Convulsive status epilepticus (CSE) is a major neurological emergency in childhood, but population-based data from low- and middle-income countries remain limited. We estimated the incidence, mortality, neurological outcomes, and predictors of adverse outcomes among children with CSE in Kano, northern Nigeria. We conducted a review of one-year surveillance data on childhood CSE across eight hospitals and a random sample of eight primary health centers in the Kano metropolis. Cases were identified using standardized definitions and weighted to account for sampling at primary health centers. Demographic and clinical data were collected at presentation and during hospitalization. Logistic regression analysis was used to identify predictors of mortality and new-onset neurological deficits. A total of 972 children with CSE were identified at participating hospitals, with additional cases detected at primary health centers. After weighting, an estimated 1921 CSE cases occurred during the study period, corresponding to an annual incidence of 98 per 100 000 children aged 1 month-14 years. Incidence was highest among infants (175 per 100 000). Among hospitalized children, in-hospital mortality was 24.7%, with more than half of the deaths occurring within 24 h of admission. New-onset neurologic deficits were observed in 9.6% of hospitalized survivors. In multivariable analysis, hypoglycemia at presentation, presumed meningoencephalitis, and CSE lasting longer than 30 min were independently associated with increased odds of death. Neurological deficits were associated with multiple CSE episodes, prolonged seizures, and presumed meningoencephalitis. Childhood CSE in Kano is associated with a high incidence and substantial mortality, particularly among infants. Hypoglycemia, central nervous system infections, and prolonged seizures are key predictors of adverse outcomes. These findings highlight the need for improved early recognition and strengthened emergency seizure management in resource-limited settings. Convulsive status epilepticus is a dangerous type of prolonged seizure in children. In this study from Kano, Nigeria, many children developed this condition and about one in four hospitalized patients died. Low blood sugar, suspected brain infections, and long seizures increased the risk of poor outcomes. Improving early treatment and emergency care may help reduce these deaths.

Control of nocturnal hypoglycemia by CGM-based AI-enabled nocturnal hypoglycemia prediction: A retrospective analysis of real-world data.

Ramadan fasting represents a distinctive model of daytime intermittent fasting that involves substantial lifestyle changes, including modifications in meal timing, sleep patterns, and circadian rhythms. For individuals with diabetes mellitus, these behavioral shifts may interact with metabolic regulation and potentially affect glycemic stability. Sleep plays a key role in glucose homeostasis, and disturbances in sleep duration or architecture have been associated with reduced insulin sensitivity and increased glycemic variability. This narrative review aims to synthesize current evidence on sleep alterations during Ramadan and to explore their metabolic implications in patients with diabetes. A structured literature search was conducted in PubMed/MEDLINE, Scopus, and Google Scholar, complemented by manual screening of relevant references and international guidelines such as the IDF–DAR recommendations. Studies examining sleep patterns, circadian rhythm changes, and glycemic outcomes during Ramadan fasting were analyzed narratively. Available evidence suggests that Ramadan fasting is commonly associated with delayed sleep timing and reduced total sleep duration, sometimes accompanied by alterations in sleep architecture. While these changes appear relatively well tolerated in healthy individuals, patients with diabetes may be more vulnerable to adverse effects. Sleep restriction, nocturnal hypoglycemia, glycemic variability, and comorbid sleep disorders such as obstructive sleep apnea may contribute to sleep fragmentation and metabolic instability. These findings highlight the importance of integrating sleep evaluation into diabetes management during Ramadan, alongside glycemic monitoring, individualized therapeutic adjustments, and culturally adapted patient education.

In recent years, diabetes has become a serious global public health issue, and the number of patients continues to grow, causing serious social and economic burdens. Type 1 diabetes patients require life-long insulin treatment, and they are also prone to risks of hypoglycemia. Diabetes is not a directly life-threatening disease, but it can significantly increase the risk of heart disease, kidney disease, and stroke. Therefore, traditional manual insulin injection methods often cause changes in blood sugar levels during treatment, which can lead to high or low blood sugar. Thus, how to effectively and steadily control insulin has become a key research area. PID control is easy to implement, convenient to operate, and capable of real-time control, and it has been widely applied in blood glucose control systems. Through a review of the relevant literature, this paper introduces the application of PID control in closed-loop blood glucose regulation and its adaptability in the insulin-glucose system, as well as the pros and cons of various PID control methods. The conclusion indicates that PID control can effectively control blood glucose changes.

Diabetes self-management is accompanied by time-varying emotional and motivational challenges that impact mental health. These day-to-day fluctuations can be assessed via ecological momentary assessment (EMA), that allows the repeated sampling of psychosocial variables in everyday life. The benefits of EMA over questionnaires mirror the benefits of continuous glucose monitoring (CGM) over HbA1c. We describe the insights generated by combining EMA and CGM and highlight its potential. Research shows that glucose levels can influence subsequent mood, stress, cognitive functioning, and symptom reporting, with nocturnal hypoglycemia and overnight glucose being particularly relevant. Studies demonstrated the importance of differentiating between subjective person-interpreted and objectively sensor-assessed glucose levels. N-of-1 analyses revealed relevant intraindividual differences in the association between glycemic and psychosocial parameters. Combining EMA and CGM can enhance our understanding of the dynamic relationship between glycemic and psychosocial variables, supporting precision medicine approaches for mental health in diabetes.

BackgroundInsulin autoimmune syndrome (IAS) is a rare hypoglycemic disorder often confused with insulinoma or insulin overdose. Patients with diabetes on insulin therapy increasingly show insulin autoantibodies (IAAs), presenting symptoms similar to classic IAS, termed exogenous insulin antibody syndrome (EIAS). This study examines EIAS clinical features and risk factors.MethodsPatients with diabetes with IAA test results, admitted to our hospital between June 2023 and March 2024 were retrospectively enrolled. Participants were stratified into control and EIAS groups on the basis of IAA status. Clinical characteristics were compared between groups, independent risk factors for EIAS were identified by multivariate logistic regression, and the diagnostic utility of fasting insulin for predicting EIAS was assessed with receiver-operating-characteristic (ROC) curve analysis.ResultsOf 120 patients with diabetes and available IAAs results, 37 met criteria for EIAS. Compared with controls, EIAS patients were older, had longer diabetes duration, were more often treated with insulin aspart or premixed human insulin, and received higher daily insulin doses. Paradoxically, EIAS patients had markedly lower levels of fasting blood glucose and HbA1c, while higher fasting and 2-h post-prandial insulin concentrations, as well as HOMA-IR. Multivariate logistic regression analysis showed that elevated fasting insulin levels were independently associated with increased risk of EIAS. For every 1 uU/mL increase in fasting insulin, the risk of EIAS increased by 3% (OR = 1.03, 95% CI: 1.00–1.05). The fasting insulin level demonstrated high overall diagnostic and predictive efficacy for EIAS, with an area under the curve (AUC) of 0.782 (95% CI: 0.691–0.872). The optimal diagnostic cutoff value was 6.975 uU/mL, with a sensitivity of 73.0% and a specificity of 81.9%.ConclusionEIAS patients were identified with advanced age, prolonged diabetes duration, high insulin dosage, hypoglycemia, and hyperinsulinemia. Fasting insulin level is independently associated with EIAS risk and demonstrates good diagnostic performance.

Severe hypoglycemia is a major acute complication of type 1 diabetes (T1D) and is associated with increased morbidity, mortality, and impaired quality of life. Identifying individuals at the highest risk remains essential for optimising preventive strategies in real-world practice. The SEHYPAN (SEvere HYpoglycemia in ANdalusia) study was a multicenter case-control analysis including adults with T1D treated with multiple daily insulin injections (MDI). Cases were individuals who required pre-hospital emergency care for severe hypoglycemia between 2018 and 2022, each matched by sex, age, glucose-monitoring method (SMBG/isCGM), reference health-care area with controls who had not experienced severe events. Logistic regression models were used to identify independent predictors, and nomograms were generated for individualised risk estimation. A total of 1464 participants were analysed (799 cases and 665 matched controls). Cases had longer diabetes duration, more comorbidities, and higher rates of smoking and alcohol use (all p<0.001). Two nomogram-based models were developed: one for the overall cohort, including glucose monitoring modality, history of severe and nocturnal hypoglycemia, comorbid depression, alcohol use, and chronic conditions; and another specific to isCGM users, which also incorporated time in range and time below range. In the full cohort model, isCGM use was independently associated with lower odds of severe hypoglycemia. Both models showed good discrimination (AUC 0.75-0.83) and high sensitivity (≥0.75). The SEHYPAN study identifies key predictors of severe hypoglycemia in adults with T1D on MDI therapy. The innovative nomogram-based models provide personalised risk estimates that may enhance preventive care in everyday clinical practice.

The effect of gestational diabetes on maternal and neonatal outcomes.

We had a patient with type 2 diabetes (patient A) who suffered from hyperglycemia and frequent episodes of nocturnal hypoglycemia. He had a high titer of insulin antibodies (> 50.0 U/mL) and a very high serum insulin concentration (19,516 µU/mL). The aims of this study were (1) establishing a non-isotopic method to identify insulin analogs that bind less to the antibodies and validating its clinical usage and (2) elucidating the mechanism of his nocturnal hypoglycemia and its management. The optimal conditions for binding study were determined using seven exogenous insulin preparations (human insulin, lispro, aspart, glulisine, glargine, degludec, and detemir) via polyethylene glycol method. The effect of pH on binding of endogenous insulin to its antibodies was examined via gel filtration chromatography (GFC) on different pH. Several times higher concentration of exogenous insulin was most appropriate in binding study. It revealed that all seven insulin preparations significantly bound to the insulin antibodies in patient A. Among the other 53 serum samples containing insulin antibodies, about 70% of them had at least one insulin analog that did not significantly bind to their insulin antibodies. GFC revealed that reducing the pH from 7.4 to 7.2 partially dissociated antibody-bound insulin leading to increased unbound insulin. We established and validated a non-isotopic method to identify suitable insulin preparations that bound less to insulin antibodies. Correcting respiratory acidosis during sleep might be effective to prevent nocturnal hypoglycemia in some patients with diabetes having insulin antibodies.

This large observational cohort real-world study explored the effects of three forms of exercise (walking [WALK], aerobic excluding walking [AER] and anaerobic [ANAER]) on glucose levels and hypoglycaemia risk in type 1 diabetes. Data were collected from 3248 users of mySugr Logbook and Apple Health (mean ± SD age 41.23±12.25 years; glucose management index of 7.05±1.09%; 41.5% were female) over a total of 428,058 exercise sessions. Acute and 24 h glycaemic effects were examined across exercise types. Post-exercise glycaemia data over 24 h were compared with sedentary glycaemic data. Time of exercise was used to assess the probability of nocturnal hypoglycaemia. Independent of type, exercise decreased glucose by -1.06±0.89 mmol/l. For the individual types of exercise, WALK decreased levels by -1.24±0.81 mmol/l, AER by -1.43±1.02 mmol/l and ANAER by -0.52±0.81 mmol/l (all p<0.001). Comparing sedentary days vs active days, the time in range (3.9-10 mmol/l glucose) increased by +2.08±6.06% for WALK, +2.94±6.46% for AER and +3.93±7.16% for ANAER, and the time below range (<3.9 mmol/l) increased by 0.37±1.57% for WALK, 0.74±1.70% for AER and 0.68±1.79% for ANAER (all p<0.001). ANAER yielded a smaller chance of acute hypoglycaemia and WALK yielded a smaller chance of nocturnal hypoglycaemia (p<0.001). Activities done after 15:30 hours did not increase the risk of nocturnal hypoglycaemia when compared with earlier exercise sessions (+0.9±0.34%; p<0.01). Aerobic activities decreased glucose more during exercise sessions than anaerobic exercise and yielded larger acute hypoglycaemia risk; anaerobic activities yielded the largest 24 h glycaemic improvements. More-intense exercise resulted in a larger nocturnal hypoglycaemia than walking; exercise timing was not a relevant contributor to nocturnal hypoglycaemia.

Radar technology has emerged as a promising tool for contactless monitoring of sleep physiology and related disorders. Nocturnal hypoglycemia is a critical safety concern in people with diabetes, particularly in vulnerable populations such as children, older adults, and individuals in long-term care, where body-worn devices are often not used. To assess the feasibility of radar-based hypoglycemia detection, we conducted a scoping review of studies published between January 2007 and July 2025 that investigated radar monitoring of physiological parameters during sleep or sleep-related disorders with overlapping autonomic and motor physiological signatures. A structured search and data extraction were performed, covering bibliographic, technical, clinical, and performance variables. Across 78 studies, radar was consistently validated for respiration, heart rate and heart rate variability, body movement, and posture, with reported accuracies frequently exceeding 90%. Sleep staging showed lower but meaningful performance (63-85%). Disease-focused applications demonstrated robust results in sleep-disordered breathing, including obstructive sleep apnea and sleep apnea-hypopnea syndrome, as well as initial explorations in asthma, insomnia, and seizure detection. Together, these studies establish radar as a reliable, contactless sleep monitoring modality for respiration, HR and HRV, movement, posture, and sleep staging, which are physiological signals known to change during nocturnal hypoglycemia. Building on this foundation, radar is well positioned to enable indirect nocturnal hypoglycemia detection, especially where wearables are not feasible. Establishing clinical performance now requires studies that pair radar with glucose measurements and report event level diagnostic accuracy.

ABSTRACTAimsWe sought to compare the MiniMed™ 770G to the 780G insulin pump for glycemic control and quality of life in Japanese adults with T1D over a one‐year period.MethodsThirty‐six adults with T1D who switched from the MiniMed™ 770G to the 780G system were analyzed over 48 weeks using continuous glucose monitoring data, retrospectively. The percentages of time within the target glucose range (70–180 mg/dL), time above 180 mg/dL, and time below 70 mg/dL were compared before and after switching. Quality of life (QOL) scores were also evaluated in 39 patients using a validated questionnaire, prospectively.ResultsAfter switching to the MiniMed™ 780G, the time within the target glucose range significantly increased from 71.6 ± 12.4% to 76.1 ± 10.2% (P < 0.01), while the time above 180 mg/dL decreased from 25.2 ± 13.4% to 21.0 ± 11.0% (P < 0.01). The time below 70 mg/dL did not differ significantly overall, but nocturnal hypoglycemia decreased from 4.9% to 2.7% (P = 0.02). Glycated hemoglobin improved from 7.2 ± 0.8% to 7.0 ± 0.7% (P < 0.01). Although the total QOL score did not change significantly, the subscales reflecting anxiety and treatment satisfaction improved significantly.ConclusionsSwitching from the MiniMed™ 770G to the 780G system in Japanese adults with T1D improved both glycemic control and treatment‐related QOL, supporting the clinical usefulness of the 780G in real‐world clinical practice. This study provides the first one‐year real‐world evidence of MiniMed™ 780G use in Japan.

Founded in 1810, the Richmond District Lunatic Asylum at Grangegorman, Dublin, Ireland, admitted tens of thousands of patients over the course of almost two centuries. Staff introduced many new treatments for mental illness, ranging from ‘moral management’ in the early 19th century to the biological treatments of the 20th century (e.g. malaria therapy, insulin coma, lobotomy). The Richmond was a model for the network of district asylums established across Ireland during the 19th and early 20th centuries. This article summarises the legislative underpinning of Ireland's high committal rates in the late 19th and early 20th centuries; draws on the Richmond's archive to present two clinical cases of women who were committed to the Richmond in 1907 and 1908 but had different outcomes; and explores the nature of ‘institutional careers’ in Ireland's ‘mental hospital’ system in the early 20th century as one element within Ireland's cultures of confinement during this period.

Background Managing patients with type 2 diabetes mellitus (T2DM) with chronic liver disease (CLD) is challenging due to an increased risk of hypoglycemia. Among other long-acting basal insulin analogs, insulin degludec has been shown to have a low risk of hypoglycemic events in patients with T2DM. Aim To evaluate the effectiveness of insulin degludec in managing T2DM in patients with CLD based on changes in glycated hemoglobin (HbA1c), fasting plasma glucose, and postprandial glucose (PPG) levels, and to evaluate its safety, based on the incidence of hypoglycemic events. Methods This single-center, observational, retrospective study was conducted using data from 35 patients, aged between 18 and 75 years, with T2DM, a body mass index (BMI) of <40 kg/m2,and stable hepatic impairment based on the Child-Pugh classification. Data, including demographics, laboratory results, and medical history, were collected from electronic medical records at baseline and three months after treatment with insulin degludec. The primary endpoints were the number and severity of hypoglycemic events, as well as changes in glycated hemoglobin (HbA1 c.), fasting plasma glucose (FPG), and postprandial glucose (PPG) levels. A p-value of <0.05 was considered statistically significant. Results Among the 35 patients enrolled in the study, the majority (n=25) were males. Most of the patients had been living with T2DM for a mean duration of 10.79±5.63 years and had mild-to-moderate hepatic impairment based on Child-Pugh scores. Most of the patients (15, 42.9%) were on a combination of sulfonylurea and insulin at baseline. Significant reductions in glycemic parameters were observed from baseline to three months after treatment (p<0.001). About 14.3% of patients developed level 1 hypoglycemia, another 14.3% developed nocturnal hypoglycemia, and none reported level 2 or 3 hypoglycemia. Conclusion Insulin degludec improved glycemic parameters while reducing the risk of severe hypoglycemic events. The study findings suggest that insulin degludec can be considered a safe option for patients with T2DM with CLD. However, prospective studies with larger sample sizes and a comparator arm are warranted to highlight insulin degludec's potential in this patient population.

The dawn phenomenon (DP), defined as an early-morning rise in glucose unrelated to preceding nocturnal hypoglycemia, represents a significant yet often underrecognized contributor to fasting hyperglycemia and overall glycemic instability in individuals with diabetes. The expansion of continuous glucose monitoring (CGM) technologies over the past decade has enabled more precise characterization of nocturnal glucose patterns, offering new insights into the prevalence, magnitude, and clinical implications of DP across diverse glycemic states. This review synthesizes original observational, retrospective, and prospective studies published between 2010 and 2025 that used CGM to evaluate DP in type 1 diabetes, type 2 diabetes, impaired glucose tolerance, and non-diabetic populations. Data extraction focused on DP definitions, nocturnal glucose trajectories, glycemic variability metrics, associations with HbA1c and time-in-range, and emerging evidence linking severe DP to adverse clinical outcomes. Findings indicate that DP is highly prevalent in both type 1 and type 2 diabetes, with magnitude varying widely depending on residual β-cell function, insulin sensitivity, and methodological differences in CGM-based definitions. DP correlates with increased total glucose exposure and greater glycemic variability, and may influence long-term metabolic risk. Understanding DP within the context of CGM-derived metrics is essential for optimizing individualized therapeutic strategies and improving morning glycemic control. Further standardized research is required to unify definitions and clarify the prognostic significance of DP.

To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)-based machine learning and examine their association with diabetes distress, a key psychosocial outcome. In this observational study, 137 adults with T2D wore blinded CGM (FreeStyle Libre Pro), yielding 1657 days of data. Glycemic patterns were identified using unsupervised machine learning via Gaussian mixture modeling, validated with Bayesian information criterion and silhouette scores. Diabetes distress was assessed with the 17-item Diabetes Distress Scale and analyzed through analysis of covariance (ANCOVA), adjusting for age, sex, body mass index, diabetes duration, and glucose management indicator. Clustering identified four distinct glycemic profiles: Cluster 1 (suboptimal control, nocturnal hypoglycemia; 15.8%), Cluster 2 (suboptimal control, nocturnal hyperglycemia; 27.1%), Cluster 3 (poorly controlled, prolonged hyperglycemia; 21.1%), and Cluster 4 (well controlled; 36.1%). Diabetes distress scores varied significantly: participants in Cluster 3 reported the highest distress (mean = 2.37, 95% CI = 1.99-2.76), while Cluster 4 reported the lowest (mean = 1.67, 95% CI = 1.48-1.86; P = .03). Effect sizes indicated differences corresponded to clinically meaningful categories of "little or no distress" vs "moderate distress." CGM-based machine learning identified physiologically distinct glycemic phenotypes that were also associated with psychosocial burden. This work demonstrates the added value of integrating CGM-derived profiles with patient-reported outcomes. These findings highlight the potential of CGM phenotyping to support precision diabetes care by enabling early identification of high-risk subgroups, guiding tailored behavioral and psychosocial interventions, and informing technology-enabled decision tools that connect physiological monitoring with emotional well-being in T2D management.

IntroductionNon-islet cell tumor hypoglycemia (NICTH) is a rare paraneoplastic syndrome in which a tumor secretes high molecular weight IGF-II, causing hypoglycemia. It is most frequently associated with solitary fibrous tumors (SFTs). SFTs are rare mesenchymal neoplasms that most commonly arise in the thoracic cavity but may occur at various extrapleural sites. When SFTs are complicated by hypoglycemia due to IGF-2 secretion, the condition is termed Doege–Potter syndrome.Clinical CaseA 50-year-old man presented with recurrent episodes of daytime fatigue, hunger, and sugar cravings, in addition to nocturnal hypoglycemia requiring awakening every two hours to eat, often accompanied by profuse sweating. Home glucose monitoring confirmed hypoglycemia during symptomatic episodes.He had no significant past medical, surgical, or family history, and no history of substance abuse or chronic medication use. Concurrently, he reported persistent cough and exertional dyspnea, for which he was evaluated in the pulmonology clinic.Initial laboratory investigations were unremarkable. A supervised 72-hour fasting test confirmed hypoglycemia. IGF-1 levels were also measured, but IGF-2 test was unavailable locally. Biochemical results are summarized in Table 1, demonstrating hypoinsulinemic hypoglycemia.Chest computed tomography, performed for respiratory complaints, revealed a well-circumscribed pulmonary mass (image1). Histopathological examination confirmed a solitary fibrous tumor. Taken together, the biochemical results and pathology established a diagnosis of NICTH.Following the diagnosis of NICTH, the patient was stabilized with prednisolone 20 mg twice daily for one week, which improved his hypoglycemia. He then underwent surgical excision of the pulmonary solitary fibrous tumor.Postoperatively, hypoglycemic episodes resolved completely. At follow-up, the patient remained asymptomatic.ConclusionThis case highlights the importance of considering Doege–Potter syndrome in otherwise healthy individuals with unexplained fasting or nocturnal hypoglycemia, as early diagnosis and definitive surgical management can be curative.Conflict of Interest StatementThe authors declare no conflict of interest regarding this case report.No external funding was received for this work.Figure 1:Chest CT showing a large pulmonary solitary fibrous tumorContrast-enhanced CT of the chest demonstrating a well-circumscribed, heterogenous mass in the right hemithorax measuring approximately 12.7 × 12.0 cm Table 1:Relevant Biochemical FindingsLaboratory evaluation demonstrating hypoglycemia with suppressed insulin and C-peptide levels, negative sulfonylurea screen, low-normal IGF-1, and absence of insulin autoantibodies.

IntroductionInsulin Autoimmune Syndrome (IAS), also known as Hirata's disease, is a rare cause of endogenous hyperinsulinemic hypoglycemia characterized by the presence of insulin autoantibodies in individuals who have not received exogenous insulin. Chronic corticosteroid therapy can suppress the hypothalamic-pituitary-adrenal axis, potentially leading to secondary adrenal insufficiency upon abrupt discontinuation. We present a case with RA who presented with hypoglycemia after the discontinuation of long-term corticosteroid therapy. This presentation initially raised suspicion of adrenal insufficiency, but the underlying cause was ultimately identified as Hirata's disease.Clinical CaseA 63-year-old male with a history of RA and coronary artery disease presented to the emergency department with recurrent syncopal attacks due to severe hypoglycemia. On physical examination, the patient was confused and tachycardic. His blood glucose level was 36 mg/dL, and he was started on a dextrose infusion. The patient had a similar presentation with hypoglycemia and syncope approximately 10 days earlier. A history revealed that he had recently and abruptly discontinued his long-term prednisone therapy. Due to the suspicion of secondary adrenal insufficiency, he was administered intravenous methylprednisolone and admitted to the ward.Upon admission, the patient experienced another episode of hypoglycemia. To investigate the cause, blood tests for adrenal insufficiency and other potential etiologies of hypoglycemia were ordered. The laboratory results from the hypoglycemic episode are shown in Table 1. The cortisol level was 12 µg/dL, which was not consistent with primary adrenal insufficiency. Elevated insulin and C-peptide levels were consistent with hyperinsulinemic hypoglycemia. The insulin level of >1000 mlU/L strongly suggested autoimmune hypoglycemia, and anti-insulin antibody (IAs) levels were ordered. The IAs level was positive (123 U/mL), leading to the diagnosis of Hirata's disease. The abdominal CT scan showed no pancreatic pathology.The patient was started on acarbose and his blood sugar was monitored. Since his hypoglycemic episodes persisted, prednisolone was added to the treatment regimen, after which no further hypoglycemia was observed. His IAs levels subsequently decreased to 57.6 U/mL and then to 14 U/mL, and the prednisolone was gradually tapered and discontinued. The patient was followed up while continuing acarbose, and no further hypoglycemic episodes occurred.ConclusionThis case report describes how chronic corticosteroid therapy can mask Insulin Autoimmune Syndrome (IAS), a condition where high-titer insulin autoantibodies cause postprandial hypoglycemia. Treatment focuses on preventing low blood sugar and suppressing the immune response. It is crucial for clinicians to consider IAS in the differential diagnosis of unexplained hypoglycemia, particularly in patients with autoimmune diseases or those on immunomodulatory drugs.Table 1:Laboratory values of the patient

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings.

Background Diabetes mellitus is a common chronic condition that presents significant challenges for patients, particularly in managing hypoglycemia. The frequency of hypoglycemic episodes can significantly impact patients' quality of life and long-term health outcomes. Aim This study aimed to assess the awareness and management of hypoglycemia among people living with diabetes in Cluster 1, Riyadh, Saudi Arabia, focusing on their knowledge, practices, and preparedness for managing hypoglycemia. Methods This cross-sectional study involved 299 patients with diabetes from healthcare centers in Cluster 1, Riyadh, Saudi Arabia, in the period between February1, 2025, and August 20, 2025. Data were collected through a structured survey that assessed demographic characteristics, knowledge of hypoglycemia, blood glucose monitoring practices, and preparedness for hypoglycemic episodes. The study also explored sources of information, frequency of healthcare consultations, and confidence in managing low blood sugar. Descriptive and statistical analyses were used to evaluate relationships between variables such as diabetes type, experience with hypoglycemia, and healthcare consultation frequency. Results The study included 299 patients with diabetes, with a higher proportion of male patients (187;62.5%) compared to female patients (112;37.5%). Age distribution was varied, with63 (21.1%) participants aged 18-30 years,63 (21.1%)aged 31-45 years,104 (34.8%)aged 46-60 years, and69 (23.1%) over 60 years of age. Type 2 diabetes was the most common diagnosis (62.5%), followed by Type 1 diabetes (n=76;25.4%). The majority of participants exhibited poor knowledge regarding hypoglycemia, with217 (72.6%) patients classified as having low awareness. Only82(27.4%)demonstrated good knowledge of hypoglycemia management. Conclusions This study reveals a low level of awareness and management of hypoglycemia among diabetic patients in Riyadh. There is a clear need for targeted education programs that address both the prevention and management of low blood sugar.