Search Results (101)

Background: Adolescent girls and young women (AGYW) who engage in alcohol and substance abuse face more significant health and social consequences compared to the general population. This study evaluated the prevalence and associated factors of alcohol abuse and substance use among AGYW in Namibia. Methods: We conducted a retrospective analysis of programmatic data from AGYW aged 10–24 who participated in the Determined, Resilient, Empowered AIDS-free, Mentored, and Safe (DREAMS) component of the Reducing HIV Vulnerability: Integrated Child and Youth Health (REACH) Project HOPE Namibia from March to December 2024. Data analysis was conducted employing chi-squared tests alongside binomial and multinomial logistic regression. Results: Among the 19,662 participants included in this analysis, 2068 (10.5%) abused alcohol and/or substances in the previous six months. Participants who were HIV-negative or did not know their status (AOR = 1.57, 95% CI (1.15–2.14), and AOR = 1.50, 95% CI (109–2.07), respectively), from outside Windhoek, those who had failed or repeated school in the previous year (COR = 1.77, 95% CI (1.54–2.05)), those not disabled (AOR = 1.27, 95% CI (1.06–1.52)), those who had dropped out of school or had completed their studies, and those with no adult emotional support (AOR = 1.25, 95% CI (1.11–1.40)), were more likely to have abused alcohol and/or substances recently. In contrast, participants who were not depressed were less likely to have recently abused alcohol and substances. Conclusions: The prioritization of strategies to identify AGYW experiencing depression and to provide them with treatment is essential. Moreover, it is important to encourage parents and guardians to provide emotional support to AGYW, as it prevents them from abusing alcohol and substances. Full article

Background/Objectives: To determine if the results of cytogenetic analyses of choroidal melanoma biopsies after ruthenium-106 plaque brachytherapy (RPB) are affected by this procedure. Methods: A retrospective study was conducted on 368 patients with choroidal melanoma treated with RPB who underwent cytogenetic testing at the Liverpool Ocular Oncology Centre (LOOC) between May 2012 and November 2024. Data on demographics, tumor characteristics, treatment date, biopsy timing (pre- or post-RPB), and cytogenetic results were extracted from the LOOC database. Statistical analysis included descriptive statistics, binary, and multinomial logistic regression to assess associations between biopsy timing and biopsy success rates. Results: Biopsies were performed before RPB in 58.7% (216/368) cases, and post-PBR in 41.3%. Cytomorphological identification and molecular genetic testing were successful in 96.4% and 85.1% cases, respectively. Timing of biopsy, patient demographics, and tumor characteristics did not significantly influence cytogenetic test outcomes. Molecular testing could not be performed on 6.8% (25/368) cases as the DNA was insufficient in these samples. Genetic testing success slightly declined beyond three months post-RPB, though a few cases had delayed biopsy (n = 8). Pre-RPB biopsies more frequently demonstrated monosomy 3, whereas post-RPB biopsies had higher rates of disomy 3 (χ², p < 0.05). Conclusions: Prognostic biopsies post-RPB provide reliable cytomorphological and molecular genetic results using MLPA or MSA. Test failure is not significantly influenced by biopsy timing, patient or tumor characteristics, biopsy modality, or genetic technique. Insufficient DNA yield remains a key limitation, emphasizing the importance of obtaining adequate tissue samples. Biopsies within three months are preferable to optimize success in molecular testing. Full article

Introduction: This study investigates the distribution and interaction of three polymorphisms—XRCC1 (rs1799782), CHEK2 (rs17879961), and XPD (rs238406)—in Romanian breast cancer patients, aiming to understand their association with histopathological subtypes, age, and BMI. Materials and Methods: This retrospective study analyzed 36 breast cancer patients from a Romanian clinic (2020–2024) with complete genetic data for XRCC1 (rs1799782), CHEK2 (rs17879961), and XPD (rs238406). The patients had invasive, non-metastatic breast cancer and no history of other cancers. Statistical analysis with Jamovi included descriptive stats, McNemar’s test for genotype associations, and multinomial logistic regression to explore links between variants, age, BMI, and tumor subtypes. Results: McNemar tests showed no significant association between XRCC1 and CHEK2 (p = 0.180), nor between XRCC1 and XPD (p = 0.03) or XPD and CHEK2 (p = 0.049) after applying the Bonferroni correction (α = 0.0167), indicating no statistically significant genetic dependency among these variants. A multinomial logistic regression model found that genetic variants, BMI, and age significantly predicted breast cancer subtypes, particularly CDI TNB. All predictors remained significant in the comparisons of CDI TNB vs. CDI LB/CDI LA. Notably, these associations remained unchanged even after applying the Bonferroni correction (α = 0.0021), confirming the robustness of the findings. Conclusions: This study identifies significant associations between XRCC1, CHEK2, and XPD gene variants and CDI TNB, suggesting a role of DNA repair deficiencies in its pathogenesis. Protective genotypes were under-represented in TNB cases. Limited links with luminal subtypes highlight TNB’s distinct genetic profile. Results support further research on these polymorphisms as markers for TNB risk and precision treatment. Full article

This study explores the toothbrushing frequency and its association with sociodemographic factors, health status, and dietary habits in Saudi Arabia. Using data from the 2017 National Demographic and Health Survey by the Ministry of Health, we analyzed responses from 44,779 individuals aged five and older. Statistical analysis using SPSS and multinomial regression revealed that 57.3% of the population brushed their teeth less than once a day. Differences were noted across regions, ages, and genders. Key factors associated with increased brushing frequency included age (45–54 vs. older than 60), nationality (Saudi vs. non-Saudi), region (Western vs. Central), and marital status (married vs. non-married). Conversely, individuals with co-morbidities, disabilities, smokers, and those without prior dental treatment were less likely to maintain recommended oral hygiene practices. Our findings suggest that toothbrushing practices fall short of professional recommendations, highlighting a need for enhanced educational efforts. Oral health care providers in Saudi Arabia are encouraged to implement regular awareness programs to improve brushing habits and overall oral hygiene. Full article

Background/Objectives: Accurate assessment of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer is critical for optimizing treatment strategies. While magnetic resonance imaging (MRI) and mammography are commonly used for response evaluation, they have inherent limitations. Ultrasound (US) has emerged as a promising, cost-effective, and real-time alternative. This study aimed to evaluate the effectiveness of US in tracking tumor regression during NAC and its correlation with pathologic tumor regression grade (TRG). Methods: This study included 282 breast cancer patients undergoing NAC. Tumor size was measured using ultrasound at three key time points: pre-chemotherapy, after four cycles, and post-chemotherapy. Spearman’s correlation was used to assess the relationship between US-measured tumor changes and TRG. Multinomial logistic regression and receiver operating characteristic (ROC) curve analyses were performed to determine the predictive accuracy of the measurements from our US in identifying pathologic complete response (pCR). Conclusions: Ultrasound is a reliable, real-time imaging tool for monitoring NAC response in breast cancer patients. Its ability to predict pCR and track tumor shrinkage highlights its potential for treatment adaptation. Standardization of US protocols and integration with AI-based analysis may further improve its clinical utility, making it a valuable adjunct in breast cancer treatment monitoring. Full article

Background/Objective: Tuberculosis (TB) is associated with significant psychological distress, including anxiety and depression, which may be influenced by coping styles. This study aimed to evaluate the relationship between coping mechanisms, psychological outcomes, and sociodemographic factors in TB patients. Methods: A total of 100 TB patients admitted to the Victor Babeș Clinical Hospital of Infectious Diseases and Pneumophtisiology, Timișoara, were assessed using the COPE questionnaire for coping styles and the GAD7 and PHQ9 scales for anxiety and depression. The Wilcoxon signed-rank test analyzed the changes in the psychological scores between admission and discharge. Results: Multinomial and linear regression analyses identified the predictors of coping styles based on psychological and sociodemographic factors. Anxiety and depression significantly improved during hospitalization (PHQ9: p < 0.001, GAD7: p < 0.001). Social-support-focused coping showed the largest depression reduction (PHQ9: from 13 to 4), while avoidant coping had the lowest residual distress (PHQ9 = 0.5, GAD7 = 0). Age and marital status were significant predictors of problem-focused coping, with older and married patients being more likely to adopt this strategy (β = 0.08, p = 0.008). Coping styles significantly influence psychological outcomes in TB patients. Problem-focused coping was associated with better psychological recovery, while social-support-focused coping was linked to persistent distress. Conclusions: Integrating mental health screening into TB care and tailoring interventions to coping styles may enhance psychological resilience and potentially support treatment adherence, a relationship that should be further explored in future research. Full article

This study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone locations to improve safety and regulate traffic. This study focuses on stop-controlled intersections (SCIs) in Montréal, Québec, to propose a new backlit sign for evaluation. An unbiased experiment utilizing multinomial logistic regression (MNL) was designed to compare drivers’ reactions to different signage. Microscopic models based on observed turning movement counters (TMCs) were calibrated for conflict estimation using a genetic algorithm (GA). Generalized linear models (GLMs) estimated accident and conflict frequencies under different treatment scenarios. The results showed significant conflict reductions at intersections with LED-backlit signs (BLSs), including 65.5% at night and 46.8% in daylight. Pedestrian crossing conflicts decreased by 55.6% and 27.8%. This study introduces an evaluation framework that integrates driver compliance behavior into simulation and crash modeling to assess a newly designed BLS treatment. It provides a framework for assessing safety treatments in contexts where crash data are limited. Findings offer insights for improving SCIs and enhancing transportation safety using LED stop signs. Full article

Job satisfaction is a critical determinant in talent acquisition and corporate value enhancement. The COVID-19 pandemic has triggered a significant increase in online-based non-face-to-face services and consumption, leading to sustained growth in ICT industry job demand. Given the ICT sector’s heavy reliance on human capital and its growing workforce demands, understanding the evolving factors of job satisfaction in this sector has become increasingly crucial. This study analyzed job satisfaction factors derived from employee reviews on an online job review platform using the Dirichlet Multinomial Regression (DMR) topic model, examining temporal changes in these factors before and after the COVID-19 pandemic. As a result, 25 distinct job satisfaction-related topics were identified, and their temporal distribution patterns were categorized into three trajectories: ascending, descending, and stable. Topics exhibiting ascending patterns included work–life balance, organizational systems, corporate culture, employee benefits, work environment, and software development practices. Conversely, factors demonstrating descending patterns encompassed annual compensation, task characteristics, supervisory relationships, employee treatment, commuting conditions, work-related stress, and welfare programs. The remaining topics maintained relatively stable patterns throughout the observation period. These findings contribute to both academic literature and industry practice by elucidating the evolutionary trends in job satisfaction determinants during the COVID-19 pandemic, thereby facilitating more informed strategic human resource management decisions in the ICT sector. Full article

Background/Objectives: There is a limited understanding of the variables relating to dental patients and the treatment provided during the initial phase of the COVID-19 lockdown in our region. This study aimed to qualitatively analyze these patient variables and determine the associations between treatment recommendations and the specialty of the doctor at the point of care. Methods: The present study was retrospective, cross-sectional, and analytical in nature. Data regarding symptoms, diagnosis, treatment, and the attending specialist were retrieved from the patient management software for patients seeking emergency dental services during the COVID-19 lockdown (23 March 2020 to 23 April 2020) in primary and tertiary public hospitals of the National Guard Health Affairs in Riyadh, Saudi Arabia. The association between exploratory (symptoms, diagnostic tool, specialist at point of care) and dependent variables (given diagnosis and treatment) was assessed using Fisher’s exact test and multinomial regression analysis. Results: A total of 151 dental patients attended the outpatient/emergency clinics during this period. The mean age of the patients in this study was 31.4 (±19.0) years. Compared to physicians, general dentists [OR 0.56, CI 0.29–10.47] were more likely to give an inappropriate diagnosis and treatment, whereas residents [OR 2.70, CI 1.65–98.17] and resident endodontists [OR 2.30, CI 1.28–78.11] were more likely to give an appropriate diagnosis and treatment. Conclusions: The findings of this study highlight the need for a greater number of endodontists at the forefront of screening and providing dental care during such health catastrophes. Full article

Background/Objectives: This nationwide retrospective study evaluated the effects of anticancer therapy on osteoporosis in 126,132 Korean breast cancer survivors from 2002 to 2020. Methods: The Cox proportional hazards model assessed the effects of treatment on osteoporosis. To circumvent the guarantee-time bias for osteoporosis development, a landmark analysis was employed. A stabilized inverse probability of treatment weighting was performed to control any confounding bias. The propensity score was calculated using a multinomial logistic regression model with age, national health insurance, and the Charlson comorbidity index. Results: During a median follow-up of 4.22 years, 28,603 cases of osteoporosis were documented. Aromatase inhibitors (AIs) were associated with a higher risk of osteoporosis development in comparison to tamoxifen (TMX) or chemotherapy. Notably, AIs administered subsequent to a combination of chemotherapy and anti-HER2 therapy exhibited the highest risk of osteoporosis development. Subgroup analysis revealed that the mean interval from breast cancer diagnosis to osteoporosis development was 5.00 years for women diagnosed with cancer at age < 50 and 3.89 years for those diagnosed at age ≥ 60. TMX increased the risk of osteoporosis in women diagnosed with cancer at age < 50, whereas chemotherapy was not a significant risk factor for osteoporosis development in those diagnosed at age ≥ 60. The impact of anticancer therapy on osteoporosis development was more pronounced in women diagnosed with breast cancer at a younger age compared to those diagnosed at an older age. Conclusions: Effective prevention and active management strategies should be implemented to address bone loss in both younger and older breast cancer patients. Full article

Background: The prognostic value of the comprehensive geriatric assessment (CGA) is recognized by many in hematology. However, there is no consensus on the utilization of alternative abbreviated methods to assess disabilities in elderly patients with B-cell non-Hodgkin’s lymphomas (B-NHLs). Aim: The aim of this study was to prospectively analyze the prognostic value of selected CGA tools in predicting adverse events (AEs) and outcomes of R-CHOP or R-CHOP-like treatment in elderly patients with diffuse large B-cell lymphomas (DLBCLs) or mantle cell lymphomas (MCLs). Methods: All patients who participated in this study underwent the Katz Index of Independence in Activities of Daily Living (ADL), the Lawton Instrumental Activities of Daily Living (iADL) scale, the Vulnerable Elders Survey-13 (VES-13), the Groningen Frailty Index (GFI), and the Mini Nutritional Assessment Short Form (MNA-SF) before starting anticancer treatment. Selected clinical predictors were also included in the study. Results: A total of 62 patients with newly diagnosed DLBCLs or MCLs, treated with R-CHOP in the Department of Hematology, Blood Neoplasm and Bone Marrow Transplantation of Wroclaw University Hospital between 1 July 2018, and 1 July 2020, were included in the study. The median age upon initiation of the treatment was 72 years (range: 61–68). Multinomial logistic regression and Cox proportional hazard regression analysis demonstrated that the iADL scale was significantly associated with response to treatment (OR = 1.21, 95% CI: 1.02–1.44, p = 0.03), was inversely related to non-hematological AEs (OR = 0.81, 95% CI: 0.71–0.92, p = 0.001), and was a statistically significant predictor of longer overall survival (OS) (HR = 0.83, 95% CI: 0.79–0.89, p < 0.001) and longer progression-free survival (PFS) (HR = 0.91, 95% CI: 0.83–0.99, p = 0.03). Conclusions: These results underscore the effectiveness of the iADL scale as a quick, easy-to-use, and universal CGA tool for evaluating crucial functional status before treatment in elderly hematological patients with DLBCLs or MCLs. Full article

Introduction: This study hypothesized that changes in the elasticity of the quadriceps and patellar tendons before and after the diagnosis of patellar tendinopathy would correlate with the range of motion (ROM) following conservative treatment. We aimed to prospectively assess post-treatment ROM using multinomial logistic regression, incorporating elasticity measurements obtained via shear wave elastography (SWE). Materials and Methods: From March 2023 to April 2024, 95 patients (86 men; aged 20–45 years, mean 25.62 ± 5.49 years) underwent SWE preoperatively and two days post-diagnosis of patellar tendinopathy. Elasticity measurements of the rectus femoris, vastus medialis, vastus lateralis, patellar tendon, and biceps tendon were obtained during full flexion and extension. Based on ROM 56 days post-treatment, patients were categorized into two groups: Group A (ROM > 120 degrees) and Group B (ROM < 120 degrees). A multinomial logistic regression algorithm was employed to classify the groups using patient information and tendon elasticity measurements both at diagnosis and 1-week post-diagnosis. Results: The predictive accuracy using only patient information was 62%, while using only elasticity measurements yielded 68% accuracy. When combining patient information with elasticity measurements taken at diagnosis and two days post-diagnosis, the algorithm achieved an accuracy of 79%, sensitivity of 92%, and specificity of 56%. Conclusions: The combination of patient information and tendon elasticity measurements obtained via SWE at pre-conservative treatment and early post-conservative treatment periods effectively predicts post-treatment ROM. This algorithm can guide rehabilitation strategies for soldiers with patellar tendinopathy. Full article

Parkinson’s disease (PD) is a neurodegenerative illness that affects the central nervous system and leads to a gradual degeneration of neurons that results in movement slowness, mental health problems, speaking difficulties, etc. In the past 20 years, the frequency of PD has doubled. Global estimates revealed that over 8.5 million cases have been identified so far. Thus, early and accurate detection of PD is crucial for treatment. Traditional detection methods are subjective and prone to delays, as they are reliant on clinical evaluation and imaging. Alternatively, artificial intelligence (AI) has recently emerged as a transformative technology in the healthcare sector, showing decent and promising results. However, an effective algorithm needs to be investigated for the most accurate prediction of a particular disease. Thus, this paper explores the ability of different machine learning algorithms in regard to the effective detection of PD. A total of 26 algorithms were implemented using the Scikit-Learn library on the Oxford PD detection dataset. This is a collection of 195 voice measurements recorded from 31 individuals, of which 23 have PD. The implemented algorithms are logistic regression, decision tree, k-nearest neighbors, random forest, support vector machine, Gaussian naïve bayes, multi-layered perceptron (MLP), extreme gradient boosting, adaptive boosting, stochastic gradient descent, gradient boosting machine, extra tree classifier, light gradient boosting machine, categorical boosting, Bernoulli naïve bayes, complement naïve bayes, multinomial naïve bayes, histogram-based gradient boosting, nearest centroid, radius neighbors classifier, logistic regression with elastic net regularization, extreme learning machine, ridge classifier, huber classifier, perceptron classifier, and voting classifier. Among them, MLP outperformed the other algorithms with a testing accuracy of 95%, precision of 94%, sensitivity of 100%, F1 score of 97%, and AUC of 98%. Thus, it successfully discriminates healthy individuals from those with PD, thereby helping for accurate early detection of PD for new patients using their voice measurements. Full article

Plant-based meat alternatives (PBMAs) such as the Impossible Burger^® imitate animal meat appearance, taste, feel, and texture. Part of their consumer appeal are the views that PBMAs are more environmentally friendly, reduce inhumane treatment of animals, and/or have preferred nutritional attributes. College-educated adults are one of the larger markets for these products. This cross-sectional online survey utilized the Theory of Planned Behavior to predict self-reported intakes of PBMAs among 536 undergraduates aged 18–25 at a Midwest university. Sixty-one percent had eaten PBMAs, and 17% wanted to try them. Twenty-two percent were uninterested non-consumers. Their top reason for not eating PBMAs was that they had no reason to decrease their meat intake. Multinomial logistic regression analysis showed subjective norms and positive attitudes about PBMAs increased the odds of more frequent intake, whereas non-consumers had less support from social contacts, but greater perceived behavioral control over general food access. Thus, those with supportive social influences, concerns about the environment, and animal welfare are more likely to consume PBMAs. More frequent PBMA consumption was observed among U.S.-born multicultural students, food insecure students, and those with less perceived behavioral control over food access. Future research should investigate the nuances between these associations further by examining the types of PBMAs consumed, their costs, and retail sources across student demographics. Full article

Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma. Full article