Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

ObjectiveINF2 is a member of the formins family. Abnormal expression and regulation of INF2 have been associated with the progression of various tumors, but the expression and role of INF2 in hepatocellular carcinoma (HCC) remain unclear. HCC is a highly lethal malignant tumor. Given the limitations of traditional treatments, this study explored the expression level, clinical value and potential mechanism of INF2 in HCC in order to seek new therapeutic targets. MethodsIn this study, we used public databases to analyze the expression of INF2 in pan-cancer and HCC, as well as the impact of INF2 expression levels on HCC prognosis. Quantitative real time polymerase chain reaction (RT-qPCR), Western blot, and immunohistochemistry were used to detect the expression level of INF2 in liver cancer cells and human HCC tissues. The correlation between INF2 expression and clinical pathological features was analyzed using public databases and clinical data of human HCC samples. Subsequently, the effects of INF2 expression on the biological function and Drp1 phosphorylation of liver cancer cells were elucidated through in vitro and in vivo experiments. Finally, the predictive value and potential mechanism of INF2 in HCC were further analyzed through database and immunohistochemical experiments. ResultsINF2 is aberrantly high expression in HCC samples and the high expression of INF2 is correlated with overall survival, liver cirrhosis and pathological differentiation of HCC patients. The expression level of INF2 has certain diagnostic value in predicting the prognosis and pathological differentiation of HCC. In vivo and in vitro HCC models, upregulated expression of INF2 triggers the proliferation and migration of the HCC cell, while knockdown of INF2 could counteract this effect. INF2 in liver cancer cells may affect mitochondrial division by inducing Drp1 phosphorylation and mediate immune escape by up-regulating PD-L1 expression, thus promoting tumor progression. ConclusionINF2 is highly expressed in HCC and is associated with poor prognosis. High expression of INF2 may promote HCC progression by inducing Drp1 phosphorylation and up-regulation of PD-L1 expression, and targeting INF2 may be beneficial for HCC patients with high expression of INF2.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Vascular calcification is a highly regulated process of ectopic calcification in cardiovascular system while no effective intervention can be clinically performed up to date.As vascular calcification undergoes a common regulatory mechanism within bone formation,bone morphogenetic protein 7(BMP-7)main-tains contractile phenotype of vascular smooth muscle cells and further inhibits vascular calcification via promoting the process of osteoblast differentiation,reducing ectopic calcification pressure by increasing bone formation and reducing bone resorption.This work systematically reviews the role of BMP-7 in vascular calcifi-cation and the possible mechanism,and their current clinical application as well.The current proceedings may help develope early diagnostic strategy and therapeutic treatment with BMP-7 as a new molecular marker and potential drug target.The expec-tation could achieve early prevention and intervention of vascular calcification and improve poor prognosis on patients.

Aim To investigate the mechanism of icar-itin(ICT)on D-galactose(D-gal)-induced TM4 ser-toli cell junctional function damage in vitro.Methods TM4 cells were divided into the normal control group and D-gal treatment group with different concentra-tions.The expression changes of TM 4 cell junction function-related proteins(ZO-1,Occludin,β-catenin and Cx43)and ERα/FAK signaling pathway-related proteins(ERα,FAK and pY397-FAK)were detected by Western blot.The concentration of ICT was screened by MTT method.TM4 cells were divided into normal control group,D-gal treatment group,and D-gal treatment+different concentrations of ICT group.The expression levels of the above proteins were detected by Western blot.Molecular docking was used to study the interaction between ERα and ICT,meanwhile predict the affinity between them.Finally,TM4 cells were di-vided into normal control group,D-gal treatment group,ERα inhibitor group,D-gal+ICT group,and ERα inhibitor+ICT group.The expression levels of the above proteins were detected by Western blot.Re-sults Compared with the normal control group,the ex-pression of junctional function-related proteins(ZO-1,Occludin,β-catenin and Cx43)and ERα/FAK signa-ling pathway-related proteins(ERα,FAK and pY397-FAK)were significantly down-regulated.After treat-ment with ICT,the expression of above proteins were significantly up-regulated.The docking results of ERα and ICT molecules revealed the formation of two hydro-gen bonds between Asp351 amino acid residue of ERα and ICT,with bond distances measuring 3.4? and 2.4?.Additionally,the docking binding energy be-tween them was found to be lower than-7 kcal·mol-1.After TM4 cells were treated with ERα inhibi-tor,the expression of above proteins and ERα/FAK signaling pathway-related proteins were significantly down-regulated,while the expression levels of the a-bove proteins did not change significantly after being given ICT protected group.Conclusions D-gal can cause damage to the junctional function of TM4 cells,and ICT can improve this damage,which may be related to the up-regulation of ERα/FAK signaling pathway.

The genomic island(GI)characteristics and virulence factor differences of clinical isolates of Burkholderia pseudomallei in Hainan Province,China were analyzed to provide a scientific basis for diagnosis and treatment of melioidosis.In total,52 B.pseudomallei isolates were collected for detection of virulence-related GIs by PCR.The whole genome sequence annotation format file was submitted on Islandviwer 4 platform,and the genomes of the same species and close relatives were added for comparison.Two algorithms,SIGI-HMM and IslandPath-DIMOB,were integrated to predict GIs and sequence a-lignments were conducted to identify specific GIs and differences in virulence factors.The genomes of 52 clinical strains could be divided into three branches based on evolutionary distance,with 82.69％(43/52)of strains concentrated in branch 1.In to-tal,828 GIs were identified among the 52 B.pseudomallei genomes,which formed 157 GI clusters based on sequence similari-ty.GIs accounted for 2.05％-6.38％of the genome content.While GI clusters 1 and 2 were present in all strains,a total of 84(53.50％)GI clusters only clustered within a single genome isolate.Of 10 GI likely specific clusters,five were from the same genus,two from another genus,and three with uncertain origins.Moreover,25 GI clusters were associated with virulence,which included eight shared by B.pseudomallei BP76 and BP169,which had the highest number of virulence-associated GIs among all isolates.O the 52 B.pseudomallei isolates,variations were identified in the virulence genes fhaB1,fhaB2,BPSL1661,cheY1,wzM,tssH-5/clpV,tssA-5,boaA,and boaB.Comparisons of these findings with clinical isolates from Thailand and Australia showed that B.pseudomallei isolates from Hainan had significant differences in the sequences of boaA,boaB,cheY1,and chbp.Additionally,fhaB1,fhaB3,and bimA displayed significant variations specifically within the Australian isolates.B.pseudomallei GI was conserved and specific to Hainan.The identification of specific GI and virulence factors was useful to clarify the source of horizontal gene transfer and differences in virulence at the molecular level.

Objective:To analyze the clinical characteristics and prognosis of patients with CD5+diffuse large B-cell lymphoma(DLBCL).Methods:The clinical data of 161 newly treated DLBCL patients in Gansu Provincial Hospital from January 2013 to January 2020 were retrospectively analyzed.According to CD5 expression,the patients were divided into CD5+group and CD5-group.The clinical characteristics and prognosis of the two groups were statistically analyzed.Results:The median age of patients in CD5+group was 62 years,which was higher than 56 years in CD5-group(P=0.048).The proportion of women in CD5+group was 62.96％,which was significantly higher than 41.79％in CD5-group(P=0.043).The proportion of patients with IPI score＞2 in CD5+group was 62.96％,which was higher than 40.30％in CD5-group(P=0.031).Survival analysis showed that the median overall survival and progression-free survival time of patients in CD5+group were 27(3-77)and 31(3-76)months,respectively,which were both shorter than 30(5-84)and 32.5(4-83)months in CD5-group(P=0.047,P=0.026).Univariate analysis showed that advanced age,positive CD5 expression,triple or double hit at initial diagnosis,high IPI score and no use of rituximab during chemotherapy were risk factors for the prognosis of DLBCL patients.Further Cox multivariate regression analysis showed that these factors were also independent risk factors except for advanced age.Conclusion:CD5+DLBCL patients have a worse prognosis than CD5-DLBCL patients.Such patients are more common in females,with advanced age and high IPI score,which is a special subtype of DLBCL.