Nakitto, Anika E. Wagner, Yusuf B. Byaruhanga, John H. Muyonga

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The prevalence and burden of diabetes are rapidly increasing globally, stemming from changes in lifestyle and dietary habits. Although several drugs are available to treat type 2 diabetes mellitus (T2DM), many are accompanied by several side effects and are often costly. Solanum anguivi Lam. fruits (SALF) are bitter berries that commonly grow in the wild and are traditionally cultivated by many globally as a remedy for T2DM. This effect is likely attributable to the presence of bioactive compounds such as phenolics, flavonoids, saponins, alkaloids, and vitamin C in SALF. In this study, we investigated the morphological characteristics of different SALF accessions and the effect of ripeness stages and thermal treatments on the bioactive compounds contents (BCC) and antioxidant activity (AA) of SALF accessions. Using the fruit fly Drosophila melanogaster (D. melanogaster) model, we explored the potential impact of dietary SALF in preventing and treating T2DM phenotypes. Morphological characterization was conducted based on descriptors of Solanum species. The BCC and AA of SALF at different ripeness stages (unripe, yellow, orange, and red) and after thermal treatments were determined using spectrophotometry, HPLC, and gravimetry. Male and female fruit flies were fed a high-sugar diet (HSD) to induce a T2DM-like phenotype, while control flies were fed on SY10 medium for up to 24 days. Experimental flies were exposed to HSD supplemented with 5 or 10 mg/ml SALF. The therapeutic and prevention effect of SALF in T2DM-like phenotype was investigated on weight, climbing activity, glucose and triglyceride contents, survival, and gene expression of PPARγ co-activator 1α fly homolog Srl and Drosophila insulin-like peptides. Methods in fly studies included Gustatory assay, Climbing assay, Glucose GOD-PAP assay, Triglyceride GPO-PAP assay, Roti-Quant®, and Real Time-PCR analysis. The ripeness stage significantly influenced SALF BCC and AA, and this was dependent on the accession. The unripe stage had the highest AA and total phenolics and flavonoids; the orange stage was rich in saponins, while the red stage had the highest alkaloid contents. Boiling and steaming increased the total phenolics and AA up to 4-fold and 3-fold, respectively. Drying at low temperatures resulted in higher phenolics and AA than the control. In the therapeutic model, the HSD-fed female flies exhibited elevated glucose levels, which exhibited a dose-dependent reduction upon exposure to a SALF-supplemented diet. Female flies fed on a SALF+ HSD exhibited a significant increase in survival compared to HSD-fed and control diet-fed flies. SALF supplementation did not alter the weights, fitness, and triglyceride levels of female flies in comparison with HSD-only-fed flies. The mRNA levels of Srl decreased in HSD-fed flies compared to the control-fed, with no effect observed in females exposed to HSD+SALF. Similarly, in the preventative model, the SALF diet resulted in higher survival of supplemented flies compared to controls. Consumption of boiled unripe SALF may result in the highest health benefits due to the high phenolic contents and antioxidant activity observed. Dietary intake of SALF significantly lowered glucose levels and increased survival of the D. melanogaster model. Additional studies in higher organisms are needed to explore the preventative and therapeutic potential of SALF in T2DM.

Keywords: antioxidant activity, bioactive compounds, bitter berries, Drosophila melanogaster, Solanum anguivi, type 2 diabetes mellitus, survival

Procedia PDF Downloads 28

Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

Procedia PDF Downloads 70

Authors: Dimitrios Tzilopoulos, Panagiotis Symeonidis, Michael Loufakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

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The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.).

Keywords: deep learning models, film industry, geospatial data management, location scouting

Procedia PDF Downloads 69

Authors: Zeynep Busra Velioglu, Deniz Pulat, Beril Demirbakan, Burak Ozcan, Ece Bayrak, Cevat Erisken

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Bone tissue has the ability to perform a wide array of functions including providing posture, load-bearing capacity, protection for the internal organs, initiating hematopoiesis, and maintaining the homeostasis of key electrolytes via calcium/phosphate ion storage. The most common cause for bone defects is extensive trauma and subsequent infection. Bone tissue has the self-healing capability without a scar tissue formation for the majority of the injuries. However, some may result with delayed union or fracture non-union. Such cases include reconstruction of large bone defects or cases of compromised regenerative process as a result of avascular necrosis and osteoporosis. Several surgical methods exist to treat bone defects, including Ilizarov method, Masquelete technique, growth factor stimulation, and bone replacement. Unfortunately, these are technically demanding and come with noteworthy disadvantages such as lengthy treatment duration, adverse effects on the patient’s psychology, repeated surgical procedures, and often long hospitalization times. These limitations associated with surgical techniques make bone substitutes an attractive alternative. Here, it was hypothesized that a 3D printed scaffold will mimic trabecular bone in terms of biomechanical properties and that such scaffolds will support cell attachment and survival. To test this hypothesis, this study aimed at fabricating poly(lactic acid), PLA, structures using 3D printing technology for trabecular bone defects, characterizing the scaffolds and comparing with bovine trabecular bone. Capacity of scaffolds on human bone marrow stem cell (hBMSC) attachment and survival was also evaluated. Cubes with a volume of 1 cm³ having pore sizes of 0.50, 1.00 and 1.25 mm were printed. The scaffolds/grafts were characterized in terms of porosity, contact angle, compressive mechanical properties as well cell response. Porosities of the 3D printed scaffolds were calculated based on apparent densities. For contact angles, 50 µl distilled water was dropped over the surface of scaffolds, and contact angles were measured using ‘Image J’ software. Mechanical characterization under compression was performed on scaffolds and native trabecular bone (bovine, 15 months) specimens using a universal testing machine at a rate of 0.5mm/min. hBMSCs were seeded onto the 3D printed scaffolds. After 3 days of incubation with fully supplemented Dulbecco’s modified Eagle’s medium, the cells were fixed using 2% formaldehyde and glutaraldehyde mixture. The specimens were then imaged under scanning electron microscopy. Cell proliferation was determined by using EZQuant dsDNA Quantitation kit. Fluorescence was measured using microplate reader Spectramax M2 at the excitation and emission wavelengths of 485nm and 535nm, respectively. Findings suggested that porosity of scaffolds with pore dimensions of 0.5mm, 1.0mm and 1.25mm were not affected by pore size, while contact angle and compressive modulus decreased with increasing pore size. Biomechanical characterization of trabecular bone yielded higher modulus values as compared to scaffolds with all pore sizes studied. Cells attached and survived in all surfaces, demonstrating higher proliferation on scaffolds with 1.25mm pores as compared with those of 1mm. Collectively, given lower mechanical properties of scaffolds as compared to native bone, and biocompatibility of the scaffolds, the 3D printed PLA scaffolds of this study appear as candidate substitutes for bone repair and regeneration.

Keywords: 3D printing, biomechanics, bone repair, stem cell

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Authors: Hilary Modir, Kyle Dutton, Michelle Swab, Shabnam Asghari

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Since its emergence, the cutaneous manifestations of COVID-19 have been documented in the literature. However, the majority are case reports with significant limitations in appraisal quality, thus leaving the role of dermatological manifestations of COVID-19 erroneously underexplored. The primary aim of this review was to systematically examine clinical patterns of dermatological manifestations as reported in the literature. This study was designed as a systematic review of case reports. The inclusion criteria consisted of all published reports and articles regarding COVID-19 in English, from September 1st, 2019, until June 22nd, 2020. The population consisted of confirmed cases of COVID-19 with associated cutaneous signs and symptoms. Exclusion criteria included research in planning stages, protocols, book reviews, news articles, review studies, and policy analyses. With the collaboration of a librarian, a search strategy was created consisting of a mixture of keyword terms and controlled vocabulary. Electronic databases searched were MEDLINE via PubMed, EMBASE, CINAHL, Web of Science, LILACS, PsycINFO, WHO Global Literature on Coronavirus Disease, Cochrane Library, Campbell Collaboration, Prospero, WHO International Clinical Trials Registry Platform, Australian and New Zealand Clinical Trials Registry, U.S. Institutes of Health Ongoing Trials Register, AAD Registry, OSF preprints, SSRN, MedRxiV and BioRxiV. The study selection featured an initial pre-screening of titles and abstracts by one independent reviewer. Results were verified by re-examining a random sample of 1% of excluded articles. Eligible studies progressed for full-text review by two calibrated independent reviewers. Covidence was used to store and extract data, such as citation information and findings pertaining to COVID-19 and cutaneous signs and symptoms. Data analysis and summarization methodology reflect the framework proposed by PRISMA and recommendations set out by Cochrane and Joanna Brigg’s Institute for conducting systematic reviews. The Oxford Centre for Evidence-Based Medicine’s level of evidence was used to appraise the quality of individual studies. The literature search revealed a total of 1221 articles. After the abstract and full-text screening, only 95 studies met the eligibility criteria, proceeding to data extraction. Studies were divided into 58% case reports and 42% series. A total of 833 manifestations were reported in 723 confirmed COVID-19 cases. The most frequent lesions were 23% maculopapular, 15% urticarial and 13% pseudo-chilblains, with 46% of lesions reporting pruritus, 16% erythema, 14% pain, 12% burning sensation, and 4% edema. The most common lesion locations were 20% trunk, 19.5% lower limbs, and 17.7% upper limbs. The time to resolution of lesions was between one and twenty-one days. In conclusion, over half of the reported cutaneous presentations in COVID-19 positive patients were maculopapular, urticarial and pseudo-chilblains, with the majority of lesions distributed to the extremities and trunk. As this review’s sample size only contained COVID-19 confirmed cases with skin presentations, it becomes difficult to deduce the direct relationship between skin findings and COVID-19. However, it can be correlated that acute onset of skin lesions, such as chilblains-like, may be associated with or may warrant consideration of COVID-19 as part of the differential diagnosis.

Keywords: COVID-19, cutaneous manifestations, cutaneous signs, general dermatology, medical dermatology, Sars-Cov-2, skin and infectious disease, skin findings, skin manifestations

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Authors: Liane J. Ioannou, Jonathan Serpell, Cino Bendinelli, David Walters, Jenny Gough, Dean Lisewski, Win Meyer-Rochow, Julie Miller, Duncan Topliss, Bill Fleming, Stephen Farrell, Andrew Kiu, James Kollias, Mark Sywak, Adam Aniss, Linda Fenton, Danielle Ghusn, Simon Harper, Aleksandra Popadich, Kate Stringer, David Watters, Susannah Ahern

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BACKGROUND: There are significant variations in the management, treatment and outcomes of thyroid cancer, particularly in the role of: diagnostic investigation and pre-treatment scanning; optimal extent of surgery (total or hemi-thyroidectomy); use of active surveillance for small low-risk cancers; central lymph node dissections (therapeutic or prophylactic); outcomes following surgery (e.g. recurrent laryngeal nerve palsy, hypocalcaemia, hypoparathyroidism); post-surgical hormone, calcium and vitamin D therapy; and provision and dosage of radioactive iodine treatment. A proven strategy to reduce variations in the outcome and to improve survival is to measure and compare it using high-quality clinical registry data. Clinical registries provide the most effective means of collecting high-quality data and are a tool for quality improvement. Where they have been introduced at a state or national level, registries have become one of the most clinically valued tools for quality improvement. To benchmark clinical care, clinical quality registries require systematic measurement at predefined intervals and the capacity to report back information to participating clinical units. OBJECTIVE: The aim of this study was to develop a core set clinical indicators that enable measurement and reporting of quality of care for patients with thyroid cancer. We hypothesise that measuring clinical quality indicators, developed to identify differences in quality of care across sites, will reduce variation and improve patient outcomes and survival, thereby lessening costs and healthcare burden to the Australian community. METHOD: Preparatory work and scoping was conducted to identify existing high quality, clinical guidelines and best practice for thyroid cancer both nationally and internationally, as well as relevant literature. A bi-national panel was invited to participate in a modified Delphi process. Panelists were asked to rate each proposed indicator on a Likert scale of 1–9 in a three-round iterative process. RESULTS: A total of 236 potential quality indicators were identified. One hundred and ninety-two indicators were removed to reflect the data capture by the Australian and New Zealand Thyroid Cancer Registry (ANZTCR) (from diagnosis to 90-days post-surgery). The remaining 44 indicators were presented to the panelists for voting. A further 21 indicators were later added by the panelists bringing the total potential quality indicators to 65. Of these, 21 were considered the most important and feasible indicators to measure quality of care in thyroid cancer, of which 12 were recommended for inclusion in the final set. The consensus indicator set spans the spectrum of care, including: preoperative; surgery; surgical complications; staging and post-surgical treatment planning; and post-surgical treatment. CONCLUSIONS: This study provides a core set of quality indicators to measure quality of care in thyroid cancer. This indicator set can be applied as a tool for internal quality improvement, comparative quality reporting, public reporting and research. Inclusion of these quality indicators into monitoring databases such as clinical quality registries will enable opportunities for benchmarking and feedback on best practice care to clinicians involved in the management of thyroid cancer.

Keywords: clinical registry, Delphi survey, quality indicators, quality of care

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Authors: Lauren B. Birney

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The envisioned long-term outcome of this three-year research, and implementation plan is for 1) teachers and students to design and build their own computational models of real-world environmental-human health phenomena occurring within the context of the “Human Harbor” and 2) project researchers to evaluate the degree to which these integrated Computer Science (CS) education experiences in New York City (NYC) public school classrooms (PreK-12) impact students’ computational-technical skill development, job readiness, career motivations, and measurable abilities to understand, articulate, and solve the underlying phenomena at the center of their models. This effort builds on the partnership’s successes over the past eight years in developing a benchmark Model of restoration-based Science, Technology, Engineering, and Math (STEM) education for urban public schools and achieving relatively broad-based implementation in the nation’s largest public school system. The Billion Oyster Project Curriculum and Community Enterprise for Restoration Science (BOP-CCERS STEM + Computing) curriculum, teacher professional developments, and community engagement programs have reached more than 200 educators and 11,000 students at 124 schools, with 84 waterfront locations and Out of School of Time (OST) programs. The BOP-CCERS Partnership is poised to develop a more refined focus on integrating computer science across the STEM domains; teaching industry-aligned computational methods and tools; and explicitly preparing students from the city’s most under-resourced and underrepresented communities for upwardly mobile careers in NYC’s ever-expanding “digital economy,” in which jobs require computational thinking and an increasing percentage require discreet computer science technical skills. Project Objectives include the following: 1. Computational Thinking (CT) Integration: Integrate computational thinking core practices across existing middle/high school BOP-CCERS STEM curriculum as a means of scaffolding toward long term computer science and computational modeling outcomes. 2. Data Science and Data Analytics: Enabling Researchers to perform interviews with Teachers, students, community members, partners, stakeholders, and Science, Technology, Engineering, and Mathematics (STEM) industry Professionals. Collaborative analysis and data collection were also performed. As a centerpiece, the BOP-CCERS partnership will expand to include a dedicated computer science education partner. New York City Department of Education (NYCDOE), Computer Science for All (CS4ALL) NYC will serve as the dedicated Computer Science (CS) lead, advising the consortium on integration and curriculum development, working in tandem. The BOP-CCERS Model™ also validates that with appropriate application of technical infrastructure, intensive teacher professional developments, and curricular scaffolding, socially connected science learning can be mainstreamed in the nation’s largest urban public school system. This is evidenced and substantiated in the initial phases of BOP-CCERS™. The BOP-CCERS™ student curriculum and teacher professional development have been implemented in approximately 24% of NYC public middle schools, reaching more than 250 educators and 11,000 students directly. BOP-CCERS™ is a fully scalable and transferable educational model, adaptable to all American school districts. In all settings of the proposed Phase IV initiative, the primary beneficiary group will be underrepresented NYC public school students who live in high-poverty neighborhoods and are traditionally underrepresented in the STEM fields, including African Americans, Latinos, English language learners, and children from economically disadvantaged households. In particular, BOP-CCERS Phase IV will explicitly prepare underrepresented students for skilled positions within New York City’s expanding digital economy, computer science, computational information systems, and innovative technology sectors.

Keywords: computer science, data science, equity, diversity and inclusion, STEM education

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Yimer Mohammed Assen, Abiyot Legesse Kura, Engida Esyas Dube, Asebe Regassa Debelo, Girma Kelboro Mensuro, Lete Bekele Gure

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Climate change has posed severe threats to many cultural landscapes of UNESCO world heritage sites recently. The UNESCO State of Conservation (SOC) reports categorized flooding, temperature increment, and drought as threats to cultural landscapes. This study aimed to examine variations and trends of rainfall and temperature extreme events and their threats to the UNESCO-designated Konso Cultural Landscape in southern Ethiopia. The study used dense merged satellite-gauge station rainfall data (1981-2020) with spatial resolution of 4km by 4km and observed maximum and minimum temperature data (1987-2020). Qualitative data were also gathered from cultural leaders, local administrators, and religious leaders using structured interview checklists. The spatial patterns, coefficient of variation, standardized anomalies, trends, and magnitude of change of rainfall and temperature extreme events both at annual and seasonal levels were computed using the Mann-Kendall trend test and Sen’s slope estimator under the CDT package. The standard precipitation index (SPI) was also used to calculate drought severity, frequency, and trend maps. The data gathered from key informant interviews and focus group discussions were coded and analyzed thematically to complement statistical findings. Thematic areas that explain the impacts of extreme events on the cultural landscape were chosen for coding. The thematic analysis was conducted using Nvivo software. The findings revealed that rainfall was highly variable and unpredictable, resulting in extreme drought and flood. There were significant (P<0.05) increasing trends of heavy rainfall (R10mm and R20mm) and the total amount of rain on wet days (PRCPTOT), which might have resulted in flooding. The study also confirmed that absolute temperature extreme indices (TXx, TXn, and TNx) and the percentile-based temperature extreme indices (TX90p, TN90p, TX10p, and TN10P) showed significant (P<0.05) increasing trends which are signals for warming of the study area. The results revealed that the frequency as well as the severity of drought at 3-months (katana/hageya seasons) was more pronounced than the 12-months (annual) time scale. The highest number of droughts in 100 years is projected at a 3-months timescale across the study area. The findings also showed that frequent drought has led to loss of grasses which are used for making traditional individual houses and multipurpose communal houses (pafta), food insecurity, migration, loss of biodiversity, and commodification of stones from terrace. On the other hand, the increasing trends of rainfall extreme indices resulted in destruction of terraces, soil erosion, loss of life and damage of properties. The study shows that a persistent decline in farmland productivity, due to erratic and extreme rainfall and frequent drought occurrences, forced the local people to participate in non-farm activities and retreat from daily preservation and management of their landscape. Overall, the increasing rainfall and temperature extremes coupled with prevalence of drought are thought to have an impact on the sustainability of cultural landscape through disrupting the ecosystem services and livelihood of the community. Therefore, more localized adaptation and mitigation strategies to the changing climate are needed to maintain the sustainability of Konso cultural landscapes as a global cultural treasure and to strengthen the resilience of smallholder farmers.

Keywords: adaptation, cultural landscape, drought, extremes indices

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Anisa Suraya Ab Razak, Izza Hayat

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Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: M. H. Pazandeh, M. Doudi, S. Barahimi, L. Rahimzadeh Torabi

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The use of antibiotics is essential in reducing the occurrence of adverse effects and inhibiting the emergence of antibiotic resistance in microbial populations. The necessity for a novel methodology concerning local administration of antibiotics has arisen, with particular focus on dealing with localized infections prompted by bacterial colonization of medical devices or implant materials. Bioactive glasses (BG) are extensively employed in the field of regenerative medicine, encompassing a diverse range of materials utilized for drug delivery systems. In the present investigation, various drug carriers for imipenem and tetracycline, namely single systems BG/SnO2, BG/NiO with varying proportions of metal oxide, and nanocomposite BG/SnO2/NiO, were synthesized through the sol-gel technique. The antibacterial efficacy of the synthesized samples was assessed through the utilization of the disk diffusion method with the aim of neutralizing Staphylococcus aureus as the bacterial model. The current study involved the examination of the bioactivity of two samples, namely BG10SnO2/10NiO and BG20SnO2, which were chosen based on their heightened bacterial inactivation properties. This evaluation entailed the employment of two techniques: the measurement of the pH of simulated body fluid (SBF) solution and the analysis of the sample tablets through X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. The sample tablets were submerged in SBF for varying durations of 7, 14, and 28 days. The bioactivity of the composite bioactive glass sample was assessed through characterization of alterations in its surface morphology, structure, and chemical composition. This evaluation was performed using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, and X-ray diffraction spectroscopy. Subsequently, the sample was immersed in simulated liquids to simulate its behavior in biological environments. The specific body fat percentage (SBF) was assessed over a 28-day period. The confirmation of the formation of a hydroxyapatite surface layer serves as a distinct indicator of bioactivity. The infusion of antibiotics into the composite bioactive glass specimen was done separately, and then the release kinetics of tetracycline and imipenem were tested in simulated body fluid (SBF). Antimicrobial effectiveness against various bacterial strains have been proven in numerous instances using both melt and sol-gel techniques to create multiple bioactive glass compositions. An elevated concentration of calcium ions within a solution has been observed to cause an increase in the pH level. In aqueous suspensions, bioactive glass particles manifest a significant antimicrobial impact. The composite bioactive glass specimen exhibits a gradual and uninterrupted release, which is highly desirable for a drug delivery system over a span of 72 hours. The reduction in absorption, which signals the loss of a portion of the antibiotic during the loading process from the initial phosphate-buffered saline solution, indicates the successful bonding of the two antibiotics to the surfaces of the bioactive glass samples. The sample denoted as BG/10SnO2/10NiO exhibits a higher loading of particles compared to the sample designated as BG/20SnO2 in the context of bioactive glass. The enriched sample demonstrates a heightened bactericidal impact on the bacteria under investigation while concurrently preserving its antibacterial characteristics. Tailored bioactive glass that incorporates hydroxyapatite, with a regulated and efficient release of drugs targeting bacterial infections, holds promise as a potential framework for bone implant scaffolds following rigorous clinical evaluation, thereby establishing potential future biomedical uses. During the modification process, the introduction of metal oxides into bioactive glass resulted in improved antibacterial characteristics, particularly in the composite bioactive glass sample that displayed the highest level of efficiency.

Keywords: antibacterial, bioactive glasses, implant infections, multi drug resistant

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