Search results for: predictive biomarker

Authors: Bing Yan

Abstract:

Nanomaterials are widely used in various industrial sectors, biomedicine, and more than 1300 consumer products. Although there is still no standard safety regulation, their potential toxicity is a major concern worldwide. We discovered that nanoparticles target and enter human cells1, perturb cellular signaling pathways2, affect various cell functions3, and cause malfunctions in animals4,5. Because the majority of atoms in nanoparticles are on the surface, chemistry modification on their surface may change their biological properties significantly. We modified nanoparticle surface using nano-combinatorial chemistry library approach6. Novel nanoparticles were discovered to exhibit significantly reduced toxicity6,7, enhance cancer targeting ability8, or re-program cellular signaling machineries7. Using computational chemistry, quantitative nanostructure-activity relationship (QNAR) is established and predictive models have been built to predict biocompatible nanoparticles.

Keywords: nanoparticle, nanotoxicity, nano-bio, nano-combinatorial chemistry, nanoparticle library

Procedia PDF Downloads 409

Authors: Bahadir Batar, Elif Serdal, Berna Erdal, Hasan Ogul

Abstract:

Micro-ribonucleic acids (miRNAs) are small short non-coding ribonucleic acid molecules about 22 nucleotides long. miRNAs play a key role in response to chemotherapeutic agents. WW domain-containing oxidoreductase (WWOX) gene encodes a tumor suppressor protein. Loss or reduction of Wwox protein is observed in many breast cancer cases. WWOX protein deficiency is increased in triple-negative breast cancer (TNBC). TNBC is a heterogeneous, highly aggressive, and difficult to treat tumor type. WWOX loss contributes to resistance to cisplatin therapy in patients with TNBC. Here, the aim of the study was to investigate the potential role of miRNAs in cisplatin therapy resistance of WWOX-deficient TNBC cells. This was a cell culture study. miRNA expression profiling was analyzed by LightCycler 480 system. miRNA Set Enrichment Analysis tool was used to integrate experimental data with literature-based biological knowledge to infer a new hypothesis. Increased miR-182 and decreased miR-214 were significantly correlated with cisplatin resistance in WWOX-deficient TNBC cells. miR-182 and miR-214 may involve in cisplatin resistance of WWOX-deficient TNBC cells by deregulating the DNA repair, apoptosis, or protein kinase B signaling pathways. These data highlight the mechanism by which WWOX regulates cisplatin resistance of TNBC and the potential use of WWOX as a predictor biomarker for cisplatin resistance.

Keywords: cisplatin, microRNA, triple-negative breast cancer, WWOX

Procedia PDF Downloads 133

Authors: Tejinder Pal Singh, Ravinder Kumar Malik, Gurpreet Kaur

Abstract:

Adhesion is key factor for colonization of the gastrointestinal tract and the ability of probiotic strains to inhibit pathogens. Therefore, the adhesion ability is considered as a suitable biomarker for the selection of potential probiotic. In the present study, eight probiotic Lactobacillus reuteri strains were evaluated as viable, LiCl treated or heat-killed forms and compared with probiotic reference strains (L. reuteri ATCC55730). All strains investigated were able to adhere to Caco-2 cells. All probiotic L. reuteri strains tested were able to inhibit and displace (P < 0.05) the adhesion of Escherichia coli ATCC25922, Salmonella typhi NCDC113, Listeria monocytogenes ATCC53135 and Enterococcus faecalis NCDC115. The probiotic strain L. reuteri LR6 showed the strongest adhesion and pathogen inhibition ability among the eight L. reuteri strains tested. In addition, the abilities to inhibit and to displace adhered pathogens depended on both the probiotic and the pathogen strains tested suggesting the involvement of various mechanisms. The adhesion and antagonistic potential of the probiotic strains were significantly decreased upon exposure to 5M LiCl, showing that surface molecules, proteinaceous in nature, are involved. The heat-killed forms of the probiotic L. reuteri strains also inhibited the attachment of selected pathogens to Caco-2 cells. In conclusion, in vitro assays showed that L. reuteri strains, as viable or heat-killed forms, are adherent to Caco-2 cell line model and are highly antagonistic to selected pathogens in which surface molecules, proteinaceous molecules in particular, plays an important role.

Keywords: probiotics, Lactobacillus reuteri, adhesion, Caco-2 cells

Procedia PDF Downloads 251

Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

Abstract:

This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

Procedia PDF Downloads 73

Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

Abstract:

Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

Procedia PDF Downloads 8

Authors: Suha K. Shihab, Zahid A. Khan, Aas Mohammad, Arshad Noor Siddiquee

Abstract:

This paper presents the influence of cutting parameters (cutting speed, feed and depth of cut) on flank wear (VB) in turning of 52100 hard alloy steel using multilayer coated carbide insert under dry condition. Nine experiments were performed based on Taguchi’s L9 orthogonal array. Analysis of variance (ANOVA) was used to determine the effects of the cutting parameters on flank wear. The results of the study revealed that the cutting speed (A) and feed rate (B) are the dominant factors affecting flank wear, while the depth of cut (C) has not a significant effect. The optimal combination of the cutting parameters for flank wear is found to be A1B1C1. The mathematical model for flank wear is found to be statistically significant. The predicted and measured values of flank wear are found to be very close to each other.

Keywords: flank wear, hard turning, Taguchi approach, optimization

Procedia PDF Downloads 665

Authors: Sina Dehghani, Zhikang Rong

Abstract:

Historically, a variety of approaches have been taken to forecast commodity prices due to the significant implications of these values on the global economy. An accurate forecasting tool for a valuable commodity would significantly benefit investors and governmental agencies. Silver, in particular, has grown significantly as a commodity in recent years due to its use in healthcare and technology. This manuscript aims to utilize the Geometric Brownian Motion predictive model to forecast silver commodity prices over multiple 3-year periods. The results of the study indicate that the model has several limitations, particularly its inability to work effectively over longer periods of time, but still was extremely effective over shorter time frames. This study sets a baseline for silver commodity forecasting with GBM, and the model could be further strengthened with refinement.

Keywords: geometric Brownian motion, commodity, risk management, volatility, stochastic behavior, price forecasting

Procedia PDF Downloads 24

Authors: Dingiswayo Likhona, Arko-Cobbah Emmanuel, Carolina Pohl, Nthabiseng Z. Mokoena, Jolly Musoke

Abstract:

A distinct strain of Klebsiella pneumoniae (K. pneumoniae), referred to as hypervirulent (hvKp), is associated with invasive infections such as an invasive pyogenic liver abscess in young and healthy individuals. In South Africa, limited information is known about the prevalence and virulence of this hvKp strain. Thus, this study aimed to determine the prevalence of hvKp and virulence-associated factors in K. pneumoniae isolates from one of the largest Tertiary hospitals in a South African province. A total of 74 K. pneumoniae isolates were received from Pelonomi National Health Laboratory Services (NHLS), Bloemfontein. Virulence-associated genes (rmpA, capsule serotype K1/K2, iroB, and irp2) were screened, and the virulence of hvKp vs. classical Klebsiella pneumoniae (cKp) was investigated using Caenorhabditis elegans nematode model. The iutA (aerobactin transporter) gene was used as a primary biomarker of hvKp. An average of 12% (9/74) of cases were defined as hvKp. Moreover, hvKp was found to be significantly more virulent in vivo Caenorhabditis elegans relative to cKp. The virulence-associated genes (rmpA, iroB, hmv phenotype, and capsule K1/K2) were significantly (p< 0.05) associated with hvKp. Findings from this study confirm the presence of hvKp in one large Tertiary hospital in South Africa. However, the low prevalence and mild to moderate clinical presentation suggest a marginal threat to public health. Further studies in different settings are required to establish the true potential impact of hvKp in developing countries.

Keywords: hypervirulent klebsiella pneumoniae, virulence, caenorhabditis elegans, aerobactin (iutA)

Procedia PDF Downloads 86

Authors: Xu Du, Aparna S. Varde

Abstract:

In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

Procedia PDF Downloads 309

Authors: Nasrin Akter, Prem Chhetri, Shams Rahman

Abstract:

The present study tested Ajzen’s Theory of Planned Behavior (TPB) model to explain the formation of business customers’ intention to use 3PL services in Bangladesh. The findings show that the TPB model has a good fit to the data. Based on theoretical support and suggested modification indices, a refined TPB model was developed afterwards which provides a better predictive power for intention. Consistent with the theory, the results of a structural equation analysis revealed that the intention to use 3PL services is predicted by attitude and subjective norms but not by perceived behavioral control. Further investigation indicated that the paths between (attitude and intention) and (subjective norms and intention) did not statistically differ between 3PL user and non-user. Findings of this research provide an evidence base to formulate business strategies to increase the use of 3PL services in Bangladesh to enhance productivity and to gain economic efficiency.

Keywords: Bangladesh, intention, third-party logistics, Theory of Planned Behavior

Procedia PDF Downloads 582

Authors: Ryan B. Simpson, Margaret A. Waskow, Aishwarya Venkat, Elena N. Naumova

Abstract:

The Centers for Disease Control and Prevention (CDC) estimate that 31 known foodborne pathogens cause 9.4 million cases of these illnesses annually in US. Over 90% of these illnesses are associated with exposure to Campylobacter, Cryptosporidium, Cyclospora, Listeria, Salmonella, Shigella, Shiga-Toxin Producing E.Coli (STEC), Vibrio, and Yersinia. Contaminated products contain parasites typically causing an intestinal illness manifested by diarrhea, stomach cramping, nausea, weight loss, fatigue and may result in deaths in fragile populations. Since 1998, the National Outbreak Reporting System (NORS) has allowed for routine collection of suspected and laboratory-confirmed cases of food poisoning. While retrospective analyses have revealed common pathogen-specific seasonal patterns, little is known concerning the stability of those patterns over time and whether they can be used for preventative forecasting. The objective of this study is to construct a calendar of foodborne outbreaks of nine infections based on the peak timing of outbreak incidence in the US from 1996 to 2017. Reported cases were abstracted from FoodNet for Salmonella (135115), Campylobacter (121099), Shigella (48520), Cryptosporidium (21701), STEC (18022), Yersinia (3602), Vibrio (3000), Listeria (2543), and Cyclospora (758). Monthly counts were compiled for each agent, seasonal peak timing and peak intensity were estimated, and the stability of seasonal peaks and synchronization of infections was examined. Negative Binomial harmonic regression models with the delta-method were applied to derive confidence intervals for the peak timing for each year and overall study period estimates. Preliminary results indicate that five infections continue to lead as major causes of outbreaks, exhibiting steady upward trends with annual increases in cases ranging from 2.71% (95%CI: [2.38, 3.05]) in Campylobacter, 4.78% (95%CI: [4.14, 5.41]) in Salmonella, 7.09% (95%CI: [6.38, 7.82]) in E.Coli, 7.71% (95%CI: [6.94, 8.49]) in Cryptosporidium, and 8.67% (95%CI: [7.55, 9.80]) in Vibrio. Strong synchronization of summer outbreaks were observed, caused by Campylobacter, Vibrio, E.Coli and Salmonella, peaking at 7.57 ± 0.33, 7.84 ± 0.47, 7.85 ± 0.37, and 7.82 ± 0.14 calendar months, respectively, with the serial cross-correlation ranging 0.81-0.88 (p < 0.001). Over 21 years, Listeria and Cryptosporidium peaks (8.43 ± 0.77 and 8.52 ± 0.45 months, respectively) have a tendency to arrive 1-2 weeks earlier, while Vibrio peaks (7.8 ± 0.47) delay by 2-3 weeks. These findings will be incorporated in the forecast models to predict common paths of the spread, long-term trends, and the synchronization of outbreaks across etiological agents. The predictive modeling of foodborne outbreaks should consider long-term changes in seasonal timing, spatiotemporal trends, and sources of contamination.

Keywords: foodborne outbreak, national outbreak reporting system, predictive modeling, seasonality

Procedia PDF Downloads 130

Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

Abstract:

This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

Procedia PDF Downloads 337

Authors: Yuzhi Cai

Abstract:

This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

Procedia PDF Downloads 347

Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

Abstract:

The paper discusses the main aspects involved in the development of a supply chain management system using the newly developed PredictionSCMS software as a basis for the discussion. The discussion is focused on three topics: the first is demand forecasting, where we present the predictive algorithms implemented and discuss related concepts such as the calculation of the safety stock, the effect of out-of-stock days etc. The second topic concerns the design of a supply chain, where the core parameters involved in the process are given, together with a methodology of incorporating these parameters in a meaningful order creation strategy. Finally, the paper discusses some critical events that can happen during the operation of a supply chain management system and how the developed software notifies the end user about their occurrence.

Keywords: demand forecasting, machine learning, risk management, supply chain design

Procedia PDF Downloads 97

Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

Abstract:

Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

Procedia PDF Downloads 68

Authors: James Moir

Abstract:

The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

Procedia PDF Downloads 422

Authors: M. Pellerone, S. G. Razza, L. Miano, A. Miccichè, M. Adamo

Abstract:

The teacher’s attitude plays a decisive role in promoting the development of the non-native or disabled student and counteracting hypothetical negative attitudes and prejudice towards those who are “different”.The objective of the present research is to measure the relationship between teachers’ prejudices towards disabled and/or immigrant students as predictors of teaching-learning strategies. A cross-sectional study involved 200 Italian female teachers who completed an anamnestic questionnaire, the Assessment Teaching Scale, the Italian Modern and Classical Prejudices Scale towards people with ID, and the Pettigrew and Meertens’ Blatant Subtle Prejudice Scale. Confirming research hypotheses, data underlines the predictive role of prejudice on teaching strategies, and in particular on the socio-emotional and communicative-relational dimensions. Results underline that general training appears necessary, especially for younger generations of teachers.

Keywords: disabled students, immigrant students, instructional competence, prejudice, teachers

Procedia PDF Downloads 73

Authors: V. A. Doss, R. Sowndarya, K. Juila Rose Mary

Abstract:

Objective: Amino acid neurotransmitter system dysfunction plays a major role in the pathophysiology of depression. The objective of the present study was to identify the amino acids as possible metabolite biomarkers for depression using GCMS (Gas Chromatography Mass Spectrometry) before and after exercise regimen in brain samples of depression induced animal models. Methods: Depression-like behaviour was induced by Chronic Unpredictable mild stress (CUMS). Severity of depression was measured by forced swim test (FST) and sucrose consumption test (SCT). Swimming protocol was followed for 4 weeks of exercise treatment. Brain obtained from depressed and exercise treated rats were used for the metabolite analysis by GCMS. Subsequent statistical analysis obtained by ANOVA followed by post hoc test revealed significant metabolic changes. Results: Amino acids such as alanine, glycine, serine, glutamate, homocysteine, proline and branched chain aminoacids (BCAs) Leucine, Isoleucine, Valine were determined in brain samples of control, depressed and exercised groups. Among these amino acids, the levels of D-Serine and branched chain amino acids were found to be decreased in depression induced rats. After four weeks of swimming exercise regimen, there were improvements in the levels of serine and Branched chain amino acids. Conclusion: We suggest that Serine and BCAs may be investigated as potential metabolite markers using GCMS and their beneficial metabolic changes in Exercise.

Keywords: metabolomics, depression, forced swim test, exercise, amino acid metabolites, GCMS, biomarker

Procedia PDF Downloads 327

Authors: Mohammed S. Assiri

Abstract:

In EFL programs, rating scales used in writing assessment are often constructed by intuition. Intuition-based scales tend to provide inaccurate and divisive ratings of learners’ writing performance. Hence, following an empirical approach, this study attempted to develop a rating scale for elementary-level writing at an EFL program in Saudi Arabia. Towards this goal, 98 students’ essays were scored and then coded using comprehensive taxonomy of writing constructs and their measures. An automatic linear modeling was run to find out which measures would best predict essay scores. A nonparametric ANOVA, the Kruskal-Wallis test, was then used to determine which measures could best differentiate among scoring levels. Findings indicated that there were certain measures that could serve as either good predictors of essay scores or differentiators among scoring levels, or both. The main conclusion was that a rating scale can be empirically developed using predictive and discriminative statistical tests.

Keywords: analytic scoring, rating scales, writing assessment, writing constructs, writing performance

Procedia PDF Downloads 463

Authors: Razieh Rezaeekalantari

Abstract:

Objective: The purpose of this study was to investigate the prediction of parenting orientations in terms of the components of couples' sexual review. Methods: This was a descriptive correlational research method. The population consisted of 500 couples referring to Sari Health Center. Two hundred and fifteen (215) people were selected randomly by using Krejcie-Morgan-sample-size-table. For data collection, the childbearing orientations scale and the Multidimensional Sexual Self-Concept Questionnaire were used. Result: For data analysis, the mean and standard deviation were used and to analyze the research hypothesis regression correlation and inferential statistics were used. Conclusion: The findings indicate that there is not a significant relationship between the tendency to childbearing and the predictive value of sexual review (r = 0.84) with significant level (sig = 219.19) (P < 0.05). So, with 95% confidence, we conclude that there is not a meaningful relationship between sexual orientation and tendency to child-rearing.

Keywords: couples referring, health center, sexual review component, parenting orientations

Procedia PDF Downloads 220

Authors: Vishal V. Choudhari, Gaurao A. Dongre, S. P. Diwan

Abstract:

A number of reference current generation have been developed for analysis of shunt active power filter to mitigate the load compensation. Depending upon the type of load the technique has to be chosen. In this paper, six reference current generation techniques viz. instantaneous reactive power theory(IRP), Synchronous reference frame theory(SRF), Perfect harmonic cancellation(PHC), Unity power factor method(UPF), Self-tuning filter method(STF), Predictive filtering method(PFM) are compared for different operating conditions. The harmonics are introduced because of non-linear loads in the system. These harmonics are eliminated using above techniques. The results and performance of system simulated on MATLAB/Simulink platform. The system is experimentally implemented using DS1104 card of dSPACE system.

Keywords: SAPF, power quality, THD, IRP, SRF, dSPACE module DS1104

Procedia PDF Downloads 592

Authors: Debneil Saha Roy

Abstract:

Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

Procedia PDF Downloads 178

Authors: Sayed Ali Garossi, Azar Heidarizadi, Shahla Mohammad Ganji

Abstract:

Context: Colorectal cancer is the second type of cancer-related mortality globally. Expression of cas8 (caspase 8) is closely connected to growth and metastasis of colorectal cancer.Cas8/Rip1 plays a vital role in the apoptosis pathway and resistance to chemotherapy. The aim of the present study is to investigate the pattern of gene expression in colorectal cancer and compare the differences using Real-Time PCR to find a potential biomarker candidate for colorectal cancer. Methodology: This study conducted real-time PCR to evaluate gene expression of Cas8 in colorectal cancer patients. The gene-specific primer sequences exon–exon junction was designed by OLIGO7 software for the expression of the gene under investigation. Forty-six patient samples without any chemotherapy were selected, including tumoral tissue and adjacent normal tissue samples. The age of the patients was 50 and the size of the tumors was 5.5 cm. The categories were before and after age 50. Findings: Here, we found that Caspase 8 was overexpressed in CRC tissues compared to corresponding adjacent colon tissues (Cas8: 5.2 vs. 1 ratio); high expression of Cas8 was associated with poor overall survival and independent risk factors for the prognosis of CRC patients. Conclusion: In conclusion, our study pioneered the reporting of high Casp8 expression as a predictor of poor prognosis and chemical resistance in CRC patients.Cas8 overexpression suppressed Cas 8 / Rip1-dependent apoptosis and activated the proliferation of tumor cells by activating necroptosis. The necroptosis pathway has also emerged as a new approach to anti-tumor in cancer treatment.

Keywords: Cas8, necroptosis, apoptosis, Real-Time PCR

Procedia PDF Downloads 55

Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le, Chi V. Phan

Abstract:

Wilson’s disease (WD) is an autosomal recessive disorder of the copper metabolism, which is caused by a mutation in the copper-transporting P-type ATPase (ATP7B). The mechanism of this disease is the failure of hepatic excretion of copper to bile, and leads to copper deposits in the liver and other organs. The ATP7B gene is located on the long arm of chromosome 13 (13q14.3). This study aimed to investigate the gene mutation in the Vietnamese patients with WD, and make a presymptomatic diagnosis for their familial members. Forty-three WD patients and their 65 siblings were identified as having ATP7B gene mutations. Genomic DNA was extracted from peripheral blood samples; 21 exons and exon-intron boundaries of the ATP7B gene were analyzed by direct sequencing. We recognized four mutations ([R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G) in the sum of 20 detectable mutations, accounting for 87.2% of the total. Mutation S105* was determined to have a high rate (32.6%) in this study. The hotspot regions of ATP7B were found at exons 2, 16, and 8, and intron 14, in 39.6 %, 11.6 %, 9.3%, and 7 % of patients, respectively. Among nine homozygote/compound heterozygote siblings of the patients with WD, three individuals were determined as asymptomatic by screening mutations of the probands. They would begin treatment after diagnosis. In conclusion, 20 different mutations were detected in 43 WD patients. Of this number, four novel mutations were explored, including [R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G. The mutation S105* is the most prevalent and has been considered as a biomarker that can be used in a rapid detection assay for diagnosis of WD patients. Exons 2, 8, and 16, and intron 14 should be screened initially for WD patients in Vietnam. Based on risk profile for WD, genetic testing for presymptomatic patients is also useful in diagnosis and treatment.

Keywords: ATP7B gene, mutation detection, presymptomatic diagnosis, Vietnamese Wilson’s disease

Procedia PDF Downloads 380

Authors: Thachamon Sinsoongsud, Noppawan Piaseu

Abstract:

This predictive study was aimed to 1) describe self efficacy for risk reduction and preventive behavior for osteoporosis, and 2) examine factors predicting preventive behavior for osteoporosis in nursing students. Through purposive sampling, the sample included 746 nursing students in a public university in Bangkok, Thailand. Data were collected by a self-reported questionnaire on self efficacy and preventive behavior for osteoporosis. Data were analyzed using descriptive statistics and multiple regression analysis with stepwise method. Results revealed that majority of the students were female (98.3%) with mean age of 19.86 + 1.26 years. The students had self efficacy and preventive behavior for osteoporosis at moderate level. Self efficacy and level of education could together predicted 35.2% variance of preventive behavior for osteoporosis (p< .001). Results suggest approaches for promoting preventive behavior for osteoporosis through enhancing self efficacy among nursing students in a public university in Bangkok, Thailand.

Keywords: osteoporosis, self-efficacy, preventive behavior, nursing students

Procedia PDF Downloads 379

Authors: Alaa Ashraf Khaleel Abdallah

Abstract:

Background: The global landscape has witnessed an alarming rise in armed conflicts, further devastating populations through enforced displacement, compromised infrastructure, and strained healthcare systems. In Sudan, the situation is particularly dire, with conflict exacerbating shortages in medical supplies and personnel, pushing the already fragile healthcare system into crisis, especially affecting surgical care. Initially, war impacts were significant in conflict zones like Khartoum, but since mid-April 2023, the entire country has descended into chaos. Weak monitoring and health information systems hinder accurate assessment of surgical care in conflict zones, leading to inadequate resource allocation, suboptimal care, and missed opportunities for global learning. This study investigates the impact of the Sudanese conflict on postoperative complications, exploring prevalence, types, outcomes, and psychological effects in conflict and safe areas. Methods: Conducted across 10 Sudanese states—5 in conflict zones such as Khartoum and West Darfur, and 5 in safer regions like River Nile and Kassala—this study analyzed data from 1,457 patients who underwent surgery post-April 2023. Data were collected using a pretested, mixed-mode questionnaire that incorporated elements from validated frameworks and tailored questions specific to the study's context. Hospital records and surgical logs were also used, with data analyzed via SPSS. Results: The overall prevalence of postoperative complications was 35.89%, with a higher rate in conflict zones (57.5%) compared to safe areas (26.4%). Surgical site infections predominated in conflict zones (24.7%) and higher than its prevalence in safe areas, and while fever was prevalent in safer regions even though much less compared to conflict areas, bleeding from surgical site was very frequent in conflict areas. Most patients recovered within two months at a rate higher in safe areas, but most of them required further medical or surgical management within the first month, but psychological impacts were more pronounced in conflict zones with 22.22% reported anxiety among injuries patients, and 20.6% experienced depression, 13.5% and 16.9% respectively, in those had surgeries for other medical conditions, compared to 0.22%anxiety rates and 8.1%for depression in safer regions. Risk factors included age, travel to conflict zones, access to care, delays, and comorbidities. Conclusion: Strengthening healthcare systems and ensuring accessible surgical care are critical in both conflict and safe areas. Specific attention must be given to addressing patient suffering and demographic shifts caused by armed conflict. Further research is needed to refine the predictive model for postoperative complications in conflict zones.

Keywords: postoperative complications, conflict zones, risk factors, surgical outcomes, Sudan

Procedia PDF Downloads 11

Authors: Dianis Wulan Sari, Takeo Minematsu, Mikako Yoshida, Hiromi Sanada

Abstract:

Introduction: Pruritus is diagnosed as itching without macroscopic abnormalities on skin. It is the most skin complaint of elderly people. In the present study, we conducted a dermatological study to examine the risk factors of pruritic skin and predicted how to prevent pruritus especially in the elderly population. Pruritus is caused several types of inflammation, including epidermal innate immunity based on keratinocyte responses and acquired immunity regulated by type 1 or 2 helper T (Th) cells. The triggers of pruritus differ among inflammation types, therefore we did separately assess the pruritus-associated factors of each inflammation type in an effort to contribute to the identification of intervention targets for preventing pruritus. Therefore, this study aimed to investigate the factors related with actual condition of pruritic skin by examine the skin properties. Method: This study was conducted in elderly population of Indonesian nursing home. Basic characteristics and behaviors were obtained by interview. The properties of pruritic skin were collected by examination of skin biomarker using skin blotting as novel method of non-invasive skin assessment method and examination of skin barrier function using stratum corneum hydration and skin pH. Result: The average age of participants was 74 years with independent status was 66.8%. Age (β = -0.130, p = 0.044), cumulative lifetime sun exposure (β = 0.145, p = 0.026), bathing duration (β = 0.151, p = 0.022), clothing change frequency (β = 0.135, p = 0.029), and clothing type (β = -0.139, p = 0.021) were risk factors of pruritic skin in multivariate analysis. Conclusion: Risk factors of pruritic skin in elderly population were caused by internal factors such as skin senescence and external factors such as sun exposure, hygiene care and skin care behavior.

Keywords: aging, hygiene care, pruritus, skin care, sun exposure

Procedia PDF Downloads 226

Authors: Naveen Kumar Challa, Pavan Verıkıcherla, Madhubalan, Ashısh Sharma

Abstract:

Aim: Aim of the study was to find whether any difference exists in pupil mydriasis measured with Orbscan in non-diabetic and type 2 diabetic patients at various intervals after installation of Tropicamide 0.8% and Phenylephrine 5%. Methods: the Observational study conducted at a tertiary care eye hospital during September 2014 to March 2015. 240 eyes from 120 patients (40 non-diabetic, 80 diabetic) were dilated with Tropicamide 0.8% and Phenylephrine 5%. One drop of a drug was installed twice. The second drop is installed at 20 minutes after installation of the first drop. In two groups’ pupil diameter was measured before installation of drops and also at 15, 30, 45 and 60 minutes after installation of the first drop using both Orbscan. Result: Mean age of the non-diabetic group is 48.67 ± 7.93 years; Diabetic group is 59.97 ± 8.77 years. Mean duration of Diabetes was 7.01 ± 5.05 years. Mean pupil diameter measured with Orbscan before installation of the drops and also at 15, 30, 45 and 60 minutes after installation of first drop in non-diabetic group was 4.18 ± 0.64mm, 6.15 ± 0.41mm, 7.76 ±0.34, 9.59 ± 0.30, and 9.97 ± 0.10 mm respectively and for the diabetic group it was 4.00 ± 0.56 mm, 5.53 ± 0.52 mm, 7.018 ± 0.58mm, 8.25±0.51mm and 9.18 ± 0.46mm respectively. The mean difference between the mean pupil diameters of the non-diabetic and diabetic group shows a significant difference (P< 0.01) at all intervals except before dilatation. There is a significant negative correlation (r = 0.78 – 0.92) between the duration of diabetes and pupil dilatation at all intervals after installation of the drops. There is also significant difference (P< 0.005) in the mean values of pupil diameter between non retinopathy diabetic subjects and diabetic retinopathy subjects at all intervals after installation of drops. Conclusion: People attending eye clinic, whose pupil mydriasis values falls below the normal may be referred for diabetic evaluation. If normative data is established for the pupil size in Indian population using Orbscan then the values fall under normative data could be a predictor for diabetes. This would in turn help ophthalmologist to detect the diabetes at an early stage and prevent the complications resulting from the diabetes.

Keywords: diabetes mellitus, pupil diameter, orbscan, tropicamide

Procedia PDF Downloads 527

Authors: Mahacine Amrani

Abstract:

This paper aims at reviewing some Moroccan industrial applications of wavelet especially in the dynamic identification of a process model using Haar wavelet mother response. Two recent Moroccan study cases are described using dynamic data originated by a distillation column and an industrial polyethylene process plant. The purpose of the wavelet scheme is to build on-line dynamic models. In both case studies, a comparison is carried out between the Haar wavelet mother response model and a linear difference equation model. Finally it concludes, on the base of the comparison of the process performances and the best responses, which may be useful to create an estimated on-line internal model control and its application towards model-predictive controllers (MPC). All calculations were implemented using AutoSignal Software.

Keywords: process performance, model, wavelets, Haar, Moroccan

Procedia PDF Downloads 318

Authors: Sergio J. Calleja, Adria Roca, José D. Santotoribio

Abstract:

Pancreatic endocrine diseases include pathologies like insulin resistance (IR), prediabetes, and type 2 diabetes mellitus (DM2). Some of them are highly prevalent in the U.S.—40% of U.S. adults have IR, 38% of U.S. adults have prediabetes, and 12% of U.S. adults have DM2—, as reported by the National Center for Biotechnology Information (NCBI). Building upon this imperative, the objective of the present study was to develop a non-invasive test for the assessment of the patient’s pancreatic endocrine function and to evaluate its accuracy in detecting various pancreatic endocrine diseases, such as IR, prediabetes, and DM2. This approach to a routine blood and urine test is based around serum and urine biomarkers. It is made by the combination of several independent public algorithms, such as the Adult Treatment Panel III (ATP-III), triglycerides and glucose (TyG) index, homeostasis model assessment-insulin resistance (HOMA-IR), HOMA-2, and the quantitative insulin-sensitivity check index (QUICKI). Additionally, it incorporates essential measurements such as the creatinine clearance, estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), and urinalysis, which are helpful to achieve a full image of the patient’s pancreatic endocrine disease. To evaluate the estimated accuracy of this test, an iterative process was performed by a machine learning (ML) algorithm, with a training set of 9,391 patients. The sensitivity achieved was 97.98% and the specificity was 99.13%. Consequently, the area under the receiver operating characteristic (AUROC) curve, the positive predictive value (PPV), and the negative predictive value (NPV) were 92.48%, 99.12%, and 98.00%, respectively. The algorithm was validated with a randomized controlled trial (RCT) with a target sample size (n) of 314 patients. However, 50 patients were initially excluded from the study, because they had ongoing clinically diagnosed pathologies, symptoms or signs, so the n dropped to 264 patients. Then, 110 patients were excluded because they didn’t show up at the clinical facility for any of the follow-up visits—this is a critical point to improve for the upcoming RCT, since the cost of each patient is very high and for this RCT almost a third of the patients already tested were lost—, so the new n consisted of 154 patients. After that, 2 patients were excluded, because some of their laboratory parameters and/or clinical information were wrong or incorrect. Thus, a final n of 152 patients was achieved. In this validation set, the results obtained were: 100.00% sensitivity, 100.00% specificity, 100.00% AUROC, 100.00% PPV, and 100.00% NPV. These results suggest that this approach to a routine blood and urine test holds promise in providing timely and accurate diagnoses of pancreatic endocrine diseases, particularly among individuals aged 40 and above. Given the current epidemiological state of these type of diseases, these findings underscore the significance of early detection. Furthermore, they advocate for further exploration, prompting the intention to conduct a clinical trial involving 26,000 participants (from March 2025 to December 2026).

Keywords: algorithm, diabetes, laboratory medicine, non-invasive

Procedia PDF Downloads 34