Search results for: drug prediction

Authors: Junfeng Yan, Wenkui Hou

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Prognostics and Health Management (PHM) is a technology to monitor the equipment status and predict impending faults. It is used to predict the potential fault and provide fault information and track trends of system degradation by capturing characteristics signals. So how to detect characteristics signals is very important. The select of test point plays a very important role in detecting characteristics signal. Traditionally, we use dependency model to select the test point containing the most detecting information. But, facing the large complicated system, the dependency model is not built so easily sometimes and the greater trouble is how to calculate the matrix. Rely on this premise, the paper provide a highly effective method to select test point without dependency model. Because signal flow model is a diagnosis model based on failure mode, which focuses on system’s failure mode and the dependency relationship between the test points and faults. In the signal flow model, a fault information can flow from the beginning to the end. According to the signal flow model, we can find out location and structure information of every test point and module. We break the signal flow model up into serial and parallel parts to obtain the final relationship function between the system’s testability or prediction metrics and test points. Further, through the partial derivatives operation, we can obtain every test point’s importance degree in determining the testability metrics, such as undetected rate, false alarm rate, untrusted rate. This contributes to installing the test point according to the real requirement and also provides a solid foundation for the Prognostics and Health Management. According to the real effect of the practical engineering application, the method is very efficient.

Keywords: false alarm rate, importance degree, signal flow model, undetected rate, untrusted rate

Procedia PDF Downloads 377

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad

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Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study shows that modified equation has good agreement with experimental data.

Keywords: equation of state, modification, ammonia, genetic algorithm

Procedia PDF Downloads 382

Authors: Sungmin Park, Gyungmok Nam, Seungpyo Woo, Young Choi, Sangheon Park, Sang-Hee Yoon

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Puncturing human skin with microneedles is critically important for microneedle-mediate drug delivery. Despite of extensive efforts in the past decades, the scale-up fabrication of sharp-tipped and high-aspect-ratio microneedles, especially made of biodegradable polymers, is still a long way off. Here, we present a UV dose insensitive and cost-effective microfabrication method for the biodegradable polymer microneedles with sharp tips and long lengths which can pierce human skin with low insertion force. The biodegradable polymer microneedles are fabricated with the polymer solution casting where a poly(lactic-co-glycolic acid) (PLGA, 50:50) solution is coated onto a SU-8 mold prepared with a reverse, ramped, and rotational (R3) UV exposure. The R3 UV exposure is modified from the multidirectional UV exposure both to suppress UV reflection from the bottom surface without anti-reflection layers and to optimize solvent concentration in the SU-8 photoresist, therefore achieving robust (i.e., highly insensitive to UV dose) and cost-effective fabrication of biodegradable polymer microneedles. An optical model for describing the spatial distribution of UV irradiation dose of the R3 UV exposure is also developed to theoretically predict the microneedle geometry fabricated with the R3 UV exposure and also to demonstrate the insensitiveness of microneedle geometry to UV dose. In the experimental characterization, the microneedles fabricated with the R3 UV exposure are compared with those fabricated with a conventional method (i.e., multidirectional UV exposure). The R3 UV exposure-based microfabrication reduces the end-tip radius by a factor of 5.8 and the deviation from ideal aspect ratio by 74.8%, compared with conventional method-based microfabrication. The PLGA microneedles fabricated with the R3 UV exposure pierce full-thickness porcine skins successfully and are demonstrated to completely dissolve in PBS (phosphate-buffered saline). The findings of this study will lead to an explosive growth of the microneedle-mediated drug delivery market.

Keywords: R³ UV exposure, optical model, UV dose, reflection, solvent concentration, biodegradable polymer microneedle

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Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

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Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

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Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

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Authors: Sanela Slavkovic, Congor Nad, Spela Golubovic

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Background: The diagnosis of multiple sclerosis (MS) is a major life challenge and has repercussions on all aspects of the daily functioning of those attained by it – personal activities, social participation, and quality of life. Regular follow-up of only the neurological status is not informative enough so that it could provide data on the sort of support and rehabilitation that is required. Objective: The aim of this study was to establish the current level of functioning of persons attained by MS and the factors that influence it. Methods: The study was conducted in Serbia, on a sample of 108 persons with relapse-remitting form of MS, aged 20 to 53 (mean 39.86 years; SD 8.20 years). All participants were fully ambulatory. Methods applied in the study include Expanded Disability Status Scale-EDSS and World Health Organization Disability Assessment Schedule, WHODAS 2.0 (36-item version, self-administered). Results: Participants were found to experience the most problems in the domains of Participation, Mobility, Life activities and Cognition. The least difficulties were found in the domain of Self-care. Symptom duration was the only control variable with a significant partial contribution to the prediction of the WHODAS scale score (β=0.30, p < 0.05). The total EDSS score correlated with the total WHODAS 2.0 score (r=0.34, p=0.00). Statistically significant differences in the domain of EDSS 0-5.5 were found within categories (0-1.5; 2-3.5; 4-5.5). The more pronounced a participant’s EDSS score was, although not indicative of large changes in the neurological status, the more apparent the changes in the functional domain, i.e. in all areas covered by WHODAS 2.0. Pyramidal (β=0.34, p < 0.05) and Bowel and bladder (β=0.24, p < 0.05) functional systems were found to have a significant partial contribution to the prediction of the WHODAS score. Conclusion: Measuring functioning and disability is important in the follow-up of persons suffering from MS in order to plan rehabilitation and define areas in which additional support is needed.

Keywords: disability, functionality, multiple sclerosis, rehabilitation

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Authors: Ruth Goldschmidt, Vyacheslav Kalchenko, Lilah Agemy, Rachel Elmoalem, Avigdor Scherz

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Vascular Targeted Photodynamic therapy (VTP) is a new modality for selective cancer treatment that leads to the complete tumor ablation. A photosensitizer, a bacteriochlorophyll derivative in our case, is first administered to the patient and followed by the illumination of the tumor area, by a near-IR laser for its photoactivation. The photoactivated drug releases reactive oxygen species (ROS) in the circulation, which reacts with blood cells and the endothelium leading to the occlusion of the blood vasculature. If the blood vessels are only partially closed, the tumor may recover, and cancer cells could survive. On the other hand, excessive treatment may lead to toxicity of healthy tissues nearby. Simultaneous VTP monitoring and image processing independent of the photoexcitation laser has not yet been reported, to our knowledge. Here we present a method for blood flow monitoring, using a real-time laser speckle imaging (RTLSI) in the tumor during VTP. We have synthesized over the years a library of bacteriochlorophyll derivatives, among them WST11 and STL-6014. Both are water soluble derivatives that are retained in the blood vasculature through their partial binding to HSA. WST11 has been approved in Mexico for VTP treatment of prostate cancer at a certain drug dose, and time/intensity of illumination. Application to other bacteriochlorophyll derivatives or other cancers may require different treatment parameters (such as light/drug administration). VTP parameters for STL-6014 are still under study. This new derivative mainly differs from WST11 by its lack of the central Palladium, and its conjugation to an Arg-Gly-Asp (RGD) sequence. RGD is a tumor-specific ligand that is used for targeting the necrotic tumor domains through its affinity to αVβ3 integrin receptors. This enables the study of cell-targeted VTP. We developed a special RTLSI module, based on Labview software environment for data processing. The new module enables to acquire raw laser speckle images and calculate the values of the laser temporal statistics of time-integrated speckles in real time, without additional off-line processing. Using RTLSI, we could monitor the tumor’s blood flow following VTP in a CT26 colon carcinoma ear model. VTP with WST11 induced an immediate slow down of the blood flow within the tumor and a complete final flow arrest, after some sporadic reperfusions. If the irradiation continued further, the blood flow stopped also in the blood vessels of the surrounding healthy tissue. This emphasizes the significance of light dose control. Using our RTLSI system, we could prevent any additional healthy tissue damage by controlling the illumination time and restrict blood flow arrest within the tumor only. In addition, we found that VTP with STL-6014 was the most effective when the photoactivation was conducted 4h post-injection, in terms of tumor ablation success in-vivo and blood vessel flow arrest. In conclusion, RTSLI application should allow to optimize VTP efficacy vs. toxicity in both the preclinical and clinical arenas.

Keywords: blood vessel occlusion, cancer treatment, photodynamic therapy, real time imaging

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

Procedia PDF Downloads 130

Authors: Xiaomin Zhong, Alexander Pate, Ya-Ting Yang, Ali Fahmi, Darren M. Ashcroft, Ben Goldacre, Brian Mackenna, Amir Mehrkar, Sebastian C. J. Bacon, Jon Massey, Louis Fisher, Peter Inglesby, Kieran Hand, Tjeerd van Staa, Victoria Palin

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Background: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. Methods: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted the patient’s probability of receiving an inappropriate antibiotic type or repeating the antibiotic course for each common infection. Findings: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same-day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%), and 8.6% had a potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the ten risk prediction models, good levels of calibration and moderate levels of discrimination were found. Important predictors included age, prior antibiotic prescribing, and region. Patients varied in their predicted risks. For sore throat, the range from 2.5 to 97.5th percentile was 2.7 to 23.5% (inappropriate type) and 6.0 to 27.2% (repeat prescription). For otitis externa, these numbers were 25.9 to 63.9% and 8.5 to 37.1%, respectively. Interpretation: Our study found no evidence of changes in the level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

Keywords: antibiotics, infection, COVID-19 pandemic, antibiotic stewardship, primary care

Procedia PDF Downloads 120

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Zakieh Rostamzadeh, Zahra Shirmohammadi

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Human cytomegalovirus is a species of the cytomegalovirus family of viruses, which in turn is a member of the viral family known as herpesviridae or herpesviruses. Although they may be found throughout the body, HCMV infections are frequently associated with the salivary glands. HCMV infection is typically unnoticed in healthy people, but can be life-threatening for the immunocompromised such as HIV-infected persons, organ transplant recipients, or newborn infants. After infection, HCMV has an ability to remain latent within the body over long periods. Cytomegalovirus (CMV) causes infection in immunocompromised, hemophilia patients and those who received blood transfusion frequently. This study aimed at determining the prevalence of cytomegalovirus (CMV) antibodies in hemophilia patients. Materials and Methods: A retrospective observational study was carried out in Urmia, North West of Iran. The study population comprised a sample of 50 hemophilic patients born after 1985 and have received blood factors in West Azerbaijan. The exclusion criteria include: drug abusing, high risk sexual contacts, vertical transmission of mother to fetus and suspicious needling. All samples were evaluated with the method of ELISA, with a certain kind of kit and by a certain laboratory. Results: Fifty hemophiliacs from 250 patients registered with Urmia Hemophilia Society were enrolled in the study including 43 (86%) male, and 7 (14%) female. The mean age of patients was 10.3 years, range 3 to 25 years. None of patients had risk factors mentioned above. Among our studied population, 34(68%) had hemophilia A, 1 (2%) hemophilia B, 8 (16%) VWF, 3(6%) factor VII deficiency, 1 (2%) factor V deficiency, 1 (2%) factor X deficiency, 1 (2%). Sera of 50 Hemodialysis patients were investigated for CMV-specific immunoglobulin G (IgG) and IgM. % 91.89 patients were anti-CMV IgG positive and %40.54 was seropositive for anti-CMV IgM. 37.8% patient had serological evidence of reactivation and 2.7% of patients had the primary infection. Discussion: There was no relationship between the antibody titer and: drug abusing, high risk sexual contacts, vertical transmission of mother to fetus and suspicious needling.

Keywords: bioinformatics, biomedicine, cytomegalovirus, immunocompromise

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar

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In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.

Keywords: early stage prediction, heart rate variability, linear and non-linear analysis, sudden cardiac death

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Authors: Sunitha Sampathi, Pankaj Kumar Tiriya, Sonia Gera, Sravanthi Reddy Pailla, V. Likhitha, A. J. Maruthi

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Surgical site infections (SSIs) are the most common nosocomial infections localized at the incision site. With an estimated 27 million surgical procedures each year in USA, approximately 2-5% rate of SSIs are predicted to occur annually. SSIs are treated with antibiotic medication. Current trend suggest that the direct drug delivery from the suture to the scared tissue can improve patient comfort and wound recovery. For that reason coating the surface of the medical device such as suture and catguts with broad spectrum antibiotics can prevent the formation of bactierial colonies with out comprimising the mechanical properties of the sutures.Hence, the present study was aimed to develop and evaluate a surgical suture coated with an antibiotic Ciprofloxacin hydrochloride loaded on gold nanoparticles. Gold nanoparticles were synthesized by chemical reduction method and conjugated with ciprofloxacin using Polyvinylpyrolidone as stabilizer and gold as carrier. Ciprofloxacin conjugated gold nanoparticles were coated over an absorbable surgical suture made of Polyglactan using sodium alginate as an immobilising agent by slurry dipping technique. The average particle size and Polydispersity Index of drug conjugated gold NPs were found to be 129±2.35 nm and 0.243±0.36 respectively. Gold nanoparticles are characterized by UV-Vis absorption spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), Scanning electron microscopy and Transmission electron microscopy. FT-IR revealed that there is no chemical interaction between drug and polymer. Antimicrobial activity for coated sutures was evaluated by disc diffusion method on culture plates of both gram negative (E-coli) and gram positive bacteria (Staphylococcus aureus) and results found to be satisfactory. In vivo studies for coated sutures was performed on Swiss albino mice and histological evaluation of intestinal wound healing parameters such as wound edges in mucosa, muscularis, presence of necrosis, exudates, granulation tissue, granulocytes, macrophages, restoration, and repair of mucosal epithelium and muscularis propria on day 7 after surgery were studied. The control animal group, sutured with plain suture (uncoated suture) showed signs of restoration and repair, but presence of necrosis, heamorraghic infiltration and granulation tissue was still noticed. Whereas the animal group treated with ciprofloxacin and ciprofloxacin gold nanoparticle coated sutures has shown promising decrease in terms of haemorraghic infiltration, granulation tissue, necrosis and better repaired muscularis layers on comparision with plain coated sutures indicating faster rate of repair and less chance of sepsis. Hence coating of sutures with broad spectrum antibiotics can be an alternate technique to reduce SSIs.

Keywords: ciprofloxacin hydrochloride, gold nanoparticles, surgical site infections, sutures

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Authors: Mahsa Ahmadi, Mehdi Mehdikhani-Nahrkhalaji, Jaleh Varshosaz, Shadi Farsaei

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Gelatin and hyaluronic acid are commonly used in skin tissue engineering scaffolds, but because of their low mechanical properties and high biodegradation rate, adding a synthetic polymer such as polycaprolactone could improve the scaffold properties. Therefore, we developed a gelatin-hyaluronic acid-polycaprolactone scaffold, containing 0.5 % atorvastatin loaded nanostructured lipid carriers (NLCs) for skin tissue engineering. The atorvastatin loaded NLCs solution was prepared by solvent evaporation method and freeze drying process. Synthesized atorvastatin loaded NLCs was added to the gelatin and hyaluronic acid solution, and a membrane was fabricated with solvent evaporation method. Thereafter it was coated by a thin layer of polycaprolactone via spine coating set. The resulting scaffolds were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses. Moreover, mechanical properties, in vitro degradation in 7 days period, and in vitro drug release of scaffolds were also evaluated. SEM images showed the uniform distributed NLCs with an average size of 100 nm in the scaffold structure. Mechanical test indicated that the scaffold had a 70.08 Mpa tensile modulus which was twofold of tensile modulus of normal human skin. A Franz-cell diffusion test was performed to investigate the scaffold drug release in phosphate buffered saline (pH=7.4) medium. Results showed that 72% of atorvastatin was released during 5 days. In vitro degradation test demonstrated that the membrane was degradated approximately 97%. In conclusion, suitable physicochemical and biological properties of membrane indicated that the developed gelatin-hyaluronic acid-polycaprolactone nanocomposite scaffold containing 0.5 % atorvastatin loaded NLCs could be used as a good candidate for skin tissue engineering applications.

Keywords: atorvastatin, gelatin, hyaluronic acid, nano lipid carriers (NLCs), polycaprolactone, skin tissue engineering, solvent casting, solvent evaporation

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Authors: Syeda Hira, Muhammad Gulfraz

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Background: Liver diseases cause serious health issues. Plants contain active compounds that significantly help in the treatment of various diseases. Ficus carica is traditionally used for the treatment of liver diseases. The purpose of the present study was the isolation and identification of active components from F.carica leaves which are responsible for hepatoprotective activity. Methods: The study was designed to identify the most active hepatoprotective sub-fraction from ethyl acetate fraction of Ficus carica by in vitro study and evaluation of its in vivo hepatoprotective effect in animal models. Ethyl acetate fraction was subjected to column, and a total of eight sub-fractions were obtained. In vitro, the hepatoprotective effect of all sub-fractions was determined on HepG2 cell lines. Toxicity was induced by CCl₄ (Carbon tetrachloride), and silymarin was used as a positive control. On the basis of the results, the most active sub-fraction was subjected to LC-MS and FT-IR analysis for the identification of bioactive compounds. In vivo, the hepatoprotective effect was determined in mice. Toxicity was induced by CCl₄; at the end of the experiment, biochemical parameters such as ALT, AST, ALP, bilirubin, and total protein were estimated in serum. Histopathology of liver tissues was also done. Results: Sub-fraction FVI exhibited significant (P<0.05) hepatoprotective activity as compared to other sub-fractions, which was almost similar to the standard drug silymarin. Six known bioactive compounds were identified from this sub-fraction after LC-MS analysis. In vivo, the hepatoprotective activity of sub-fraction FVI was evaluated in CCl₄-induced toxicated mice. Administration of CCl₄ significantly increased level of ALT (Alanine transaminase), AST (Aspartate aminotransferase), ALP (Alkaline phosphatase), and bilirubin and decreased the total protein. Treatment with sub-fraction FVI significantly (p<0.05) reversed the level of these biomarkers toward normal at both doses of 25 mg/kg and 50 mg/kg. Conclusion: Our findings confirmed the hepatoprotective effect of ethyl acetate fraction of F.carica. It could be a good candidate for the development of a natural hepatoprotective drug; pre-clinical investigation on ethyl acetate fraction is recommended.

Keywords: Ficus carica, hepatoprotective, CCl₄, bioactive compounds, liver markers

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Wenjie Wu, Peng Cheng, Zehua Zhang, Fei Luo, Feng Wu, Min Zhong, Jianzhong Xu

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Background: There has been limited research into the therapeutic efficacy of rapid diagnosis of spinal tuberculosis complicated with pulmonary tuberculosis. We attempted to discover whether the utilization of a DNA microarray assay to detect multidrug-resistant spinal tuberculosis complicated with pulmonary tuberculosis can improve clinical outcomes. Methods: A prospective study was conducted from February 2006 to September 2015. One hundred and forty-three consecutive culture–confirmed, clinically and imaging diagnosed MDR-TB patients with spinal tuberculosis complicated by pulmonary tuberculosis were enrolled into the study. The initial time to treatment for MDR-TB, the method of infection control, radiological indicators of spinal tubercular infectious foci, culture conversion, and adverse drug reactions were compared with the standard culture methods. Results: Of the total of 143 MDR-TB patients, 68 (47.6%) were diagnosed by conventional culture methods and 75 (52.4%) following the implementation of detection using the DNA microarray. Patients in the microarray group began rational use of the second-line drugs schedule more speedily than sufferers in the culture group (17.3 vs. 74.1 days). Among patients were admitted to a general tuberculosis ward, those from the microarray group spent less time in the ward than those from the culture group (7.8 vs. 49.2 days). In those patients with six months follow-up (n=134), patients in the microarray group had a higher rate of sputum negativity conversion at six months (89% vs. 73%). In the microarray group, the rate of drug adverse reactions was significantly lower (22.2% vs. 67.7%). At the same time, they had a more obvious reduction of the area with spinal tuberculous lesions in radiological examinations (77% vs. 108%). Conclusions: The application of the CapitalBio™ DNA Microarray assay caused noteworthy clinical advances including an earlier time to begin MDR-TB treatment, increased sputum culture conversion, improved infection control measures and better radiographical results

Keywords: tuberculosis, multidrug-resistant, tuberculous spondylitis, DNA microarray, clinical outcomes

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Authors: Aleksander Ejsmont, Aleksandra Galarda, Joanna Goscianska

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Smart porous carriers with defined structure and physicochemical properties are required for releasing the therapeutic drug with precise control of delivery time and location in the body. Due to their non-toxicity, ordered structure, chemical, and thermal stability, mesoporous carbons can be considered as modern carriers for active pharmaceutical ingredients (APIs) whose effectiveness needs frequent dosing algorithms. Such an API-carrier system, if programmed precisely, may stabilize the pharmaceutical and increase its dissolution leading to enhanced bioavailability. The substance conjugated with the material, through its prior adsorption, can later be successfully applied internally to the organism, as well as externally if the API release is feasible under these conditions. In the present study, ordered mesoporous carbons of different morphologies and structures, prepared by hard template method, were applied as carriers in the adsorption and controlled release of active pharmaceutical ingredients. In the first stage, the carbon materials were synthesized and functionalized with carboxylic groups by chemical oxidation using ammonium persulfate solution and then with amine groups. Materials obtained were thoroughly characterized with respect to morphology (scanning electron microscopy), structure (X-ray diffraction, transmission electron microscopy), characteristic functional groups (FT-IR spectroscopy), acid-base nature of surface groups (Boehm titration), parameters of the porous structure (low-temperature nitrogen adsorption) and thermal stability (TG analysis). This was followed by a series of tests of adsorption and release of paracetamol, benzocaine, and losartan potassium. Drug release experiments were performed in the simulated gastric fluid of pH 1.2 and phosphate buffer of pH 7.2 or 6.8 at 37.0 °C. The XRD patterns in the small-angle range and TEM images revealed that functionalization of mesoporous carbons with carboxylic or amine groups leads to the decreased ordering of their structure. Moreover, the modification caused a considerable reduction of the carbon-specific surface area and pore volume, but it simultaneously resulted in changing their acid-base properties. Mesoporous carbon materials exhibit different morphologies, which affect the host-guest interactions during the adsorption process of active pharmaceutical ingredients. All mesoporous carbons show high adsorption capacity towards drugs. The sorption capacity of materials is mainly affected by BET surface area and the structure/size matching between adsorbent and adsorbate. Selected APIs are linked to the surface of carbon materials mainly by hydrogen bonds, van der Waals forces, and electrostatic interactions. The release behavior of API is highly dependent on the physicochemical properties of mesoporous carbons. The release rate of APIs could be regulated by the introduction of functional groups and by changing the pH of the receptor medium. Acknowledgments—This research was supported by the National Science Centre, Poland (project SONATA-12 no: 2016/23/D/NZ7/01347).

Keywords: ordered mesoporous carbons, sorption capacity, drug delivery, carbon nanocarriers

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Authors: Christos Lampropoulos, Maria Konsta, Vicky Dradaki, Irini Dri, Konstantina Panouria, Tamta Sirbilatze, Ifigenia Apostolou, Vaggelis Lambas, Christina Kordali, Georgios Mavras

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Objectives: Malnutrition in hospitalized patients is related to increased morbidity and mortality. The purpose of our study was to compare various nutritional scores in order to detect the most suitable one for assessing the nutritional status of elderly, hospitalized patients and correlate them with mortality and extension of admission duration, due to patients’ critical condition. Methods: Sample population included 150 patients (78 men, 72 women, mean age 80±8.2). Nutritional status was assessed by Mini Nutritional Assessment (MNA full, short-form), Malnutrition Universal Screening Tool (MUST) and short Nutritional Appetite Questionnaire (sNAQ). Sensitivity, specificity, positive and negative predictive values and ROC curves were assessed after adjustment for the cause of current admission, a known prognostic factor according to previously applied multivariate models. Primary endpoints were mortality (from admission until 6 months afterwards) and duration of hospitalization, compared to national guidelines for closed consolidated medical expenses. Results: Concerning mortality, MNA (short-form and full) and SNAQ had similar, low sensitivity (25.8%, 25.8% and 35.5% respectively) while MUST had higher sensitivity (48.4%). In contrast, all the questionnaires had high specificity (94%-97.5%). Short-form MNA and sNAQ had the best positive predictive value (72.7% and 78.6% respectively) whereas all the questionnaires had similar negative predictive value (83.2%-87.5%). MUST had the highest ROC curve (0.83) in contrast to the rest questionnaires (0.73-0.77). With regard to extension of admission duration, all four scores had relatively low sensitivity (48.7%-56.7%), specificity (68.4%-77.6%), positive predictive value (63.1%-69.6%), negative predictive value (61%-63%) and ROC curve (0.67-0.69). Conclusion: MUST questionnaire is more advantageous in predicting mortality due to its higher sensitivity and ROC curve. None of the nutritional scores is suitable for prediction of extended hospitalization.

Keywords: duration of admission, malnutrition, nutritional assessment scores, prognostic factors for mortality

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Authors: Anupam Chanda

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Presently Packaging plays a significant role for drug discoveries. The process of selecting materials and the type of packaging also offers an opportunity for the Packaging scientist to look for biological delivery choices. Most injectable protein products were supplied in some sort of glass vial, prefilled syringe, cartridge. Those product having high Ph content there is a chance of “delamination “from inner surface of glass vial. With protein-based drugs, the biggest issue is the effect of packaging derivatives on the protein’s threedimensional and surface structure. These are any effects that relate to denaturation or aggregation of the protein due to oxidation or interactions from contaminants or impurities in the preparation. The potential for these effects needs to be carefully considered in choosing the container and the container closure system to avoid putting patients in jeopardy. Cause of Delamination : -Formulations with a high pH include phosphate and citrate buffers increase the risk of glass delamination. -High alkali content in glass could accelerate erosion. -High temperature during the vial-forming process increase the risk of glass delamination. -Terminal sterilization (irradiated at 20-40 kGy for 150 min) also is a risk factor for specific products(veterinary parenteral administration),could cause delamination. -High product-storage temperatures and long exposure times can increase the rate and severity of glass delamination. How to prevent Delamination -Treating the surface of the glass vials with materials, such as ammonium sulfate or siliconization can reduce the rate of glass erosion. -Consider alternative sterilization methods only in rare cases. -The correct specification for the glass to ensure its suitability for the pH of the product. -Use Cyclic olefin copolymer(COC)/Cyclic olefin Polymer(COP) Adsorption of protein and Solutions: Option#1 Coat with linear methoxylated polyglycerol and hyperbranchedmethoxylated polyglycerol. Option#2 Thehyperbranched non-methoxylated coating performed best. Option#3 Coat with hyperbranched polyglycerol Option#4 Right selection of Sterilization of glass vial/syringe.

Keywords: delamination of glass, ptrotien adoptions inside the glass surface, extractable & leachable solutions, injectable designs for new drugs

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Authors: Alkaben Patel

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The proposed RP-HPLC method is easy, rapid, economical, precise and accurate stability indicating RP-HPLC method for simultaneous estimation of Astorvastatin and Methylcobalamine in their combined dosage form has been developed.The separation was achieved by LC-20 AT C18(250mm*4.6mm*2.6mm)Colum and water (pH 3.5): methanol 70:30 as mobile phase, at a flow rate of 1ml/min. wavelength of this dosage form is 215nm.The drug is related to stress condition of hydrolysis, oxidation, photolysis and thermal degradation.

Keywords: RP- HPLC, atorvastatin, methylcobalamine, method, development, validation

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Authors: Fadia Gafri, Kathryn Mckintosh, Louise Young, Alan Harvey, Simon Mackay, Andrew Paul, Robin Plevin

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Nuclear factor-kappa B (NF-κB) is a ubiquitous transcription factor found originally to play a key role in regulating inflammation. However considerable evidence links this pathway to the suppression of apoptosis, cellular transformation, proliferation and invasion (Aggarwal et al., 2006). Moreover, recent studies have also linked inflammation to cancer progression making NF-κB overall a promising therapeutic target for drug discovery (Dobrovolskaia & Kozlov, 2005). In this study we examined the effect of the natural product canthin-6-one (SU182) as part of a CRUK small molecule drug discovery programme for effects upon the NF-κB pathway. Initial studies demonstrated that SU182 was found to have good potency against the inhibitory kappa B kinases (IKKs) at 30M in vitro. However, at concentrations up to 30M, SU182 had no effect upon TNFα stimulated loss in cellular IκBα or p65 phosphorylation in the keratinocyte cell line NCTC2544. Nevertheless, 30M SU182 reduced TNF-α / PMA-induced NF-κB-linked luciferase reporter activity to (22.9 ± 5%) and (34.6± 3 %, P<0.001) respectively, suggesting an action downstream of IKK signalling. Indeed, SU182 neither decreased NF-κB-DNA binding as assayed by EMSA nor prevented the translocation of p65 (NF-κB) to the nucleus assessed by immunofluorescence and subcellular fractionation. In addition to the inhibition of transcriptional activity of TNFα-induced NF-κB reporter activity SU182 significantly reduced PMA-induced AP-1-linked luciferase reporter activity to about (48± 9% at 30M, P<0.001) . This mode of inhibition was not sufficient to prevent the activation of NF-κB dependent induction of other proteins such as COX-2 and iNOS, or activated MAP kinases (p38, JNK and ERK1/2) in LPS stimulated RAW 264.7 macrophages. Taken together these data indicate the potential for SU182 to interfere with the transcription factors NF-κB and AP-1 at transcriptional level. However, no potential anti-inflammatory effect was indicated, further investigation for other NF-κB dependent proteins linked to survival are also required to identify the exact mechanism of action.

Keywords: Canthin-6-one, NF-κB, AP-1, phosphorylation, Nuclear translocation, DNA-binding activity, inflammatory proteins.

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Authors: Zakaria Baka, Halima Alem

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Cancer is a major worldwide health problem that has caused around ten million deaths in 2020. In addition, efforts to develop new anti-cancer drugs still face a high failure rate. This is partly due to the lack of preclinical models that recapitulate in-vivo drug responses. Indeed conventional cell culture approach (known as 2D cell culture) is far from reproducing the complex, dynamic and three-dimensional environment of tumors. To set up more in-vivo-like cancer models, 3D bioprinting seems to be a promising technology due to its ability to achieve 3D scaffolds containing different cell types with controlled distribution and precise architecture. Moreover, the introduction of microfluidic technology makes it possible to simulate in-vivo dynamic conditions through the so-called “cancer-on-chip” platforms. Whereas several cancer types have been modeled through the cancer-on-chip approach, such as lung cancer and breast cancer, only a few works describing ovarian cancer models have been described. The aim of this work is to combine 3D bioprinting and microfluidic technics with setting up a 3D dynamic model of ovarian cancer. In the first phase, alginate-gelatin hydrogel containing SKOV3 cells was used to achieve tumor-like structures through an extrusion-based bioprinter. The desired form of the tumor-like mass was first designed on 3D CAD software. The hydrogel composition was then optimized for ensuring good and reproducible printability. Cell viability in the bioprinted structures was assessed using Live/Dead assay and WST1 assay. In the second phase, these bioprinted structures will be included in a microfluidic device that allows simultaneous testing of different drug concentrations. This microfluidic dispositive was first designed through computational fluid dynamics (CFD) simulations for fixing its precise dimensions. It was then be manufactured through a molding method based on a 3D printed template. To confirm the results of CFD simulations, doxorubicin (DOX) solutions were perfused through the dispositive and DOX concentration in each culture chamber was determined. Once completely characterized, this model will be used to assess the efficacy of anti-cancer nanoparticles developed in the Jean Lamour institute.

Keywords: 3D bioprinting, ovarian cancer, cancer-on-chip models, microfluidic techniques

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Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Ayman K. El Essawy, Amal M. Hosny, Hala M. Abu Shady

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The rapid detection of TB and drug resistance, both optimizes treatment and improves outcomes. In the current study, respiratory specimens were collected from 155 patients. Conventional susceptibility testing and MIC determination were performed for rifampicin (RIF) and isoniazid (INH). Genotype MTBDRplus assay, which is a molecular genetic assay based on the DNA-STRIP technology and specific gene sequencing with primers for rpoB, KatG, and mab-inhA genes were used to detect mutations associated with resistance to rifampicin and isoniazid. In comparison to other categories, most of rifampicin resistant (61.5%) and isoniazid resistant isolates (47.1%) were from patients relapsed in treatment. The genotypic profile (using Genotype MTBDRplus assay) of multi-drug resistant (MDR) isolates showed missing of katG wild type 1 (WT1) band and appearance of mutation band katG MUT2. For isoniazid mono-resistant isolates, 80% showed katG MUT1, 20% showed katG MUT1, and inhA MUT1, 20% showed only inhA MUT1. Accordingly, 100% of isoniazid resistant strains were detected by this assay. Out of 17 resistant strains, 16 had mutation bands for katG distinguished high resistance to isoniazid. The assay could clearly detect rifampicin resistance among 66.7% of MDR isolates that showed mutation band rpoB MUT3 while 33.3% of them were considered as unknown. One mono-resistant rifampicin isolate did not show rifampicin mutation bands by Genotype MTBDRplus assay, but it showed an unexpected mutation in Codon 531 of rpoB by DNA sequence analysis. Rifampicin resistance in this strain could be associated with a mutation in codon 531 of rpoB (based on molecular sequencing), and Genotype MTBDRplus assay could not detect the associated mutation. If the results of Genotype MTBDRplus assay and sequencing were combined, this strain shows hetero-resistance pattern. Gene sequencing of eight selected isolates, previously tested by Genotype MTBDRplus assay, could detect resistance mutations mainly in codon 315 (katG gene), position -15 in inhA promotes gene for isoniazid resistance and codon 531 (rpoB gene) for rifampicin resistance. Genotyping techniques allow distinguishing between recurrent cases of reinfection or reactivation and supports epidemiological studies.

Keywords: M. tuberculosis, rpoB, KatG, inhA, genotype MTBDRplus

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Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

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Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

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Authors: Shobha Rani R. Hiremath, Keerthana R., Madhuvan H. S.

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BACKGROUND: Type 2 Diabetes is a chronic health condition where the body cannot effectively use the insulin it produces, leading to elevated blood sugar levels It is often associated with lifestyle factors and insulin resistance. Globally, diabetes is on the rise, with urban areas like Bangalore seeing a surge due to lifestyle changes, stress, and dietary habits. To manage diabetes effectively, over 50 medications are available, each serving to regulate blood sugar through different mechanisms. This reflects the complex and individualized nature of diabetes treatment. Given the increase in medications for Type 2 diabetes mellitus, it is important to evaluate their effectiveness and safety so that clinicians can make informed choices while treating their patients. OBJECTIVES: To evaluate the effectiveness of various anti-diabetic medications used in the hospital in Type 2 diabetes patients by monitoring their HbA1c levels. To assess the safety of these medications by monitoring Adverse drug reactions if any. METHODOLOGY Design : Prospective Cohort study, Study period: 8 months, Sample Size: 100 patients, Inclusion Criteria: Patients above 18 years of both genders who were diagnosed with T2DM and who were prescribed oral hypoglycaemic agents. Exclusion Criteria: Diabetic patients with hepatic/renal failure, patients prescribed with insulin /not prescribed with OHAs and patients who were terminally ill, pregnant and lactating patients. Source of Data: Prescriptions, lab reports, ECG reports. Data collection forms were used to collect data which consisted of patient demographic details, drugs prescribed, laboratory investigations such as HbA1C, FBS, PPBS , ECG and any ADRs experienced. Data was collected at baseline, 3 months, and 6 months. It was statistically analyzed using SPSS (version 26) software. RESULTS: Greater number of patients (46%) were in the age group of greater than 61 years. 43 patients had no co-morbidities whereas 51 patients had Hypertension as the co morbidity. Basically 5 Drug combinations were prescribed as follows. Combination 1: Tablet Metformin HCL + Glimepiride (500, 2 mg) : 1-0-1, Combination 2: Tablet Sitagliptin + Metformin HCL (50, 500 mg) : 1-0-1, Combination 3: Tablet Vildagliptin + Metformin HCL (50, 500 mg): 1-0-1, Combination 4: Tablet Voglibose+ Glimepiride+ Metformin HCL (0.2 ,2 ,500mg): : 1-0-1, Combination 5: Tablet Voglibose+ Glimepiride+ Metformin HCL (0.2, 2 ,500mg): : 1-0-1 and T. Sitagliptin +Metformin HCL (50, 500 mg): 0-1-0. Combination 5 (Voglibose, Glimepiride, Metformin, Sitagliptin) was most effective in reducing HbA1c levels, showing a significant decrease (-0.00682, p = 0.001), followed by Combinations 4 and 3. However, Combination 5 also had the highest incidence of gastrointestinal side effects (72.7%) and ECG abnormalities (27.3%). Combination 1 (Metformin + Glimepiride) had the highest occurrence of hypoglycemia due to Glimepiride's insulin-stimulating effects. Weight loss was most notable in Combination 5, affecting 36.36% of patients. CONCLUSION: The enhanced effectiveness of Combinations 3, 4, and 5 suggests that a multi-drug approach that incorporates different mechanisms of action is more effective in managing HbA1c levels in patients with diabetes. Adverse effect profiles should be considered for personalized treatment strategies.

Keywords: type 2 diabetes, safety, oral anti diabetic medications, effectiveness

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Authors: Theophilus Asante, Comfort Nyarko, Daniel Antwi

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Drug-resistant tuberculosis, together with the associated comorbidities like pulmonary fibrosis and silicosis, has been one of the most serious global public health threats that requires immediate action to curb or mitigate it. This prolongs hospital stays, increases the cost of medication, and increases the death toll recorded annually. Crinum asiaticum bulb (CAE) and betulin (BET) are known for their biological and pharmacological effects. Pharmacological effects reported on CAE include antimicrobial, anti-inflammatory, anti-pyretic, anti-analgesic, and anti-cancer effects. Betulin has exhibited a multitude of powerful pharmacological properties ranging from antitumor, anti-inflammatory, anti-parasitic, anti-microbial, and anti-viral activities. This work sought to investigate the anti-tuberculosis and resistant modulatory effects and also assess their effects on mitigating pulmonary fibrosis and silicosis. In the anti-tuberculosis and resistant modulatory effects, both CAE and BET showed strong antimicrobial activities (31.25 ≤ MIC ≤ 500) µg/ml against the studied microorganisms and also produced significant anti-efflux pump and biofilm inhibitory effects (ρ < 0.0001) as well as exhibiting resistance modulatory and synergistic effects when combined with standard antibiotics. Crinum asiaticum bulbs extract and betulin were shown to possess anti-pulmonary fibrosis effects. There was an increased survival rate in the CAE and BET treatment groups compared to the BLM-induced group. There was a marked decrease in the levels of hydroxyproline and collagen I and III in the CAE and BET treatment groups compared to the BLM-treated group. The treatment groups of CAE and BET significantly downregulated the levels of pro-fibrotic and pro-inflammatory cytokine concentrations such as TGF-β1, MMP9, IL-6, IL-1β and TNF-alpha compared to an increase in the BLM-treated groups. The histological findings of the lungs suggested the curative effects of CAE and BET following BLM-induced pulmonary fibrosis in mice. The study showed improved lung functions with a wide focal area of viable alveolar spaces and few collagen fibers deposition on the lungs of the treatment groups. In the anti-silicosis and pulmonoprotective effects of CAE and BET, the levels of NF-κB, TNF-α, IL-1β, IL-6 and hydroxyproline, collagen types I and III were significantly reduced by CAE and BET (ρ < 0.0001). Both CAE and BET significantly (ρ < 0.0001) inhibited the levels of hydroxyproline, collagen I and III when compared with the negative control group. On BALF biomarkers such as macrophages, lymphocytes, monocytes, and neutrophils, CAE and BET were able to reduce their levels significantly (ρ < 0.0001). The CAE and BET were examined for anti-oxidant activity and shown to raise the levels of catalase (CAT) and superoxide dismutase (SOD) while lowering the level of malondialdehyde (MDA). There was an improvement in lung function when lung tissues were examined histologically. Crinum asiaticum bulbs extract and betulin were discovered to exhibit anti-tubercular and resistance-modulatory properties, as well as the capacity to minimize TB comorbidities such as pulmonary fibrosis and silicosis. In addition, CAE and BET may act as protective mechanisms, facilitating the preservation of the lung's physiological integrity. The outcomes of this study might pave the way for the development of leads for producing single medications for the management of drug-resistant tuberculosis and its accompanying comorbidities.

Keywords: fibrosis, crinum, tuberculosis, antiinflammation, drug resistant

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