This study is a systematic review assessing the efficacy and safety of oral antidiabetic drugs in managing type 2 diabetes mellitus in people with chronic kidney disease (CKD), focusing on their dosing and effects on renal function and glycemic control. A systematic search of online databases was conducted for published from  January  2023  to September  2024.  Studies  that  focused  on oral  antidiabetic  drugs  for  individuals  with  diabetes and  CKD, and  reported  outcomes  such  as  glycemic  control,  renal  function, and adverse events were included. A risk-of-bias assessment was performed to evaluate study quality. A  total  of  21  studies  that  met  the  criteria  were  included.  The evidences indicate that SGLT2 (Sodium-Glucose Cotransporter-2) inhibitors and GLP-1 (Glucagon-like peptide-1) receptor agonists provide renal protective benefits. SGLT2 inhibitors are more effective especially in early-stages of CKD. DPP-4 (Dipeptidyl peptidase 4) inhibitors are safe across various CKD stages and require minimal dose adjustments. Metformin remains a popular drug for glycemic control but should be monitored and even discontinued in advanced stages of CKD because of the risk of lactic acidosis. Sulfonylureas were related to hypoglycemia risk. Oral  antidiabetic  drugs,  especially  SGLT2  inhibitors  and  GLP-1 receptor  agonists,  are  suggested  in  managing  blood  glucose  in CKD  patients  owing  to  their  renal  and  cardiovascular  benefits. Individualized therapy is an important factor as drug safety and efficacy  are  influenced  by  CKD  stage,  comorbid  conditions,  and hypoglycemia risk. Individualized therapy helps maximize renal and cardiovascular protection while minimizing adverse outcomes. Regular monitoring of HbA1c (hemoglobin A1c), GFR (glomerular filtration rate), and albuminuria is recommended.

Traumatic brain injury (TBI) affects millions globally each year, often resulting in long-term health issues or death. While the immediate physical damage caused by these injuries receives much attention, subsequent metabolic changes in the brain are equally vital to recovery but understudied. After TBI, brain energy regulation and consequential metabolic processes are disrupted. This review provides a detailed examination of metabolic alterations following TBI, including glucose and lipid processing disruptions, increased lactate levels, neurotransmitter imbalances, and oxidative stress. These changes can lead to hyper/hypoglycemia, lactate accumulation, chemical imbalances, and heightened oxidative stress, all of which impede recovery. Understanding these biochemical shifts is essential for developing more effective treatments. This review offers a comprehensive overview of brain metabolic changes post-TBI and discusses some promising therapies, including drugs, nutrition, and lifestyle adjustments, that could aid recovery and improve the quality of life of those impacted.

Managing patients with type 1 diabetes (T1D) with end stage kidney disease (ESKD) on hemodialysis presents unique challenges due to altered insulin kinetics and rapid glucose fluxes. We report the case of a 48-year-old female with long-standing T1D and ESKD who suffered from severe glycemic variability and recurrent hypoglycemia despite intensive insulin management. The patient was transitioned to the Medtronic MiniMed™ 780G automated insulin delivery (AID) system. Although glycemic control generally improved, she experienced frequent intradialytic hypoglycemia and hypotension. A specialized protocol was implemented to mitigate these risks: setting a temporary glucose target of 150 mg/dL (SI: 8.3 mmol/L) (reference range, 70-180 mg/dL [SI: 3.9-10 mmol/L]) two hours prior to dialysis, suspending the pump upon dialysis initiation, and resuming delivery only when sensor glucose exceeded 200 mg/dL (SI: 11.1 mmol/L). Following this adjustment, intradialytic adverse events were resolved, and the patient achieved a time in range of 69% with a glucose management indicator (GMI) of 7.0%. This case demonstrates that while standard AID algorithms are not calibrated for dialysis, they can be safely employed in ESKD patients through tailored modifications, specifically strategic pump suspension and proactive target elevation.

Analyses of recurrent hypoglycemia are critical for effective treatment management in diabetic patients. Typically, within-subject dependency in such analyses is captured through subject-level frailty. Recent research has modeled recurrent hypoglycemia using the first hitting times of a reflected Brownian motion. A close examination of this approach reveals that it does not adequately account for varying frailties among individuals, which indicate notable heterogeneity. To address this gap, we propose a finite mixture model of the first hitting time distribution of the reflected Brownian motion. This model allows for component-specific regression coefficients and frailty parameters, providing nuanced insights into how risk factors differently affect patient subgroups. We employ a Bayesian framework for inference, utilizing Markov chain Monte Carlo for estimation. Model selection is conducted using the Deviance Information Criterion and the Logarithm of the Pseudo-Marginal Likelihood. The effectiveness of these criteria is assessed through simulation studies. Application to recurrent hypoglycemia modeling revealed two subgroups with different risk profiles, as reflected in their volatilities. Bayesian model comparison criteria favor the model with component-specific regression coefficients for volatilities. The subgroup with lower volatility exhibits a larger variance and, hence, a greater level of heterogeneity.

BackgroundWomen living with type 2 diabetes mellitus (T2DM) often encounter unique physiological and lifestyle-related hurdles that impact disease progression and management. Dietary interventions tailored to cultural and gender-specific needs are essential for effective diabetes management.AimThis pilot trial primarily evaluated feasibility and safety and, secondarily, explored the preliminary metabolic effects and practical implementation of a lacto-vegetarian diet combined with intermittent fasting in Indian women with T2DM.MethodsIn this 12-week, parallel-group, open-label randomized controlled trial, 10 women with T2DM (aged 25-60 years) were randomly allocated in a 1:1 ratio using block randomization to either an intervention group (lacto-vegetarian diet within a 10-h eating window) or a control group (usual diet). Glycemic, lipid, and anthropometric parameters were measured at baseline and after the intervention. Safety, tolerability, and participant-reported experiences were also assessed.ResultsExploratory within-group analyses in the intervention arm demonstrated reductions in glycated hemoglobin (-11.3%), fasting insulin (-47.4%), body weight (-3.96.0%), and body mass index (-3.98%) while the control group showed no meaningful changes. Lipid parameters showed no meaningful changes. No serious adverse events occurred; mild fatigue, hypoglycemia, and constipation were reported but resolved without intervention. Participants expressed improvements in energy, appetite control, and emotional well-being. No reductions in diabetes medication were reported, likely due to the short duration of the intervention.ConclusionThis pilot randomized controlled trial was feasible, safe, and well-tolerated and showed signals of potential metabolic benefit in women with T2DM. This pilot study is part of an ongoing clinical trial, and the complete results will be presented upon completion of the full trial.

A 1.5-year-old female Spitz dog was presented with a sudden onset of inappetence, weakness, anemia, and hemoglobinuria persisting for two days. The dog had a prior history of tick infestation. A thorough physical examination revealed pyrexia, lymphadenopathy, pale conjunctival and gingival mucous membranes, and a normal capillary refill time. Microscopic analysis of a Giemsa-stained peripheral blood smear identified the presence of Babesia gibsoni, which was further confirmed through PCR. Hematological analysis revealed severe anemia, neutropenia, eosinophilia, lymphocytosis, and thrombocytopenia. Serum biochemistry indicated significant hypoglycemia, hypoproteinemia, and hypoalbuminemia. Serum oxidant-antioxidant parameters revealed a marked increase in the level of oxidant and a decreased level of antioxidants. Genetic characterization based on the 18S rRNA gene fragment demonstrated complete sequence identity with B. gibsoni strains from Asia, Europe, and North America. Phylogenetic analysis revealed that the isolate (PQ143276) was most closely related to B. gibsoni strains from Sri Lanka and Spain, following a distinct geographical pattern. The therapy included a combination of oral metronidazole, doxycycline, and clindamycin which resulted in a favourable response. The dog regained appetite and vigor by the 10th day post-treatment. To the best of the authors' knowledge, this is one of the few documented reports from Tripura that provides a comprehensive analysis of genetic characterization, phylogenetics, hemato-biochemical findings, and serum oxidant-antioxidant status of the infection, while also contributing to the understanding of its evolutionary relationships within the Babesia genus.

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder that often requires treatment intensification over time. In complex patients with multiple comorbidities and cognitive impairment, therapeutic decisions must balance glycemic control with safety and feasibility. We present a 59-year-old male with long-standing T2DM, marked body weight fluctuations, and emerging neurocognitive decline. Baseline glycated hemoglobin (HbA1c) was 9.4%, with progressive improvement to 7.1% under quadruple non-insulin therapy consisting of metformin, sitagliptin, glimepiride, and semaglutide. Insulin therapy was avoided due to cognitive impairment and a high risk of dosing errors. Sustained glycemic control was achieved without severe hypoglycemia. This case highlights the importance of individualized, multidisciplinary management in advanced T2DM and demonstrates that non-insulin strategies may be a viable and safe alternative in selected high-risk patients.

BACKGROUND Thyroid storm, or thyrotoxic crisis, is an acute and life-threatening manifestation or complication of hyperthyroidism that may involve sudden multisystem dysfunction. For these reasons, the initial diagnosis can be challenging. This report describes a 73-year-old woman with an atypical presentation of life-threatening thyroid storm. CASE REPORT A 73-year-old woman with no history of hyperthyroidism experienced 1 month of abdominal distension and palpitations. Upon arrival at the emergency department, she displayed profound, refractory hypoglycemia (capillary glucose 0.24 mmol/L). Within 24 hours, she developed fulminant liver failure (aspartate aminotransferase 3767 U/L, alanine aminotransferase 791 U/L), coagulopathy, and thrombocytopenia (platelets 83 000/μL). Thyroid function tests confirmed severe thyrotoxicosis (free T3 10.30 pg/mL, free T4 6.11 ng/dL, thyroid-stimulating hormone <0.008 μIU/mL), and a Burch-Wartofsky Point Scale score of 70 established the diagnosis of thyroid storm. Additional diagnoses included acute liver failure, disseminated intravascular coagulation, and heart failure (ejection fraction 43%). Conventional antithyroid drugs were contraindicated due to severe hepatic dysfunction; rescue therapies such as plasmapheresis were precluded by a prohibitive bleeding risk. After multidisciplinary counseling, the family chose palliative discharge; the patient died 3 days later. CONCLUSIONS This case demonstrates that thyroid storm can present with atypical features in patients without known hyperthyroidism, leading to delayed diagnosis and fatal multiorgan failure. Early consideration of thyroid dysfunction is essential to prevent irreversible outcomes.

Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked to type 2 diabetes and obesity. It creates new opportunities to shift from reactive to proactive, data-driven care. Recent advances include predictive algorithms for hypoglycemia and hyperglycemia, decision-support tools for insulin titration, and generative and agentic AI applications that can enhance patient engagement, streamline clinical workflows, and provide personalized education. For individuals with chronic conditions, AI-powered technologies offer hope in reducing disease burden, supporting self-management, and improving quality of life. For clinicians, AI offers opportunities to analyze and interpret large amounts of glucose, medication, and behavioral data, thus supporting personalized care and freeing more time to focus on psychosocial and lifestyle factors. Despite these benefits, challenges remain, such as ensuring equitable access, integrating AI into primary care, building trust among clinicians and patients, and addressing ethical issues related to data use. This review will synthesize current evidence on AI's impact on diabetes and CKM care and education, highlight opportunities for interdisciplinary teams to utilize AI tools, and outline future directions for research and clinical practice. By examining AI's potential and limitations, this article aims to equip clinicians with the knowledge needed to adopt AI-enabled approaches to better manage chronic diseases.