B lymphocytes play crucial roles in host immune responses by mediating humoral immunity via the production of various types of antibodies and also contribute to the pathogenesis of a wide range of inflammatory, autoimmune, and neoplastic disorders. Recently, considerable literature has grown around the naturally occurring compounds immunomodulatory potential. In this regard, berberine (BBR) is an isoquinoline alkaloid with unique pharmacological actions including anti-inflammatory, anti-oxidant, anti-microbial, and anti-tumor activity, which arises from its polytrophic pharmacological and biochemical properties. Up to now, no previous study has investigated the immunomodulatory actions of BBR on B cells. Herewith, this review attempts to explore the immunomodulatory effects of BBR on B cells by gathering all evidence from research investigations. BBR has high potency to inhibit the activation, proliferation, and differentiation of pathological B cells, which is represented by the reduced number of antibody-producing B cells and the production of IgE (allergies) and IgG/IgA autoantibodies (autoimmune and inflammatory disorders). The molecular mechanisms by which BBR play its modulatory action are interacting with NF-κB, MAPK, JAK/STAT, and PI3K signaling pathways, along with the regulation of transcription factors such as STAT6, GATA3, and Blimp-1. Interestingly, BBR has stimulatory effects on B cells in infection via the augmentation of IgM, IgA, and IgG production. Notably, BBR and its derivatives exerts anti-tumor activities against B cell malignancies by inducing DNA damage, expression of proapoptotic proteins, cell cycle arrest, cell death, dysregulation of mitochondrial metabolism, reactive oxygen species (ROS), which are mainly mediated by modulating c-Myc/CD47 axis, ROS/JNK/DNA damage pathway, PIK3CG signaling cascade, and the expression of CDK4, CDK6, CyclinD1, CD19, CD69, Ki67, and Bcl-2. BBR also shows contradictory effects on the therapeutic efficacy of conventional anti-lymphoma therapies such as rituximab. Together these findings support that BBR could be considered as a promising natural agent for immunomodulation in pathologic conditions by targeting B cells.

Climate change and global warming pose increasing threats to human health. These could have significant impact on healthcare systems, especially emergency services. In this narrative review, we aim to examine how climate change affects emergency attendances and operations, and to suggest strategies to reduce environmental impact through sustainability efforts.

We performed literature search of published studies on healthcare environmental sustainability and climate change in PubMed, Embase, Google Scholar and Scopus databases using the following search terms: ("climate change" OR "global warming" OR "heatwave" OR "heat wave" OR "greenhouse effect") AND ("emergency department" OR "emergency medicine" OR "emergency, hospital services"), ("sustainability in healthcare" OR "environmental footprint" OR "carbon footprint" OR "carbon emission" OR "greenhouse gas" OR "energy us*" OR "waste") AND ("health care system" OR "health system" OR "health care" OR "healthcare" OR "health sector"). Study team members conducted independent searches of articles and any discrepancy between two members was resolved by a third independent co-investigator.

Climate change increases incidences of both communicable and non-communicable diseases through heat-related illnesses, respiratory and infectious diseases, and physical injuries from natural disasters, leading to higher demand on emergency services. Structural damage and physical injuries from natural disasters also negatively impact healthcare resources. Vulnerable populations like the very young and elderly are extremely susceptible. Ironically, the healthcare sector contributes significantly to greenhouse gas emissions and waste production. There are challenges faced by both patients and healthcare providers in adopting sustainability in healthcare. We proposed the SCRAP strategy (Stewardship of resources, Carbon footprint reduction, Research, Advocacy for change, and Policies and education) to attain sustainable healthcare: (I) stewardship of resources; (II) carbon footprint reduction; (III) research; (IV) advocacy for change; and (V) policies and education.

As the frontline of most healthcare systems, emergency departments bear the brunt of resultant increased attendances. Urgent actions by the emergency medicine fraternity are needed to understand and tackle the causes and consequences of climate change in tandem with environmental sustainability efforts to mitigate these issues.

The risk of developing coronary artery disease (CAD) is increased in type 1 diabetes, due to accelerated atherosclerosis. The molecular mechanisms are yet to be unraveled, but potential functional and quantitative abnormalities in lipoproteins are suggested to be involved. Some individuals have coronary arteries free from atherosclerosis even after living with type 1 diabetes for many decades. We therefore aimed to investigate the associations between a set of lipoproteins and metabolites and the presence of coronary arteries free from atherosclerosis in individuals with long-term type 1 diabetes.

Cross-sectional, controlled study of 102 participants with type 1 diabetes and 61 control subjects. We used a high-throughput nuclear magnetic resonance (NMR) spectroscopy platform to quantify circulating lipids and metabolites in serum. In participants without previously established coronary heart disease (CHD) we performed computed tomography coronary angiography (CTCA).

In the diabetes group, mean age was 62 (7) [mean (standard deviation, SD)] year and diabetes duration 50.6 (4.9) years. Lower particle concentration of all LDL subclass particles associated significantly with higher odds of having coronary arteries free from atherosclerosis (p < 0.05). Low particle concentration of all LDL subclasses also associated significantly with normal Coronary Artery Calcium (CAC) score (p < 0.05 for all), after adjustment for age, sex, BMI, eGFR and statin treatment. The whole diabetes group, independent of presence of CAD, had significantly lower particle concentration of IDL and all LDL and VLDL subclass particles compared to the control group (p < 0.05 for all).

In this cohort of long-term survivors of type 1 diabetes, lower levels of all types of LDL particles associated significantly with higher odds of having coronary arteries free from atherosclerosis, after adjustment for statin treatment. These results emphasize the importance of early treatment start and lipid management in the development of CAD in type 1 diabetes and suggest a subgroup of long-term survivors of type 1 diabetes to may hold environmental or genetic protective beneficial traits, independent of statin use. More research on the role of lipoproteins in the development of atherosclerosis in patients with type 1 diabetes is needed.

Urosepsis is a potentially life-threatening condition caused by urinary tract infections and is usually associated with urolithiasis-induced obstruction. No established guidelines exist regarding the optimal timing of antibiotic administration, appropriate duration of urinary drainage, or scheduling of definitive ureteroscopic lithotripsy (URSL) in patients with urolithiasis-induced obstructive acute pyelonephritis (APN). Therefore, this study aimed to investigate the key factors influencing the management of urosepsis secondary to upper ureteral stones, specifically focusing on the optimal duration of antibiotic therapy, risk factors for post-URSL fever, and timing of percutaneous nephrostomy (PCN) drainage to improve clinical outcomes.

This retrospective study included 107 patients with urolithiasis-induced obstructive APN complicated by urosepsis who underwent treatment between 2013 and 2019. These patients underwent PCN drainage, followed by URSL. Data on patient demographics, comorbidities, laboratory results, microbiological cultures, and stone characteristics were obtained and analyzed.

Among the 107 patients, 34 (32%) had post-URSL fever. The afebrile group received longer antibiotic therapy (7.1 vs. 5.7 days, p = 0.022) and had shorter postoperative hospital stays (3.1 vs. 5.3 days, p = 0.015). Logistic regression analysis showed that antibiotic duration and the interval from PCN to URSL were significant predictors of fever. Diabetes mellitus was a significant risk factor for post-URSL fever in patients treated with antibiotics for more than 7 days (odds ratio = 9.07, p = 0.03). Emergent PCN insertion was associated with earlier URSL and a shorter hospital stay.

This study emphasizes the importance of determining the optimal duration of antibiotic therapy before URSL in patients with urosepsis complicated by upper ureteral stones. Post-URSL fever correlates with a shorter duration of antibiotic therapy, and emergent PCN insertion results in earlier URSL and a shorter hospital stay. Diabetes mellitus significantly predicted post-URSL fever.

Diabetic retinopathy (DR), a major complication of type 2 diabetes mellitus (T2DM), remains a growing global health concern. This study aimed to evaluate the relationship between systemic inflammatory markers and both the presence and severity of DR. Special attention was given to the pan-immune-inflammation value (PIV), a novel composite index of immune response, whose association with DR remains underexplored.

A prospective cross-sectional study was conducted involving 310 patients with T2DM, grouped based on the International Clinical Diabetic Retinopathy Disease Severity Scale (ICDR): no DR (NDR, n = 100), non-proliferative DR (NPDR, n = 100), and proliferative DR (PDR, n = 110). Clinical and laboratory data-including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), neutrophil-to-HDL cholesterol ratio (NHR), systemic immune-inflammation index (SII), and PIV-were analyzed. Statistical methods included ANOVA, Kruskal-Wallis, and receiver operating characteristic (ROC) analyses.

PDR patients had significantly elevated NLR (p = 0.005), MLR (p < 0.001), NHR (p = 0.016), C-reactive protein-to-albumin ratio (CAR) (p < 0.001), and PIV (p = 0.002) levels. In NPDR, PLR (p = 0.012) and SII (p = 0.005) were significantly higher than in the other groups. NLR showed the highest predictive performance in ROC analysis (sensitivity: 84.8%), followed by SII (78.1%), PLR (76.2%), and PIV (53.3%).

Among patients with T2DM, inflammatory markers-particularly NLR, PLR, SII, and PIV-suggested potential relevance in identifying DR and its progression. PLR and SII may have utility in the early identification of NPDR, while PIV appears to be a potentially valuable inflammatory marker. These cost-effective and easily accessible indices may contribute to the screening and monitoring of DR in clinical settings.

To analyse clinical features and identify risk factors for brain injury in paediatric diabetic ketoacidosis (DKA), aiming to support early recognition and intervention.

A retrospective study was conducted on 20 children with DKA-related brain injury and 14 children with DKA without brain injury admitted to Xiamen Children's Hospital between January 2022 and April 2024. Clinical data, laboratory findings, electroencephalography and magnetic resonance imaging (MRI) results were reviewed. Logistic regression analyses were used to identify influencing factors.

Among the 20 children with DKA-related brain injury, 75% were girls, the median age was 7.3 years and 70% had severe DKA. Impaired consciousness was the primary symptom, presenting as lethargy (75%), dizziness (10%), somnolence (10%) and delirium (5%). Electroencephalography abnormalities were observed in 8 cases, and MRI changes were noted in 3 cases. Multivariate analysis identified a longer time to blood glucose stabilisation (odds ratio [OR] = 1.047, 95% confidence interval [CI]: 1.014-1.080, P = 0.005) and elevated insulin levels (OR = 1.217, 95% CI: 1.048-1.413, P = 0.010) as independent risk factors. Higher pH (OR = 0.002, P = 0.003), partial pressure of carbon dioxide (OR = 0.790, P = 0.007), serum bicarbonate (OR = 0.836, P = 0.010) and base excess (OR = 0.665, P = 0.015) were found to be protective.

Brain injury in paediatric DKA is associated with delayed metabolic correction. Early monitoring and timely regulation of acid-base balance and glucose levels may reduce the risk and improve outcomes.

Adipose tissue-derived mesenchymal stromal/stem cells (ADMSCs) represent a novel therapeutic intervention for Type 1 Diabetes (T1D). The attractiveness of ADMSCs is characterized by their immunomodulatory activities, regenerative properties, and relative ease of access. ADMSC therapies in animal models and clinical trials have revealed decreased insulin dependence, increased β cell mass, and improved islet graft acceptance. Despite their potential, challenges in quality control, small-scale investigations, functional heterogeneity, and standardization limit the application of these therapies. This review synthesizes the current knowledge and recent outcomes of ADMSC therapies in treating T1D and highlights areas that need further investigation.

Gene variants of the vitamin D receptor (VDR) have been linked to the alterations of the vitamin D metabolic pathway and thus a candidate risk gene for developing type 2 diabetes mellitus (T2DM). However, the association between VDR gene variants and vitamin D status as well as susceptibility to T2DM in the Ghanaian population have not been explored. This study assessed VDR gene variants, vitamin D status and susceptibility to T2DM. In a 1:1 matched case-control study, 100 clinically diagnosed T2DM patients visiting the Greenshield Hospital at Sefwi Bekwai and 100 apparently healthy non-diabetic controls living in same environs were recruited. Venous blood samples were obtained for biochemical analyses. Lipid profile was estimated spectrophotometrically and serum vitamin D was determined by enzyme linked immunosorbent assay. The variants of the VDR gene were determined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Logistic regression models were used to assess the associations between single nucleotide polymorphisms and T2DM. The prevalence of vitamin D deficiency (28.0%) and insufficiency (70.0%) was higher in T2DM than in non-diabetics (5.3% and 44.2%), respectively (p < 0.0001). rs10735810 and rs7975232 was found to increase risk of T2DM under the codominant model (OR = 1.93 (95% CI = 0.69-5.46) and OR = 1.81 (95% CI = 0.51-6.38), respectively), and additive model 1 (OR = 2.02 (95% CI = 0.68-5.98) and OR = 1.29 (95% CI = 0.34-4.84), respectively) whereas rs10735810 and rs7975232 was found to decrease risk of T2DM under the recessive model (OR = 0.96 (95% CI = 0.55-1.67) and OR = 0.58 (95% CI = 0.32-1.02), respectively). Besides, rs7975232 variant was associated with hypertension among individuals with T2DM (p = 0.033). Low vitamin D levels are common in T2DM. There is no association between variants rs10735810 and rs7975232 of VDR gene and T2DM; however, rs7975232 was associated with hypertension and that the 'A' allele of rs7975232 variant seems to be protective against hypertension among T2DM.

This pilot investigation utilized ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to monitor metformin plasma concentrations in patients with type 1 diabetes. The study was conducted on a group of 11 patients. Plasma metformin was measured before treatment and at 2, 4 weeks, and 3, 6, and 12 months after initiation. Method validation was performed according to ICH M10 guidelines. The method demonstrated precision (CV < 11.07%), accuracy (89.78-99.44%), and linearity (r² > 0.98) across three independent validation runs in the 100-3000 ng/ml range. The lowest and highest measured concentrations were 163.2 and 2162.9 ng/ml, respectively, with a mean of 918 ± 616.7 ng/ml. For several patients subtherapeutic metformin levels have been reported, whereas other patients approached potentially excessive levels (> 2000 ng/ml). These findings highlight the variability in metformin exposure and the utility of UPLC-MS/MS for monitoring patient adherence and pharmacokinetics.

The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions. This study aims to develop a novel predictive-prescriptive analytics framework that leverages ML to enhance medication prescriptions for T2D patients. The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. Utilizing a comprehensive dataset of electronic health records from 17,773 patients across various U.S. Veterans Administration Medical Centers collected over 12 years, the study employs the Bayesian Network (BN) as the ML model of choice. The BN's unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. The prescriptive stage includes three strategies, i.e., forward, backward, and guideline-based, to identify optimal treatment recommendations. Next, the complex treatment pathways identified through the prescriptive stage were illustrated using rule-based and decision-tree presentations to improve interpretability for actionable insights and clinical usability. Finally, our empirical analysis examines the alignment between recommended treatment strategies and actual physician prescriptions. ML exhibited strong predictive performance with a precision of 0.789, a recall of 0.879, and an F1-score of 0.831. The recommended treatment strategies aligned with physician prescriptions in simpler treatment scenarios. However, the alignment decreased as the complexity of medication prescription increased, highlighting the challenges of achieving physician compliance with optimal strategies in complex scenarios. This underscores the greater need for CDSS, particularly in situations involving complex combination therapy. This study presents a novel ML-based CDSS framework for personalized T2D treatment. Leveraging ML, the framework offers a promising approach to optimizing medication prescriptions and improving patient outcomes.