This reflective paper explored the philosophical foundations of lifelong learning and impactful research in the field of nursing. Anchored in personal experience and supported by scholarly literature, it illustrated the transformative power of continuous learning, the cultivation of research competence, and the moral responsibility of contributing meaningfully to society. A nurse researcher's journey is not defined by awards or accomplishment but by an unwavering dedication to knowledge creation, community involvement, and evidence-based practice. The "emptying one's cup" metaphor embodies intellectual humility, a mindset that keeps the mind open to learning, self-improvement, and meaningful service throughout one's career.

Varicocele is a prevalent condition in the infertile male population. However, to date, which patients may benefit most from varicocele repair is still a matter of debate. The purpose of this study was to evaluate whether certain preintervention sperm parameters are predictive of successful varicocele repair, defined as an improvement in total motile sperm count (TMSC). We performed a retrospective study on 111 patients with varicocele who had undergone varicocele repair, collected from the Department of Endocrinology, Metabolic Diseases and Nutrition, University of Catania (Catania, Italy), and the Unit of Urology at the Selcuk University School of Medicine (Konya, Türkiye). The predictive analysis was conducted through the use of the Brain Project, an innovative tool that allows a complete and totally unbiased search of mathematical expressions that relate the object of study to the various parameters available. Varicocele repair was considered successful when TMSC increased by at least 50% of the preintervention value. For patients with preintervention TMSC below 5 × 10 6 , improvement was considered clinically relevant when the increase exceeded 50% and the absolute TMSC value was >5 × 10 6 . From the preintervention TMSC alone, we found a model that predicts patients who appear to benefit little from varicocele repair with a sensitivity of 50.0% and a specificity of 81.8%. Varicocele grade and serum follicle-stimulating hormone (FSH) levels did not play a predictive role, but it should be noted that all patients enrolled in this study were selected with intermediate- or high-grade varicocele and normal FSH levels. In conclusion, preintervention TMSC is predictive of the success of varicocele repair in terms of TMSC improvement in patients with intermediate- or high-grade varicoceles and normal FSH levels.
Humans ; Varicocele/complications* ; Male ; Retrospective Studies ; Machine Learning ; Adult ; Treatment Outcome ; Sperm Count ; Infertility, Male/etiology* ; Sperm Motility ; Follicle Stimulating Hormone/blood* ; Young Adult

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

OBJECTIVES: To study the impact of family socioeconomic status on children's reading fluency and the chain mediation effect of family reading environment and children's living and learning styles in this relationship. METHODS: A total of 473 children from grades 2 to 6 in two primary schools in Nanjing were selected through stratified random sampling. The children's reading fluency was assessed, and a questionnaire was used to collect information on family socioeconomic status, family reading environment, and children's living and learning styles. The mediation model was established using the Process macro in SPSS, and the Bootstrap method was employed to test the significance of the mediation effects. RESULTS: Family socioeconomic status, family reading environment, and children's living and learning styles were significantly positively correlated with reading fluency (P<0.001). The family reading environment and children's living and learning styles mediated the relationship between family socioeconomic status and children's reading fluency. Specifically, the independent mediation effect of family reading environment accounted for 11.02% of the total effect, while the independent mediation effect of children's living and learning styles accounted for 10.79%. The chain mediation effect of family reading environment and children's living and learning styles accounted for 7.41% of the total effect. CONCLUSIONS Family socioeconomic status can affect children's reading fluency through three pathways: family reading environment, children's living and learning styles, and the chain mediation effect of family reading environment and children's living and learning styles.
Humans ; Child ; Male ; Female ; Reading ; Learning ; Social Class ; Family

Specific learning disorder (SLD) is a common neurodevelopmental disorder in children that significantly affects academic performance and quality of life. At present, diagnosis mainly relies on standardized tests and professional evaluations, a process that is complex and time-consuming. Multiple studies have shown that machine learning can analyze diverse data, including test scores, handwriting samples, eye movement data, neuroimaging data, and genetic data, to automatically learn the relationships between input features and output labels and achieve efficient prediction. It shows great potential for early screening, auxiliary diagnosis, and research on underlying mechanisms in SLD. This article reviews the applications of machine learning in the auxiliary diagnosis of SLD and discusses its performance when handling different data types.
Humans ; Machine Learning ; Specific Learning Disorder/diagnosis* ; Child

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value. METHODS: 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. RESULTS: Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05). CONCLUSIONS The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Machine Learning ; Lung Neoplasms/diagnosis* ; Male ; Female ; Mutation ; Middle Aged ; ErbB Receptors/genetics* ; Prognosis ; Aged ; Retrospective Studies ; Adult ; Biomarkers, Tumor/genetics*

BACKGROUND AND OBJECTIVES

The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

BACKGROUND

Six-pyruvoyl-tetrahydrobiopterin synthase (6-PTPS) deficiency is an inherited metabolic disorder which results in tetrahydrobiopterin (BH4) deficiency causing hyperphenylalaninemia.

This study aimed to describe the clinical, biochemical, and radiologic profiles, and neurologic and developmental outcomes of patients diagnosed with 6-pyruvoyl tetrahydrobiopterin (PTPS) deficiency through newborn screening and confirmed by BH4 loading test, pterin analysis, and gene sequencing who were following-up with the metabolic team.

The research was a single-center descriptive case series study design that was done at the Philippine General Hospital, a tertiary government hospital. The clinical, biochemical, radiologic profiles and neurodevelopmental evaluation of each patient were described.

Nine patients from 1 year 2 months to 14 years 5 months of age were enrolled in the study. Clinical manifestations before treatment were hypotonia, poor suck, and seizure. The most common clinical manifestation even after treatment initiation was seizure. The mean phenylalanine level on newborn screening was 990.68 umol/L, but after treatment was started, mean levels ranged from 75.69 to 385.09 umol/L. Two of the patients had focal atrophy of the posterior lobe on brain imaging. Pathogenic variants on molecular analysis were all missense, with two predominant variants, c.155A>G and c.58T>C. Eight of the nine patients had varying degrees of developmental delay or intellectual disability, while the remaining patient had signs of a learning disorder.

Newborn screening has played a crucial role in the early identification and management of patients with hyperphenylalaninemia due to 6-PTPS deficiency. Confirmation of diagnosis through determination of DHPR activity, urine pterins and/or molecular analysis is necessary for appropriate management. However, despite early initiation of treatment, neurodevelopmental findings of patients with 6-PTPS deficiency were still unsatisfactory.

BACKGROUND AND OBJECTIVES

One of the effects of the COVID-19 pandemic on medical education is an increased awareness and use of social media (SocMed) to facilitate learning. However, literature on the use of SocMed in medical education has focused primarily on educator-led teaching activities. Our study aimed to describe SocMed initiatives that were student-led, particularly for information dissemination and peer collaborative learning, and to elicit perceptions of medical students towards such activities.

An online survey on SocMed usage in medical education was sent to all first- and second-year medical students at the University of the Philippines Manila College of Medicine from October to December 2021. The questionnaire collected data on demographics, SocMed habits and preferences, and perceived advantages and disadvantages of SocMed. Descriptive statistics were calculated while the free-text responses were grouped into prominent themes and summarized.

We received a total of 258 responses (71%) out of 361 eligible participants. Overall, 74% found SocMed platforms to be very and extremely helpful; 88% recommended its continued use. The most popular SocMed platforms for different tasks were as follows: Discord for independent study groups and for conducting peer tutoring sessions; Facebook Messenger for reading reminders; Telegram for reading announcements related to academics and administrative requirements, and for accessing material provided by classmates and professors.

The high uptake of SocMed among medical students may be attributed to its accessibility and costefficiency. The use of a particular SocMed platform was dependent on the students’ needs and the platform's features. Students tended to use multiple SocMed platforms that complemented one another. SocMed also had disadvantages, such as the potential to distract from academic work and to become a source of fatigue. Educators must engage with students to understand how SocMed platforms can be integrated into medical education, whether in the physical or virtual learning environment.

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