BACKGROUND

data-mce-style="text-align: justify;">The COVID-19 pandemic disrupted education worldwide, prompting a rapid shift to emergency remote teaching that challenged students’ learning and mental health. Nursing students, in particular, faced heightened pressures due to the suspension or online adaptation of essential clinical experiences, alongside the need to master theoretical and practical competencies. Emerging evidence indicates that such stressors adversely affect students’ emotional and psychological well-being, potentially influencing academic outcomes. Understanding the relationship between mental health and academic performance among nursing students is crucial for developing targeted interventions that support their well-being and professional readiness.

OBJECTIVE

This study analyzed the mental health status and academic performance of graduating nursing students during the challenging period of remote learning amid the pandemic in a government school in Leyte.

METHODS

data-mce-style="text-align: justify;">The study utilized a descriptive correlational design to explore the relationships between mental health status and academic performance among nursing students. A modified self-administered questionnaire was utilized to gather data. Ethical approval from Eastern Visayas Health Research and Development ConsortiumEthics Review with ERC number 2023-024 was secured, and data collection occurred through various methods. Data analysis used SPSS version 24, emphasizing the importance of understanding these relationships in educational settings.

RESULTS

data-mce-style="text-align: justify;">The study assessed the demographic profile, online learning attributes, mental health status, and academic performance of 20 nursing students during the pandemic. All students passed their courses, despite reporting moderate emotional loneliness and irritability, but minimal fear of COVID-19. Significant correlations were found between demographic factors and mental health indicators. The null hypothesis, suggesting no relationship between demographic factors and mental health, is void, as significant associations were identified. Recommendations include enhancing mental health support in nursing education to address these challenges.

CONCLUSION

data-mce-style="text-align: justify;">This study highlights the experiences of 20 nursing students from a government college in Leyte during the COVID-19 pandemic. Predominantly young women from rural, low-income backgrounds, these students faced challenges like poor internet access but successfully completed their academic requirements, showcasing resilience. While they reported low fear of COVID-19, moderate emotional loneliness and irritability indicated underlying mental health issues. The findings stress the need for educational institutions to provide mental health support and address the digital divide to enhance student well-being and success.

Human ; Male ; Female ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Statistics As Topic ; Psychological Well-being ; Indicators And Reagents ; Students, Nursing ; Suspensions ; Academic Performance ; Learning ; Pandemics ; Nursing ; Education, Nursing ; Covid-19 ; Mental Health

OBJECTIVES

This study aimed to evaluate the psychometric properties of the Tagalog version of the PsAQoL to assess its reliability and consistency.

METHODS

This is a prospective validation study involving 47 patients with PsA from June to August 2023. The
psychometric properties tested were internal consistency (Cronbach’s alpha coefficients), test-retest reliability, convergent validity (Spearman’s rank correlation), and known group validity (Mann-Whitney U Test or Kruskal- Wallis One-Way Analysis of Variance).

RESULTS

The PsAQoL on both week 0 and week 2 had Cronbach’s alpha coefficients of 0.926 indicating high
internal consistency. Test-retest reliability was 0.929, which demonstrates excellent reliability and low level of random measurement error. The PsAQoL scores highly correlated with the Health Assessment Questionnaire-Disability Index (r=0.754, pCONCLUSION

The Tagalog version of the PsAQoL demonstrates excellent psychometric properties and is recommended for monitoring of Tagalog-speaking patients with psoriatic arthritis in healthcare settings.

Human ; Male ; Female ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Middle Aged: 45-64 Yrs Old ; Aged: 65-79 Yrs Old ; Analysis Of Variance ; Aptitude ; Health ; Index ; Patients ; Psychometrics ; Reproducibility Of Results ; Statistics, Nonparametric ; Validation Study

Abstract Intestinal ultrasound (IUS) is increasingly recognized as a vital, non-invasive tool for managing inflammatory bowel disease (IBD) due to its real-time assessment capabilities and patient comfort. However, the lack of standardized training pathways remains a significant barrier to its widespread adoption, particularly in low- and middle-income settings. This editorial evaluates the implementation of a mastery learning-based workshop in Indonesia designed to enhance IUS skills among physicians. The mastery learning framework—incorporating flipped learning, deliberate practice, and real-time feedback led to significant improvements in technical performance with large effect sizes. Despite these gains, only about two-thirds of participants achieved competency in sigmoid colon scanning, and only half met the standard for terminal ileum assessment. This suggests that while short-term intensive workshops are effective for early skill acquisition, they may be insufficient for ensuring consistent proficiency in technically demanding tasks. Mastery learning offers a structured and reproducible approach to gastroenterology procedural training. To translate these initial educational gains into durable clinical expertise, future programs should consider longitudinal curricula that include sustained practice, mentorship, and ongoing assessment.
Intestinal ultrasound ; mastery learning ; Inflammatory bowel disease ; medical education ; competency-based training ; gastroenterology

data-mce-style="text-align: justify;">Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, requiring timely and accurate diagnosis to guide appropriate therapy and reduce antimicrobial resistance. Although urine culture remains the diagnostic gold standard, its prolonged turnaround time and susceptibility to pre-analytical variability limit its clinical efficiency. Recent advances in artificial intelligence (AI) have positioned urinalysis as a promising alternative diagnostic approach, utilizing machine learning and deep learning algorithms for automated analysis and prediction. This review synthesizes current evidence on AI applications in urinalysis for UTI diagnosis, examining computational techniques, diagnostic performance, clinical integration, limitations, and future directions. The literature demonstrates that AI-powered urinalysis can achieve diagnostic accuracy comparable to urine culture, with high sensitivity and specificity while reducing diagnostic time. Integration of AI into clinical workflows has the potential to enhance decision-making, streamline laboratory processes, and support antimicrobial stewardship. However, challenges related to data heterogeneity, algorithm interpretability, validation, and regulatory requirements remain significant barriers to widespread adoption. Overall, AI-driven urinalysis represents a transformative opportunity to complement the existing diagnostic standard and advance more rapid, efficient, and personalized approaches to UTI management.

Human ; Artificial Intelligence ; Urinalysis ; Urinary Tract Infections ; Machine Learning ; Deep Learning

Breast cancer (BC) is the most frequently diagnosed malignancy worldwide and a leading cause of death in women. Machine learning (ML) enables data-driven risk stratification and outcome prediction, potentially improving screening, treatment choices, and overall care. This scoping review cataloged ML models used to predict BC risk and post-treatment outcomes. A PubMed search using MeSH terms (“breast neoplasms,” “breast cancer,” “machine learning,” and “predictive value of tests”) retrieved 44 records, with 9 added by manual searching; 24 studies were included. We excluded 29 reports limited to imaging/radiomics or biomolecular/omics, plus other ineligible items. Model usage among included studies: 13 random forest (RF); 13 support vector machine; 7 decision tree; 5 k-nearest neighbors; 4 deep learning; 3 naïve Bayes; 2 artificial neural networks. Average RF accuracy surpassed 90% in most reports, indicating RF is both common and consistently strong for predictive modeling, supporting targeted clinical translation.
breast neoplasm ; machine learning ; predictive value of tests

INTRODUCTION: Medical errors commonly occur in medical imaging departments. These errors are frequently influenced by patient safety culture. This study aimed to develop a suitable patient safety culture assessment tool for medical imaging departments. METHODS: Staff members of a teaching hospital medical imaging department were invited to complete the generic short version of the Safety Attitude Questionnaire (SAQ). Internal consistency and reliability were evaluated using Cronbach's α. Confirmatory factor analysis (CFA) was conducted to examine model fit. A cut-off of 60% was used to define the percentage positive responses (PPR). PPR values were compared between occupational groups. RESULTS: A total of 300 complete responses were received and the response rate was 75.4%. In reliability analysis, the Cronbach's α for the original 32-item SAQ was 0.941. Six subscales did not demonstrate good fit with CFA. A modified five-subscale, 22-item model (SAQ-MI) showed better fit (goodness-to-fit index ≥0.9, comparative fit index ≥ 0.9, Tucker-Lewis index ≥0.9 and root mean square error of approximation ≤0.08). The Cronbach's α for the 22 items was 0.921. The final five subscales were safety and teamwork climate, job satisfaction, stress recognition, perception of management and working condition, with PPR of 62%, 68%, 57%, 61% and 60%, respectively. Statistically significant differences in PPR were observed between radiographers, doctors and others occupational groups. CONCLUSION The modified five-factor, 22-item SAQ-MI is a suitable tool for the evaluation of patient safety culture in a medical imaging department. Differences in patient safety culture exist between occupation groups, which will inform future intervention studies.
Humans ; Surveys and Questionnaires ; Patient Safety ; Attitude of Health Personnel ; Diagnostic Imaging ; Reproducibility of Results ; Male ; Female ; Adult ; Job Satisfaction ; Factor Analysis, Statistical ; Middle Aged ; Hospitals, Teaching ; Safety Management ; Organizational Culture ; Medical Errors/prevention & control*

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence

OBJECTIVE: To observe the effects of Huayu Tongluo (transforming stasis and unblocking collaterals) moxibustion on learning-memory ability and hippocampal mammalian sterile 20-like kinase 1 (Mst1)/nuclear factor κB (NF-κB) p65 pathway related to inflammatory response in rats with vascular dementia (VD). METHODS: A total of 60 male Wistar rats of SPF grade were randomly divided into a sham operation group (12 rats) and a modeling group (48 rats). VD model was established by the method of modified bilateral common carotid artery permanent ligation in the modeling group. Thirty-six rats with successful modeling were randomly divided into a model group, a moxibustion group and a western medication group, with 12 rats in each group. Huayu Tongluo moxibustion was applied at "Dazhui" (GV14), "Baihui" (GV20) and "Shenting" (GV24) in the moxibustion group, 20 min each time, once a day, 7 day-intervention was as one course, and 1 day-interval was taken between two courses, for a total of 3 courses. In the western medication group, piracetam was given 0.72 mg/kg by intragastric administration, twice a day, the course of intervention was same as that of the moxibustion group. The learning-memory ability was detected by Morris water maze test; the morphology of hippocampal CA1 region was observed by HE staining; the mRNA expression of Mst1, M1 microglia markers CD86, interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) was detected by real-time PCR; the levels of IL-6 and TNF-α in hippocampus were detected by ELISA; and the protein expression of Mst1 and NF-κB p65 in hippocampus was detected by Western blot in rats of each group. RESULTS: Compared with the sham operation group, the escape latency was prolonged in the model group (P<0.05); compared with the model group, the escape latency was shortened in the moxibustion group and the western medication group (P<0.05). The cells in the CA1 region of hippocampus were disordered, cell collapse and irregular nuclei could be observed in the model group; compared with the model group, the cell arrangement in the CA1 region of hippocampus was more regular, and the damage was improved in the moxibustion group and the western medication group. Compared with the sham operation group, the mRNA expression of Mst1, CD86, IL-6 and TNF-α, as well as the protein expression of Mst1, NF-κB p65 in hippocampus were increased in the model group (P<0.05). Compared with the model group, the mRNA expression of Mst1, CD86, IL-6 and TNF-α, as well as the protein expression of Mst1, NF-κB p65 in hippocampus were decreased in the moxibustion group and the western medication group (P<0.05). Compared with the sham operation group, the levels of IL-6 and TNF-α in hippocampus were increased in the model group (P<0.05). Compared with the model group, the levels of IL-6 and TNF-α in hippocampus were decreased in the moxibustion group and the western medication group (P<0.05). CONCLUSION Huayu Tongluo moxibustion can improve the learning-memory ability of VD rats, the mechanism may be related to regulating the activation of microglia through Mst1/NF-κB p65 pathway, reducing the release of pro-inflammatory factors i.e. IL-6 and TNF-α, so as to alleviating the damage of inflammatory factors in the hippocampus of VD rats.
Animals ; Male ; Rats ; Moxibustion ; Hippocampus/metabolism* ; Rats, Wistar ; Dementia, Vascular/genetics* ; Memory/drug effects* ; Humans ; Transcription Factor RelA/genetics* ; Learning ; Protein Serine-Threonine Kinases/genetics* ; Acupuncture Points ; Interleukin-6/genetics* ; Signal Transduction/drug effects* ; Drugs, Chinese Herbal

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
Acupuncture Points ; Humans ; Deep Learning ; Abdomen/diagnostic imaging* ; Neural Networks, Computer ; Acupuncture Therapy ; Image Processing, Computer-Assisted