This study aimed to characterize the expression levels and identify the risk factors associated with Toll-like receptor 2 and 4 (TLR2/4) mRNA in peripheral blood mononuclear cells of patients with type 2 diabetes mellitus (T2DM) complicated with pulmonary infection, and to develop and internally validate a nomogram-based diagnostic model.

A total of 239 patients with T2DM admitted to our hospital between January and August 2025 were selected. Based on the presence of concurrent pulmonary infection at admission, they were divided into the T2DM group (n=128) and the T2DM with pulmonary infection group (n=111). TLR2/4 mRNA expression levels, general characteristics, and peripheral blood inflammatory markers were compared between the two groups. Predictors were identified using LASSO regression and logistic regression to construct a discriminant model, with receiver operating characteristic (ROC) curves plotted. Internal validation employed 10-fold cross-validation and bootstrap-based optimism correction (B = 1000). Model performance was assessed via Hosmer-Lemeshow tests and decision curve analysis (DCA).

Patients with T2DM and pulmonary infection exhibited significantly elevated levels of fasting blood glucose, inflammatory markers (WBC, NEUT, hsCRP, PCT, ESR), and TLR2/4 mRNA expression, as well as higher rates of invasive procedures, compared with the T2DM group (all P < 0.05). Using LASSO feature selection followed by multivariable logistic regression, a diagnostic nomogram was developed incorporating TLR2, IL-6, TNF-α, ESR, age, and diabetes duration. The nomogram demonstrated excellent discrimination, with an apparent AUC of 0.987. Internal validation confirmed robust performance, yielding a 10-fold cross-validation AUC of 0.980 ± 0.006 and a bootstrap optimism-corrected AUC of 0.980 (B = 1,000).The Hosmer-Lemeshow test indicated good calibration (P > 0.05). DCA showed substantial net clinical benefit across threshold probabilities ranging from 0.10 to 0.60.

TLR2, IL-6, TNF-α, ESR, age, and duration of diabetes can serve as a combined biomarker panel to aid in the early diagnosis of pulmonary infection in T2DM patients at hospital admission. The proposed nomogram demonstrates strong diagnostic performance and potential clinical utility.

The longitudinal impact of diabetes duration on the progression of insulin resistance (IR) in type 2 diabetes (T2DM) remains to be fully elucidated. It is also unclear whether this relationship is uniform across all patients or modified by specific metabolic factors.

To investigate the association between diabetes duration and IR, and to identify critical effect modifiers that define high-risk trajectories.

This cross-sectional study enrolled 3,309 patients with T2DM, categorized into non-IR (HOMA-IR < 2.31, n = 1,530) and IR (HOMA-IR ≥ 2.31, n = 1,779) groups. Multivariable linear regression was used to assess the independent association between diabetes duration and HOMA-IR. Subgroup and interaction analyses were performed to identify effect modifiers. A two-piecewise linear regression model was employed to delineate threshold effects.

In the overall cohort, diabetes duration was independently associated with higher HOMA-IR after full adjustment (β = 0.04, 95% CI: 0.03-0.05, P < 0.001). This association was significant only in the IR group (β = 0.04, 95% CI: 0.02-0.06, P < 0.001) and absent in the non-IR group. Subgroup analysis revealed that the TyG index was the only significant effect modifier (β_interaction = 0.06, 95% CI: 0.02-0.09, P _interaction = 0.001). Crucially, a threshold effect was identified in the high-TyG group: HOMA-IR increased sharply by 0.08 per additional year of diabetes only after 4.0 years (P < 0.001).

This study defines a novel, actionable high-risk phenotype in T2DM, characterized by a combination of a TyG index ≥ 9.187 and diabetes duration exceeding 4.0 years, which is associated with a markedly accelerated increase in IR. Given that the TyG index is adjustable, this framework provides a clinically meaningful, personalized strategy for targeted early intervention, enabling precise prevention for those most susceptible to worsening insulin resistance.

Hypertension is a critical comorbidity in patients with type 2 diabetes mellitus that significantly increases cardiovascular risk. Although several predictive models have been developed using conventional logistic regression or basic machine learning algorithms, these approaches often face significant limitations. Many existing models suffer from a lack of external validation which limits their generalizability, or they operate as black boxes without providing interpretable clinical insights. Furthermore, most prior studies have focused exclusively on biological indicators while overlooking the potential impact of socioeconomic determinants and lifestyle factors on disease progression.

To address these gaps, this study aimed to develop a high-performance Random Forest model for predicting hypertension risk in diabetic patients by integrating multidimensional data, including clinical metrics, lifestyle habits, and socioeconomic status. The study further sought to validate the model's robustness using an independent external cohort and assess its clinical utility through SHAP analysis, providing transparent interpretations of risk factors to guide personalized medical decision-making.

A multicenter retrospective cohort study was conducted using electronic medical records from two tertiary hospitals. Eligible adults with type 2 diabetes and no prior hypertension were included. A total of 900 eligible patients were included, with 420, 180, and 300 participants in the training, testing, and external validation cohorts, respectively. Feature selection combined Boruta and LASSO methods, yielding seven predictors. Seven algorithms were tested, and model performance was assessed through cross-validation, independent testing, and external validation. The random forest model was explained using SHAP analysis.

Among 900 participants, the random forest model achieved the best discrimination, with AUCs of 0.89 in internal testing and 0.83 in external validation. Calibration and decision curve analyses confirmed stability and clinical utility. Key predictors included alcohol consumption, triglycerides, diabetes duration, health insurance type, fasting blood glucose, estimated glomerular filtration rate, and exercise frequency.

The validated random forest model effectively predicts hypertension in type 2 diabetes patients, integrating metabolic, behavioral, and socioeconomic factors. Its interpretability and robust performance support its potential use for early identification and personalized prevention of hypertension in clinical practice.

Gastric cancer during pregnancy is uncommon but associated with a high maternal mortality rate. Symptoms are often non-specific, leading to delayed diagnosis due to pregnancy-related limitations in diagnostic approaches. The present report describes the case of a 35-year-old pregnant patient diagnosed with advanced gastric cancer. Although supportive therapies were administered, the condition of the patient deteriorated and they ultimately passed away shortly thereafter. The report highlights how early detection is vital for improving outcomes, underscoring the importance of prompt evaluation of maternal symptoms and targeted diagnostic examinations. Moreover, treatment strategies should be tailored according to the cancer stage and the developmental stage of the fetus. Notably, metastatic sites of gastric cancer during pregnancy can include the placenta, ovaries, lungs, bones and breasts, with fetal metastasis posing a notable clinical concern.

Panax ginseng Meyer (P. ginseng, PG), a historically used phytotherapeutic agent with a long history and wide-ranging applications, has garnered increasing attention in recent years because of its considerable pharmacological value. Amid the global rise in metabolic disorders, P. ginseng, as a natural product, has been demonstrated to contain various bioactive components-including ginsenosides, P. ginseng polysaccharides, and P. ginseng peptides-that have significant pharmacological effects on glucose and lipid metabolic diseases such as obesity, type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD). These bioactive compounds modulate glucose and lipid metabolism through multitarget mechanisms, including enhancing glucose uptake and glycogen synthesis, inhibiting gluconeogenesis, and regulating adipocyte differentiation and fatty acid oxidation, as well as through gut microbiota-mediated regulation of glucose‒lipid metabolism. These effects help alleviate pathological conditions such as insulin resistance (IR), inflammation, oxidative stress, and endoplasmic reticulum (ER) stress, involving key signaling pathways such as phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT), AMP-activated protein kinase (AMPK), and peroxisome proliferator-activated receptor gamma (PPARγ). As a result, P. ginseng shows significant promise and holds great potential for preventing and treating glucose‒lipid metabolic disorders. Ongoing advances in research and technology may further elucidate its underlying mechanisms and facilitate clinical translation, paving the way for the development of more effective therapeutics for metabolic regulation.

Diabetic neuropathy (DN) is one of the most prevalent complications of diabetes mellitus, affecting up to half of patients and contributing to disability, poor quality of life, and the risk of foot ulceration. Despite extensive research, its heterogeneous manifestations and complex pathophysiology continue to challenge timely diagnosis and effective treatment. This systematic review aimed to synthesize recent clinical evidence on diagnostic and therapeutic strategies for DN. A comprehensive search of PubMed, EMBASE, CENTRAL, and Web of Science identified 76 eligible clinical studies published between 2020 and 2025, including randomized controlled trials, observational studies, and case reports. Data extraction and risk-of-bias assessment were performed according to PRISMA guidelines.  Pharmacological agents, particularly pregabalin, duloxetine, and α-lipoic acid, demonstrated the most consistent efficacy, with significant pain reduction and improvements in nerve conduction velocity. Neuromodulation with high-frequency spinal cord stimulation provided sustained pain relief in refractory cases, while structured, exercise-based rehabilitation improved gait velocity and balance. Advanced wound care strategies, such as platelet-rich plasma dressings and bioengineered skin substitutes, accelerated ulcer healing. Complementary therapies (e.g., acupuncture and balneotherapy) and emerging biologics (e.g., gene- and cell-based interventions) showed preliminary promise but require further validation.  Nerve conduction studies and validated scoring instruments remain the most reliable diagnostic tools, with biomarker- and microvascular-based measures emerging as valuable adjuncts. Current evidence underscores the value of integrating pharmacological, device-based, and rehabilitative strategies, while highlighting critical gaps in small-fiber and autonomic neuropathies. Robust, multicenter trials are needed to establish disease-modifying therapies and optimize comprehensive care pathways for DN.

This study aims to investigate the changes in cardiometabolic index (CMI) levels in newly diagnosed type 2 diabetes mellitus (T2DM) patients before and after undergoing short-term continuous subcutaneous insulin infusion (CSII) intensive treatment and analyse its correlation with insulin resistance.

This study retrospectively collected data from 604 patients who were initially diagnosed with T2DM and received short-term CSII treatment during their hospitalisation in the Endocrinology Department of the Affiliated Hospital of Jiangsu University. Clinical data, blood glucose, insulin, C-peptide, blood biochemistry, and other indicators were collected before and after treatment, and CMI was calculated.

Short-term CSII treatment significantly improved blood glucose levels, insulin resistance, and pancreatic β-cell function in T2DM patients, while downregulating the CMI index (p = 0.002). Correlation analysis showed a positive correlation between CMI and HOMA-IR (p < 0.001) and a negative correlation between CMI and HOMA-β (p = 0.008). Stepwise linear regression model analysis revealed that CMI was independently associated with fasting C-peptide (FCP) before treatment (β = 0.291, p < 0.001); after treatment, CMI was associated with FCP (β = 0.160, p < 0.001) and HOMA-IR (β = 0.159, p < 0.001).

After short-term intensive CSII treatment, the CMI level in newly diagnosed T2DM patients significantly decreased, indicating that intensive CSII treatment may have certain significance in improving cardiometabolic function.

Gestational diabetes mellitus (GDM) is a frequent pregnancy complication that increases short- and long-term risks for both mother and child. However, its underlying molecular mechanisms remain poorly understood. This study aims to unravel the molecular basis of GDM and explore potential therapeutic targets.

We integrated genomic, transcriptomic, and single-cell RNA sequencing datasets to delineate cell-type-specific alterations in GDM. Candidate genes were prioritized using Mendelian randomization (MR), followed by quantitative PCR (qPCR) validation in placental samples. Pathway and immune-network analyses were performed to contextualize biological function.

Single-cell profiling showed marked remodeling of immune compartments in GDM, with prominent changes in monocytes and T-cell subsets. Two-sample MR prioritized 15 genes with putative causal links to GDM, including BNIP3L, COMT, CTSB, LMNA, and SLC7A5. qPCR further demonstrated significant differential expression of CTSB, LMNA, and SLC7A5 between GDM and control placentas (human or mouse). Pathway enrichment implicated CTSB in immune regulation and metabolic processes, whereas LMNA and SLC7A5 mapped to insulin resistance and glucose/amino-acid transport pathways. Immune-network analysis revealed significant correlations between these genes and immune mediators, supporting immune dysregulation as a contributor to GDM pathogenesis.

This study provides a comprehensive analysis of the immune-metabolic landscape of GDM. Key genes identified in this study may serve as potential biomarkers and therapeutic targets for early diagnosis and personalized treatment of GDM. Further studies are warranted to elucidate the underlying mechanisms and develop targeted therapies for this disease.

Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disease. Increasing evidence suggests that persistent inflammation and autoimmune mechanisms play a critical role in its pathogenesis.

T2DM was induced in rabbits through a combination of a high-sugar, high-fat diet and streptozotocin (STZ) administration. The study included three groups: control, T2DM, and T2DM + autologous T cell immunotherapy (ATIM). Individualized ATIM was prepared by heat shock treatment of peripheral blood after erythrocyte removal. Rabbits in the ATIM group received intradermal injections of 0.36 mL ATIM in the thigh every two days. Blood glucose, glycated serum protein (GSP), glycogen synthase (GS), glycogen synthase kinase-3β (GSK3β), T cell subsets, interleukin-10 (IL-10), and interferon-gamma (IFN-γ) levels were measured.

ATIM treatment reduced blood glucose and GSP levels, with a trend toward improved glucose tolerance. Compared with the T2DM group, ATIM-treated rabbits exhibited more preserved liver morphology and increased GS expression. The ratio of phosphorylated GSK3β to total GSK3β was decreased. Immunologically, ATIM increased the proportion of CD4+ T cells, decreased IFN-γ levels, and increased IL-10 levels.

ATIM enhanced GS expression, promoted CD4+ T cell responses, and suppressed pro-inflammatory cytokines in T2DM rabbits, potentially contributing to improved blood glucose control and protection of islet function.

These findings suggest that ATIM ameliorates T2DM through synergistic regulation of metabolic pathways and immune balance, supporting its potential as a therapeutic approach targeting both metabolic and immune dysfunction in T2DM.

Mature B-cell neoplasms constitute a biologically and clinically heterogeneous group of hematologic malignancies, defined by the clonal proliferation and accumulation of monotypic mature B lymphocytes, which may involve the peripheral blood, bone marrow (BM), lymphoid tissues, or present primarily in extranodal sites. While the pathogenesis of common subtypes, such as chronic lymphocytic leukemia, multiple myeloma, Hodgkin Lymphoma and Diffuse Large B-cell Lymphoma has been extensively studied, those of rare entities like Hairy Cell Leukemia (HCL) remains poorly understood. HCL is a distinct B-cell neoplasm marked by BM infiltration of atypical "hairy" cells, pancytopenia, BM fibrosis, and the BRAF V600E mutation, which is a defining molecular hallmark of the classic form of the disease. Analogous to solid tumors, growing evidence shows that the tumor microenvironment (TME) is a pivotal contributor in both the initiation and progression of all these malignancies. However, in rare mature B-cell neoplasms, understanding how tumor cells interact with their microenvironment, in terms of immune invasion, stromal crosstalk, and tissue remodeling, remains a challenge, partly due to the scarcity of patient samples and limited availability of preclinical models. In the context of TME, extracellular vesicles (EVs) have emerged as central mediators of intercellular communication within both solid and hematological malignancies. In line with numerous findings from solid tumors, EVs are receiving heightened attention as key mediators of disease progression, immune modulation, and treatment response in blood tumors, by modulating cellular interactions and delivering bioactive cargo in the tumor milieu. This review presents HCL within the broader spectrum of mature B-cell neoplasms, highlighting the current state of knowledge on the dynamic crosstalk between malignant B cells and their TME. Particular attention is given to EVs, which play key immuno-regulatory roles by interacting with both immune and non-immune components of the TME, including stromal cells. We explore how EVs contribute to disease pathogenesis, offering a unifying framework for integrating complex interactions that are often under-investigated in rare disease contexts. Building on this synthesis, we propose that the insights gained from well-characterized lymphoproliferative disorders may serve as a valuable foundation for investigating related yet poorly understood conditions, such as HCL. Furthermore, given the scarcity of both biological samples and reliable preclinical models for rare hematological malignancies, we highlight the strategic role of European biobanks in providing access to well-annotated clinical samples-an essential resource for fostering interdisciplinary collaboration and enabling advanced experimental modelling.