Evaluating Chatbot responses to patient questions in the field of glaucoma.

Objective: The aim of this study was to evaluate the accuracy, comprehensiveness, and safety of a publicly available large language model (LLM)-ChatGPT in the sub-domain of glaucoma. Design: Evaluation of diagnostic test or technology. Subjects participants and/or controls: We seek to evaluate the responses of an artificial intelligence chatbot ChatGPT (version GPT-3.5, OpenAI). Methods intervention or testing: We curated 24 clinically relevant questions in the domain of glaucoma. The questions spanned four categories: pertaining to diagnosis, treatment, surgeries, and ocular emergencies. Each question was posed to the LLM and the responses obtained were graded by an expert grader panel of three glaucoma specialists with combined experience of more than 30 years in the field. For responses which performed poorly, the LLM was further prompted to self-correct. The subsequent responses were then re-evaluated by the expert panel. Main outcome measures: Accuracy, comprehensiveness, and safety of the responses of a public domain LLM. Results: There were a total of 24 questions and three expert graders with a total number of responses of n = 72. The scores were ranked from 1 to 4, where 4 represents the best score with a complete and accurate response. The mean score of the expert panel was 3.29 with a standard deviation of 0.484. Out of the 24 question-response pairs, seven (29.2%) of them had a mean inter-grader score of 3 or less. The mean score of the original seven question-response pairs was 2.96 which rose to 3.58 after an opportunity to self-correct (z-score - 3.27, p = 0.001, Mann-Whitney U). The seven out of 24 question-response pairs which performed poorly were given a chance to self-correct. After self-correction, the proportion of responses obtaining a full score increased from 22/72 (30.6%) to 12/21 (57.1%), (p = 0.026, χ2 test). Conclusion: LLMs show great promise in the realm of glaucoma with additional capabilities of self-correction. The application of LLMs in glaucoma is still in its infancy, and still requires further research and validation.

Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats.

BACKGROUND: This study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q). METHODS: Eighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used. RESULTS: At day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035). CONCLUSION: Tensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury. RELEVANCE STATEMENT: Tensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI. KEY POINTS: The alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury. Changing q values were observed in the impact site and remote WM. The lower q values in the remote WM were associated with astrogliosis.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis.

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.

Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study.

AIMS: To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. METHODS: We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. RESULTS: In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885-0.976) and 0.906 (0.863-0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730-0.934) to 0.930 (0.906-0.953) and 0.891 (0.836-0.945) to 0.962 (0.918-1.000), respectively. CONCLUSIONS: Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Contributions of Lipid-Related Metabolites and Complement Proteins to Early and Intermediate Age-Related Macular Degeneration.

Objective: Our objective was to determine the effects of lipids and complement proteins on early and intermediate age-related macular degeneration (AMD) stages using machine learning models by integrating metabolomics and proteomic data. Design: Nested case-control study. Subjects and Controls: The analyses were performed in a subset of the Singapore Indian Chinese Cohort (SICC) Eye Study. Among the 6753 participants, we randomly selected 155 Indian and 155 Chinese cases of AMD and matched them with 310 controls on age, sex, and ethnicity. Methods: We measured 35 complement proteins and 56 lipids using mass spectrometry and nuclear magnetic resonance, respectively. We first selected the most contributing lipids and complement proteins to early and intermediate AMD using random forest models. Then, we estimated their effects using a multinomial model adjusted for potential confounders. Main Outcome Measures: Age-related macular degeneration was classified using the Beckman classification system. Results: Among the 310 individuals with AMD, 166 (53.5%) had early AMD, and 144 (46.5%) had intermediate AMD. First, high-density lipoprotein (HDL) particle diameter was positively associated with both early and intermediate AMD (odds ratio [OR]early = 1.69; 95% confidence interval [CI],1.11-2.55 and ORintermediate = 1.72; 95% CI, 1.11-2.66 per 1-standard deviation increase in HDL diameter). Second, complement protein 2 (C2), complement C1 inhibitor (IC1), complement protein 6 (C6), complement protein 1QC (C1QC) and complement factor H-related protein 1 (FHR1), were associated with AMD. C2 was positively associated with both early and intermediate AMD (ORearly = 1.58; 95% CI, 1.08-2.30 and ORintermediate = 1.56; 95% CI, 1.04-2.34). C6 was positively (ORearly = 1.41; 95% CI, 1.03-1.93) associated with early AMD. However, IC1 was negatively associated with early AMD (ORearly = 0.62; 95% CI, 0.38-0.99), whereas C1QC (ORintermediate = 0.63; 95% CI, 0.42-0.93) and FHR1 (ORintermediate = 0.73; 95% CI, 0.54-0.98) were both negatively associated with intermediate AMD. Conclusions: Although both HDL diameter and C2 levels show associations with both early and intermediate AMD, dysregulations of IC1, C6, C1QC, and FHR1 are only observed at specific stages of AMD. These findings underscore the complexity of complement system dysregulation in AMD, which appears to vary depending on the disease severity. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

DHX9 SUMOylation is required for the suppression of R-loop-associated genome instability.

RNA helicase DHX9 is essential for genome stability by resolving aberrant R-loops. However, its regulatory mechanisms remain unclear. Here we show that SUMOylation at lysine 120 (K120) is crucial for DHX9 function. Preventing SUMOylation at K120 leads to R-loop dysregulation, increased DNA damage, and cell death. Cells expressing DHX9 K120R mutant which cannot be SUMOylated are more sensitive to genotoxic agents and this sensitivity is mitigated by RNase H overexpression. Unlike the mutant, wild-type DHX9 interacts with R-loop-associated proteins such as PARP1 and DDX21 via SUMO-interacting motifs. Fusion of SUMO2 to the DHX9 K120R mutant enhances its association with these proteins, reduces R-loop accumulation, and alleviates survival defects of DHX9 K120R. Our findings highlight the critical role of DHX9 SUMOylation in maintaining genome stability by regulating protein interactions necessary for R-loop balance.

Interpretable Machine Learning-Based Risk Score for Predicting Ten-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes.

PURPOSE: To predict 10-year graft survival after deep anterior lamellar keratoplasty (DALK) and penetrating keratoplasty (PK) using a machine learning (ML)-based interpretable risk score. METHODS: Singapore Corneal Transplant Registry patients (n = 1687) who underwent DALK (n = 524) or PK (n = 1163) for optical indications (excluding endothelial diseases) were followed up for 10 years. Variable importance scores from random survival forests were used to identify variables associated with graft survival. Parsimonious analysis using nested Cox models selected the top factors. An ML-based clinical score generator (AutoScore) converted identified variables into an interpretable risk score. Predictive performance was evaluated using Kaplan-Meier (KM) curves and time-integrated AUC (iAUC) on an independent testing set. RESULTS: Mean recipient age was 51.8 years, 54.1% were male, and majority were Chinese (60.0%). Surgical indications included corneal scar (46.5%), keratoconus (18.3%), and regraft (16.2%). Five-year and ten-year KM survival was 93.4% and 92.3% for DALK, compared with 67.6% and 56.6% for PK (log-rank P < 0.001). Five factors were identified by ML algorithm as predictors of 10-year graft survival: recipient sex, preoperative visual acuity, choice of procedure, surgical indication, and active inflammation. AutoScore stratified participants into low-risk and high-risk groups-with KM survival of 73.6% and 39.0%, respectively (log-rank P < 0.001). ML analysis outperformed traditional Cox regression in predicting graft survival beyond 5 years (iAUC 0.75 vs. 0.69). CONCLUSIONS: A combination of ML and traditional techniques identified factors associated with graft failure to derive a clinically interpretable risk score to stratify PK and DALK patients-a technique that may be replicated in other corneal transplant programs.

Skin colonization by Staphylococcus aureus in hemodialysis patients with pruritus and the effect of Staphylococcus aureus-secreted α-toxin on filaggrin expression.

Staphylococcus aureus (S. aureus) commonly reside on human skin in residents in long-term care facilities, yet its colonization and impact on the skin of hemodialysis (HD) patients have yet to be studied. The aim of the present study was to investigate the colonization of S. aureus on the skin of pruritic and non-pruritic HD patients, and the influence of S. aureus and S. aureus-secreted α-toxin on skin barrier function-related protein expression. In this study, a higher relative S. aureus count in pruritic HD patients compared to non-pruritic HD patients and healthy subjects were revealed by real-time polymerase chain reaction. S. aureus and α-toxin decreased mRNA and protein expression levels of aryl hydrocarbon receptor (AHR), ovo-like transcriptional repressor 1 (OVOL1), and filaggrin (FLG) in keratinocytes. In addition, anti-alpha-hemolysin (anti-hla) was used as an α-toxin neutralizer, and it successfully abrogated S. aureus-induced AHR, OVOL1, and FLG mRNA and protein expression downregulation. Mechanistically, α-toxin could decrease FLG activity by preventing the recruitment of AHR to the FLG promoter region. In conclusion, pruritic HD patients had higher S. aureus colonization, with S. aureus-secreted α-toxin suppressing FLG expression through the AHR-FLG axis.

Number needed to treat with intravenous tenecteplase to reduce the need for mechanical thrombectomy in large vessel occlusion acute ischemic stroke patients: A retrospective look at real-world experience data.

OBJECTIVE: We sought to describe short term outcomes in patients with large vessel occlusion acute ischemic stroke (LVOAIS) who were treated with intravenous tenecteplase (TNK) as compared to alteplase (tPA), focusing on reduction in the need for mechanical thrombectomy (MT). BACKGROUND: In LVOAIS, TNK has shown improved reperfusion and outcomes with a similar safety profile to tPA. Ultra-early reperfusion has been described with TNK which would prevent the need for MT. We analyze the magnitude of this effect in a "real-world" setting. DESIGN/METHODS: In this retrospective study, demographic, clinical, and imaging information from patients with LVOAIS treated with intravenous thrombolysis was collected. Data was compared between the group treated with TNK and tPA. RESULTS: One hundred eighty-six patients met the criteria for the study. Of these,144patients received tPA and 42 received TNK. Nine had clinical improvement prior to groin puncture and did not require angiography. When combining the number of patients who had recanalization on angiography before MT and those who had clinical improvement prior to angiography, there were a total of 23 patients. This was noted in 9.7 % of patients who received tPA and 21.4 % of those who received TNK (p = 0.043). For patients treated with TNK we observed a rapid clinical improvement, improved NIHSS, improved functional outcomes and decreased length of stay compared to patients treated with tPA. For patients with spontaneous recanalization either angiographically or with clinical improvement from intravenous thrombolysis, MT may not be required. CONCLUSIONS: Intravenous TNK in patients with LVOAIS decreases the need for MT, and is associated with improved outcomes and reduced length of stay.

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes.

PURPOSE: To evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes. DESIGN: Longitudinal cohort study METHODS: We analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010-2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state. RESULTS: The median assessment interval was 12 months, with most patients having 3 assessments. Annual probabilities for DR progression (no-to-mild, mild-to-moderate and moderate-to-severe/proliferative) were 6.1 %, 7.0 % and 19.3 %, respectively; and for regression (mild-to-no, moderate-to-mild and severe-to-moderate) were 55.4 %, 17.3 % and 4.4 %, respectively. Annual mortality rates from each DR state were 1.2 %, 2.0 %, 18.7 %, and 30.0 %. The sojourn time in each state were 8.2, 0.8, 0.8 and 2.2 years. Higher HbA1c and SBP levels were associated with progression of no-mild and mild-moderate DR, and diabetes duration with no-to-mild and moderate-to-severe/proliferative DR. Lower HbA1c levels were associated with regression from mild-to-no and moderate-to-mild, and higher BMI with mild-to-no DR. CONCLUSIONS: Our results suggest a prolonged duration (∼8 years) in developing mild DR, with faster transitions (within a year) from mild or moderate states. Moderate/above DR greatly increases the probability of progression and death as compared to mild DR/below. HbA1c was associated with both progression as well as regression.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

BACKGROUND: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Restraint Stress-Induced Neutrophil Inflammation Contributes to Concurrent Gastrointestinal Injury in Mice.

Psychological stress increases risk of gastrointestinal tract diseases. However, the mechanism behind stress-induced gastrointestinal injury is not well understood. The objective of our study is to elucidate the putative mechanism of stress-induced gastrointestinal injury and develop an intervention strategy. To achieve this, we employed the restraint stress mouse model, a well-established method to study the pathophysiological changes associated with psychological stress in mice. By orally administering gut-nonabsorbable Evans blue dye and monitoring its plasma levels, we were able to track the progression of gastrointestinal injury in live mice. Additionally, flow cytometry was utilized to assess the viability, death, and inflammatory status of splenic leukocytes, providing insights into the stress-induced impact on the innate immune system associated with stress-induced gastrointestinal injury. Our findings reveal that neutrophils represent the primary innate immune leukocyte lineage responsible for stress-induced inflammation. Splenic neutrophils exhibited elevated expression levels of the pro-inflammatory cytokine IL-1, cellular reactive oxygen species, mitochondrial burden, and cell death following stress challenge compared to other innate immune cells such as macrophages, monocytes, and dendritic cells. Regulated cell death analysis indicated that NETosis is the predominant stress-induced cell death response among other analyzed regulated cell death pathways. NETosis culminates in the formation and release of neutrophil extracellular traps, which play a crucial role in modulating inflammation by binding to pathogens. Treatment with the NETosis inhibitor GSK484 rescued stress-induced neutrophil extracellular trap release and gastrointestinal injury, highlighting the involvement of neutrophil extracellular traps in stress-induced gastrointestinal inflammation. Our results suggest that neutrophil NETosis could serve as a promising drug target for managing psychological stress-induced gastrointestinal injuries.

Gut Microbiota in Patients with Prediabetes.

Prediabetes is characterized by abnormal glycemic levels below the type 2 diabetes threshold, and effective control of blood glucose may prevent the progression to type 2 diabetes. While the association between the gut microbiota, glucose metabolism, and insulin resistance in diabetic patients has been established in previous studies, there is a lack of research regarding these aspects in prediabetic patients in Asia. We aim to investigate the composition of the gut microbiota in prediabetic patients and their differences compared to healthy individuals. In total, 57 prediabetic patients and 60 healthy adult individuals aged 18 to 65 years old were included in this study. Biochemistry data, fecal samples, and 3 days of food records were collected. Deoxyribonucleic acid extraction and next-generation sequencing via 16S ribosomal ribonucleic acid metagenomic sequencing were conducted to analyze the relationship between the gut microbiota and dietary habits. Prediabetic patients showed a lower microbial diversity than healthy individuals, with 9 bacterial genera being less abundant and 14 others more abundant. Prediabetic patients who consumed a low-carbohydrate (LC) diet exhibited higher diversity in the gut microbiota than those who consumed a high-carbohydrate diet. A higher abundance of Coprococcus was observed in the prediabetic patients on an LC diet. Compared to healthy individuals, the gut microbiota of prediabetic patients was significantly different, and adopting an LC diet with high dietary fiber consumption may positively impact the gut microbiota. Future studies should aim to understand the relationship between the gut microbiota and glycemic control in the Asian population.

A body shape index (ABSI) but not body mass index (BMI) is associated with prostate cancer-specific mortality: Evidence from the US NHANES database.

BACKGROUND: Prostate cancer (PCa) is a common malignancy in males and obesity may play a role in its development and progression. Associations between visceral obesity measured by a body shape index (ABSI) and PCa mortality have not been thoroughly investigated. This study assessed the associations between ABSI, body mass index (BMI), and long-term PCa-specific mortality using a nationally representative US database. METHODS: This population-based longitudinal study collected data of males aged ≥40 years diagnosed with PCa and who underwent surgery and/or radiation from the National Health and Nutrition Examination Survey database 2001-2010. All included participants were followed through the end of 2019 using the National Center for Health Statistics Linked Mortality File. Associations between PCa-specific mortality, BMI, and ABSI were determined using Cox proportional hazards regression and receiver operating characteristic (ROC) curve analysis. RESULTS: Data of 294 men (representing 1,393,857 US nationals) were analyzed. After adjusting for confounders, no significant associations were found between BMI (adjusted hazard ratio [aHR] = 1.06, 95% confidence interval [CI]: 0.97-1.16, p = 0.222), continuous ABSI (aHR = 1.29, 95% CI: 0.83-2.02, p = 0.253), or ABSI in category (Q4 vs. Q1-Q3: aHR = 1.52, 95% CI: 0.72-3.24, p = 0.265), and greater risk of PCa-specific mortality. However, among participants who had been diagnosed within 4 years, the highest ABSI quartile but not in BMI was significantly associated with greater risk for PCa-specific mortality (Q4 vs. Q1-Q3: aHR = 5.34, 95% CI: 2.26-12.62, p = 0.001). In ROC analysis for this subgroup, the area under the curve of ABSI alone for predicting PCa-specific mortality was 0.638 (95% CI: 0.448-0.828), reaching 0.729 (95% CI: 0.490-0.968 when combined with other covariates. CONCLUSIONS: In US males with PCa diagnosed within 4 years, high ABSI but not BMI is independently associated with increased PCa-specific mortality.

Prevalence, associated risk factors; and patient and economic impact of multiple sensory impairment in a multi-ethnic elderly population in Singapore: the PIONEER study.

BACKGROUND: To determine the prevalence, risk factors; and impact on patient health and economic outcomes across the laterality spectrum of multiple sensory impairment (MSI) in a multi-ethnic older Asian population. METHODS: In this population-based study of Singaporeans aged ≥ 60 years, MSI was defined as concomitant vision (visual acuity > 0.3 logMAR), hearing (pure-tone air conduction average > 25 dB), and olfactory (score < 12 on the Sniffin' Sticks test) impairments across the spectrum of laterality (any, unilateral, combination [of unilateral and bilateral], and bilateral). RESULTS: Among 2,057 participants (mean ± SD 72.2 ± 0.2 years; 53.1% female), the national census-adjusted prevalence rates of any, unilateral, combination, and bilateral MSI were 20.6%, 1.2%, 12.2%, and 7.2%, respectively. Older age, male gender, low socioeconomic status (SES), and smoking (all p < 0.05) were independently associated with higher likelihood of any MSI. Compared to those with no sensory loss, those with MSI had significantly decreased mobility (range 5.4%-9.2%), had poor functioning (OR range 3.25-3.45) and increased healthcare costs (range 4-6 folds) across the laterality spectrum. Additionally, bilateral MSI had a significant decrease in HRQoL (5.5%, p = 0.012). CONCLUSIONS: MSI is a highly prevalent medical condition, with 1 in 5; and almost 1 in 10 community-dwelling older Asians having any and bilateral MSI, respectively, with a higher likelihood in men, smokers, and those with low SES. Critically, MSI has a substantial negative impact on patient health and economic outcomes across the laterality spectrum. Sensory testing is critical to detect and refer individuals with MSI for management to improve their functional independence and QoL.

Overexpression of malic enzyme is involved in breast cancer growth and is correlated with poor prognosis.

Malic enzyme (ME) genes are key functional metabolic enzymes playing a crucial role in carcinogenesis. However, the detailed effects of ME gene expression on breast cancer progression remain unclear. Here, our results revealed ME1 expression was significantly upregulated in breast cancer, especially in patients with oestrogen receptor/progesterone receptor-negative and human epidermal growth factor receptor 2-positive breast cancer. Furthermore, upregulation of ME1 was significantly associated with more advanced pathological stages (p < 0.001), pT stage (p < 0.001) and tumour grade (p < 0.001). Kaplan-Meier analysis revealed ME1 upregulation was associated with poor disease-specific survival (DSS: p = 0.002) and disease-free survival (DFS: p = 0.003). Multivariate Cox regression analysis revealed ME1 upregulation was significantly correlated with poor DSS (adjusted hazard ratio [AHR] = 1.65; 95% CI: 1.08-2.52; p = 0.021) and DFS (AHR, 1.57; 95% CI: 1.03-2.41; p = 0.038). Stratification analysis indicated ME1 upregulation was significantly associated with poor DSS (p = 0.039) and DFS (p = 0.038) in patients with non-triple-negative breast cancer (TNBC). However, ME1 expression did not affect the DSS of patients with TNBC. Biological function analysis revealed ME1 knockdown could significantly suppress the growth of breast cancer cells and influence its migration ability. Furthermore, the infiltration of immune cells was significantly reduced when they were co-cultured with breast cancer cells with ME1 knockdown. In summary, ME1 plays an oncogenic role in the growth of breast cancer; it may serve as a potential biomarker of progression and constitute a therapeutic target in patients with breast cancer.

Effects of an unfolding case study on clinical reasoning, self-directed learning, and team collaboration of undergraduate nursing students: A mixed methods study.

BACKGROUND: Clinical reasoning is an essential nursing competency that students must develop to provide safe patient care. Developing and utilizing unfolding case studies, which present constantly changing patient conditions to improve students' clinical reasoning and to foster communication and self-reflection, can help to achieve that imperative. OBJECTIVES: To develop an unfolding case study and to test its effectiveness in improving clinical reasoning, team collaboration, and self-directed learning. DESIGN: A mixed methods design. SETTING: One university in Southern Taiwan. PARTICIPANTS: Forty nursing students. METHODS: An unfolding case study was developed based on the clinical reasoning model and unfolding cases model. The Nurses Clinical Reasoning Scale, Self-Directed Learning Instrument, and Questionnaire of Group Responsibility and Cooperation in Learning Teams were used. Forty nursing students completed questionnaires and nine of them participated in focus group discussions. Wilcoxon signed-rank, Spearman correlation, regression, and inductive content analysis were used to analyze data. RESULTS: Students' abilities in clinical reasoning, self-directed learning, and team collaboration were statistically significantly improved after implementation of the unfolding case study. Emergent themes included "patient-centered communication," "group inspiration and learning," "thinking critically and reflecting on oneself," and "applying theoretical knowledge in care to meet patients' changing needs." CONCLUSIONS: Unfolding case studies provide a safe environment in which nursing students may learn and apply knowledge to safe patient care.

Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization.

INTRODUCTION: The clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined. OBJECTIVES: To this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP. METHODS: A meta-analysis comprising 26 case-control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP. RESULTS: Our meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein-protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained. CONCLUSION: With the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.

BACKGROUND: Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. OBJECTIVE: This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. METHODS: We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI. RESULTS: Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). CONCLUSIONS: This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Using the Theory of Planned Behavior to determine COVID-19 vaccination intentions and behavior among international and domestic college students in the United States.

Vaccination is the most effective strategy for preventing infectious diseases such as COVID-19. College students are important targets for COVID-19 vaccines given this population's lower intentions to be vaccinated; however, limited research has focused on international college students' vaccination status. This study explored how psychosocial factors from the Theory of Planned Behavior (TPB; attitudes, perceived behavioral control, subjective norms, and behavioral intentions) related to students' receipt of the full course of COVID-19 vaccines and their plans to receive a booster. Students were recruited via Amazon mTurk and the Office of the Registrar at a U.S. state university. We used binary logistic regression to examine associations between students' psychosocial factors and full COVID-19 vaccination status. Hierarchical multiple regression was employed to evaluate relationships between these factors and students' intentions to receive a booster. The majority of students in our sample (81% of international students and 55% of domestic students) received the complete vaccination series. Attitudes were significantly associated with all students' full vaccination status, while perceived behavioral control was significantly associated with domestic students' status. Students' intentions to receive COVID-19 vaccines were significantly correlated with their intentions to receive a booster, with international students scoring higher on booster intentions. Among the combined college student population, attitudes, intentions to receive COVID-19 vaccines, and subjective norms were significantly related to students' intentions to receive a booster. Findings support the TPB's potential utility in evidence-based interventions to enhance college students' COVID-19 vaccination rates. Implications for stakeholders and future research directions are discussed.