ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Diabetic retinopathy(DR)is one of the most common and severe microvascular complications in diabetic patients and has become one of the leading causes of blindness worldwide. With the continuous rise in the prevalence of diabetes, in-depth exploration of the pathogenesis of DR and effective intervention measures is of great clinical significance. The mechanistic target of rapamycin(mTOR), as a protein kinase, is widely involved in cellular processes such as growth, metabolism, and autophagy. Research indicates that the mTOR signaling pathway plays a crucial regulatory role in the pathological progression of DR, and its abnormal activity can disrupt retinal cell autophagy function, thereby accelerating cellular damage and disease progression. Autophagy, as an important regulatory mechanism for cellular homeostasis, maintains cellular functional balance by clearing damaged organelles and protein aggregates. This article provides a systematic review of the structural and functional aspects of the mTOR signaling pathway, the molecular regulatory mechanisms of autophagy, and their roles in retinal pathological changes. By summarizing current research findings, the article aims to clarify the key regulatory role of the mTOR-autophagy axis in DR, providing theoretical support for elucidating the molecular pathogenesis of DR and offering potential targets and research directions for developing novel targeted therapeutic strategies, thereby holding significant scientific and clinical value.

BACKGROUND: Doxorubicin hydrochloride (DOX) is extensively used in the treatment of various tumors. However, its clinical application is limited due to dose-dependent cardiotoxicity. Currently, few effective strategies exist to mitigate or eliminate DOX-induced cardiomyopathy (DIC). Although ferroptosis is implicated in DIC and its inhibition partially alleviates the condition, the direct targets of DOX in the progression of cardiotoxicity remain unclear. This study aimed to discover the direct targets of DOX in ferroptosis-mediated DIC. METHODS: A DOX pulldown assay was performed to identify proteins specifically binding to DOX in murine hearts, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify candidate proteins. A cardiac injury mouse model was established by DOX treatment. Based on this, multiple ferroptosis biomarkers were detected by flow cytometry, quantitative real-time polymerase chain reaction, western blotting, immunochemistry, etc. Besides, specific activator and inhibitor of signaling pathways were applied to illuminate molecular mechanisms. RESULTS: Glutathione S-transferase P1 (GSTP1) was identified as a DOX target. GSTP1 activity was inhibited in DOX-treated cardiomyocytes, while its overexpression significantly alleviated DIC. Moreover, GSTP1 overexpression inhibited acyl-CoA synthetase long-chain family member 4 (ACSL4)-dependent ferroptosis. Mechanistically, GSTP1 overexpression suppressed c-Jun N-terminal kinase (JNK) phosphorylation, thereby reducing reactive oxygen species (ROS) production and inhibiting ferroptosis in DIC. CONCLUSIONS This study identifies the DOX/GSTP1/JNK axis as a critical pathway mediating ACSL4-dependent ferroptosis in DIC. GSTP1 is highlighted as a potential key mediator of ferroptosis and a promising therapeutic target for DIC.

OBJECTIVES: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making. METHODS: In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments. RESULTS: An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis. CONCLUSIONS LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
Humans ; Multiple Myeloma/mortality* ; Prognosis ; Lipid Metabolism/genetics* ; Transcriptome ; Machine Learning ; Male ; Female ; Gene Expression Profiling ; Algorithms

Abdominal aortic aneurysm (AAA) is a deadly condition of the aorta, carrying a significant risk of death upon rupture. Currently, there is a dearth of efficacious pharmaceutical interventions to impede the advancement of AAA and avert it from rupturing. Here, we investigated dihydromyricetin (DHM), one of the predominant bioactive flavonoids in Ampelopsis grossedentata (A. grossedentata), as a potential agent for inhibiting AAA. DHM effectively blocked the formation of AAA in angiotensin II-infused apolipoprotein E-deficient (ApoE^-/-) mice. A combination of network pharmacology and whole transcriptome sequencing analysis revealed that DHM's anti-AAA action is linked to heme oxygenase (HO)-1 (Hmox-1 for the rodent gene) and hypoxia-inducible factor (HIF)-1α in vascular smooth muscle cells (VSMCs). Remarkably, DHM caused a robust rise (∼10-fold) of HO-1 protein expression in VSMCs, thereby suppressing VSMC inflammation and oxidative stress and preserving the VSMC contractile phenotype. Intriguingly, the therapeutic effect of DHM on AAA was largely abrogated by VSMC-specific Hmox1 knockdown in mice. Mechanistically, on one hand, DHM increased the transcription of Hmox-1 by triggering the nuclear translocation and activation of HIF-1α, but not nuclear factor erythroid 2-related factor 2 (NRF2). On the other hand, molecular docking, combined with cellular thermal shift assay (CETSA), isothermal titration calorimetry (ITC), drug affinity responsive target stability (DARTS), co-immunoprecipitation (Co-IP), and site mutant experiments revealed that DHM bonded to HO-1 at Lys243 and prevented its degradation, thereby resulting in considerable HO-1 buildup. In summary, our findings suggest that naturally derived DHM has the capacity to markedly enhance HO-1 expression in VSMCs, which may hold promise as a therapeutic strategy for AAA.

Sigma-1 receptor (σ ₁R) has become a focus point of drug discovery for central nervous system (CNS) diseases. A series of novel 1-phenylethan-1-one O-(2-aminoethyl) oxime derivatives were synthesized. In vitro biological evaluation led to the identification of 1a, 14a, 15d and 16d as the most high-affinity (K _i < 4 nmol/L) and selective σ ₁R agonists. Among these, 15d, the most metabolically stable derivative exhibited high selectivity for σ ₁R in relation to σ ₂R and 52 other human targets. In addition to low CYP450 inhibition and induction, 15d also exhibited high brain permeability and excellent oral bioavailability. Importantly, 15d demonstrated effective antipsychotic potency, particularly for alleviating negative symptoms and improving cognitive impairment in experimental animal models, both of which are major challenges for schizophrenia treatment. Moreover, 15d produced no significant extrapyramidal symptoms, exhibiting superior pharmacological profiles in relation to current antipsychotic drugs. Mechanistically, 15d inhibited GSK3β and enhanced prefrontal BDNF expression and excitatory synaptic transmission in pyramidal neurons. Collectively, these in vivo proof-of-concept findings provide substantial experimental evidence to demonstrate that modulating σ ₁R represents a potential new therapeutic approach for schizophrenia. The novel chemical entity along with its favorable drug-like and pharmacological profile of 15d renders it a promising candidate for treating schizophrenia.

OBJECTIVES: To investigate the subcellular localization and biological functions of transmembrane protein 240 (TMEM240). METHODS: NCBI BLAST and TMHMM bioinformatics software were used for protein sequence analysis and prediction of transmembrane domain of TMEM240. Brain tissues from male C57BL/6 mice (18-20 days old) were examined for distribution of TMEM240 using in situ hybridization, and qPCR and Western blotting were used to detect TMEM240 expression in different mouse tissues and in cortical neurons at different time points (n=3). In the in vitro experiment, HepG2 and Neuro-2a cells were transfected with plasmids for overexpression of TMEM240, and subcellular localization of TMEM240 was analyzed using cell imaging. In primary cultures of cortical neurons isolated from C57BL/6 mice, TMEM240 expression and its biological functions were investigated using qPCR, Western blotting, and immunofluorescence staining. RESULTS: Human and mouse TMEM240 proteins share a 97.69% similarity in the protein sequences, and both are transmembrane proteins with two transmembrane domains. TMEM240 mRNA and protein were highly expressed in mouse brain tissues and cortical neurons. In isolated mouse cortical neurons, TMEM240 expression reached the peak level after primary culture for 9 days and distributed in scattered spots within the cells. In HepG2 cells, TMEM240 was characterized as intracellular membrane structures and showed 80% colocalization with peroxisomes. In Neuro-2a cells, TMEM240 overexpression caused significant enhancement of the expressions of Rho GDP dissociation inhibitor β (ARHGDIB) at both the mRNA and protein levels. CONCLUSIONS TMEM240 is a novel intracellular subcellular structure specifically expressed in neurons with significant potential for targeted cellular function regulation.
Animals ; Humans ; Mice ; Peroxisomes/metabolism* ; Membrane Proteins/genetics* ; Mice, Inbred C57BL ; Neurons/metabolism* ; Male ; rho-Specific Guanine Nucleotide Dissociation Inhibitors ; Hep G2 Cells ; Brain/metabolism*

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Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK _a values. The high reliability and interpretability of GraFpK _a ensure accurate pK _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK _a values, making it a valuable tool in the field of pK _a prediction.

ObjectiveTo understand the epidemic characteristics of influenza in Beijing from 2023 to 2024, and to provide a scientific basis for the prevention and control of influenza. MethodsData on influenza-like illness (ILI) from secondary level and above hospitals, etiology surveillance data, and influenza clusters outbreaks data from 2023‒2024 were used to analyze the epidemic trend and pathogenic characteristics of influenza. Furthermore, an influenza comprehensive index was used to categorize the epidemic intensity at the severity level. ResultsA total of 2 065 857 ILI cases were reported in 2023‒2024 epidemic season, and the percentage of ILI was 3.67%. The age group of 5‒14 years accounted for the highest proportion of ILI (30.48%). A total of 41 766 throat swabs from ILI were detected, with a positive rate of 17.28%.A (H3N2) (51.86%) and B Victoria (41.93%) were the most prevalent subtypes of influenza virus. Clustered influenza outbreaks occurred mainly in primary schools (57.78%) and middle schools (35.55%), mainly caused by the influenza A (H3N2) subtype (85.93%). According to the influenza comprehensive index (I), the period of influenza activity and above (I>0.5) lasted for a total of 37 weeks, accounting for 71.15% of the entire influenza season. ConclusionCompared with previous years, the epidemic level of influenza in Beijing was increased in 2023‒2024, and the peak time became earlier. The comprehensive index method can objectively evaluate the level of influenza epidemic and provide suggestions for the future prevention and control of influenza in Beijing.