Science, Engineering and Health Studies

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

2023-07-15T20:19:24+07:00

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

2023-12-05T10:17:11+07:00

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

2023-12-26T12:32:21+07:00

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.

RECSALMO: Rapid typing and characterization tool for whole genome sequencing data of Salmonella

2024-04-03T22:22:48+07:00

Salmonella is one of the most prevalent foodborne pathogens. Endemic events involving Salmonella now occur more frequently and must be handled efficiently and quickly. Whole genome sequencing (WGS) cost and speed have improved significantly during the past decades, with the number of bioinformatics tools based on WGS data also increasing. This research presented the “RECSALMO” tool, designed specifically to analyze Salmonella genome assemblies. This tool can quickly type and characterize a collection of Salmonella genomes, providing extensive information and assist related healthcare personnel to effectively and timely manage a Salmonella epidemic. The essential outputs of the tool include sequence-based typing comprising serotyping, MLST and cgMLST, antibiotic resistance genes with their associated classes/subclasses of antibiotic drugs, Salmonella pathogenicity islands (SPIs) from SPI-1 to SPI-17, and spacer profiles (CRISPR locus 1 and 2). Moreover, the tool generates SNP-based trees, CRISPR-based dendrograms and pie charts of serotypes and ST indexes. Due to the superior resolution, quick operational time, and low processing cost of WGS, RECSALMO is considered a practical bioinformatics tool for comprehensive and rapid examination of Salmonella genomes.

Enhancing human activity recognition with lightweight CNN models and integrated blocks

2023-11-24T00:46:34+07:00

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

2023-10-10T15:21:05+07:00

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

2024-02-07T20:15:38+07:00

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.

Bathroom design for enhanced comfort in Indonesian older adults and the disabled community

2024-03-30T09:06:00+07:00

Individuals with certain physical conditions encounter challenges in using the bathroom optimally due to various design limitations. For instance, older adults may fear falling in the bathroom, while individuals with disabilities may have troubles due to wheelchairs. The objective of this study was to propose an efficacious bathroom design tailored to the needs of older adults and the disabled community in Indonesia. In this context, “efficacious” means to reduce the gap between physical requirements and object limitations to enhance user comfort. Anthropometric measurements were used to ascertain the appropriate dimensions for the bathroom. Importantly, the proposed design is versatile enough to accommodate young adults without disabilities, addressing space constraints in smaller homes.

Effect of glycerol concentration as a biocoating on lotus leaf fracture improvement

2024-05-15T11:20:58+07:00

This study investigated the fracture improvement of dried lotus leaves using glycerol, beeswax, and chitosan as coating agents. The aim of this study was to investigate the effects of glycerol on lotus leaf fracture improvement. The optimum glycerol content for biocoating was investigated using the recovery percentage of lotus leaves after coating, which indicates the elastic properties of lotus leaves. The 27% recovery rate was increased after treatment with 5 g chitosan, 24% v/v glycerol, and 76% v/v acetic solution for 1 d of immersion. The moisture content and thickness of the coated lotus leaves increased by 38% and 7.1%, respectively. Meanwhile, the contact angle decreased by 24% because of the increase in moisture content. Moreover, the mechanical properties showed that Young’s modulus decreased by 76% and the elongation increased by 78% after coating because of the improved fracture of dried lotus leaves. Additionally, thermal stability was increased compared with that of dried lotus leaves according to thermal analysis.

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

2023-10-14T21:48:00+07:00

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

2023-12-15T10:07:11+07:00

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.

AI-driven design and optimization of nanoparticle-based drug delivery systems

2024-09-03T15:30:14+07:00

Nanoparticle-based drug delivery systems represent a transformative advancement in targeted therapeutics, providing meticulous drug delivery, enhanced bioavailability, and diminished side effects. However, designing nanoparticles (NPs) optimal for specific drugs and diseases remains a complex challenge. The advancements in artificial intelligence (AI) have provided innovative approaches to design and optimize these systems, improving their efficacy and adaptability. This review encompasses the integration of AI in the conceptualization and development of NP drug delivery systems, signifying its potential to revolutionize the field. The review discusses the different AI methods such as machine learning, neural networks, and optimization algorithms that simplify the fabrication of NPs with tailored characteristics such as size, surface chemistry, and drug release profiles. AI can also standardize these characteristics to enhance drug loading capacity, targeting specificity, and controlled release at the chosen site of action. AI-based predictive modeling enables the quick screening of numerous parameters, thus quickening the discovery of optimal NP configurations tailored to specific therapeutic needs. Furthermore, the review also discusses the case studies where AI has efficaciously forecasted NP behavior in biological environments, crucial for enhanced targeting and diminished off-target effects. The amalgamation of AI and nanotechnology not only streamlines the drug development process but also paves the way for personalized medicine. The review also entails the different challenges associated with implementing AI in this field, such as data quality, algorithm transparency, and regulatory specifications. By utilizing AI, researchers and healthcare providers can unlock new potentials in novel drug delivery systems, ultimately advancing the precision and effectiveness of treatments for various diseases. Finally, the review discusses the future directions of AI-based NP design, highlighting its benefits to transform drug delivery and augment patient outcomes.

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

2024-01-03T19:26:13+07:00

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

2024-02-05T12:12:31+07:00

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

2024-01-01T17:51:59+07:00

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

2023-11-13T19:24:45+07:00

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

2024-01-17T15:14:34+07:00

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

2023-11-26T21:38:26+07:00

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

2023-11-30T20:23:16+07:00

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

2024-02-14T20:33:53+07:00

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

2024-04-01T21:36:43+07:00

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

2024-01-31T20:20:05+07:00

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

2024-05-15T20:40:43+07:00

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

2024-03-20T19:35:59+07:00

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

2024-03-27T22:11:51+07:00

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.

Impact of the COVID-19 pandemic on direct medical costs of acute coronary syndrome admissions in Yogyakarta, Indonesia

2024-04-04T18:11:08+07:00

The number of admissions, therapeutic management, and clinical results of acute coronary syndrome (ACS) have been extensively reported. However, the economic impact of ACS therapy has been ignored. This study examined the effect of the COVID-19 pandemic on direct medical costs for ACS hospitalizations in Yogyakarta, Indonesia. A retrospective study of patients with ACS admitted to one of three hospitals during March to August in 2019, 2020, and 2021 was conducted. Direct medical costs from a provider’s perspective were estimated using the charges. All costs were reported in Indonesian Rupiah (IDR) at a 2021 value. Multiple linear regression analysis was used to evaluate the impact of the pandemic on total direct medical costs. The total direct medical costs associated with ACS admission per patient during the pandemic period was approximately 40% higher than that during the pre-COVID period (28.41 vs. 20.33 million IDR or 1976.90 vs. 1414.65 USD). A substantial impact of the COVID-19 pandemic on total direct medical costs still remained after adjusting the ACS types, hospital types, length of stay, percutaneous coronary intervention, in-hospital mortality, and delay time of onset to first medical contact. Given the limited health budget, reallocation of health care resources must be prioritized to maintain the provision of essential health care for those who timely treatment is crucial.

A model for monitoring the medication rechallenge system in Queen Savang Vadhana Memorial Hospital

2023-12-15T10:19:49+07:00

A rechallenge process is crucial for patients who need to use drugs known to cause allergic reactions. Establishing a standardized rechallenge protocol is essential to protect patients and increase the likelihood of using effective medications. This study aimed to evaluate the outcomes of the rechallenge monitoring system implemented at Queen Savang Vadhana Memorial Hospital. In this retrospective study, rechallenges focused on two drugs categories: antimicrobial drugs and non-steroidal anti-inflammatory drugs. The confirmed rechallenge protocol was introduced in 2018, and data from October 2019 and November 2022 were extracted from the hospital’s electronic database. The primary outcome measured was the number of adverse events during rechallenges. Trends in severe allergic reactions were documented, and descriptive statistics were used. Out of 81 total rechallenge events, with a history of 77 mild reactions (95.1%) and 4 serious events (4.9%), only 3 mild reaction (3.7%) occurred. A positive trend in successful rechallenges was observed following the implementation of the monitoring model, along with a decrease in medication error levels B and C. Importantly, no harmful levels D and E were detected. The rechallenge monitoring system improved the chance of safe medication use and reduced the occurrence of seriously adverse drug events.

Development of taste-masked enteric granules containing diclofenac sodium utilizing Eudragit® E PO as a taste-masking agent

2024-06-23T18:52:43+07:00

This study aimed to develop a taste-masking method for Eudragit^® L 100-based enteric microparticles containing diclofenac sodium (DS) using Eudragit^® E PO and to determine the optimal polymer ratio for effective taste-masking. The taste-masked enteric granules (TEGs) were fabricated via rotary evaporation with dichloromethane as the solvent, with enteric microparticle-to-Eudragit^®E PO weight ratios of 1:0.1, 1:0.25, 1:0.5, and 1:1. Reducing the polymer ratio to 0.1 caused enteric microparticles to disperse independently from the polymer, while higher ratios embedded them in the polymeric carrier. To evaluate the taste-masking efficiency of TEGs, drug release in 10-mL simulated salivary fluid (SSF) was tested. The results revealed that increasing the polymer ratio enhanced the inhibition of drug release in SSF, indicating improved taste masking. However, in the in vitro drug dissolution in a two-stage biorelevant medium, TEGs with high polymer ratios of 1 and 0.5 released only 69.80±1.47% and 78.87±1.21% of DS after 3 h in the buffer stage dissolution, while TEGs with lower polymer ratios of 0.25 and 0.1 exhibited higher drug release percentages of 92.61±1.23% and 97.14±0.58%, respectively. From this study, the optimal enteric particle-to-Eudragit^® E PO ratio was 1:0.25, effectively masking the bitter taste of DS while minimally affecting drug release in a gastrointestinal-mimicking environment.

Assessment of neurological disorders in patients with chronic viral hepatitis: A prospective cohort study

2024-05-01T17:02:50+07:00

This prospective cohort study aimed to determine the prevalence and factors associated with neurological manifestations in patients with chronic hepatitis. The study involved 233 patients with chronic viral hepatitis from the Shymkent City Infectious Diseases Hospital and the Hepatological Center of the Shymkent Regional Clinical Hospital, conducted between March 2021 and January 2022. To identify neurological disorders, the neurological status of patient was assessed. The prevalence of neurological manifestations in the study population was 49.4%, which 43% of cases involving disorders of the peripheral nervous system. Peripheral neuropathy was common, affecting nearly half of the patients (49.4%). Factors associated with neurological manifestations included age over 50 years disease duration (greater than 5 years) alanine aminotransferase (ALT) level (p < 0.014), and aspartate aminotransferase (AST) level above 40 IU/L. Thus, patients with chronic viral hepatitis exhibit a range of neurological manifestations. We recommend routine screening for neurological disorders, particularly among older patient and those with a longer disease duration.

Forward selection models for classifying mild cognitive impairment and Alzheimer’s disease based on single nucleotide polymorphisms

2024-02-26T16:15:52+07:00

Early detection of Alzheimer’s disease (AD) is crucial for patients to begin treatment early to slow the disease’s progression. While mild cognitive impairment (MCI) is considered an early translational stage of AD, clinically diagnosing MCI is difficult due to its inconsistent symptoms and the lack of standardized diagnostic tests. In this work, we proposed forward selection models to classify patients with AD, patients with MCI and healthy controls (HCs) based on single nucleotide polymorphisms (SNPs). In the proposed method, the initial SNP data were prescreened via genome-wide association studies with a suggestive significance threshold. Then, the qualified SNPs were reselected using the forward SNP selection algorithm to create classification models. Consequently, the forward selection models significantly outperformed the preselection models, those based on all prescreened SNPs, with an area under the precision-recall curve (AUPRC) value of 0.93 in the AD-HC classification, an AUPRC value of 0.94 in the MCI-HC classification, and an AUPRC value of 0.81 in the AD-MCI classification. Moreover, the proposed method could identify AD-associated and MCI-associated SNPs, which would support the clinical diagnosis of AD and MCI in the future.

Antioxidant activity of gallic acid carbon-based nanomaterials

2024-06-27T19:21:41+07:00

Reactive oxygen species (ROS) play a role in multiple physiological processes. In contrast, an excessive generation of ROS can harm various biological components, including proteins, lipids, and DNA, leading to accelerated aging, illness, and inflammatory disorders. Carbon-based nanomaterials (CNMs) are well-known nanomaterials widely developed for antioxidant activity because of their great biocompatibility, low toxicity, easy synthesis, unique physicochemical properties, and especially great ROS scavenging ability. The most general method to prepare CNMs is bottom-up synthesis because it is more environmentally friendly and economical than top-down methods. In this study, the antioxidant activity of CNMs was evaluated. A microwave-assisted pyrolysis method was applied at 200°C for 20 min to prepare gallic acid carbon-based nanomaterials (GACNMs) by using 2.5 mg/mL of gallic acid as a carbon source according to the previous research. The morphology, cytotoxicity on normal human fibroblast cell lines by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, and antioxidant activity using a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay were elucidated. The results revealed that the GACNMs were spherical, with a particle size of 104.43±0.43 nm, and their structure was in line with the previous study. The GACNMs exhibited higher antioxidant activity (50% scavenging capacity (SC50) of 0.99±0.07 μg/mL) than the gallic acid solution (SC50 of 1.25±0.03 μg/mL). The GACNMs were non-toxic to fibroblast cells. Therefore, the GACNMs could be a promising nanomaterial for skin antioxidants.

Comparison of stress distribution on articular disc between the Hunsuck/Epker and NM-Low Z plasty technique for mandibular setback procedure

2024-03-13T08:45:18+07:00

Novel modification of Low Z plasty (NM-Low Z) technique is a bilateral sagittal split osteotomy (BSSO) technique in which the incision line is designed to create more inferior support at the proximal segment compared to the Hunsuck/Epker (HE) modification. This study aimed to evaluate and compare the stress distribution patterns at the articular disc of the temporomandibular joint (TMJ) using the NM-Low Z technique and HE modification in mandibular prognathism models. Finite element models of the mandible including the TMJ from a CT scan of a patient with skeletal class III deformity that required mandibular setback surgery, were segmented using the NM-Low Z and HE modification techniques. The mandible was moved 7 mm posteriorly and fixed with 2.0 mm miniplates
and screws. The material properties of the articular discs were simulated as homogenous, isotropic, and linear elastic materials. Finite element analysis was used to evaluate the equivalent von Mises stress on the articular disc of the TMJ. Various muscular and biting forces were applied to simulate 1, 3, and 12 months postoperatively in both models. The maximum von Mises stresses of the articular disc in the NM-Low Z model were 60.64, 36.43, and 76.38 MPa, while those of the HE modification model were 73.40, 29.94, and 82.51 MPa at 1, 3, and 12 months, respectively. Both techniques resulted in normal stress distribution patterns on the articular disc surface. In conclusion, the NM-Low Z technique is an option for BSSO in terms of providing a lower maximum von Mises stress at the articular disc than the HE modification technique, while also providing a normal stress distribution pattern similar to the HE modification technique.

First aid training using virtual reality

2023-07-25T15:29:57+07:00

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

2023-10-05T09:27:28+07:00

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

2023-11-01T16:21:46+07:00

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

2024-03-09T12:24:08+07:00

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

2023-10-18T14:21:10+07:00

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.

Multi-algorithm for predicting the level accuracy of fault output in software

2024-04-19T11:05:06+07:00

Software fault output refers to software errors found by testers during the software testing process. Software testing is an overly critical stage in software development; hence, a software testing model is required to systematically classify software errors. For the process of classifying software fault output, accuracy measurements are needed to predict manual fault outputs compared to algorithm-generated fault outputs. Algorithmic methods can be used to measure the accuracy of fault output. The main objective of this research was to compare different algorithms for predicting the accuracy of fault output on a dataset derived from past software testing. The findings indicate that the neural network algorithm outperforms SVM, MLP, RF, and MNB algorithms, achieving 98% accuracy when using 10 software testing variables (function, interface, structure, performance, requirement, documentation, positive, negative, basis path, and times) to predict expected fault outputs.

Forecasting of plastic packaging waste in Thailand using economic index data application

2024-04-03T13:43:08+07:00

Population growth and urbanization used to be the only indicators of waste quantity in the past. This research forecasted plastic packaging waste with data from 2010 to 2022 using statistical methods for analysis and aimed to study 47 economic indexes that correlate with and influence the quantity of plastic packaging waste. The quantity of municipal solid waste is constantly increasing by approximately 25 million tons per year, with the quantity of plastic packaging waste having a similar proportion generated each year at 7.91–11.79%. This study observed that the quantity of plastic packaging waste had a Pearson correlation value with an economic index of more than 0.4 (R>0.4) at a significance level of 0.05. The economic index could additionally explain 52.2% of the changes in the quantity of plastic packaging waste, including the wholesale index for food, retail sales index for beverages, and sales index without a store, using the stepwise multiple regression method. The accurate forecasting of plastic packaging waste for use in the planning and design of management systems averaged 78.57%, including the fact that it could be forecasted simultaneously with forecasting economic index changes.

Field evaluation of a simple infiltration test and its relationship with soil physical properties of three different types of land uses

2023-06-22T09:58:57+07:00

Infiltration capacity is an important variable for understanding and predicting arange of soil processes. A study was conducted to assess infiltration rates across three distinct land types: forested areas, grasslands, and construction zones. The infiltration rate of the soil was assessed utilizing a mini disk infiltrometer, with a subsequent analysis of soil physical properties to establish a correlation with its infiltration capacity. The infiltration rates were observed to be the highest for forestland, followed by grassland, and then construction land. The cumulative infiltration values further support this trend, indicating that forestland exhibits the highest cumulative infiltration, followed by grassland and construction land. Soil physical properties, including particle size distribution, moisture content, and organic matter content, significantly influenced infiltration rates. Soil compaction, characterized by a higher dry bulk density, was associated with reduced infiltration capacity, particularly in construction land. Conversely, higher moisture and organic matter content enhanced infiltration rates. These findings underscore the importance of considering soil properties, land use, and vegetation cover when managing landscapes to mitigate soil erosion and preserve water quality. Understanding the intricate relationship between these factors is crucial for sustainable land use practices and environmental conservation.

Application of response surface methodology for optimization of phosphate and ammonia removal from domestic wastewater by modified steel slag

2023-09-12T19:03:00+07:00

Domestic wastewater is one of the significant sources causing phosphorus and nitrogen pollution in natural water. Though steel slags have been applied for the adsorption of phosphate, ammonia, and both in various synthetic and real wastewaters, no report has been found for the application of response surface methodology (RSM) to investigate the main and interaction effects, and optimization of various factors on phosphate and ammonia adsorption by steel slag. The current study aims to evaluate the removal of phosphate and ammonia from domestic wastewater using adsorbents of modified steel slags. Steel slags from two companies were modified, characterized, and preliminarily applied for synthetic wastewater treatment to select the suitable type of steel slag. Optimization experiments were then designed using RSM to find the significant factors, their interactions affecting the treatment processes, and optimum conditions. The results of energy-dispersive X-ray spectroscopy (EDS) and Brunauer–Emmett–Teller (BET) analyses showed that thermal treatment affected the mass percentages of elements and increased the surface areas of the materials. The adsorption kinetics and isotherm studies demonstrated that the phosphate adsorption process followed pseudo-second-order reaction kinetics, and the Langmuir model was appropriate to the present experimental results. The results from RSM suggested that output phosphate concentration was significantly affected by the modified temperature, steel slag dosage, contact time, and the modified temperature-dosage interaction, while the modified temperature and steel slag dosage were significant factors for output ammonia concentration. Validation of the domestic wastewater treatment at the optimum conditions including the modified steel slags at 776°C, contacting time of 15.7 h, and dosage of 0.87 g/L confirmed the high removal efficiency for phosphate (81.7%), while ammonia and COD were eliminated at the efficiencies of 31.6% and 32.7%, respectively. This study hence proposed the application of the modified steel slag as a promising and cheap adsorbent for nutrient removal in domestic wastewater.