BACKGROUND

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

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

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

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

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

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

OBJECTIVES: To explore the therapeutic mechanism of Chaihu Shugan (CHSG) Decoction for improving cognitive impairment in rats with epilepsy induced by lithium chloride and pilocarpine. METHODS: Male SD rat models of cognitive impairment model after epilepsy induced by intraperitoneal injection with lithium chloride and pilocarpine were randomly divided into 5 groups (n=12) for treatment with daily gavage of saline, donepezil (90 mg/kg), or CHSG Decoction at 2.5, 5.0, 10, 20 and 40 g/kg for 4 consecutive weeks, with 10 rats with intraperitoneal injection with saline as the blank control group. Morris water maze test was used to evaluate cognitive and behavioral changes of the rats after treatment. The mRNA and protein expressions of ASIC1, NR1, NR2A and NR2B in the hippocampus of rats were detected using RT-qPCR and Western blotting. RESULTS: Compared with those with saline treatment, the rat models treated with CHSG Decoction at 5 and 10 g/kg showed significantly shortened escape latency and prolonged stay in the target quadrant with increased number of platform crossings in Morris water maze test. CHSG Decoction treatment at the two doses significantly increased ASIC1, NR1, NR2A and NR2B protein expressions in the hippocampus of the rat models, and their mRNA expression levels were all increased significantly after the treatment at the doses above 2.5 g/kg. CONCLUSIONS CHSG Decoction can improve cognitive impairment in rats after epilepsy possibly by regulating the expression and channel activity of NMDAR protein and its subunit protein via upregulating ASIC1 to modulate neuronal excitability and synaptic plasticity in the hippocampus.
Animals ; Hippocampus/drug effects* ; Receptors, N-Methyl-D-Aspartate/metabolism* ; Acid Sensing Ion Channels/metabolism* ; Rats, Sprague-Dawley ; Male ; Rats ; Epilepsy/complications* ; Cognitive Dysfunction/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Up-Regulation ; Maze Learning

OBJECTIVES: To develop risk prediction models for postoperative hypothermia after transurethral holmium laser enucleation of the prostate (HoLEP) using machine learning algorithms. METHODS: We retrospectively analyzed the clinical data of 403 patients from our center (283 patients in the training set and 120in the internal validation set) and 120 patients from Weifang People's Hospital (as the external validation set). The risk prediction models were built using logistic regression, decision tree and support vector machine (SVM), and model performance was evaluated in terms of accuracy, recall, precision, F1 score and AUC. RESULTS: Operation duration, prostate weight, intraoperative irrigation volume, and being underweight were identified as the predictors of postoperative hypothermia following HoLEP. Among the 3 algorithms, SVM showed the best precision rate and accuracy in all the 3 data sets and the best area under the ROC (AUC) in the training set and validation set, followed by logistic regression, which had a similar AUC in the two data sets. SVM outperformed logistic regression and decision tree models in the validation set in precision, accuracy, recall, F1 score, and AUC, and performed well in the external validation set with better precision rate and accuracy than logistic regression and decision tree models but slightly lower recall rate, F1 index, and AUC value than the decision tree model. SVM outperformed logistic regression and decision tree models in precision, accuracy, F1 score, and AUC in the training set, but had slightly lower recall rate than the decision tree. CONCLUSIONS Among the 3 models, SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.
Humans ; Male ; Retrospective Studies ; Machine Learning ; Transurethral Resection of Prostate/adverse effects* ; Hypothermia/etiology* ; Prostatic Hyperplasia/surgery* ; Algorithms ; Lasers, Solid-State ; Risk Assessment ; Postoperative Complications ; Decision Trees ; Logistic Models ; Aged ; Middle Aged ; Support Vector Machine

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Learning-associated functional plasticity at hippocampal synapses remains largely unexplored. Here, in a single session of reward-based trace conditioning, we examine learning-induced synaptic plasticity in the dorsal CA1 hippocampus (dCA1). Local field-potential recording combined with selective optogenetic inhibition first revealed an increase of dCA1 synaptic responses to the conditioned stimulus (CS) induced during conditioning at both Schaffer collaterals to the stratum radiatum (Rad) and temporoammonic input to the lacunosum moleculare (LMol). At these dCA1 inputs, synaptic potentiation of CS-responding excitatory synapses was further demonstrated by locally blocking NMDA receptors during conditioning and whole-cell recording sensory-evoked synaptic responses in dCA1 neurons from naive animals. An overall similar time course of the induction of synaptic potentiation was found in the Rad and LMol by multiple-site recording; this emerged later and saturated earlier than conditioned behavioral responses. Our experiments demonstrate a cued memory-associated dCA1 synaptic plasticity induced at both Schaffer collaterals and temporoammonic pathways.
Animals ; CA1 Region, Hippocampal/physiology* ; Male ; Association Learning/physiology* ; Neuronal Plasticity/physiology* ; Cues ; Memory/physiology* ; Synapses/physiology* ; Conditioning, Classical/physiology* ; Excitatory Postsynaptic Potentials/physiology* ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; Rats ; Optogenetics

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, alterations in the expression of non-coding RNAs (ncRNAs) have emerged as pivotal events. In this study, we focused on the aberrant expression of ncRNAs and revealed that Lamr1-ps1, a pseudogene of the laminin receptor, significantly exacerbates early spatial learning and memory deficits in APP/PS1 mice. Through a combination of bioinformatics prediction and experimental validation, we identified the miR-29c/Bace1 pathway as a potential regulatory mechanism by which Lamr1-ps1 influences AD pathology. Importantly, augmenting the miR-29c-3p levels in mice ameliorated memory deficits, underscoring the therapeutic potential of targeting miR-29c-3p in early AD intervention. This study not only provides new insights into the role of pseudogenes in AD but also consolidates a foundational basis for considering miR-29c as a viable therapeutic target, offering a novel avenue for AD research and treatment strategies.
Animals ; Alzheimer Disease/pathology* ; Pseudogenes/genetics* ; Mice ; Memory Disorders/metabolism* ; MicroRNAs/genetics* ; Disease Models, Animal ; Spatial Learning/physiology* ; Mice, Transgenic ; Presenilin-1/genetics* ; Male ; Amyloid Precursor Protein Secretases/metabolism* ; Mice, Inbred C57BL ; Aspartic Acid Endopeptidases/metabolism*