Journal of Current Science and Technology

Mitigating Data Imbalance for Robust Diabetes Diagnosis Using Machine Learning and Explainable Artificial Intelligence

Poorani K — 2025-06-15

Diabetes is increasing at a global level and is associated with a high mortality rate. Early diagnosis can significantly reduce the risk of complications and save lives. This study proposes an efficient ensemble model for diabetes diagnosis using Machine Learning (ML). To address class imbalance in the dataset, a hybrid sampling technique Synthetic Minority Over-sampling Technique (SMOTE) combined with Edited Nearest Neighbors (ENN) is implemented. This combined method, referred to as SMOTE-ENN, enhances the model’s ability to accurately predict diabetes outcomes by generating synthetic samples and removing noisy instances. Before implementing any ML model, it is necessary to prepare the data and find a suitable model. Data preprocessing techniques like normalization, and filling missing values are essential in preparing data for a ML model. The proposed approach implements suitable preprocessing techniques such as mean value imputation and encoding. Feature selection with mutual information is carried out to select important variables. The PIMA Indian Diabetic dataset is balanced using SMOTE-ENN, a sampling strategy in Python with the help of the imbalanced-learn library, which improves model performance. The dataset is split for analysis at 80:20 ratio (train: test). Before ML implementation, the data is prepared for model building. Then, various ML models are introduced, ranging from single classifiers to ensemble models. The proposed approach, SMOTE-ENN with ensemble ML models proves that stacking provides high accuracy (98.9%), precision (97.6%), recall (99.5%), and F1-score (98%). Explainable Artificial Intelligence is also used to interpret the results with the help of Local Interpretable Model-Agnostic Explanations (LIME). The proposed approach combines feature selection, data imbalance handling, and ensemble techniques to improve performance. Stacking with the proposed approach performs better than state-of-the-art algorithms.

Smartphone Based Real-Time Detection of Postural and Leg Abnormalities using Deep Learning Techniques

Saiprasad Potharaju — 2025-06-15

This research presents an innovative real-time method for detecting leg postural abnormalities using deep learning techniques and smartphone sensors. The objectives are to: (1) develop a smartphone-based system for real-time classification of leg postures using accelerometer and gyroscope data, (2) evaluate the effectiveness of three deep learning models DNN, CNN, and CNN-LSTM in identifying spatial and temporal features, and (3) offer a low-cost, objective alternative to traditional assessment methods by addressing issues such as observer inconsistency and computational complexity. Accelerometer and gyroscope data from smartphones were used to develop a system that classified four leg postures: Pronation, Supination, Normal, and Postural Sway. Participants from various age groups carried a smartphone in their left pocket while standing and walking for 10, 20, and 30 seconds. This process produced a dataset of 29,823 records, which were verified and labeled by medical professionals based on observed postural characteristics. The CNN-LSTM model achieved the highest accuracy (96.4%) with strong class differentiation, demonstrating its effectiveness in capturing temporal dependencies. All three models were employed for unknown instances, and a majority voting approach was used for final classification. This proposed smartphone-based assessment system addresses limitations of traditional methods, such as inconsistencies due to subjective visual evaluations. This approach supports applications where leg posture is critical, such as in military, sports assessments, and disability certification, by offering an objective and accessible solution. Unlike video-based methods, it leverages widely available mobile technology, offering a low-computation, tamper-proof, and nonintrusive real-time surveillance system. Designed for automated and transparent evaluation, it has the potential to enhance the integrity of physical disability certifications.

The Effect of Kegel Exercise on Sexual Function in Menopausal Women: A Systematic Review and Meta-Analysis

Nichada Prasong — 2025-06-15

Menopause often leads to sexual dysfunction due to hormonal and physiological changes. This research aimed to investigate the effects of pelvic floor muscle training (Kegel exercises) on sexual function in postmenopausal women by analyzing overall sexual function, specific domains, and various comorbidities. A comprehensive selection of primary studies was conducted across five databases: MEDLINE, Cochrane Library, ScienceDirect, Google Scholar, and Scopus. Inclusion criteria encompassed both randomized control trials (RCTs) and non-randomized Control Trials (non-RCTs) involving postmenopausal women participating in pelvic floor exercise programs with assessed sexual function outcomes. Eleven studies met the inclusion criteria, with 8 studies (n=643) contributing to the meta-analysis. Results from 6 RCTs (n=446) indicated that participants engaged in Kegel exercises exhibited significantly higher total sexual function scores, as assessed by the Female Sexual Function Index (FSFI), compared to control groups (mean difference = 2.58, 95% CI = 1.56, 3.59; p< 0.00001). Notable improvements were observed in several FSFI domains, including desire, arousal, lubrication, satisfaction, and pain; however, no significant difference was noted in the orgasm domain. Furthermore, a meta-analysis of 2 studies (n=197) involving mild pelvic organ prolapse revealed no significant difference in sexual function scores, measured by the Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12) (mean difference = -1.26, 95% CI = -2.75 to 0.22; p = 0.69). In conclusion, Kegel exercises significantly enhance sexual function in postmenopausal women, particularly regarding desire, arousal, lubrication, satisfaction, and pain, while showing no significant impact on orgasm or pelvic organ prolapse outcomes compared to the control group.

Effect of Bifidobacterium Breve on Lipid Profile and Body Fat Reduction in Patients with Metabolic Syndrome: A Randomized, Double-Blind, Placebo-Controlled, Clinical Trial

Dorn Numnark — 2025-06-24

Metabolic Syndrome (MetS) is a cluster of metabolic abnormalities, including impaired glucose tolerance and elevated triglyceride levels, that increase the risk of cardiovascular disease and diabetes. This randomized, double-blind, placebo-controlled clinical trial aimed to evaluate the efficacy of Bifidobacterium breve strains BR03 and B632 in reducing body fat and improving metabolic parameters in individuals with MetS. Ninety participants were randomly assigned to either a placebo group (n = 45; receiving 1.6 g of microcrystalline cellulose daily) or a treatment group (n = 45; receiving 1.6 g of microencapsulated B. breve BR03 and B632, 2×10⁹ CFU/day). Anthropometric and biochemical parameters, including BMI, waist circumference (WC), visceral fat ratio (VFR), blood pressure, HbA1c, fasting blood sugar (FBS), total cholesterol (TC), triglycerides (TGs), LDL-C, and HDL-C, were assessed at baseline and at 1, 2, and 3 months. At 3 months, the treatment group showed significant reductions compared to the placebo group in BMI (p = 0.001), WC (p < 0.01), VFR (p < 0.016), HbA1c (p = 0.001), FBS (p < 0.001), TC (p < 0.001), TGs (p < 0.001), and LDL-C (P < 0.001), along with a modest increase in HDL-C (p = 0.034). No significant differences were found in systolic (p = 0.19) or diastolic blood pressure (p = 0.15). These findings suggest that B. breve BR03 and B632 supplementation may offer a beneficial adjunctive strategy for improving metabolic profiles in patients with metabolic syndrome.

Enhancing Machinery Maintenance in the Gold Manufacturing Industry: Strategies for Overcoming Barriers and Integrating Sustainability

Vivin S — 2025-06-15

Effective machinery maintenance is critical for ensuring operational efficiency and productivity in the gold manufacturing industry. However, several barriers hinder maintenance practices, including insufficient employee accountability, limited availability of spare parts, and inadequate financial planning. This study investigates strategies to overcome these challenges and enhance maintenance processes. Risk factors were identified through a comprehensive literature review and expert consultations, and then analysed using the Fuzzy VIKOR method a multi-criteria decision-making tool that addresses complexity and uncertainty. The research highlights key barriers and proposes proactive measures for improving maintenance performance. Furthermore, the integration of sustainability principles into maintenance operations is emphasized to support economic, environmental, and social objectives. The findings underscore the importance of employee training, spare parts management, and financial planning in achieving effective and sustainable maintenance practices. This study provides valuable insights for industry professionals and decision-makers seeking to optimize machinery maintenance in the gold manufacturing sector.

The Six-Point Injection Technique: A Non-Immersion Method for Enhancing Cadaveric Tissue Quality

Patipath Suwannahoy — 2025-06-15

Two-point injections of embalming fluid without venous drainage, along with immersion in a post-fixative pool, has been used for several decades in the Department of Anatomy, Chiang Mai University (CMU). However, tissue decomposition was frequently observed during dissection. To address this, a Modified Embalming Method (MEM), utilizing a six-point injection technique with venous drainage, was tested. This study evaluates the effectiveness of MEM compared to the traditional two-point method with immersion. Ten cadavers were preserved and assessed for range of motion (ROM), histological integrity, and dissection quality. The cadavers were divided into two groups: five were embalmed using MEM and stored in plastic bags at room temperature, while the other five were preserved using the Present Embalming Method (PEM), which involved two-point injection without venous drainage followed by immersion. Both groups received the same embalming fluid. After one year, ROM was measured, and dissection quality was evaluated by ten dissectors. The MEM group showed greater joint mobility and superior tissue quality for both gross anatomical and histological analysis. The enhanced perfusion achieved by MEM ensured uniform distribution of fixative throughout the body. Furthermore, MEM eliminated the need for immersion, reduced chemical use, and allowed safe storage of cadavers in mortuary bags at room temperature.

Comparison of Multiple Linear Regression and Periodic Models for Estimating PM2.5 and PM10 Concentrations in Bangkok

Sittipong Ruktamatakul — 2025-06-15

This study compares the performance of Multiple Linear Regression (MLR) and Periodic Models in estimating PM_2.5and PM₁₀concentrations in Bangkok using a 60-month dataset (2019–2023). Eight independent variables, including air temperature, rainfall, air pressure, wind speed, ozone concentrations, nitrogen dioxide concentrations, the number of vehicles, and the number of factories, were analyzed to determine their influence on PM_2.5 and PM₁₀ levels. Model accuracy was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results revealed that the Periodic Model more accurately predicted PM_2.5 (MAE = 4.65, MAPE = 12.69), while the MLR model performed better for PM₁₀ (MAE = 6.93, MAPE = 10.54). These findings highlight the complementary strengths of the two modeling approaches: Periodic Models effectively capture seasonal trends, while MLR reveals specific influencing factors. These findings provide valuable insights into the strengths and limitations of each model, offering guidance for developing targeted and efficient measures to control PM_2.5 and PM₁₀ levels in Bangkok, ultimately enhancing public health and urban living conditions.

Effect of Steaming and Moisture Reduction Process on Phytochemical Content, Physicochemical and Microbiological Qualities of White (Sang Mon) Bamboo (Dendrocalamus sericeus) Leaf Tea

Tarit Apisittiwong — 2025-06-15

Bamboo leaves are a rich source of phytochemicals and antioxidants, traditionally used to support immune health and alleviate chronic conditions. This study aimed to develop high-phytochemical-content bamboo leaf tea by optimizing the tea processing steps, specifically examining steaming times and moisture reduction methods, roasting and hot air drying. Results revealed that steaming white bamboo (Dendrocalamus sericeus) leaves for 15 minutes produced the highest total phenolic content (TPC) at 14.97 ± 0.05 mg GAE/g. In terms of moisture reduction, roasting for 30 minutes resulted in the highest TPC, total flavonoid content (TFC), and catechin levels (11.73 ± 0.07 mg GAE/g, 9.46 ± 0.19 mg QE/g, and 19.10 ± 0.12 mg/100 g, respectively), though isovitexin was undetectable. Conversely, hot air drying for 30 minutes preserved higher levels of orientin (71.41 ± 0.01 ppm), isoorientin (36.73 ± 0.01 ppm), and isovitexin (166.61 ± 0.00 ppm). Vitexin was not detected in either method. Both methods effectively reduced moisture to below 10%, aligning with Thai Ministry of Public Health standards for tea infusions. Microbiological assessments confirmed that the processed tea met Thai community standards for dried herbs. Importantly, brewed bamboo leaf tea contained no detectable caffeine, making it suitable for consumers seeking stimulant-free alternatives. The optimal production process was identified as steaming for 15 minutes followed by hot air drying at 60°C for 30 minutes. This approach not only enhances phytochemical retention but also offers a sustainable strategy for utilizing bamboo foliage, often discarded during culm processing, as a valuable resource in the herbal tea industry. Overall, this study underscores the importance of process optimization in preserving functional compounds and supports community-based herbal tea production as a viable and health-promoting enterprise.

A Study on Area Assessment of Psoriasis Lesions Using Image Augmentation and Deep Learning: Addressing the Lack of Thai Skin Disease Data

Tanatorn Tanantong — 2025-06-15

Psoriasis is a chronic skin disease with significant global and regional impacts, including in Thailand, where its burden is compounded by diagnostic challenges and limited dermatological resources. Psoriasis was selected for this study because it develops in distinct phases, requiring ongoing monitoring and treatment. The distribution of skin lesions plays a crucial role in disease identification and assessment, making it an essential factor for AI-based analysis. The development of AI-based diagnostic tools offers a potential solution. However, there is no publicly available skin disease dataset in Thailand, and image annotation is a challenging and time-consuming task for dermatologists. This scarcity of annotated datasets remains a critical barrier to AI development. This study utilizes the Dermnet dataset and enhances it through the application of image augmentation and style transfer techniques to generate a more diverse and representative dataset, particularly reflecting Thai skin tones. It also evaluates how augmentation techniques affect AI performance in psoriasis classification. The results showed that augmentation significantly enhanced model performance, with EfficientNetB4 achieving the highest accuracy (93.00%) and sensitivity (91.19%). Style transfer emerged as a valuable technique, enabling the creation of skin tone representative datasets that improved model generalizability. These findings align with existing literature. They demonstrate that augmentation techniques can overcome data limitations and enhance model robustness. This study introduces a novel use of style transfer techniques. These are applied to generate augmented datasets that represent Thai skin tones, addressing a critical gap in publicly available dermatology data. By enhancing dataset diversity, style transfer significantly improves the generalizability and accuracy of AI-based psoriasis classification models. These advancements have important implications for clinical practice. They are especially relevant in Thailand and other resource-limited regions, where AI-assisted diagnostics can improve dermatological care access and effectiveness.

Efficacy and Safety of Topical Steroid with 1064 nm Long-pulsed Nd:YAG Laser compared to Topical Steroid Alone in the Treatment of Paronychia associated with EGFR Inhibitors: A Randomized Controlled Pilot Study

Chanita Autchayawat — 2025-06-15

Paronychia is a common adverse event associated with epidermal growth factor receptor inhibitor (EGFRI) therapy, substantially affecting patients’ quality of life and adherence to cancer treatment. Currently, there are no definitive guidelines for its management. This prospective randomized controlled study evaluated the efficacy and safety of a topical steroid combined with a long-pulsed 1064 nm Nd:YAG laser compared to topical steroids alone for treating paronychiae associated with EGFRIs. Each of the ten patients with two lesions (20 in total) was randomized to receive laser treatment with topical steroids on one lesion (n = 10), while the other lesion received topical steroids alone (n = 10). PSG grade, Atis grade, and pain scores were assessed at baseline (Day 0) and on Days 7, 14, and 21. Photographic documentation was obtained at each time point. The laser group demonstrated significantly greater improvements. The reduction in PSG grade from Day 0 to Day 21 was more pronounced in the laser-treated lesions (-1.40 ± 0.84) than in those treated with steroids alone (-0.50 ± 0.97, p = 0.045). The reduction in Atis grade was also greater in the laser group (-2.70 ± 0.82) than in the control group (-0.30 ± 1.70, p = 0.003). Pain reduction was also more significant in the laser group. Nd:YAG 1064 nm laser therapy combined with topical steroids demonstrates superior efficacy in reducing inflammation and pain in EGFRI-associated paronychia. This suggests that a combined treatment approach is a promising option. Further studies with larger sample sizes are warranted to confirm these findings.

Environmental Surveillance of Gram-Negative Bacteria and bla Genes in Hospital Facilities and Surrounding Waters in Thailand

Sangrasami Meeprawat — 2025-06-15

To reveal the real time prevalent situation of antibiotic-resistant bacteria (ARB) and bla genes in Thailand, we monitored 83 isolates of Gram-negative bacteria (GNB) from hospital facilities and surrounding environmental waters. 16S rRNA gene sequencing was performed. Polymerase chain reactions were employed for bla gene detection. Disk diffusion was used for antimicrobial susceptibility testing. As a result, Enterobacter mori (20%) and Klebsiella pneumoniae (17.14%) were prevalent in hospital facilities, while K. pneumoniae (27.08%) and Enterobacter cloacae (14.58%) prevailed in water samples. Ampicillin resistance rates were highest, at 65.71% and 66.67% in hospital and water isolates, respectively. Enterobacter species from water samples exhibited multidrug-resistant characteristics. bla_SHV and bla_TEM were highly prevalent, 91.43% and 89.58% in various bacterial species from hospital facilities and water samples, respectively. The coexistence of bla_SHV and bla_TEM and bla_NDM was the most common overall (16.87%). The prevalence of the same bacterial species and bla genes in both sectors suggests the cross-transfer of ARGs and resistant bacteria between different environments, hospital and water. The findings emphasize concerns about the safety of water sources and bacterial contamination in hospital facilities.

Experimental Study on Slurry Ice Formation in Right Circular Cylinder and Its Empirical Model

Sirachat Aruxvanit — 2025-06-15

Slurry ice has the potential to serve as a secondary working fluid for cooling purposes or as a cold storage medium due to its high energy intensity. In the latter application, it can overcome the drawbacks associated with using regular ice, such as ice bridging and insulation, thereby enhancing heat transfer between the exchanger surface and the surrounding medium. However, the solidification process depends on various factors, including the concentration of the freezing point depressant, the freezing point of the working medium, the size and shape of the storage medium, and its thermal properties. This study investigated the formation of slurry ice using water-ethanol and water-propylene glycol mixtures with different concentrations of freezing point depressants. The experiments were conducted in a freezer at temperatures around -15 and -20^oC. The findings revealed that higher concentrations of freezing point depressants resulted in a faster growth rate of ice, however when the concentration exceeded 8 wt%, the opposite effect was observed. To better understand the process phenomena, a set of new empirical models was developed using polynomial curve fitting of related parameters in dimensionless forms to predict the amount of slurry ice formed over time. The results from the models showed good agreement with the experimental data across different concentrations of freezing point depressants and container sizes.

Exploring the Clinical Characteristics and Survival Outcomes in Colorectal Cancer Patients in Hatyai Hospital: A Retrospective Cohort Study

Makusee Masae — 2025-06-15

Colorectal cancer (CRC) is considered a significant public health concern worldwide, with substantial morbidity and mortality rates. In Thailand, several campaigns have been implemented to address this issue, such as the establishment of local treatment centers. The Cancer Center of Hatyai Hospital (CCHH) is the latest cancer center affiliated with a tertiary public hospital in the southernmost part of Thailand. However, a systematic assessment of cancer treatment outcomes, including those for CRC patients, has yet to be conducted. Therefore, the current study utilized a retrospective analysis approach to elucidate the survival probability of CRC patients treated at CCHH. A secondary data analysis was conducted using electronic medical records (EMRs), and the selected data were validated and filtered by a certified oncologist and pharmacist. Time-to-event analysis was used to model survival probability across subgroups, and visualized using Kaplan-Meier (KM) plots. Additionally, restricted mean survival time (RMST) analysis was performed to estimate the 3-year survival time of this patient cohort, with an estimated survival time of 24.8 months. The univariate Cox proportional hazards (PH) model was used as an exploratory analysis to identify the influence of clinical variables on survival outcomes. Subsequently, a multivariable Cox PH model was constructed with a set of selected variables. T2 tumor status, the presence of distant metastasis, ECOG score of 4, and poorly differentiated tumor were identified as the strongest predictors of reduced survival among the included variables. As such, this study provides practical insights based on real-world data regarding cancer survivorship and the survival outcomes of CRC patients treated at a public hospital. Additionally, it offers a snapshot of the recent implementation of an early diagnosis campaign.

Evaluation of ELISA for Detecting Porcine Content in Halal Compliance

Meily Cahya Setyawati — 2025-06-15

Pork is strictly forbidden for consumption by the Muslim population. According to the Quran, the consumption is strictly prohibited, even in trace amounts or minimal concentrations. In this study, we aimed to evaluate the sensitivity of ELISA to detect porcine in food. We used six types of pork-containing food samples: raw pork meat, grilled pork skewers, pork oil, pork fat, pork fried rice, and pork meatballs. To ensure sensitivity and reproducibility, each sample was tested in duplicate using undiluted, 10x, and 100x dilutions. Samples were evaluated using spectrometry at an absorbance wavelength of 450 nm. As a result, porcine antigen was detected in raw pork meat, grilled pork skewers, and pork meatballs at OD values > 0.07 (1.012; 1.1266; 0.8166) respectively. In pork meatballs, the presence of porcine antigen at high dilutions was inconsistently observed. Moreover, porcine antigen was not detected in pork oil, pork lard, pork fried rice, or beef soup even in undiluted samples at OD value < 0.07. This study successfully detected the presence of porcine antigens, however, its application is currently limited to meat products. Detection was also less sensitive when applied to processed food. Porcine protein was not detectable in oil and lard samples, nor in processed pork meat products at higher dilutions.

Advancing Breast Cancer Detection: A Comparison of PCA and LDA Methods in Analyzing Ultrasound Imagery

Najeeb Saiyed — 2025-06-15

Early and accurate detection of breast cancer via ultrasound imaging is essential, yet the high dimensionality of raw ultrasound features can hinder classifier performance and increase computational burden. Comparison between Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for feature reduction in a breast-cancer ultrasound diagnostic pipeline, alongside t-SNE for exploratory visualization. The research utilized 1,200 breast ultrasound images with 400 benign, 400 malignant, and 400 normal images obtained from Baheya Hospital (Cairo, Egypt). Minority classes were balanced using data augmentation techniques like rotation and flipping. PCA reduced the data to 172 components, preserving 90% of data variance, while LDA used two components. t-SNE generated a two-dimensional visual representation. Classifiers, including Support Vector Machine (SVM), Random Forest (RF), and XGBoost, were trained on: (a) the full feature set, (b) PCA-reduced data, and (c) LDA-reduced data. Evaluation metrics included precision, recall, and F1-score. Compression ratio and signal-to-noise ratio (SNR) measured image compression via PCA.Without reduction, XGBoost achieved the highest F1-score (76.97%), precision (77.40%), and recall (76.55%). PCA yielded a modest precision gain (XGBoost: 78.65%) but reduced recall and net F1-score (76.37%). LDA significantly degraded performance (XGBoost F1: 63.99%; RF F1: 60.13%; SVM F1: 42.05%). PCA compression reduced image size by 2.68x with an SNR of 48.91 dB, while LDA offered no compression benefit. t-SNE visualization revealed clear non-linear class clusters, underscoring the dataset’s intrinsic complexity. For ultrasound-based breast cancer diagnosis, preserving full high-dimensional features and using a powerful non-linear model (e.g., XGBoost) yields optimal accuracy. PCA is best reserved for storage or runtime efficiency, LDA for scenarios with very low dimensional constraints, and t-SNE for exploratory data analysis. This comparative study highlights that dimensionality reduction may harm performance in complex imaging data and recommends context-specific use of PCA and LDA to avoid loss of critical diagnostic information.

Visual Feature Refinement with MECNET for Gastrointestinal Cancer Classification

Ravi Kumar — 2025-06-15

Early detection and classification of gastrointestinal tract pathologies are crucial for better prognosis and reduced mortality rates, such as in colorectal cancer. In this paper, we introduce MECNET, a new hybrid deep learning framework for efficient classification of endoscopic images. The proposed framework integrates the feature refiner module with state-of-the-art CNN architectures such as VGG19, ResNet50, and EfficientNet for improved performance in image analysis and classification tasks. The feature refiner module successively applies grayscale, Gaussian, and LPQ filters to extract meaningful texture features, which play an important role in differentiating disease categories. Our proposed scheme has been tested on several available datasets, namely WCE, Kvasir, GastroVision, and SCPolyp including 13,000 images from four categories: normal colon, polyps, esophagus, and ulcerative conditions. The MECNET model attained an appreciable performance metric, outperforming state-of-the-art methods at accuracy and F1 scores of 97.4% and 97.34% on the WCE test set and 97.2% and 97.26% on the Kvasir test set, respectively. This proves that MECNET does not only excel in classification but also generalizes well across diverse datasets. The novelty of this work lies inincorporating a feature refiner module with established CNN architectures and utilizinga hybrid ensemble approach. This approach will provide a boost to the model's performance. The proposed framework addresses key challenges in medical image classification: improving feature extraction by making full use of advanced transfer learning techniques.

Reliability and Validity of the Modified O'Sullivan Functional Balance (mOFB) Test in Individuals with Stroke

Butsara Chinsongkram — 2025-06-15

The modified O’Sullivan Functional Balance (mOFB) test is a refined version of the original OFB, developed to assess balance in sitting and standing positions in individuals with stroke using standardized perturbations and updated scoring criteria. Despite its clinical utility, the mOFB lacked standardized administration procedures and had not been validated in individuals with stroke. This study aimed to examine the intra-rater and inter-rater reliability, as well as the convergent validity of the mOFB following the development of standardized instructions and scoring criteria. The mOFB comprises four tasks that assess static and dynamic balance in both sitting and standing positions, each rated on a 5-point ordinal scale (where 0 indicates an inability to maintain balance and 4 indicates normal balance). Seventy-five individuals with a first-time stroke (aged 25–84 years) participated. For inter-rater reliability, assessment sessions were video recorded and scored independently by four raters. Intra-rater reliability was assessed by having the same rater evaluate the recordings twice. Convergent validity was examined using the Berg Balance Scale (BBS) and the Trunk Impairment Scale (TIS). Statistical analyses included intraclass correlations coefficients (ICCs) and Spearman’s rank correlation. The mOFB demonstrated excellent intra-rater (ICC = 0.97; 95% CI: 0.96–0.98) and inter-rater reliability (ICC = 0.91; 95% CI: 0.85–0.95). Strong correlations with the BBS (r = 0.82, p < 0.001) and moderate correlations with the TIS (r = 0.60, p < 0.001) supported its convergent validity. No floor or ceiling effects were observed in the total scores. These findings support the mOFB as a reliable and valid tool for assessing balance in individuals with stroke. Its simplicity, brief administration time, and appropriate difficulty across stroke stages make it suitable as a clinical screening tool in high-volume clinical settings.

The Effects of Mindfulness on Sleep Quality in Working Adults with Stress: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Satita Laoveeratam — 2025-06-24

This systematic review and meta-analysis evaluated the effects of mindfulness practices on sleep quality in working adults experiencing stress, a population often overlooked in clinical sleep research. A comprehensive search of six databases including PubMed, Scopus, ScienceDirect, Cochrane Library, ThaiJo, and Google Scholar were conducted for randomized controlled trials (RCTs) published before October 2024. Eight studies met inclusion criteria, with four comprising 195 participants included in the meta-analysis. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Meta-analysis revealed that mindfulness significantly improved global sleep quality compared to controls (Mean Difference = -0.49; 95% CI: -0.82 to -0.16; p = 0.003; I² = 0%). Subgroup analyses indicated improvements in sleep duration (MD = -0.18; p < 0.001) and reduced use of sleep medications (MD = -0.27; p < 0.001), with no significant changes observed in the other PSQI domains. These findings suggest that mindfulness may be a beneficial non-pharmacological approach for enhancing certain aspects of sleep in stressed working populations. However, the small sample size and modest effect size highlights the need for further high-quality studies with larger cohorts and longer follow-up periods.