Citations

ABSTRACT Patient outcomes in advanced renal cell carcinoma (RCC) remain poor, with five‐year survival rates ranging from ~10% to 30%. Early projections of therapeutic outcomes could optimize precision medicine and accelerate drug development. While machine learning (ML) models integrating tumor growth inhibition (TGI) metrics have improved survival predictions over traditional models, their application in RCC remains unexplored. Herein, we used TGI metrics and baseline data to evaluate parametric (PM) and semi‐parametric (SPM) survival models alongside ML approaches for predicting progression‐free (PFS) and overall survival (OS) in 1839 RCC patients from four trials (evaluating sunitinib, axitinib, sorafenib, interferon‐alpha, and avelumab + axitinib). Data were split into training (70%) and testing (30%), and feature selection was used to determine parsimonious and robust models. Bootstrap resampling ( n = 100) was employed for models' validation, and performance was assessed using C‐index and Integrated Brier Score. In brief, training data results demonstrated that tree‐based ML models (random survival forest (RSF) and XGBoost) outperformed PM and SPM models in predicting PFS (C‐index: 0.783–0.785 vs. 0.725–0.738 for PM and SPM; p < 0.05) and OS (C‐index: 0.77–0.867 vs. 0.750–0.758 for PM and SPM; p < 0.05), with RSF achieving better prediction of PFS and OS using only 3–5 covariates, compared to 9–35 with other tested methods. Tree‐based methods were also superior in the testing data. SHapley Additive exPlanations revealed nonlinear relationships among top predictors, including TGI metrics, underscoring the ability of tree‐based methods to capture complex prognostic interactions. Further validation is required to confirm models' generalizability to additional therapies and patients with differing tumor severity.

Abstract The sections in this article are: Physiological Effects of Vitamin D and its Active Metabolites on Calcium and Phosphorus Homeostasis Sources of Vitamin D Transport of Vitamin D and its Metabolites in Plasma Metabolism of Vitamin D 3 to 25‐Hydroxyvitamin D 3 Metabolism of 25‐Hydroxyvitamin D 3 Regulation of the 25‐Hydroxyvitamin D 3 –1α‐Hydroxylase Enzyme Parathyroid Hormone Phosphorus Concentrations Calcium: Direct Effects 1,25‐Dihydroxyvitamin D 3 Calcitonin Hydrogen Ion Sex Steroids Glucocorticoids Pituitary Hormones Thyroid Hormone Pregnancy Catabolism of 1,25‐Dihydroxyvitamin D 3 Physiological Adaptations that Occur in Response to Changes in Serum Calcium and Phosphate Concentrations Hypocalcemia and Hypercalcemia Hypophosphatemia and Hyperphosphatemia Pathophysiology and Physiology of the 1,25‐Dihydroxyvitamin D 3 System Mechanisms of Action of 1,25‐Dihydroxyvitamin D 3 1,25‐Dihydroxyvitamin D 3 in the Intestine Processes in the Cell That Are Mediated by 1,25‐Dihydroxyvitamin D 3 Intestinal Brush Border Events Induced by 1,25‐Dihydroxyvitamin D 3 Events at the Contraluminal Border Effects of 1,25‐Dihydroxyvitamin D 3 on the Intestinal Cell 1,25‐Dihydroxyvitamin D 3 in the Kidney

Abstract The sections in this article are: Intrarenal Hemodynamic Control of Sodium Excretion Effects of Peritubular Starling Forces on Sodium Excretion Effects of Medullary Blood Flow on Sodium Excretion Effects of Renal Perfusion Pressure on Sodium Excretion Neural Control of Sodium Excretion Sodium Intake and Body Fluid Volumes Low‐Pressure Baroreceptors Renal Sympathetic Nerve Activity Hormonal Control of Sodium Excretion Renin–Angiotensin–Aldosterone System Renal Prostaglandin System Renal Kallikrein–Kinin System Natriuretic Hormone Atrial Natriuretic Peptide Summary

Background Valvular heart disease contributes to symptomatic heart failure, but its prevalence and prognostic significance in advanced heart failure is not well understood. Methods This retrospective population‐based cohort study included adult residents of Olmsted County, Minnesota, with advanced heart failure (2007–2018). Patients were classified by echocardiographic regurgitation and stenosis of aortic, mitral, tricuspid, and pulmonic valves at the time of advanced heart failure. We examined the associations of aortic stenosis, mitral regurgitation, and tricuspid regurgitation with risks of cardiovascular and all‐cause death through 2023 using Cox proportional hazards models. Results There were 936 patients with advanced heart failure (mean age, 76.9 years; 44.5% women, 43.4% with heart failure with preserved ejection fraction). Moderate or greater regurgitation was observed in 2.8% (n=26) of aortic, 26% (n=244) of mitral, 42% (n=389) of tricuspid, and 4.3% (n=40) of pulmonic valves, while moderate or greater stenosis was observed in 9.7% (n=91) of aortic and 1.8% (n=17) of mitral valves. No patients had clinically significant tricuspid or pulmonic stenosis. After adjustment for age, sex, left ventricular ejection fraction, right ventricular function, serum sodium, Charlson comorbidity index, body mass index, and systolic blood pressure, moderate and severe aortic stenosis were associated with increased cardiovascular death compared with patients without aortic stenosis (hazard ratio [HR], 1.42 [95% CI, 1.01–2.01]; and HR, 1.55 [95% CI, 1.02–2.36]), respectively. Conclusions Tricuspid regurgitation, mitral regurgitation, and aortic stenosis were the most observed valvular pathologies of moderate or greater severity in advanced heart failure. Only moderate or greater aortic stenosis was independently associated with cardiovascular death in advanced heart failure.

Abstract The sections in this article are: Pathogenesis of Type I Diabetes Effect of Type I Diabetes on Carbohydrate Metabolism Insulin Secretion and Action Postabsorptive and Postprandial Carbohydrate Metabolism Effects of Type I Diabetes and Its Treatment on Fatty Acid Metabolism Adipose Tissue Postabsorptive FFA Metabolism Postprandial FFA Availability Effects of Exercise on Adipose Tissue Lipolysis Regulation of Lipolysis During Hypoglycemia Effects Of Type I Diabetes on Protein Metabolism Regulation of Body Protein Mass, Amino Acid Levels and Nitrogen Balance Effect of Insulin on Protein Balance and Interaction of Insulin with Other Hormones and Substrates Effects of Insulin on Whole‐Body Protein Dynamics Effect of Insulin on Amino Acid Transamination Effect of Insulin on Regional Protein Dynamics Effect of Insulin on the Synthesis Rates of Specific Proteins Impact of Type I Diabetes on Protein Metabolism

Abstract The sections in this article are: Molecular Actions of Thyroid Hormone Thyroid Hormone Regulation of Growth and Development Extrahypothalamic Thyrotropin‐Releasing Hormone Metabolic Actions of Islet Cell Hormones Insulin Glucagon Amylin Calcitonin Gene‐Related Peptide Somatostatin Pancreatic Polypeptide Thyroid Hormone Regulation of Insulin Metabolic Actions Euthyroidism Hyperthyroidism Hypothyroidism Thyroid Hormone Regulation of Glucagon Metabolic Actions Hyperthyroidism Hypothyroidism Thyroid Hormone Regulation of Somatostatin Metabolic Actions Hyperthyroidism Hypothyroidism Thyroid Hormone Regulation of Pancreatic Polypeptide Metabolic Actions Summary and Conclusions