The aim of the present study was to investigate birth weight and associated factors among Indigenous peoples in the state of Mato Grosso do Sul, Brazil. A cross-sectional baseline study was conducted with an Indigenous birth cohort of 407 livebirths to Indigenous women living in villages, retaken territories, and urban communities between 2021 and 2022. Birth weight (in grams) was considered the main outcome, with means and 95% confidence intervals calculated according to maternal and household characteristics. A multiple linear regression model was run to determine associations between mean birth weight and maternal household, demographic, and obstetric characteristics, with a 95% confidence level. Mean birth weight was 3,160.5g. The prevalence of low birth weight and macrosomia was 6.4% and 3.7%, respectively. After the adjustment for confounding variables, lower birth weight was found among livebirths of women living in households with a communal outdoor faucet and those with insufficient gestational weight gain. Children with higher birthweights were found among women with excessive gestational weight gain and multiparous women. The birth weight of Indigenous children was associated with maternal nutritional status, multiparity, and limited access to drinking water, indicating the need to improve nutritional surveillance for Indigenous women of childbearing age and strengthen intersectoral public policies that ensure access to drinking water on Indigenous lands.

Acute kidney injury is one of the main systemic complications of severe coronavirus disease 2019 (COVID-19).

To examine histopathological changes in post-mortem kidney biopsies of patients who died as a result of the disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2).

A case-control study was conducted at a tertiary hospital located in Curitiba, Paraná, Brazil.

The study group, called "COVID," consisted of kidney biopsy samples obtained from deceased patients with COVID-19, with a "Control" group included for comparison. Samples were selected based on sex, age, and comorbidities, with an emphasis on diabetes mellitus and systemic arterial hypertension (SAH). Morphological evaluation was performed by pathologists using preestablished criteria with glomerular, tubular, and vascular characteristics among the parameters.

Tubular atrophy and interstitial fibrosis, markers of chronic kidney injury, were observed with equal frequency in both groups, probably because of the initial pairing of the samples. These findings are in line with what would be expected from chronic exposure to proteinuria. In relation to SAH, the main identification was interstitial vascular damage, particularly arteriolosclerosis/arteriosclerosis. Acute tubular injury was the most frequently observed feature in patients in the COVID group, which was probably related to ischemic damage.

This study demonstrated that the main change identified in the renal parenchyma of patients with COVID-19 was acute tubular injury, which was expected considering the context of severe systemic ischemia to which these patients are subjected, with the other findings being the consequences of chronic damage.

Although dexamethasone reduces mortality in patients with COVID-19, plasma glucose (PG) levels increase upon initiation. In a multicenter observational cohort of 530 adults, we estimated glycemic variability based on baseline HbA1c among patients with normoglycemia (N = 238), prediabetes (N = 159), unknown (N = 63), and known diabetes (N = 159). Glycemic variability, diabetic- and hyperglycemic events (≥ 11.1 and ≥ 16 mmol/L) were analyzed using a linear mixed model and competing risks analysis adjusted for confounders. Before dexamethasone, mean PG levels were similar in those with normoglycemia (6.5 mmol/L) and prediabetes (6.6 mmol/L), but higher in unknown (8.5 mmol/L) and known diabetes (9.9 mmol/L). After treatment, PG increased across all groups. Prediabetes showed a larger increase (1.5 mmol/L) than normoglycemia (0.7 mmol/L, p = 0.002), and known diabetes had the highest increase (2.4 mmol/L, p < 0.001), reaching an average of 12.6 mmol/L. All groups except prediabetes returned to baseline after dexamethasone. The cumulative incidence of diabetic events was 98% in known diabetes, 67% in unknown diabetes, 31% in prediabetes, and 8% in normoglycemia, with significant differences between groups (p < 0.001). We conclude that dexamethasone treatment increased average PG and caused frequent hyperglycemic events in patients with prediabetes, unknown, and known diabetes, while persistent PG elevation post-treatment occurred in prediabetes.

Over 70% of diabetic patients die from cardiovascular disease, in which diabetic heart disease (DHD) is an important cause of death in individuals with type 2 diabetes (T2D). It is hence imperative to explore the simple, rapid, and economical method for diagnosing DHD from T2D.

T2D and DHD patients were recruited, and their serum samples were used for metabolomic analysis to identify differential metabolites. Logistic regression analysis and receiver operating characteristic curve analysis were performed to identify candidate biomarkers. Moreover, four machine learning methods were used to construct the integrated biomarker profiling (IBP) models with the candidate biomarkers. Gini impurity was employed to select characteristic candidate biomarkers.

Eighty-four differential metabolites were identified in the serum of 58 T2D and 62 DHD patients. Logistic regression analysis indicated that 17 differential metabolites were protective factors, whereas 39 were risk factors for DHD. Further, 29 differential metabolites were identified as the candidate biomarkers of DHD after receiver operating characteristic curve analysis. After comparing the predictive performance of the four machine learning models, the IBP was constructed based on the eXtreme Gradient Boosting (XGBoost) with six candidate biomarkers, which were sphingomyelin (d18:0/16:1), deoxycholic acid, hexadecanedioic acid, phosphatidylcholine (20:5/18:3), L-tryptophan, and N-undecanoylglycine from the ranked results of Gini impurity. The accuracy of the IBP for distinguishing T2D and DHD reached 88.89%, with a 100% accuracy in predicting DHD from T2D patients.

The IBP, composed of six metabolites, can effectively predict DHD from T2D, and it is expected to become a screening indicator for early-stage DHD.

Diabetic retinopathy, a major microvascular complication of type 2 diabetes mellitus, remains a leading cause of preventable blindness worldwide. Early identification of individuals at high risk is essential, yet conventional screening systems are limited by workforce shortages and delayed detection. Artificial intelligence, particularly machine learning, offers substantial potential to support prognostic scoring tools capable of predicting the development of diabetic retinopathy. This review summarises current evidence on AI-driven prognostic models for diabetic retinopathy among adults with type 2 diabetes mellitus.

A comprehensive PubMed search using Medical Subject Headings and free-text terms related to "Diabetic Retinopathy," "Type 2 Diabetes Mellitus," "Artificial Intelligence," "Machine Learning," and "Prognostic Model" was conducted. Original studies involving adults with T2DM that developed or evaluated AIor ML-based prognostic or risk-scoring tools for DR were included. Extracted data included study design, sample size, artificial intelligence methods, predictors, and model performance, and were synthesised narratively.

From 759 records, five studies met the inclusion criteria. Extreme Gradient Boosting consistently demonstrated the highest predictive performance, with area under the curve values between 0.803 and 0.966. Support Vector Machine also performed well in smaller cohorts. Key predictors across studies included HbA1c, duration of diabetes, renal function markers, blood pressure, lipid profile, and body mass index.

AI-driven prognostic tools show strong potential to enhance early diabetic retinopathy risk prediction. However, broader external validation and population-specific calibration are needed before routine clinical adoption.

The complex pathological microenvironment, characterized by hyperglycemia, chronic inflammation, and infection, significantly impedes diabetic wound healing. Multi-strategy collaboration is expected to improve the complex pathological microenvironment and accelerate diabetic wound healing. This work developed a dynamic borate bond-crosslinked chitosan/polyvinyl alcohol nanohydrogel loaded with glucose oxidase (GOx) and ZnS/Arg@MOF-818 nanoparticles for synergistic therapy via glucose depletion, photothermy, nitric oxide (NO), and hydrogen sulfide (H₂S)-mediated gas therapy. GOx enables glucose depletion, lowering local pH and triggering the on-demand release of ZnS/Arg@MOF-818 nanoparticles, which exhibited a photothermal conversion efficiency of 55 % and outstanding photothermal stability in vitro. The composite nanohydrogel Gel/ZnS/Arg@MOF-818 enabled sustained and stable release of NO/H₂S with glucose existent. The synergistic effects of glucose depletion, photothermy, and controlled NO/H₂S release effectively disrupted biofilms, eradicated multidrug-resistant pathogens, reduced inflammation, and promoted angiogenesis. The composite nanohydrogel exhibited 100 % antibacterial efficacy against drug-resistant strains of Staphylococcus aureus, Escherichia coli, and Acinetobacter baumannii in vitro. In vivo, it significantly accelerated diabetic wound healing 98 % within 9 days, accompanied by CD31/VEGF-driven neovascularization, balanced cytokine expression, and organized collagen deposition, without significant systemic toxicity. Therefore, chitosan-based hydrogels crosslinked via borate ester bonds in this study exhibit considerable potential for future clinical applications in the management of chronic wounds.

Type 2 diabetes mellitus (T₂DM) is a prevalent metabolic disorder closely associated with oxidative stress. Natural source polysaccharides (NSPs) show great potential in T₂DM management due to their remarkable antioxidant activity and favourable biosafety. This review systematically elaborates the multi-target mechanisms and structure-activity relationships of NSPs against T₂DM. Key mechanisms include regulating oxidative stress, modulating insulin/PI3K/Akt signalling, inhibiting α-glucosidase/α-amylase, protecting pancreatic β-cells, and improving hepatic lipid metabolism. Furthermore, through cluster analysis and structural visualization, we delineate the critical influence of molecular weight, monosaccharide composition, and glycosidic bond types on antioxidant efficacy. Specifically, low-molecular-weight NSPs with high solubility and bioavailability often exhibit superior activity. Polysaccharides rich in glucose, galactose, or galacturonic acid demonstrate enhanced antioxidant effects, while diverse glycosidic bonds contribute to varied functional patterns through synergistic interactions with other structural features. This work provides a theoretical foundation for the precise application of NSPs in anti-diabetic therapeutics and functional foods. Future research should integrate multidimensional structural analysis, dynamic conformation monitoring, and artificial intelligence-assisted design to accelerate their clinical translation.

Anti-seizure medications (ASM) variably affect cardiovascular and cerebrovascular risk factors, as evidenced by changes in associated serological biomarkers. While enzyme-inducing ASM vascular effects are well studied, those of cenobamate (CNB) remain unknown. We examined CNB's impact on vascular risk by analyzing longitudinal changes in these biomarkers, addressing an important knowledge gap as CNB use expands in epilepsy management.

We conducted a retrospective analysis of adults (≥18 years) receiving CNB with ≥ 3 serial biomarker measurements (either 2 pre-/1 post-CNB or 1 pre-/2 post-CNB). Analyzed biomarkers comprised glycosylated hemoglobin (HbA1c), international normalized ratio (INR), and the lipid profile consisting of low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), triglycerides (TG), and total cholesterol (TC).

Among 413 patients receiving CNB, 96 patients met inclusion criteria (56 HbA1c, 50 lipid profile analysis, 21 INR). HbA1c increased significantly by 0.24 % post-CNB (p = 0.017), with greater increases in patients with diabetes mellitus (DM) (0.48 % vs. 0.09 % in non-diabetics, p < 0.001). Insulin use was associated with a further HbA1c rise (0.61 %increase, p=<0.001). Lipid profile changes showed an overall non-significant increase (HDL-c + 0.44 mg/dL, LDL-c + 6.99 mg/dL, TC + 8.84 mg/dL, and TG + 7.68 mg/dL), though statins attenuated these effects. Significant dose-dependent changes were observed for LDL-c and TC (p(Dose) = 0.007-0.012; p(statin) = 0.008-0.009). HDL-c increased non-significantly by 0.96 mg/dL per 100 mg of CNB without statins but decreased by 0.35 mg/dL per 100 mg of CNB with statins. TG levels rose modestly regardless of statin use. INR increased non-significantly (+0.05-point, p = 0.609) post-CNB, without any dose-dependent effect within the anticoagulant (AntiCOAG) users and non-users. Anticoagulant use was barely significantly associated with higher post-CNB INR values(p (AntiCOAG) = 0.043).

While this retrospective study has inherent limitations, our results indicate potential CNB-associated changes in metabolic biomarkers. Definitive evaluation of CNB's vascular effects will require prospective, controlled, multicenter studies that integrate serial biomarker monitoring with clinical endpoints and adjusting for concomitant ASM use, particularly enzyme-inducing agents.