Objective To explore the independent risk factors for complicated coronary heart disease (CHD) in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and to construct a risk prediction model. Methods A retrospective study was performed to analyze the clinical data of 308 OSAHS patients who visited Jiangsu Provincial People's Hospital from January 2021 to June 2025. The patients were divided into the CHD group (n=80) and non-CHD group (n=228) according to the presence or absence of complicated CHD. Independent risk factors were identified using univariate analysis and multivariate logistic regression, and a nomogram prediction model was constructed. The model was evaluated using the calibration curve, Hosmer-Lemeshow test, Decision Curve Analysis (DCA) curve, and Receiver Operating Characteristic (ROC) curve. Results The incidence of CHD in OSAHS patients was 25.97%. Age, history of hypertension, oxygen desaturation index (ODI), severe polysomnography (PSG) grade, decreased high-density lipoprotein cholesterol, elevated homocysteine, and elevated troponin I were independent risk factors for OSAHS complicated with CHD (P<0.05). The nomogram model demonstrated good calibration (Hosmer-Lemeshow test, P=0.995), and the DCA curve showed high clinical net benefit. The area under the ROC curve was 0.940 (95% CI: 0.908~0.972), with a sensitivity of 0.842 and a specificity of 0.900. Conclusion Age, history of hypertension, ODI, PSG grade, high-density lipoprotein cholesterol, homocysteine, and troponin I are independent risk factors for CHD in OSAHS patients. The prediction model constructed based on these factors can provide a reliable tool for clinical nurses to early identify high-risk individuals and formulate targeted intervention strategies.

Acute liver failure (ALF) is a life-threatening condition associated with macrophage-mediated inflammatory responses. Effective therapies and drugs are still lacking to date. Here, we reveal that a derivative of xanthohumol, CAM12203, alleviates lipopolysaccharide (LPS) + d-galactosamine (D-GalN)-induced ALF through limiting macrophage-mediated inflammation, with the most significant impact on interleukin-1β (IL-1β) transcription. Through biotin labeling-mediated pull-down and LC-MS/MS analysis, diacylglycerol kinase ζ (DGKζ), a lipid-metabolizing kinase, is identified as the direct target of CAM12203. Mechanistically, DGKζ is induced in macrophages upon inflammatory stimuli and is upregulated observed on clinical liver failure samples. Its product phosphatidic acid (PA) boosts phospholipase C (PLC)-inositol 1,4,5-trisphosphate (IP₃)-Ca²⁺ signaling and subsequent janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3) cascade, ultimately promoting IL-1β production and liver failure. DGKζ knockdown/ablation or inhibition significantly impairs the DGKζ-STAT3-IL-1β pathway along with ALF progression. Finally, CAM12203 is confirmed to be a new DGKζ inhibitor and acts against inflammation in a DGKζ-reliant manner. Taken together, CAM12203 inhibits IL-1β transcription in macrophages by binding to DGKζ and blocking the DGKζ-STAT3 axis, thereby exerting an ameliorative effect on ALF. These results not only highlight CAM12203 as a promising lead compound for ALF treatment, but also define DGKζ as a novel therapeutic target.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a prevalent type of cancer with a high mortality rate in its late stages. One of the major challenges in OSCC treatment is the resistance to epidermal growth factor receptor (EGFR) inhibitors. Therefore, it is imperative to elucidate the mechanism underlying drug resistance and develop appropriate precision therapy strategies to enhance clinical efficacy. METHODS: To evaluate the efficacy of the combination of the Ca 2+ /calmodulin-dependent protein kinase II (CAMK2) inhibitor KN93 and EGFR inhibitors, we performed in vitro and in vivo experiments using two FAT atypical cadherin 1 ( FAT1 )-deficient (SCC9 and SCC25) and two FAT1 wild-type (SCC47 and HN12) OSCC cell lines. We assessed the effects of EGFR inhibitors (afatinib or cetuximab), KN93, or their combination on the malignant phenotype of OSCC in vivo and in vitro . The alterations in protein expression levels of members of the EGFR signaling pathway and SRY-box transcription factor 2 (SOX2) were analyzed. Changes in the yes-associated protein 1 (YAP1) protein were characterized. Moreover, we analyzed mitochondrial dysfunction. Besides, the effects of combination therapy on mitochondrial dynamics were also evaluated. RESULTS: OSCC with FAT1 mutations exhibited resistance to EGFR inhibitors treatment. The combination of KN93 and EGFR inhibitors significantly inhibited the proliferation, survival, and migration of FAT1 -mutated OSCC cells and suppressed tumor growth in vivo . Mechanistically, combination therapy enhanced the therapeutic sensitivity of FAT1 -mutated OSCC cells to EGFR inhibitors by modulating the EGFR pathway and downregulated tumor stemness-related proteins. Furthermore, combination therapy induced reactive oxygen species (ROS)-mediated mitochondrial dysfunction and disrupted mitochondrial dynamics, ultimately resulting in tumor suppression. CONCLUSION Combination therapy with EGFR inhibitors and KN93 could be a novel precision therapeutic strategy and a potential clinical solution for EGFR-resistant OSCC patients with FAT1 mutations.
Humans ; ErbB Receptors/metabolism* ; Mouth Neoplasms/metabolism* ; Cell Line, Tumor ; Animals ; Drug Resistance, Neoplasm/genetics* ; Cadherins/metabolism* ; Carcinoma, Squamous Cell/metabolism* ; Mice ; Mutation/genetics* ; Mice, Nude ; Protein Kinase Inhibitors/therapeutic use* ; Cetuximab/pharmacology* ; Afatinib/therapeutic use* ; Cell Proliferation/drug effects* ; Signal Transduction/drug effects*

Left ventricular assist device (LVAD) serves as a critical therapeutic option for patients with end-stage heart failure, significantly enhancing survival rates and quality of life. However, LVAD implantation exerts complex and profound effects on right ventricular (RV) function, with RV dysfunction emerging as a key factor influencing the prognosis of LVAD patients. This article systematically reviews the relationship between LVAD and RV function, exploring the importance of RV function in LVAD patients, assessment methods, underlying mechanisms of impact, and strategies for prevention and management. Comprehensive evidence suggests that preoperative evaluation of RV function is crucial for predicting the risk of RV dysfunction, while effective prevention and management rely on preoperative optimization, meticulous intraoperative techniques, rigorous postoperative monitoring, and multidisciplinary collaboration. Furthermore, this review discusses the potential and future directions of emerging technologies, such as improved LVAD designs, biventricular assist devices, gene therapy, and personalized medicine, in ameliorating RV dysfunction. In conclusion, RV function is one of the key determinants of successful LVAD therapy. Through comprehensive assessment, prevention, and management of RV function, coupled with the application of novel technologies, the clinical outcomes of LVAD patients can be further improved.

Purpose This study aimed to explore the mucinous phenotype characteristics,key points of differenti-al diagnosis and prognosis of invasive non-mucinous adenocarcinoma(INMA)and invasive mucinous adenocarcinoma(IMA)under the WHO(2021)lung adenocarcinoma classification.Methods We retrospectively collected clinico-pathological data from 522 cases of lung adenocarcinoma,including 425 INMA(66 with mucin secretion,259 without mucin secretion)and 97 IMA.Immunohistochemical(IHC)staining using the EnVision method was performed on the mucin-secreting adenocarcinoma to assess expression of TTF-1,HNF4α,MUC1,MUC4,MUC5AC,MUC5B,and MUC6.Unsupervised clustering analysis was conducted to explore phenotypic subgroups.Results 522 patients with lung adenocarcinoma ranged from 32 to 83 years old(median:61).251 cases(48.1％)were male and 271 cases(51.9％)were female.Clustering analysis divided lung adenocarcinomas into two major groups:one characterized by TTF-1-/HNF4α+and gastric-type mucins MUC5AC+/MUC6+,predominantly IMA;the other,TTF-1+/HNF4α-/MUC4+,largely INMA.A three-marker IHC panel(TTF-1,HNF4α,MUC6)distinguished IMA from mucinous IN-MA with an area under the ROC curve(AUC)of 0.957(95％CI:0.928-0.986)and a Youden's index of 0.860.Further cluster analysis of INMA cases identified four phenotypic subgroups.Prognostic analysis demonstrated that pa-tients with advanced-stage mucin-secreting INMA had significantly shorter overall survival(OS)and progression-free survival(PFS)than those without mucin secretion(5-year OS:57.1％ vs 81.8％,P=0.004;3-year PFS:40.9％ vs 62.4％,P=0.004).No significant survival differences were noted among INMA subgroups stratified by varying mucin proportions.Multivariate analysis identified pathological stage,tumor necrosis,KRAS mutation,and TTF-1 negativity as independent adverse prognostic factors for both OS and PFS in mucinous INMA.Conclusion A three-marker im-munohistochemical panel of TTF-1,HNF4α,and MUC6 is recommended to distinguish IMA from mucinous INMA.Mucus component portends a worse prognosis in advanced INMA,with necrosis,KRAS mutations,and TTF-1 negativi-ty serving as independent adverse prognostic factors in mucinous INMA.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Purpose This study aimed to explore the mucinous phenotype characteristics,key points of differenti-al diagnosis and prognosis of invasive non-mucinous adenocarcinoma(INMA)and invasive mucinous adenocarcinoma(IMA)under the WHO(2021)lung adenocarcinoma classification.Methods We retrospectively collected clinico-pathological data from 522 cases of lung adenocarcinoma,including 425 INMA(66 with mucin secretion,259 without mucin secretion)and 97 IMA.Immunohistochemical(IHC)staining using the EnVision method was performed on the mucin-secreting adenocarcinoma to assess expression of TTF-1,HNF4α,MUC1,MUC4,MUC5AC,MUC5B,and MUC6.Unsupervised clustering analysis was conducted to explore phenotypic subgroups.Results 522 patients with lung adenocarcinoma ranged from 32 to 83 years old(median:61).251 cases(48.1％)were male and 271 cases(51.9％)were female.Clustering analysis divided lung adenocarcinomas into two major groups:one characterized by TTF-1-/HNF4α+and gastric-type mucins MUC5AC+/MUC6+,predominantly IMA;the other,TTF-1+/HNF4α-/MUC4+,largely INMA.A three-marker IHC panel(TTF-1,HNF4α,MUC6)distinguished IMA from mucinous IN-MA with an area under the ROC curve(AUC)of 0.957(95％CI:0.928-0.986)and a Youden's index of 0.860.Further cluster analysis of INMA cases identified four phenotypic subgroups.Prognostic analysis demonstrated that pa-tients with advanced-stage mucin-secreting INMA had significantly shorter overall survival(OS)and progression-free survival(PFS)than those without mucin secretion(5-year OS:57.1％ vs 81.8％,P=0.004;3-year PFS:40.9％ vs 62.4％,P=0.004).No significant survival differences were noted among INMA subgroups stratified by varying mucin proportions.Multivariate analysis identified pathological stage,tumor necrosis,KRAS mutation,and TTF-1 negativity as independent adverse prognostic factors for both OS and PFS in mucinous INMA.Conclusion A three-marker im-munohistochemical panel of TTF-1,HNF4α,and MUC6 is recommended to distinguish IMA from mucinous INMA.Mucus component portends a worse prognosis in advanced INMA,with necrosis,KRAS mutations,and TTF-1 negativi-ty serving as independent adverse prognostic factors in mucinous INMA.

Objective Tto evaluate the effect of low intensity pulsed ultrasound(LIPUS)on the migration and phagocytosis ability of lipopolysaccharide(LPS)-induced RAW264.7 macrophages and on macrophage behavior under inflammation.Methods An in vitro activated RAW264.7 macrophage model was developed using LPS.Cell viability was assessed using a CCK-8 assay to explore the effect of LIPUS on activated and inactivated macrophages.Wound healing assays were employed to measure the effect of LIPUS on macrophage migration,and the Transwell assay was employed when LPS was used as a chemoattractant.Phagocytosis ability was examined using confocal microscopy and flow cytometry by observing the FITC fluorescence signal of internalized pHrodo Green E.coli BioParticles Conjugate.Results An activated RAW264.7 macrophage model was successfully developed using 100 ng/mL LPS.LIPUS inhibited the migration of inactivated macrophages into scratch areas as well as guided cell migration.However,cell viability and phagocytosis remained unchanged.Conclusion LIPUS may inhibit RAW264.7 migration but not affect the phagocytosis ability of the macrophages.

Gastric cancer(GC)is one of the most common and high mortality tumors in the world,and the clinical staging during diagnosis is closely related to the prognosis of patients.Therefore,finding sensitive diagnostic biomarkers to improve the early diagnosis rate has become an urgent issue.Exosomes are nanoscale extracellular vesicles secreted by various cells,which play an important role in cellular communication by transporting bioactive substances in tumor microenvironment(TME).CircRNA is a type of non-coding RNA with a specific structure.Because it is difficulty in degradation,it can be enriched in extracellular vesicles and participate in various pathophysiological processes mediated by it.Exo-circRNA can affect the proliferation,invasion,drug resistance and tissue metastasis of tumor cells,and has more significant diagnostic specificity than conventional diagnostic biomarkers in clinical practice.It may become a supplement to biomarker for the diagnosis and prognosis of GC.This article briefly introduces the characteristics,formation mechanism and role of extracellular vesicles and circRNA in GC.It discusses in detail the role of exo-circRNA in the occurrence and development of GC,and discusses their potential clinical application value and challenges in GC.