Clinical decisions often require balancing conflicting priorities rather than simply selecting a single "correct" answer. We present an evaluation framework that probes the value judgments embedded in large language models (LLMs) by testing how they assess quality of glycemic control from continuous glucose monitoring (CGM) data. Using synthetic type 1 diabetes profiles, we asked five commercial LLMs to perform pairwise comparisons of CGM summary statistics and derived a percentile ranking for each profile. We then quantified alignment with two reference metrics: time in range (TIR) and the expert-derived Glycemia Risk Index (GRI), which was developed with clinician input regarding preferences across glycemic ranges. Across three insulin therapy modalities, newer models showed stronger correlation with GRI than older models, suggesting a generational shift toward expert consensus. However, a perturbation analysis revealed instances of disagreement around the weighting of mild hypoglycemia and mild hyperglycemia relative to the GRI. These results demonstrate that high average agreement with clinical metrics can mask clinically meaningful misalignments in how LLMs prioritize risks. Our proposed framework reveals how LLM outputs reflect competing priorities in clinical contexts.

The spectrum of neurologic symptoms caused by hypoglycaemia can be remarkably broad. Thus, hypoglycemia should be considered early in the diagnostic workup of neurologic symptoms, including those that may appear atypical. Prompt glucose administration can result in rapid symptom resolution, potentially preventing costly or unnecessary invasive testing.

Solitary fibrous tumor (SFT) is a rare mesenchymal lesion that typically exhibits no aggressive behavior and has low metastatic potential. While it is commonly described in the pleura and thorax, its occurrence in the retroperitoneal pelvic cavity is uncommon. We report two cases of pelvic retroperitoneal SFT treated at the Instituto Nacional de Cancerología in Colombia. The first case involved a 76-year-old man with a pelvic mass that developed over one year. Computed tomography (CT) showed a retroperitoneal tumor near the prostate and a suspicious pulmonary nodule. Biopsy confirmed high-risk SFT with signal transducer and activator of transcription 6 (STAT6) positivity and a Ki-67 index of 40%. The patient underwent extensive surgical resection with clear margins, during which an incidental prostatic adenocarcinoma was identified. At 12 months of follow-up, no evidence of recurrence was observed. The second case involved a 35-year-old man with severe hypoglycemia due to Doege-Potter syndrome secondary to pelvic SFT. Imaging demonstrated a large pelvic retroperitoneal mass causing obstructive uropathy, requiring bilateral nephrostomies. Biopsy confirmed high-risk SFT with STAT6 positivity and a Ki-67 index of 40%. The patient underwent extensive surgical resection complicated by massive intraoperative bleeding, requiring a two-stage procedure. Pathology revealed margins in contact; however, the postoperative course was favorable. At six months of follow-up, the patient remained free of hypoglycemic episodes and without radiological progression. SFT remains a rare condition, especially when located in the retroperitoneal pelvic region. Both cases highlight the clinicopathological variability of the disease and emphasize that early diagnostic suspicion and multidisciplinary management are crucial to optimizing treatment outcomes.

The swim bladder of Pangasius bocourti is an underutilized fishery byproduct with high protein content, yet its potential as a source of hypoglycemic peptides has not been systematically investigated. This study aimed to prepare and characterize bioactive peptides from P. bocourti swim bladder (PBSB) through integrated enzymatic hydrolysis optimization, peptidomic analysis, and in vivo evaluation. Five proteases were comparatively assessed, and alkaline protease was identified as the optimal enzyme based on degree of hydrolysis and α-glucosidase inhibitory activity. Under the optimized conditions (pH 9.5, enzyme dosage 2%, 50°C, 4 h), the obtained peptides (PBSBPs) exhibited notable α-glucosidase inhibitory activity (67.90% ± 0.26% at 5 mg/mL). Nano-LC-MS/MS analysis identified 101 peptides, of which 91.09% had molecular weights below 1500 Da. The peptide sequences were enriched in hydrophobic and hypoglycemia-associated amino acid residues, and 58 peptides were predicted to possess α-glucosidase inhibitory potential. Furthermore, PBSBPs significantly improved glucose tolerance, reduced serum triglyceride and total cholesterol levels, and alleviated hepatic lipid accumulation and pancreatic islet injury in diabetic mice. This study integrates process optimization, peptide profiling, and biological validation, providing a systematic strategy for the development of functional peptides from aquatic byproducts. These findings highlight the potential application of PBSBPs as natural antidiabetic functional ingredients and contribute to the high-value utilization of fishery processing byproducts.

This editorial argues that hypoglycaemia self care should move beyond 'any sugar will do' and define what good recovery after treatment should involve. It proposes recovery-focused standards using CGM metrics, such as time-in-range and repeat lows after treatment, alongside participant-reported recovery, so existing and emerging approaches, including MESH formulations, can be evaluated fairly against modern evidence-based standards.

Impaired cognitive function caused by insulin-induced hypoglycemia is a complication of type 1 diabetes mellitus (T1DM) for which no protective strategies are currently available. In this first-in-human mechanistic study using magnetic resonance spectroscopy, the direct contribution of the infused ketone β-hydroxybutyrate (BHB) to brain metabolism during clamped hypoglycemia was greater in participants with T1DM than healthy participants. In a randomized dietary intervention trial of participants with T1DM and recurrent hypoglycemia, receiving medium-chain triglycerides (MCTs) as a dietary ketone precursor was associated with higher working memory performance and greater regional brain activation during clamped hypoglycemia compared with an isocaloric standard diet. Counterregulatory hormone responses to hypoglycemia were not affected by MCT supplementation. We conclude that ketone bodies are well suited to support brain metabolism in persons with T1DM experiencing insulin-induced hypoglycemia. Dietary MCT supplementation raising BHB may represent a novel strategy to prevent hypoglycemia-induced brain injury in this vulnerable patient population.

Even though insulin therapy is the cornerstone of diabetes mellitus management, multiple daily injections are associated with poor adherence. Once-weekly basal insulin therapy has emerged as a novel therapeutic approach and is being evaluated in multiple ongoing clinical trials. We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of once-weekly insulin efsitora therapy compared with once-daily basal insulin therapy in patients with type 1 or type 2 diabetes mellitus. Primary endpoints include glycemic control, assessed via HbA1c reduction, and hypoglycemia, classified as level 1 (54-69 mg/dL), level 2 (<54 mg/dl), or level 3 (any hypoglycemic episode characterized by altered mental and/or physical functioning requiring assistance). We have performed a systematic review and meta-analysis through an extensive literature search of major databases including Scopus, PubMed/Medline, and Cochrane databases, and have only included randomized controlled trials. We have included 5 randomized controlled trials comprising a total of 2,562 patients: 1,358 (53%) in the insulin efsitora arm and 1,204 (47%) in the insulin degludec arm. Our data show that insulin efsitora therapy achieved a similar HbA1c reduction overall (estimated treatment difference (ETD) 0.08%, 95% CI -0.01 to 0.17; I² = 42.6%), in patients with type 1 diabetes mellitus (ETD 0.10%, 95% CI -0.64-0.85; I² =24.0%) and in patients with type 2 diabetes mellitus (ETD 0.05%, 95% CI -0.25 - 0.26; I² = 38.1%) compared with insulin degludec therapy. Moreover, no statistically significant differences were identified in overall hypoglycemic events, level 2 or 3 hypoglycemic episodes, or nocturnal hypoglycemia, irrespective of diabetes mellitus subtype. Our meta-analysis shows no statistically significant difference between once-weekly insulin efsitora therapy and insulin degludec therapy in terms of glycemic control and hypoglycemic events.

Pancreatic ductal adenocarcinoma (PDAC) exhibits five-year survival rates below 10% with profound therapeutic resistance mediated by stromal barriers and immunosuppression (Rahib et al. 2014; Siegel et al. 2023). Recent evidence suggests intratumoral mycobiome alterations may influence disease progression (Aykut et al. 2019; Riquelme et al. 2019). We performed comprehensive network pharmacology analysis to elucidate potential therapeutic mechanisms of Saccharomyces boulardii (SB) and Clostridium histolyticum collagenase (CHC) in PDAC. Gene sets representing immune activation, metabolic competition, and stromal remodeling were analyzed using protein-protein interaction networks (STRING v11.5), functional enrichment (DAVID v2021), and pathway databases (KEGG, Reactome, WikiPathways). Structural assessment employed blind molecular docking (CB-Dock2) and coarse-grained molecular dynamics (CABS-flex). Computational analysis was performed on TCGA-PAAD data (n = 177 patients, 93 events) using Cox regression and five machine learning classifiers (Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM) with Q1/Q3 survival stratification and strict fivefold nested cross-validation with within-fold feature selection (SelectKBest, k = 5). Random survival forest (RSF) incorporating 67 high-quality genes from four mechanistically curated modules was developed for time-to-event prediction, evaluated by AUC-ROC and Harrell's C-index. Permutation-based feature selection identified prognostic biomarkers. Network analysis identified TNF, IL6, IFNG, and TLR4 as central hubs (degree > 15) with dense interconnectivity (clustering coefficient = 0.78). Pathway enrichment revealed over-representation in IL-17, TLR, and IL-10 signaling (BH-FDR < 0.05). Molecular docking revealed high-affinity interactions with AKT1 (ΔG = - 7.2 kcal/mol), TLR2 (- 7.6 kcal/mol), TLR4 (- 6.9 kcal/mol), and KRAS-SOS1 interface (- 6.9 kcal/mol). Kaplan-Meier analysis demonstrated significant survival differences by composite immune-metabolic signature (log-rank p = 0.0001) and immune sub-signature (CXCL9, CXCL10, CXCL11; p = 0.0028). The metabolic sub-signature (LDHA, ALDH9A1) demonstrated a significant continuous prognostic effect by Cox regression (HR = 1.431, p = 0.0292), with median dichotomisation not reaching significance (log-rank p = 0.2164), indicating a gradient rather than threshold effect. Cox modelling of the composite five-gene score identified it as a strong negative prognostic factor (HR = 1.897, p < 0.0001). Machine learning classifiers achieved good-to-strong discrimination of survival extremes under strict nested cross-validation (AUC range: 0.631-0.745; Logistic Regression: AUC = 0.745 ± 0.036), confirmed by permutation testing (n = 1000 permutations, empirical p < 0.01). RSF modelling on the full cohort achieved C-index = 0.649 ± 0.040 using the top-5 gene signature, representing acceptable discrimination for PDAC. Permutation-based feature selection identified CXCL11, CXCL10, CXCL9, LDHA, and ALDH9A1 as top prognostic biomarkers. Tertile-based risk stratification demonstrated significant survival separation between Low and High Risk groups (log-rank p = 0.0005). Kinetic modeling showed 4.2-fold SB competitive advantage under tumor hypoglycemia (< 1 mM glucose). Network analysis, structural modeling, and machine learning support biological plausibility of SB anti-tumor activity through immune activation and stromal remodeling. RSF modelling (C-index = 0.649) and Cox regression (HR = 1.897, p < 0.0001) provide a computational framework for patient stratification. Identification of CXCL11, CXCL10, CXCL9, LDHA, and ALDH9A1 as prognostic biomarkers spanning immune and metabolic modules provides targets for clinical monitoring. This systems-level framework provides mechanistic rationale for clinical investigation of intratumoral S. boulardii as adjuvant PDAC therapy.