Intracerebral hemorrhage (ICH) is the most lethal subtype of stroke, but effective treatments are lacking, and neuroinflammation plays a key role in the pathogenesis. In the innate immune response to cerebral hemorrhage, microglia first appear around the injured tissue and are involved in the inflammatory cascade response. Microglia respond to acute brain injury by being activated and polarized to either a typical M1-like (pro-inflammatory) or an alternative M2-like (anti-inflammatory) phenotype. These two polarization states produce pro-inflammatory or anti-inflammatory. With the discovery of the molecular mechanisms and key signaling molecules related to the polarization of microglia in the brain, some targets that regulate the polarization of microglia to reduce the inflammatory response are considered a treatment for secondary brain tissue after ICH damage effective strategies. Therefore, how to promote the polarization of microglia to the M2 phenotype after ICH has become the focus of attention in recent years. This article reviews the mechanism of action of microglia’s M1 and M2 phenotypes in secondary brain injury after ICH. Moreover, it discusses compounds and natural pharmaceutical ingredients that can polarize the M1 to the M2 phenotype.

Hypertension, defined as a systolic/diastolic blood pressure ≥140/90 mm Hg, is a major risk factor for cardiovascular diseases (CVDs) and the leading cause of disability-adjusted life years (DALYs) worldwide [1–3]. It affects approximately 31% of the global adult population (around 1.4 billion) and is characterised by a rising prevalence which expected to exceed 1.6 billion […]

Background: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) constitutes a serious source of global healthcare-associated infections, and the exploration of its resistance mechanism represents an important approach to address this issue. Because current research on antibiotic resistance predominantly focuses on multidrug-resistant P. aeruginosa which is widely isolated clinically and the resistance mechanism is complicated. CRPA generally has a higher tolerance to other antibiotics than carbapenem-sensitive P. aeruginosa, yet the specific mechanism of resistance remains poorly understood. Results: This study delves into the specific antibiotic resistance mechanisms of carbapenem-resistance-only P. aeruginosa (CROPA), a rare kind of pathogen that shows resistance exclusively to carbapenem antibiotics. We collected 11 clinical isolates of CROPA, performed genome sequencing. Our analysis revealed numerous amino acid mutations and premature termination of OprD expression in the CROPA strains. The insertion of IS256 element into OprD in P. aeruginosa was a novel finding. Validation via qPCR and SDS-PAGE affirmed diminished OprD expression levels. Interestingly, common carbapenemases were not detected in our study, and there was no observed upregulation of relevant efflux pumps. The expression of wild-type OprD in CROPA strains restored the sensitivity to carbapenem antibiotics. Conclusions: Compared with previous studies on MDR-CRPA, the emergence of CROPA may be directly linked to changes in OprD, while other resistance mechanisms could contribute to broader antibiotic resistance profiles. By focusing on the antibiotic resistance mechanisms of CROPA, this study illuminates the relationship between specific antibiotic resistance mechanisms and antibiotic resistance, providing a theoretical foundation for guiding clinical treatment and developing novel anti-infective agents.

Vasculogenic mimicry (VM) formation is important for invasion and metastasis of tumor cells in gastric adenocarcinoma (GAC). The present study aimed to investigate the association between signal transducer and activator of transcription‑3 (STAT3), phosphor‑STAT3 (p‑STAT3), hypoxia‑inducible factor‑1α (HIF‑1α) and VM formation in GAC, and discuss their clinical significance and correlation with the prognosis of patients with GAC. The expression levels of STAT3, p‑STAT3, HIF‑1α and VM were assessed in 60 cases of patients with GAC and 20 cases of patients with gastritis on tissue microarrays by immunohistochemical methods. The expression levels of STAT3, p‑STAT3, HIF‑1α and VM were higher in patients with GAC (particularly in poorly differentiated GAC) than in those with gastritis (P<0.05). The expression levels of STAT3, p‑STAT3 and HIF‑1α were higher in VM tissues compared with non‑VM tissues (P<0.05). Positive correlations existed between STAT3, p‑STAT3, HIF‑1α and VM expression (P<0.05). The expression levels of STAT3, p‑STAT3 and HIF‑1α, VM, status of lymph node metastasis and tumor differentiation degree were associated with the overall survival time of patients with GAC (P<0.05). However, only p‑STAT3 and VM expression were identified as the independent risk factors of GAC OS when analyzed with multivariate analysis. p‑STAT3 and VM play a significant role in indicating the prognosis of patients with GAC. STAT3 activation may play a positive role in VM formation of GAC by the STAT3‑p‑STAT3‑HIF‑1α‑VM effect axis.

The cardiometabolic index (CMI) is a novel metric for assessing cardiometabolic health and type 2 diabetes mellitus (DM), yet its relationship with insulin resistance (IR) and prediabetes (preDM) is not well-studied. There is also a gap in understanding the nonlinear associations between CMI and these conditions. Our study aimed to elucidate these associations. We included 13,142 adults from the National Health and Nutrition Examination Survey (NHANES) 2007–2020. CMI was calculated by multiplying the triglyceride-to-high density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Using weighted multivariable linear and logistic regression explored the relationships of CMI with glucose metabolism markers, IR, preDM, and DM. Nonlinear associations were assessed using generalized additive models (GAM), smooth curve fittings, and two-piecewise logistic regression. Multivariate regression revealed positive correlations between CMI and glucose metabolic biomarkers, including FBG (β = 0.08, 95% CI: 0.06–0.10), HbA1c (β = 0.26, 95% CI: 0.22–0.31), FSI (β = 4.88, 95% CI: 4.23–5.54), and HOMA-IR (β = 1.85, 95% CI: 1.56–2.14). There were also significant correlations between CMI and increased risk of IR (OR = 3.51, 95% CI: 2.94–4.20), preDM (OR = 1.49, 95% CI: 1.29–1.71), and DM (OR = 2.22, 95% CI: 2.00-2.47). Inverse nonlinear L-shaped associations were found between CMI and IR, preDM, and DM, with saturation inflection points at 1.1, 1.45, and 1.6, respectively. Below these thresholds, increments in CMI significantly correlated with heightened risks of IR, preDM, and DM. CMI exhibited inverse L-shaped nonlinear relationships with IR, preDM, and DM, suggesting that reducing CMI to a certain level might significantly prevent these conditions.