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Redefining the albumin-bilirubin score: Predictive modeling and multidimensional integration in liver and systemic disease
No full textThis editorial comment is on the article by Xu et al. It offers an in-depth analysis of liver function assessment tools and their prognostic roles in non-malignant liver diseases, with a focus on the albumin-bilirubin (ALBI) score. ALBI’s components, grading system, and clinical relevance across various liver conditions are reviewed and compared with traditional models such as the Child-Pugh and model for end-stage liver disease scores. We included recent studies evaluating ALBI’s role in estimating liver function, suggesting it may help differentiate patients who appear similar under other staging systems, and assist in guiding clinical decisions. Although ALBI is primarily used as an indicator of hepatic reservoir in hepatocellular carcinoma, it has been demonstrated a positive correlation with overall survival, tumor recurrence, and post-hepatectomy liver failure in patients undergoing potentially curative treatments such as liver resection, liver transplantation, and local ablation. Moreover, several studies suggest that ALBI can also predict survival outcomes, treatment-related toxicity, and liver-related complications in patients receiving trans-arterial chemoembolization, radioembolization, external-beam radiotherapy, or systemic therapies. Its growing use in nonmalignant liver diseases, including primary biliary cholangitis, cirrhosis, acute and chronic liver failure, and viral hepatitis highlights the need for large, prospective studies. Further studies are warranted to validate the integration of ALBI into routine clinical practice and to clarify its role in guiding prognosis and treatment planning
Cardiovascular Risk of JAK Inhibitors in Severe Alopecia Areata: A Multicenter Cohort Study
No full textThe Relationships Between Hospital Support Staff and Vascular Trainee Educational Experience and Well-Being
No full textBACKGROUND: Many stakeholders contribute to effective training environments for surgical residents, including program administrators and nursing staff. This study evaluates associations of hospital support staff with trainee educational time and wellness. METHODS: Data were collected via confidential voluntary survey of vascular trainees, who were asked about support staff interactions and protected educational time. Responses were recorded on a five-point Likert scale and dichotomized as positive or negative. Multivariable clustered logistic regression was used to evaluate the associations of support staff with educational time and educational time with burnout. A sensitivity analysis was conducted to adjust for experiences of mistreatment. RESULTS: Of 427 trainees with complete data for items of interest (62% response rate), most responded positively to questions of support staff and educational time. On multivariable analysis of associations with support staff, reporting that programs had adequate staffing and clear division of labor were significant predictors for satisfaction with education (odds ratio (OR) 7.0, P \u3c 0.001, and OR 6.3, P \u3c 0.001, respectively). Those who were satisfied with education had lower odds of burnout (OR 0.25, P \u3c 0.001) and thoughts of attrition (OR 0.15, P \u3c 0.001). CONCLUSION: Trainees with positive support staff relationships were more likely to be satisfied with their education, and satisfaction with education was associated with increased wellness. Addressing sources of mistreatment will likely improve the educational experience. Future work should incorporate support staff experiences to promote successful team-based care
Strategies and Considerations for Forming and Managing Community Advisory Boards from Health Research with Transgender and Nonbinary Communities
No full textPurpose: There is limited research on the use of community advisory boards (CABs) in health research with transgender and nonbinary communities. Transgender communities are navigating challenging histories of health researcher exploitation and research distrust alongside exponential research growth. We explored strategies for forming and managing CABs when conducting transgender community-informed health research. Methods: We used purposive and snowball sampling to identify key informants (KIs): research leaders, implementing staff, and community partners engaging in transgender health research. Between October 2018 and December 2020, we conducted 30 semi-structured in-depth interviews. We used coding followed byiterative thematic analysis and memoing to identify themes. Results: KIs emphasized the importance of involving CABs early in the research process and communicating transparently about their decision-making power and roles. They urged research teams to anticipate and address both multilevel (e.g., gender affirmation-related and socioeconomic) and historical (e.g., local research harms) barriers to CAB participation, and to intentionally engage groups that are historically underrepresented in research. KIs warned against tokenistic CAB models and called on researchers to show up for transgender communities beyond research goals. Research-related trainings and skills-building opportunities could equip CAB members to contribute meaningfully to research decisions, but KIs find that they are often under-planned and under-budgeted. Health Equity Implications: This study contributes to our understanding of how to engage and support CABs working on transgender health research, and how sociostructural factors shape their experiences. We offer a series of recommendations and questions researchers should consider when forming CABs for transgender health research and community-informed research broadly
Antisense verses gene replacement therapy for USH1C
No full textAssociation for Research in Vision and Ophthalmology Annual Meeting, ARVO 2025, May 4-8, 2025, Salt Lake City, U
Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment
No full textNeoadjuvant chemotherapy (NAC) is a critical component of breast cancer treatment, but the molecular mechanisms underlying resistance remain poorly understood. This study aimed to identify transcriptomic changes associated with NAC resistance across four breast cancer subtypes: Luminal A, Luminal B/HER2-positive, Luminal B/HER2-negative, and Triple-Negative Breast Cancer (TNBC). RNA-seq analysis was performed on paired pre- and post-NAC breast cancer samples from 32 nonresponders. Differentially expressed genes (DEGs) were identified, and functional enrichment analyses were conducted. Protein-protein interaction (PPI) networks were constructed to identify hub genes. Tumor microenvironment (TME) infiltration was estimated using deconvolution algorithms. The results revealed distinct gene expression profiles between pre- and post-NAC samples, with FOS and NR4A1 being common DEGs across all subtypes. Enriched pathways varied among subtypes, including signal transduction, estrogen biosynthesis, extracellular matrix organization, dendritic cell activation, and B cell activation. TME analysis showed increased infiltration of specific immune cell populations after NAC, including CD4 memory T cells, regulatory T cells, neutrophils, macrophages, and mast cells, varying by subtype. These findings suggest that NAC modulates gene expression, cellular activity, and TME interactions, potentially contributing to treatment resistance. Understanding the molecular determinants of NAC resistance is crucial for developing targeted therapeutic strategies and improving outcomes for breast cancer patients
Heuristic Weight Initialization for Transfer Learning in Classification Problems
No full textTransfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully connected layer in neural networks from a small set of training data with fewer classes. The approach relies on partitioning the output values from pre-trained models for a small set into two separate intervals determined by the targets. This process is framed as an optimization problem for each output neuron and class. The optimization selects the highest values as weights, considering their direction towards the respective classes. Furthermore, empirical 145 experiments involve a variety of neural network models tested across multiple benchmarks and domains, occasionally yielding accuracies comparable to those achieved with gradient descent methods by using only small subsets
Arterial Insufficiencies: Central Retinal Artery Occlusion
No full textCentral retinal artery occlusion (CRAO) is a relatively rare emergent condition of the eye resulting in sudden painless vision loss. This vision loss is usually dramatic and permanent, and the prognosis for visual recovery is poor. Patients particularly at risk include those with giant cell arteritis, atherosclerosis, and thromboembolic disease. A wide variety of treatment modalities have been tried over the last one hundred years with little to no success, with the exception of hyperbaric oxygen therapy (HBO₂)
