Diabetic myonecrosis is an uncommon and under-recognised complication of diabetes mellitus. We aimed to describe the clinical profile, imaging characteristics, management strategies, and outcomes of patients with diabetic myonecrosis.

This retrospective study included 30 patients diagnosed with diabetic myonecrosis between 2002 and 2024. Diagnosis - based on clinical suspicion, supported by characteristic ultrasonography and magnetic resonance imaging. Demographic data, diabetes characteristics, biochemical parameters, imaging features, management approaches, recurrence, and mortality were analysed.

The mean age was 49.1 ± 11.7 years, with a mean diabetes duration of 12.6 ± 6.9 years and poor glycaemic control (HbA1c 8.83 ± 1.88%). Acute limb pain and swelling were the most common presentations (93.3%), predominantly involving the thigh and calf muscles. MRI revealed characteristic muscle oedema and infarction, frequently extending beyond the quadriceps to involve hamstrings, adductors, and posterior compartments. Diabetic nephropathy and retinopathy were present in 70% of patients. Most patients (83.3%) improved with conservative management. Recurrence occurred in 23.3% of cases, and the overall mortality rate was 33.3%. Recurrence was the strongest predictor of mortality (adjusted OR, 8.20; 95% CI, 1.45-46.40), while advanced kidney disease increased the risk, and higher serum albumin levels were protective.

Diabetic myonecrosis should be considered in patients with diabetes who present with acute localised muscle pain; the presence of advanced CKD, significant proteinuria, or hypoalbuminemia identifies those at increased risk of adverse outcomes. MRI is the diagnostic modality of choice. Despite response to conservative therapy, high recurrence and mortality rates highlight the need for early recognition and long-term surveillance.

Molecular mimicry is the process by which foreign peptides contain epitopes similar to self-peptides, potentially inducing autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E_INF) and T1DM epitopes (E_T1D) that demonstrated sequence homology; however, structural homology and binding ability to HLA molecules were not considered. Correlating sequence homology with structural properties and binding ability is important for translational investigation of potential molecular mimics. This work compares sequence homology with structural homology and binding ability by calculating the structures and binding ability of the epitope pairs identified in previous work. For each pair of E_INF and E_T1D we calculated the root mean square deviations (RMSD) between their predicted isolated structures and their percent (%) overlap when bound to the corresponding HLA molecule using the Boltz-2. Of the 53 epitope pairs considered here, only 6 were found to not exhibit any matching (defined as less than 3 residues overlap). For the other epitopes, the RMSD ranges from 0.13 Å to 1.39 Å with an average of 0.56 Å. The calculated % overlaps between the infectious and T1DM epitopes when bound to the corresponding HLA molecules ranges from 0% to 93.3%. There are 3 pairs that show nearly identical HLA footprints, 15 very similar binding mode, 19 similar binding mode, 11 moderately similar, and only 5 showing a totally different footprint. Most of the E_INF/E_T1D pairs selected by sequence homology show similar structural and binding ability to HLA molecules, indications that may lead to disease onset due to molecular mimicry. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to plausible candidates for molecular mimicry that should be considered for further experimental validation. However, full binding ability calculations at scale may be necessary to advance the in-silico molecular mimicry predictions, which may be useful to select the most promising candidates for experimental studies.

There is a rising prevalence of metabolic-associated steatotic liver disease (MASLD), particularly in patients with type 2 diabetes mellitus, which needs early detection and treatment to prevent the grave outcomes. Dapagliflozin and Saroglitazar are known to improve metabolic and liver health in patients with type 2 diabetes mellitus.

Our study was a randomised, controlled, open-label, parallel trial of 101 patients with T2DM and MASLD. Patients were randomly assigned to receive Dapagliflozin 10 mg or Saroglitazar 4 mg on top of standard diabetes care. The primary endpoints were changes in hepatic steatosis and fibrosis (FIB) as estimated by controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) using FibroScan, respectively. The secondary endpoints were changes in anthropometric parameters, glycemic parameters, lipid profiles, transaminases, and FIB scores, which were monitored at baseline, 3 months, and 6 months.

At the end of 6 months of study, both cohorts resulted in significant clinical benefits. Comparison between the two cohorts revealed no significant statistical difference between the two groups regarding their impact on the primary endpoints (CAP and LSM). Minimal but statistically significant intergroup differences were noted only for BMI (P = 0.033) and LDL cholesterol (P = 0.020) in the Dapagliflozin cohort and alanine transaminase levels (P = 0.037) in the Saroglitazar cohort.

Both Dapagliflozin and Saroglitazar are potent therapeutic options in patients with type 2 diabetes mellitus who also have MASLD.

Breast cancer (BC) and diabetes mellitus (DM) frequently coexist, posing significant clinical challenges. Evaluating long-term mortality trends helps assess the impact of healthcare practices and public health policies. This study examines mortality trends involving breast cancer and DM in US adults aged > 25, stratified by demographics and geography.

We conducted a retrospective analysis of CDC WONDER mortality data from 1999 to 2023 for individuals aged ≥ 25 years. BC and DM were identified using ICD-10 codes C50 and E10-E14. Age-adjusted mortality rates (AAMR) per 100,000 were calculated. Joinpoint regression estimated annual percentage changes (APC) with 95% confidence intervals (CI).

From 1999 to 2023, 79,139 deaths involving BC and DM. Overall, AAMR remained stable (AAPC 0.23; 95% CI, -0.002 to 0.475, p > 0.05). Females had higher AAMR (2.42) than males (0.05). Non-Hispanic (NH) Blacks had the highest AAMR (2.69), followed by Hispanics (1.31), NH Whites (1.28). Older adults, rural residents, and the Midwest region had higher mortality.

Mortality involving BC and DM has remained largely stable over two decades, but disparities by sex, age, race/ethnicity, and geography persist, highlighting the need for targeted interventions in high-risk populations.

The online version contains supplementary material available at https://doi.org/10.1007/s40200-026-02074-4.

Inflammatory signalling within and around pancreatic islets is increasingly recognised as an important component of β-cell dysfunction in type 2 diabetes mellitus (T2DM). Current evidence, however, supports a bidirectional relationship in which metabolic stress initiates inflammatory responses and inflammation subsequently amplifies β-cell dysfunction, rather than inflammation acting as a single primary cause of T2DM. This review critically examines the evidence linking metabolic stress to β-cell inflammatory injury and evaluates therapeutic strategies aimed at preserving β-cell function.

PubMed and Web of Science were searched without a lower publication-date restriction through 1 June 2026, with Google Scholar used as a supplementary source. Search strategies combined terms related to T2DM, pancreatic β-cells/islets, inflammatory and metabolic stress pathways, and therapeutic interventions using the Boolean operators AND and OR. A representative search strategy was: ("type 2 diabetes mellitus" OR T2DM) AND ("pancreatic β-cell" OR "pancreatic islet") AND (inflammation OR glucotoxicity OR lipotoxicity OR "oxidative stress" OR "endoplasmic reticulum stress" OR inflammasome OR cytokine) AND (therapy OR treatment OR protection OR regeneration). Reference lists of relevant reviews and primary studies were also screened to identify additional publications.

Evidence from human and experimental studies supports interactions among glucotoxicity, lipotoxicity, oxidative and endoplasmic reticulum stress, mitochondrial dysfunction, islet macrophage activation, cytokine signalling, and β-cell dysfunction. In contrast, evidence for the causal importance of individual inflammasome, regulated cell-death, extracellular-vesicle, and gene-regulatory pathways remains predominantly preclinical. Randomised trials have reported improvements in glycaemic control or β-cell functional indices with anakinra, salsalate, liraglutide, dapagliflozin, thiazolidinediones, and short-term intensive insulin therapy. However, these trials generally assessed metabolic or β-cell functional outcomes rather than pancreatic islet inflammation directly. Canakinumab did not reduce new-onset diabetes or provide sustained glycaemic improvement, whereas regenerative and gene-editing approaches remain experimental.

Inflammatory signalling and metabolic stress interact bidirectionally and contribute to progressive β-cell dysfunction in T2DM. Selected anti-inflammatory and glucose-lowering interventions improve glycaemic or β-cell functional outcomes, but direct suppression of pancreatic islet inflammation and durable modification of T2DM progression have not been established. IL-1 pathway modulation warrants further investigation, whereas regenerative and gene-based approaches remain experimental. Patient stratification based on β-cell functional status and inflammatory profiles requires prospective validation before clinical implementation.

Rhinocerebral mucormycosis is a rapidly progressive and frequently fatal invasive fungal infection that most commonly occurs in patients with uncontrolled diabetes mellitus in the setting of diabetic ketoacidosis.

We present a woman in her 70 s with diabetic ketoacidosis, who developed right-sided facial numbness and ptosis, followed by acute, complete bilateral vision loss within hours. Initial cranial CT and diffusion MRI were unremarkable despite rapidly evolving cranial neuropathies. This created significant diagnostic uncertainty and prompted consideration of alternative diagnoses, including giant cell arteritis. Nevertheless, markedly elevated inflammatory markers and rapidly worsening ophthalmoplegia prompted further investigation. Orbital MRI and paranasal sinus CT revealed postseptal orbital involvement and extensive pansinusitis. Nasal endoscopy revealed black, necrotic crusts, and two aspiration cultures confirmed Mucor species. Liposomal amphotericin B was started without delay; however, the disease had already extended into the orbit and frontal brain parenchyma, making surgical debridement impossible. The patient died despite aggressive antifungal therapy.

Rhinocerebral mucormycosis should be suspected immediately in diabetic ketoacidosis patients who develop acute cranial neuropathies or sudden vision loss, even when initial neuroimaging is unrevealing. Clinical deterioration may precede radiological abnormalities in fulminant rhinocerebral mucormycosis, and delayed recognition may rapidly eliminate the possibility of surgical intervention.

The hypothesis was that depressed patients compared to non-depressed patients with newly diagnosed type 2 diabetes mellitus (T2DM) had higher levels of soluble Triggering Receptor Expressed on Myeloid Cells 2 (sTREM2) as a sign of increased microglial activation. The aim was to explore a potential association between depression and sTREM2.

Cross-sectional and multicentre study which included adults with serologically verified, newly diagnosed T2DM. Associations with depression and high sTREM2 (>3.38 pg/mL) were assessed using multiple regression analyses. Potential covariations were explored for age, sex, anxiety, soluble neuropilin-1 (sNRP-1), C-peptide, body mass index (BMI), Haemoglobin A1c, physical inactivity, smoking, and prior cardiovascular disease. ELISA techniques were used to analyse sTREM2, sNRP-1, and C-peptide.

Included were 726 patients (aged 18-94 years, women 38%, depressed 16%, anxious 23%, physically inactive 32%). The prevalence was for high sTREM2 (>3.38 pg/mL) 20%, low sNRP-1 (<225.5 ng/mL) 24%, and high C-peptide (≥1.60 nmol/L) 26%.Associated with depression (n = 630) were anxiety (adjusted odds ratio (AOR) 10.5, p < 0.001), low sNRP-1 (AOR 3.3, p < 0.001), high sTREM2 (AOR 2.0, p = 0.016), high C-peptide (AOR 1.9, p = 0.024), and physical inactivity (AOR 1.8, p = 0.025).Associated with high sTREM2 (n = 656) were BMI (per kg/m²) (AOR 1.08, p < 0.001) and depression (AOR 1.8, p = 0.015).

Depressed patients compared non-depressed patients with newly diagnosed T2DM had higher levels of sTREM2. High sTREM2, high C-peptide, low sNRP-1, physical inactivity, and anxiety, were independently associated with depression. Depression and BMI were independently associated with high sTREM2. The hypothesis was supported.

Diabetic Retinopathy (DR) is a major global cause of blindness. Artificial Intelligence (AI) has markedly impacted fundus screening over the past three decades, yet systematic bibliometric mapping of the knowledge architecture, evolutionary paths, and synergy among AI models, data modalities, and DR remains scarce.

This study conducted a comprehensive bibliometric analysis to characterize the AI-driven DR knowledge structure, collaboration networks, and hotspot migration, and to elucidate co-evolutionary dynamics among AI advances, data modality development, and DR research.

Following a systematic search and screening process, 12,741 publications were identified from the Web of Science Core Collection (WoSCC), PubMed, and Scopus, spanning from January 1996 to June 2026. The analytical framework combined multiple methodologies: VOSviewer for collaborative network visualization, CiteSpace for burst detection and timeline mapping, and a Python-based text-mining pipeline for standardized extraction and normalization of AI model names, data modalities, and disease entities.

The field exhibits a distinct three-stage evolutionary trajectory: the traditional machine learning era (1996-2014), the deep learning surge (2015-2019), and the current phase marked by the growing prominence of Transformer-based models (2020-present). The collaboration landscape is multipolar, with the United States, China, and India as hubs, while Singapore produces high-impact research. The knowledge base rests on algorithmic innovation and clinical validation. Convolutional neural networks have long served as the backbone architecture in the literature, while Vision Transformers have shown a clear upward trend in publication volume in recent years. Research hotspots are expanding from single-disease classification toward multimodal integration. Although fundus imaging remains the predominant data source, the potential of electronic health record narratives and multi-omics data is increasingly recognized. Overall, the research focus is shifting from "black-box" pattern recognition toward explainable AI and end-to-end clinical translation.

This study presents a systematic bibliometric mapping of AI-driven DR research, revealing high-frequency co-occurrence patterns between architectural specialization and clinical demands. Challenges persist in data integration, rare-disease evidence, and cross-setting validation. The future is likely to be shaped by multimodal foundation models and portable acquisition, transitioning AI toward comprehensive clinical decision support.

Diabetes mellitus (DM) poses a significant global public health challenge, with impaired fasting glucose (IFG) representing a critical prediabetic state. The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index, a body fat estimator integrating body mass index (BMI), age, and gender, has shown promise in assessing cardiometabolic risk. However, its longitudinal association with bidirectional glycemic transitions, specifically reversion to normoglycemia and progression to DM, in individuals with IFG remains unclear. This study aimed to evaluate the association between baseline CUN-BAE index and glycemic outcomes in a Chinese IFG population.

This retrospective multicenter cohort study included 26,247 adults with baseline IFG from a health screening program. The CUN-BAE index was calculated using a standardized formula. Cox proportional hazards regression, restricted cubic spline models, and threshold effect analyses were employed to assess associations between the CUN-BAE index and glycemic outcomes, with adjustments for confounders. Subgroup and sensitivity analyses were conducted to verify robustness.

Higher CUN-BAE index levels were independently associated with a reduced probability of reversion to normoglycemia (adjusted HR 0.99 per unit increase; 95% CI 0.98-0.99) and an increased risk of DM progression (adjusted HR 1.04; 95% CI 1.03-1.04). Nonlinear relationships were identified, with threshold effects at CUN-BAE index = 24.53 for reversion and CUN-BAE index = 22.51 for progression. The CUN-BAE index quartile analysis demonstrated a graded association, with the highest CUN-BAE index quartile showing a significantly elevated risk of diabetes and a reduced rate of reversion to normoglycemia.

Higher CUN-BAE index levels are strongly and independently associated with adverse glycemic transitions in IFG individuals, with higher values indicating poorer outcomes. Its nonlinear associations and threshold effects support its utility for risk stratification and personalized intervention strategies in prediabetes management.

Diabetes mellitus is associated with chronic hyperglycemia, dyslipidemia, and systemic inflammation. Banana peel contains bioactive compounds with antioxidant and anti-inflammatory properties and may provide a natural therapeutic alternative. This study aimed to evaluate its potential effects on metabolic and inflammatory parameters in rabbits with HFFD-induced metabolic disturbances.

This preclinical experimental study involved 40 male New Zealand White rabbits (3-4 months; 1.5-2.0 kg) randomly assigned to four groups (n = 10): Healthy Control, HFFD control (HFFD-induced diabetes mellitus), and two treatment groups receiving either 0.3% sodium carboxymethyl cellulose (NaCMC) or banana peel extract (200 mg/kg body weight). Diabetes and hyperlipidemia were induced using a high fructose fat diet (HFFD) for two weeks, followed by a three-week treatment period. Blood samples were collected at baseline, post-induction, and posttreatment to measure glucose, triglycerides, and inflammatory markers (hsCRP and IL-10) using biochemical assays and ELISA. Histological evaluation was performed on tissue samples. Data were analysed using One-Way ANOVA or the Kruskal-Wallis test based on data distribution, with post hoc analysis and a significance level of p < 0.05.

Yellow banana peel extract (200 mg/kgBW) significantly reduced fasting blood glucose, triglycerides, and hsCRP levels while increasing IL-10 expression, indicating antidiabetic and anti- inflammatory effects in an HFFD-induced diabetic model.

The findings suggest that yellow kepok banana peel extract exhibits significant antihyperlipidemic and antidiabetic potential, which may be attributable to bioactive compounds previously reported in banana peel, which may improve lipid metabolism and glucose regulation.

Banana peel extracts showed potential as a supportive natural adjunct for improving glucose and lipid profiles in patients with diabetes mellitus. These findings suggest that extract- based natural products may improve metabolic control and reduce cardiovascular risk. Further studies are required to elucidate the underlying mechanisms and to confirm long-term safety and efficacy in clinical settings.