Science, Engineering and Health Studies

Fostering ayurvedic plant wellness: Innovative leaf disease detection using computer vision and machine learning

Sakshi Koli — 2024-10-16

Ayurveda is a conventional medicinal approach that has its roots in India and has been used for hundreds of years. It is still popular today since it is entirely natural and free of side effects. Although ayurvedic medicines are made from natural botanical substances, their safety depends on the way they are administered, taking into account the needs of the individual and the specific disease states they are treating. Diseases that affect the plants' leaves are regarded to be one of the main reasons for a decline in the output of ayurvedic plants in India's agricultural, economic, cosmetic, and pharmaceutical sectors. A plant exhibits signs of plant diseases in diverse sections of the plant, however, leaves are the most frequently observed component for spotting an infection. The objectives of this work are to modernize and innovate an autonomous ayurvedic plant leaf disease detection system by examining the scientific basis of computer vision. Additionally, a full analysis of computer vision techniques such as image pre-processing, segmentation, and feature extraction is covered to detect defects in plant leaves. This work investigates cutting-edge machine learning methods for identifying plant diseases that employ a variety of computer vision techniques. Based on the review, the article addresses the challenges and offers recommendations for changes that could be made in the future.

Herbal medicine use in patients seeking treatment in emergency departments

Sunanta Tangnitipong — 2024-11-05

To ascertain the extent of toxicological profiles around their medical use, herbal medicine use problems need to be consistently traced for consumer safety. Medical information regarding botanical use in various diseases or disorders, as well as adverse/toxicological effects of the botanicals, was sourced from scientific databases. Patients who request medical care at hospitals may be using at least one formulation of botanicals at the time of the visit, and some of the used medicinal plant products possess the potential to be the cause of toxicity as well as herb–conventional drug interactions. Emergency healthcare providers must be aware of some potential adverse events that may be seen in admitted emergency department patients when the herbal medicine is used alone or concomitantly with conventional medicine. Moreover, emergency healthcare providers must be knowledgeable of herbal medicine practices to ensure that optimal treatments will be selected for these patients.

First aid training using virtual reality

Burapa Phatichon — 2024-07-23

This work considers the use of virtual reality (VR) technology to self-teach first aid training. It is known that VR provides realistic experiences to train individuals. We created interactable first aid lessons using the Unity engine and a VR interaction framework, and provided hands-on experience, with tests based on practical exercises. The VR application materials, with the first aid knowledge gathered from many government and hospital websites, consisted of 10 lessons and 7 tests. The lessons were appraised by 14 learners, resulting in a total average satisfaction score of 9.1. The post training first aid knowledge test scores increased by 35% from pre-course level. All learners reported having greater confidence, with their practical test scores improving by an average of 22% after multiple tests, demonstrating that the application could be effectively used for learning and practice purposes.

Service priority classification using machine learning

Teratam Boonprapapan — 2024-10-03

This article details a procedure for classifying service cases with various priority levels based on machine learning (ML). It accurately defines the priority level of each service case. The presence of imbalanced datasets in service cases poses a challenge for achieving reliable classification accuracy. To address this, the use of the synthetic minority over-sampling technique (SMOTE) was proposed as the method for balancing the datasets prior to applying the ML method. From these experimental results, an improvement in the precision of the learning process was observed, which led to better outcomes in the test sets. This improvement was measured using the efficiency metrics from the confusion matrix. The experiment involved 6,182 service cases, categorized into four levels: critical, serious, moderate, and low. These were based on test comparisons with other ML methods. The accuracy achieved in the test data was 94.37%. By employing a hybrid technique to address the imbalance in SMOTE and the support vector machine model, it was found to be more effective than the comparative term frequency-inverse document frequency model that was used in conjunction with cosine similarity, which achieved an evaluation score of 70.14%.

Development of empirical models for calculating global and diffuse erythemal weighted solar ultraviolet radiation under clear sky conditions in Thailand

Pradthana Laiwarin — 2024-10-28

This study introduced semi-empirical models for calculating hourly global and diffuse erythemal weighted ultraviolet (EUV) solar radiation under clear sky conditions in Thailand. To develop these models, global and diffuse erythemal ultraviolet data collected over a decade (2011–2020) from four solar monitoring stations situated in the main regions of Thailand were used. The data was classified into two groups. The first group (2011–2018) was used for modeling, while the second group (2019–2020) was reserved for model validation. The global and diffuse EUV radiation models revolve around semi-empirical functions. These functions express both types of EUV radiation in terms of normalized variables, specifically the total ozone column, aerosol optical depth, and air mass. To assess the accuracy of the proposed models, their outputs for hourly global and diffuse EUV radiation were calculated at the four monitoring stations. These outputs were then compared against actual measurements to validate the effectiveness of the models. The evaluation revealed a root mean square difference of 15.8% for global EUV radiation and 14.9% for diffuse EUV radiation when compared to the mean measured values.

Study of alternatives for preserving enamelled goldware using thermoplastic acrylic resin via scientific examination

Teeraphong Phukeaw — 2024-11-14

Four conservation-grade thermoplastic polymers (PB-44, PB-48N, PB-67, and PB-72) were investigated as potential materials for conserving enamel objects. The research aimed to determine the appropriate concentration of Paraloid resin and solvents for forming a film on a silver test plate with a grooved surface. To enhance film visibility, an acrylic green additive, used in King "Ra cha wa dee" enamels, was added. A 20% w/w concentration of Polymer resin with toluene as the solvent showed bubble-free films with no residual resin lumps or pigment additive clumping. The films (PB-44, PB-67, and PB-72) exhibited high transparency, ranging from 98% to 99%, except for PB-48N, which showed a transparency of 77.65%. Each polymer had unique characteristics: PB-44 was strong (Young's modulus of 827.94±50.87 MPa), PB-72 was flexible (elongation at break of 22.10±1.55 %), PB-48N was viscous, and PB-67 had a hard, hydrophobic surface. The films exhibited high thermal stability with degradation temperatures exceeding 200°C and glass transition temperatures over 50°C. They could be readily removed using toluene, acetone, or xylene, without residue. This research provides valuable insights into the properties of Polymer resin films for preserving enamelled goldware, especially in Thailand's tropical climate. These findings are crucial for effective conservation and restoration efforts.

Soil erosion analysis for flood disaster assessment using GIS-based RUSLE model in Kota Belud, Sabah, Malaysia

Kamilia Sharir — 2024-11-21

Soil erosion is one of the significant environmental problems and main contributors to flood events, especially in the Kota Belud district of Sabah, Malaysia. A detailed assessment of soil loss prediction and its consequences was carried out in this district using the revised soil loss equation (RUSLE) model with a geographical information system (GIS). A thematic data layering method was used to analyze risk areas and identify possible high-risk erosion zones. The RUSLE model used GIS as the spatial information analysis method for calculating the amount of soil
loss (tons/ac/year). Approximately 61.50% (89 ac) of the area was classified as very low risk, 2.67% (4 ac) low risk, 4.76% (7 ac) moderate risk, 3.57% (5 ac) high risk, and 27.50% (40 ac) very high risk. All the outcomes revealed that GIS integration might be used for regional spatial analysis. Total value maps can be used to plan inevitable development, such as housing, farming, and hazard and risk management.

Factors influencing the properties of zein nanoparticles encapsulated with fragrances prepared by liquid-liquid dispersion

Usaraphan Pithanthanakul — 2024-07-12

The duration of the fragrance is one of the factors that influences a customer’s choice of fabric softeners. Fragrances, a mixture of various aromatic compounds, usually present low solubility and stability in the environment, so they do not last long. Micro/nanoencapsulation technology of fragrances can be used to solve this problem. This research studied the factors influencing the preparation of zein nanoencapsulation with fragrances. Fruity fragrances were encapsulated in zein nanoparticles (PF-ZNs) by the liquid-liquid dispersion method, using Tween 20 as a surfactant. The effects of zein and ethanol concentrations of 0.4%–0.8% and 70%–85%, respectively, homogenized at 15,000 rpm for 5–15 min on zein encapsulation, were investigated. The fruity fragrance was loaded at 30% of the zein content. Increased zein concentration resulted in increased particle size with decreased zeta potential. Particle agglomeration was detected when the ethanol concentration was decreased from 85% to 75%. Compared to using a vacuum concentrator centrifuge, the zein nanoparticles agglomerated less when freeze-dried. The encapsulation efficiency of the fruity fragrance was 39.7%–68.4%, and the yield percentage was 54.5%–72.3% when freeze-drying was used.

Antibacterial activity of Mitragyna speciosa Korth. leaves

Muhammad Niyomdecha — 2024-08-05

The crude extracts of red vein kratom leaves (hexane, dichloromethane, ethyl acetate, ethanol, and 50% acetic acid) and mitragynine were tested for antibacterial activity against gram-positive Staphylococcus aureus and gram-negative Escherichia coli using the disk diffusion method. The minimum inhibitory concentration (MIC) values against S. aureus and E. coli were determined. The acetic acid crude extract was effective against both S. aureus and E. coli with an inhibition zone of 5.52±0.44 and 4.65±1.02 mm, at a minimum concentration of 6 and 9 mg/mL, respectively. Mitragynine was active against S. aureus with an inhibition zone of 4.35±0.68 mm and a MIC of 6 mg/mL.

Comparison between consumer-oriented and laboratory benchtop near-infrared spectrometers for total soluble solids measurement in grape

Pramote Khuwijitjaru — 2024-11-14

Portable near-infrared (NIR) spectrometers are gaining interest as a non-destructive tool for fruit quality determination in the decade. In this study, the performance of the portable NIR spectrometer for TSS prediction of grapes was evaluated and compared to the benchtop spectrometer. A total of 105 table grapes were individually measured NIR spectra at short-wavelength (740 – 1070 nm) and long-wavelength NIR region (1000 – 2500 nm) using SCiO and NIRFlex N-500 spectrometers, respectively. Partial least square (PLS) regression analysis was performed on berry spectra from both devices afterward significant wavelengths were identified and used to develop multiple linear regression (MLR) models. The best PLS prediction model was obtained from SNV pretreating spectra for both devices and spectral data acquired from SCiO showed better prediction performance (= 0.854, SEP = 0.452°Brix) than NIRFlex N-500 spectra (= 0.667, SEP = 0.675°Brix). Low predicting ability was gained for the MLR calibration model for both device with an RPD of about 1.70. From the results, a pocket-size spectrometer has the potential to be a non-destructive sorting and screening tool in fruit industries.

Drug use patterns in COVID-19 patients: A retrospective survey 2021–2022

Phaksachiphon Khanthong — 2024-07-18

This retrospective survey examines drug use patterns in COVID-19 patients from 2021 to 2022 with 81 participants, who reported 13 symptoms between March and May 2023. Application of the k-means clustering method led to identification of three distinct symptom severities, severe (Cluster I), moderate (Cluster II), and mild (Cluster III), with respective average scores of 3.67±0.87, 3.20±0.98, and 1.87±0.81. In Clusters I and II, myalgia was the most notable symptom, while in Cluster III, sore throat was predominant. On average, individuals in Clusters I–III used 2.00–2.34 types of drugs, with use of a single drug having the highest frequency. Notably, Andrographis paniculata capsules were highly utilized across all clusters (51.85%), while favipiravir was less often used. Furthermore, one in five participants in the combined Clusters I and II employed substantial pharmaceutical interventions for COVID-19 treatment, whereas in Cluster III, this use remained below 10%. This research provides valuable insights into drug use patterns for managing COVID-19. The findings offer crucial information about symptoms from each cluster, tailoring treatment approaches to specific symptom severity clusters as well as overlapping medications.

Artificial intelligence-aided rational design and prediction model for progesterone-loaded self-microemulsifying drug delivery system formulations

Porawan Aumklad — 2024-07-18

Artificial intelligence (AI) is now applied across various domains in nanomedicine. Self-microemulsifying drug delivery systems (SMEDDS) are isotropic mixtures of active compounds that can produce spontaneous oil-in-water emulsions. SMEDDS can improve the solubility of lipophilic drugs such as progesterone (PG). However, the physicochemical properties of SMEDDS are sensitive to various factors, depending on their components. This study generated a prediction model algorithm for PG-loaded SMEDDS to provide appropriate droplet size (DS), polydispersity index (PDI), zeta potential (ZP), and % drug loading (%DL). Various machine learning algorithms were compared for their accuracy, as reported by root mean square error (RMSE) and coefficient of determination (R²). The selected machine learning algorithms were implemented with an unseen training dataset, and the model performance was re-evaluated. The correlation of each factor was investigated. Self-micro emulsifying (SME) time, cloud point, pH, and viscosity of predicted PG-loaded SMEDDS were evaluated. Results showed that linear regression algorithms gave the highest accuracy and optimal prediction performance with the highest RMSE and R². All components of PG-loaded SMEDDS correlated with DS, PDI, ZP, and %DL. The physical properties of predicted PG-loaded SMEDDS showed SME time within 39 s, cloud point at around 71.3 °C, pH between 5.53 and 6.10, and viscosity between 10.32 and 14.23 cP. This research outlined the application of a machine learning algorithm to build a prediction model to optimize PG-loaded SMEDDS drug delivery formulations.

Antimicrobial properties of Citrus maxima flavedo extracts against food pathogens and spoilage microorganisms

Mohd Fahmi Mastuki — 2024-07-23

This study assesses the antimicrobial potential of ethyl acetate and dichloromethane extracts obtained from pomelo, Citrus maxima (C. maxima), flavedo against various food pathogens and spoilage microorganisms. The antimicrobial activities of these extracts were evaluated using the agar disc diffusion method against gram-positive bacteria (Bacillus cereus), Staphylococcus aureus and gram-negative bacteria (Escherichia coli). The results indicated that both extracts demonstrated antibacterial properties against the tested microorganisms. The ethyl acetate extract exhibited significantly higher antibacterial activity against the majority of bacterial strains compared to the dichloromethane extract, particularly against S. aureus and B. cereus. However, dichloromethane extract showed a better effect on E. coli, with the inhibition zone ranging from 8.7 to 11.3 mm. S. aureus displayed the highest sensitivity to ethyl acetate and dichloromethane extracts of pomelo flavedo with inhibition zones ranging from 1.3 to 1.5 mm, respectively. In conclusion, the findings suggest that pomelo extracts have significant potential as natural antimicrobials and can be safely utilized as food preservatives. This highlights the value of pomelo as a potential source of antimicrobial compounds for food safety and preservation purposes.

Physiotherapy interventions for motion sickness: A systematic review

Tushar Palekar — 2024-07-23

Motion sickness susceptibility depends on the sensitivity of each individual and the ability of the vestibular system to adapt to continued exposure to the stimulus affecting activities of daily living. For this systematic review, data were extracted from PubMed, Pedro, Cochrane, and Google Scholar from 2000 to 2021 publication dates using the following MESH terms: ‘motion sickness’, ‘exercise’, ‘physiotherapy’, and ‘physical therapy’. A total of 41,789 articles were identified from 2 databases, of which 41,767 were excluded, and 18 were saved for secondary screening. After a detailed review of these articles, 7 articles were selected, including RCTs, case studies, and experimental studies. Strong evidence was identified for 2 strategies used, including breathing techniques and vestibular adaptation exercises. Physiotherapy interventions play an important role for individuals with motion sickness by alleviating the symptoms.

Modified mini-incision surgery for carpal tunnel syndrome: Technique and clinical outcome

Saran Malisorn — 2024-08-05

Carpal tunnel syndrome (CTS) is caused by the shortening of the median nerves in the wrist, resulting in hand pain and paralysis necessitating surgical operation for relief. Conventional open carpal tunnel release (CTR) procedures, involving long incisions, often lead to complications, delaying patients’ recovery for weeks or months. Therefore, mini-incision surgery has emerged as a preferred option, offering reduced pain, smaller wounds, and improved appearance. This study aimed to compare the outcomes of modified mini-incision surgery for CTS before and after the operation. It was a retrospective study involving 80 patients, with data collected from medical records pre- and post-operation, specifically at the 2^nd week, 3^rd month, 6^th month, 12^th month, and 24^th month intervals. Variables included operation time, incision length, pinch strength, gripping strength, two-point discrimination (2-PD), visual analogue scale (VAS), Levine score, quick disabilities of the arm, shoulder and hand (Quick-DASH), wound pain, and pillar pain. Data were analyzed using descriptive statistics and logistic regression, with a significant level of 0.05. The mean incision length was 11.54 mm. At the 2-week post-operative mark, the pinch strength was 5.43, gripping strength was 14.96, 2-PD was 5.84, the VAS score was 2.86, the Levine symptom was 3.84, and the DASH score was 69.43. There was a relationship (p-value<0.05) between preoperative and postoperative measures for pinch strength, gripping strength, 2-PD, and Levine symptom condition. The study on 80 patients who underwent modified mini-incision surgery for CTS at Naresuan University Hospital in Phitsanulok, Thailand, found that these parameters showed significant improvement postoperatively. Patients demonstrated good recovery and condition 2 weeks after the mini-incision surgery for CTS.

Review of improvements in upper limb function with balance training and adjunct intervention in stroke survivors

Candace Xiao Huey Goh — 2024-08-13

Balance is one of the main factors influencing the ability to walk. Various rehabilitation strategies have been developed to help stroke survivors regain functional ability. However, limited evidence is available on the effects of balance training on improving upper limb function. The goal of this review was to summarize the most recent research on the benefits of balance training and adjunct intervention for upper limb function in stroke survivors. Using the search phrases “stroke” AND “balance training” AND “hand function", the PubMed and Scopus databases were used to find relevant articles. Only those published in English were chosen, while the study included randomized controlled trials held between 2012 and 2021 involving stroke survivors aged 18 and above who underwent balance training. Dissertations, case studies, and review articles were excluded. Overall, 28 of 237 articles were eligible after screening based on the eligibility criteria. Ten articles were selected for the review. The intervention and control groups had 156 and 154 participants, respectively. Core muscle exercises; adjunct interventions such as virtual reality, action observation, and resistance training; and comparisons with combination therapy were among the main types of balance training. The Fugl-Meyer assessment upper extremity and Wolf motor function test were the primary outcome measures used to evaluate upper limb function. Of the ten trials chosen, six utilized combination therapy and demonstrated noticeable improvements in upper limb function.

Intelligent healthcare for cardiac patients utilizing neural networks, k-means clustering, and ad hoc routing

Annwesha Banerjee Majumder — 2024-10-02

This paper introduced a comprehensive healthcare system designed to address the unique challenges faced by cardiac patients in economically and geographically constrained regions. The system operated in three phases, each contributing to the effective care of patients. In the initial phase, an artificial neural network model identified potential cardiac patients with an impressive accuracy of 90.16%, demonstrating its potential for early detection. The second phase employed k-means clustering to categorize patients into three groups based on the severity of their condition, facilitating precise prognosis and stratification. Finally, the system utilized an innovative ad hoc routing algorithm to securely transmit patient data to remote servers, enabling expert monitoring and consultation. The study's outcomes demonstrate the system's ability to accurately identify at-risk patients, appropriately categorize their condition, and efficiently route critical data. This holistic approach leverages cutting-edge technologies and methodologies to transform healthcare delivery in underserved areas. The novel system presents a promising avenue for enhancing cardiac care in regions with limited access to advanced healthcare services, ultimately improving patient outcomes and reducing disparities in cardiovascular healthcare.

Relationships between mental health literacy and stress, depression, and anxiety among patients with chronic heart failure in a cardiac outpatient department

Amporn Buasan — 2024-10-22

This cross-sectional analytical study investigated the relationships between mental health literacy and stress, depression, and anxiety in chronic heart failure patients within a cardiac outpatient department. A simple random sample of 116 patients was included. Data were collected using questionnaires covering participant demographics, clinical information, and mental health aspect. The content validity (IOC) ranged from 0.67 to 1, and reliability, assessed using Cronbach's alpha coefficient, was 0.78 to 0.90. Data were analyzed by descriptive statistics and Pearson's correlation statistics. Findings reveal an average participant age of 60.86±12.98 years, with a male majority (66.38%). Mean scores were: mental health literacy component 184.28±23.38, stress 3.15±2.32; depression 7.83±4.84, and anxiety 52.27±11.58. Significant negative correlation existed between mental health literacy and stress (r = -0.306, p<0.001), depression (r = -0.590, p<0.001), and anxiety (r = -0.574, p<0.001). The study indicated that mental health literacy was at a good level among chronic heart failure patients, demonstrating a negative association with stress, depression, and anxiety. Therefore, cardiac outpatient departments could integrate care management strategies to promote mental health literacy and mitigate stress, depression, and anxiety in these patients.

Stillbirth prevention with counseling during antenatal care visits in pregnant women: A systematic review

Magdalena Paunno — 2024-10-22

At important stages in women's lives, antenatal care (ANC) serves as a means of communication for women, their families and communities. This study aimed to determine the effectiveness of counseling during ANC as an intervention to prevent stillbirths. Narrative studies with scientific journal database sources published in PubMed, EBSCO and Science Direct were used. This study was conducted during September–November 2023. The keywords used were stillbirth, counseling, ANC, and pregnancy, and the search included research published within five years of the study. A total of 185 articles were found, and 12 articles were selected for review. The findings indicate that an effective intervention to prevent stillbirth can be carried out through counseling during ANC visits. Antenatal counseling is the intervention of choice to change the behavior of pregnant women and health workers and improve the quality of ANC as a strategy for preventing stillbirths. ANC counseling topics that are effective in preventing stillbirth include weight control, breastfeeding, nutrition, physical activity, alcohol, smoking, HIV, drugs, and safe medication during pregnancy. By enhancing the quality of ANC and focusing on education and support, healthcare providers can play a crucial role in preventing stillbirths and promoting maternal and infant health.

Risky reproductive health behavior and information access on adolescents: Mix-method analysis

Muhlisa Muhlisa — 2024-10-29

Adolescence is a vital period of development marked by confusion, a range of emotions, and an understanding of adult behavior, the environment, enthusiasm, and an inclination to explore, particularly with drugs, alcohol, and sex. This study outlines adolescents' knowledge, attitude, access to information and risky behaviors related to reproductive health. This study employed a concurrent triangulation design, using mixed methodologies. The chi-square test was used to examine this form of quantitative study. The focus groups and in-depth interviews with teenagers in several agencies, along with the accompanying teachers and parents, provided qualitative information for this study. Adolescents had greater access to knowledge about reproductive health issues, but the majority were unable to make effective use of this information. Numerous risky habits, including drug use, also set off unsafe sexual behaviors. In contrast to their parents or professors, teenagers increasingly feel more at the ease of sharing stories with their friends. It is intended that parents and educators will act in a peer-like manner by imitating teenagers’ social cues. Quantitative analysis revealed relationships among information access, attitude, knowledge, and reproductive behavior. Teenagers typically have higher knowledge of their reproductive health. However, the majority of them struggle to put this knowledge into practice, and as a result, many engage in risky sexual behavior out of curiosity.

Optimization of hydroxypropyl β-cyclodextrin-isophane insulin complex-loaded thiolated chitosan/sodium alginate nanoparticles using full factorial design

Benchawan Chamsai — 2024-11-05

Isophane insulin (N) is challenging to administer orally due to its gastrointestinal instability. Research has shown that partially complexing N with hydroxypropyl β-cyclodextrin (HPβCD-N complexes) enhances its stability. Nanoparticles (NPs) are also frequently used to encapsulate drugs to protect them from degradation. This study aimed to optimize HPβCD-N complex-loaded thiolated chitosan/alginate (TCS/ALG) NPs using a full factorial design. The HPβCD-N complexes were prepared before being loaded into the NPs. Independent variables included the concentrations of TCS, ALG, and HPβCD-N complex, while the dependent variables were particle size and zeta potential. The results demonstrated that TCS and ALG concentrations had a positive and negative effect on particle size, respectively, with smaller particles being favored. The zeta potentials of the NPs increased positively and negatively in proportion to the TCS and ALG concentrations, respectively. The HPβCD-N complexes had a minimal effect on the dependent variables. The NPs made with TCS, ALG, and HPβCD-N complex concentrations of 0.075%, 0.0375%, and 5% w/w, respectively, were the most suitable for achieving small particle size and zeta potential within +30 mV and +50 mV. TEM images showed spherical particles with nanometer diameters. The encapsulated N was found to be approximately 1% w/w, confirming successful loading of N into the NPs. Thus, the optimized formulation shows potential as an NP carriers for N delivery.

Molecular docking study of co-trimoxazole against SARS-CoV-2 main protease and RNA-dependent RNA polymerase: An in silico approach

Nommanudien Naibkhil — 2024-11-08

The coronavirus disease 2019 (COVID-19) pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused significant morbidity and mortality worldwide. Although various therapeutic options are being explored, there is still a need for effective treatments. Co-trimoxazole, a broad-spectrum antibiotic, has shown promising results in clinical studies in patients with COVID-19; however, its direct antiviral activity remains unclear. Thus, this study aimed to evaluate the direct effect of co-trimoxazole on SARS-CoV-2 using computational approaches. The molecular interactions for co-trimoxazole were analyzed against two vital SARS-CoV-2 proteins, the main protease (M^pro) and the RNA-dependent RNA polymerase (RdRp), using AutoDock Vina. Our findings reveal that both components of co-trimoxazole, sulfamethoxazole, and trimethoprim, exhibit good binding affinities with M^pro and RdRp, implying their potential inhibitory effects on viral replication with binding energies of < - 6 kcal/mol, which were close to reference drugs. This suggests that co-trimoxazole may offer therapeutic benefits for COVID-19 patients, beyond its ability to reduce inflammation and secondary infections. More clinical studies are warranted to investigate its safety and potential as a treatment option for COVID-19.

Factors affecting women’s mental health in Nigeria in the past and present: A systematic review

Joy Johnson Agbo — 2024-11-18

This study conducted a systematic review to explore the contemporary and historical determinants influencing women's mental health in Nigeria. Utilizing databases such as Web of Science, PubMed, Sage, Medline, and BioMed Central, a comprehensive search was performed to identify relevant literature. After carefully applying predefined inclusion and exclusion criteria, 39 published research papers were selected for the review. The study examines various factors impacting women's mental health in Nigeria, including socio-economic, health-related and cultural determinants. Specifically, socio-economic factors such as poverty were analyzed in 21 studies while health-related issues such as postpartum depression were addressed in 8 studies. In addition, cultural elements, including psychological traumas leading to conditions like psychosis, sadness, and stigma, were explored in 10 studies. The findings highlight the need to prioritize women's mental health in Nigeria, emphasizing the interconnectedness of societal well-being, which includes both physical and mental health. Based on these findings, it is essential for policymakers in Nigeria to implement comprehensive mental health initiatives that target the socio-economic, health-related and cultural factors highlighted in the research. The delivery of accessible and affordable mental health services customized to the specific needs of Nigerian women should be prioritized.

Enhancing human activity recognition with lightweight CNN models and integrated blocks

Teppakorn Sittiwanchai — 2024-09-02

Human activity recognition (HAR) is crucial for health tracking, fitness monitoring, and fall detection systems. Recently, convolutional neural network (CNN) models have been proven to be highly effective for HAR tasks. This study aimed to enhance HAR performance by integrating specific architectural improvements, namely identity, convolutional, and bottleneck blocks, into lightweight CNN models. To evaluate the effectiveness of these enhancements, two data sets were utilized: HAR using smartphones data set version 1.0 (UCI-HAR) and wireless sensor data mining activity prediction data set version 1.1. The results indicated that the convolutional and identity block models outperformed the original lightweight CNN model on both data sets. The proposed models strike a balance between high performance and computational complexity, thereby making them suitable for real-world applications. The findings of this study contribute to the field of HAR and provide valuable insights for improving the recognition and classification of human activities.

Digital twin for decision support system of industrial utility management

Thanatip Satjeenphong — 2024-11-05

Manufacturing and industrial operations rely heavily on energy that is generated mostly from fossil fuels, such as coal, oil, and natural gas, which harm the environment and contribute to climate change. Thus, renewable energy is being integrated into industrial processes to lessen environmental effects and reduce fossil fuel usage. However, the renewable source performance process can be greatly affected by disturbances and constraints, such as ambient air temperature and relative humidity, minimum utility consumption, and the total energy required, making effective control difficult. This study proposes a digital twin built with machine learning regression techniques for load demand forecasting as a decision-support system for industrial utility management. From the results, the ensemble tree (ET) model produced the highest accuracy, based on validation and test dataset root mean squared error values of 23.164 and 27.558, respectively, and R² values of 0.96 and 0.95, respectively. The digital twin and load demand forecasting approaches effectively created an efficient operating schedule for industrial utility management. The ET model had a total error of 23.86%, substantially lower than the average load demand's total error of 65.29%. Therefore, the ET model with weather conditions in four scenarios could be recommended to optimize energy utilization when creating the operating schedule.

Implementation of good distribution practice in a Thai pharmaceutical manufacturer

Pana Chinajitphan — 2024-11-20

Good distribution practice (GDP) has recently been adopted and practiced by the pharmaceutical industry in Thailand, complementing the supply chain perspective and fundamentally transforming the industry’s quality landscape. This study proposed a GDP implementation methodology that integrates project management and quality risk management (QRM) to effectively identify, manage, and mitigate distribution risks. The project management framework provides valuable tools and approaches for prioritizing operational gaps, managing expectations, and securing necessary resources, while QRM focuses on the risk aspects such as operation impact, reputation, and cost for manufacturers. The methodology was successfully applied to a pharmaceutical manufacturer to ensure product quality during storage and distribution and to prevent counterfeit products from infiltrating the supply chain. This study provides 4 insights into GDP implementations: (1) fostering a project management culture, (2) utilizing existing templates and tools, (3) implementing continuous compliance monitoring, and (4) embracing digital transformation through information technology systems. This implementation represents an opportunity to engage executives and extend the quality program beyond manufacturing into logistic activities.