Objective To explore the relationship between uric acid, visceral fat thickness and insulin resistance in elderly patients with hypertriglyceridemia (HTG). Methods A total of 347 elderly patients with HTG admitted to the hospital from January 2021 to January 2025 were retrospectively selected, and the related factors of insulin resistance in elderly HTG were analyzed. Results Among the 347 elderly patients with HTG, 218 cases had insulin resistance and 129 cases did not develop insulin resistance, and were included in the insulin resistance group (n=218) and the non-insulin resistance group (n=129) respectively. Compared with the non-insulin resistance group, patients in the insulin resistance group had higher proportions of severe HTG and concurrent fatty liver, higher levels of IL-6, TNF- α, FFA and uric acid, and thicker visceral fat thickness (P<0.05). After logistic regression analysis, it was found that the related factors for insulin resistance in elderly patients with HTG included the severity of HTG, IL-6, FFA, uric acid, and visceral fat thickness (P<0.05). Conclusion The severity of HTG, IL-6, FFA, uric acid, and visceral fat thickness are related to insulin resistance in elderly HTG patients. Clinically, it is necessary to pay attention to targeted interventions for uric acid control and visceral fat reduction in elderly patients with HTG so as to improve the insulin resistance status.

Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective This study aims to investigate the effects of FGL2 on the immune response of EBV-infected T cells,including their activation,proliferation,exhaustion,and cytokine profile changes.Methods Primary T cells were infected with EBV at different multiplicities of infection(MOI).Expression of FGL2 in T cells,as well as T-cell activation,proliferation,exhaustion,and cytokine levels,were detected using RT-qPCR,Western blot,ELISA,CCK8,and flow cytometry(FCM),respectively.Further experiments involving FGL2 knockdown and overexpression were conducted to elucidate its specific regulatory role in EBV-infected T cells.Results FGL2 expression was significantly upregulated in EBV-infected T cells(P＜0.05).EBV infection also induced enhanced T cell activation(P＜0.001),proliferation(P＜0.001),and exhaustion(P＜0.01).Compared to the T cells+EBV group,the T cells+EBV+FGL2 overexpression group exhibited higher exhaustion levels(P＜0.01),reduced activation(P＜0.05)and proliferation(P＜0.05),decreased pro-inflammatory cytokine levels(P＜0.05),and increased anti-inflammatory cytokine levels(P＜0.05).Conversely,the T cells+EBV+FGL2 knockdown group demonstrated the opposite trends,with elevated activation(P＜0.01),proliferation(P＜0.05),pro-inflammatory cytokine levels(P＜0.05),and reduced exhaustion(P＜0.01)and anti-inflammatory cytokine levels(P＜0.05).Conclusion FGL2 suppresses T cell activation and proliferation,exacerbates T cell exhaustion,inhibits pro-inflammatory cytokine release,and promotes anti-inflammatory cytokine secretion during EBV infection,thereby modulating the immune response of T cells.

Objective This study aims to investigate the effects of FGL2 on the immune response of EBV-infected T cells,including their activation,proliferation,exhaustion,and cytokine profile changes.Methods Primary T cells were infected with EBV at different multiplicities of infection(MOI).Expression of FGL2 in T cells,as well as T-cell activation,proliferation,exhaustion,and cytokine levels,were detected using RT-qPCR,Western blot,ELISA,CCK8,and flow cytometry(FCM),respectively.Further experiments involving FGL2 knockdown and overexpression were conducted to elucidate its specific regulatory role in EBV-infected T cells.Results FGL2 expression was significantly upregulated in EBV-infected T cells(P＜0.05).EBV infection also induced enhanced T cell activation(P＜0.001),proliferation(P＜0.001),and exhaustion(P＜0.01).Compared to the T cells+EBV group,the T cells+EBV+FGL2 overexpression group exhibited higher exhaustion levels(P＜0.01),reduced activation(P＜0.05)and proliferation(P＜0.05),decreased pro-inflammatory cytokine levels(P＜0.05),and increased anti-inflammatory cytokine levels(P＜0.05).Conversely,the T cells+EBV+FGL2 knockdown group demonstrated the opposite trends,with elevated activation(P＜0.01),proliferation(P＜0.05),pro-inflammatory cytokine levels(P＜0.05),and reduced exhaustion(P＜0.01)and anti-inflammatory cytokine levels(P＜0.05).Conclusion FGL2 suppresses T cell activation and proliferation,exacerbates T cell exhaustion,inhibits pro-inflammatory cytokine release,and promotes anti-inflammatory cytokine secretion during EBV infection,thereby modulating the immune response of T cells.

With the in-depth promotion of precision medicine for breast cancer,pathological diagnosis has changed from traditional histological classification to a multidimensional system integrating morphology,immunohistochemistry and molecular target detection,and becoming a pivotal component in clinical treatment decision-making.Currently,the pathological diagnosis of breast cancer necessitates continuous optimization,including standardizing the interpretation of HER2-low expression,promoting the upfront detection for key targets,refining the structure of pathological reports,and further enhancing collaboration with clinical teams.These efforts aim to better meet the demands of precision treatment and provide patients with superior diagnostic support.Based on the clinical research progress and guideline consensus in recent years,this paper delves into the critical pathological hotspots in the clinical diagnosis and treatment of breast cancer,aiming to facilitate the implementation of standardized pathological diagnosis within the precision treatment framework.

Purpose This study aimed to evaluate the consistency of human epidermal growth factor receptor 2(HER2)status between primary breast cancer lesions and lung metastatic lesions and to establish a prognostic model for predicting the survival rate of HER2 low expression(HER2-low)breast cancer patients with lung metastasis.Methods Clinicopathological data from a cohort of 252 patients with breast cancer and lung metastasis were retrospec-tively analyzed.Results 50.00％of the patients had HER2-low expression in metastatic lesions,and HER2-low ex-pression was the most prevalent subgroup in both primary and metastatic lesions.A discordance in HER2 status be-tween primary and metastatic sites was observed in 28.07％of cases.The most frequent shift was from HER2-zero in the primary tumor to HER2-low expression in the metastasis(12.28％of all cases).Estrogen receptor(ER)status,menopausal status,and histological type were identified as independent prognostic factors for overall survival(OS)by univariate and multivariate Cox regression analyses.A prognostic model incorporating these factors was constructed to predict 3-year and 5-year survival.The model demonstrated area under the curve(AUC)values of 0.765 and 0.780 for 3-year and 5-year OS in the training cohort,and 0.667 and 0.706 in the validation cohort,respectively.Conclu-sion HER2-low expression is the most common subtype among breast cancer patients with lung metastasis.The ob-served shift from HER2-zero in primary lesions to HER2-low in metastases underscores the clinical necessity of re-biop-sy at metastatic sites.The developed prognostic model effectively predicts OS in this patient population.

Objective:To investigate the mutation spectrum of the PIK3CA gene in breast cancer, providing a new basis for the precise treatment of breast cancer with PIK3CA inhibitors.Methods:A retrospective analysis was conducted on 144 breast cancer patients who underwent biopsy before neoadjuvant therapy archived from 2015 to 2020 at the Fourth Hospital of Hebei Medical University. Next-generation sequencing (NGS) was utilized to detect the mutations of 520 genes closely related to the development of solid tumors and targeted therapies. The study compared the differences between reported mutation types and focused on analyzing the mutation status of the PIK3CA gene. The clinical and pathological characteristics, including age of onset, molecular subtypes, and Ki-67, were also analyzed. The correlation between PIK3CA mutations and clinicopathological characteristics was examined using Pearson×s chi-square test and Mann Whitney test. Logistic regression was employed to analyze factors influencing PIK3CA mutations. Kaplan-Meier survival analysis and Cox regression models were constructed using R programming.Results:Among the 144 breast cancer samples, 61 (42.3%, 61/144) exhibited PIK3CA gene mutations, of which 23 cases (53.5%, 23/43) were HER2-positive breast cancer, 28 cases (44.4%, 28/63) were luminal breast cancer, and 10 cases (27.8%, 10/36) were triple-negative breast cancer. Of the detected mutations, three hotspot mutations (H1047R, E545K, and E542K) accounted for 72.1% of the total PIK3CA mutations, with H1047R (52.4%), E545K (16.4%), and E542K (3.3%) most commonly detected. The remaining rare mutations accounted for 26.3%. Co-mutations involving PIK3CA and other genes were also observed in the cohort, occurring with TOP2A and FOXA1, while being mutually exclusive with GATA3 and BRCA2. PIK3CA mutations were significantly associated with HER2 status and were not significantly correlated with the patient′s age, menopausal status, HR status, Ki-67 index, molecular typing, TNM stage or pCR status. Likewise, no significant correlation was found between different PIK3CA mutation status and overall survival.Conclusions:This cohort study shows the overall mutation rate of PIK3CA in breast cancer and the mutation frequencies across different molecular subtypes. The findings reveal a significant correlation between PIK3CA mutations and HER2 status, which provides a new basis for the precise treatment of breast cancer with PIK3CA inhibitors.

Objective:To investigate the mutation spectrum of the PIK3CA gene in breast cancer, providing a new basis for the precise treatment of breast cancer with PIK3CA inhibitors.Methods:A retrospective analysis was conducted on 144 breast cancer patients who underwent biopsy before neoadjuvant therapy archived from 2015 to 2020 at the Fourth Hospital of Hebei Medical University. Next-generation sequencing (NGS) was utilized to detect the mutations of 520 genes closely related to the development of solid tumors and targeted therapies. The study compared the differences between reported mutation types and focused on analyzing the mutation status of the PIK3CA gene. The clinical and pathological characteristics, including age of onset, molecular subtypes, and Ki-67, were also analyzed. The correlation between PIK3CA mutations and clinicopathological characteristics was examined using Pearson×s chi-square test and Mann Whitney test. Logistic regression was employed to analyze factors influencing PIK3CA mutations. Kaplan-Meier survival analysis and Cox regression models were constructed using R programming.Results:Among the 144 breast cancer samples, 61 (42.3%, 61/144) exhibited PIK3CA gene mutations, of which 23 cases (53.5%, 23/43) were HER2-positive breast cancer, 28 cases (44.4%, 28/63) were luminal breast cancer, and 10 cases (27.8%, 10/36) were triple-negative breast cancer. Of the detected mutations, three hotspot mutations (H1047R, E545K, and E542K) accounted for 72.1% of the total PIK3CA mutations, with H1047R (52.4%), E545K (16.4%), and E542K (3.3%) most commonly detected. The remaining rare mutations accounted for 26.3%. Co-mutations involving PIK3CA and other genes were also observed in the cohort, occurring with TOP2A and FOXA1, while being mutually exclusive with GATA3 and BRCA2. PIK3CA mutations were significantly associated with HER2 status and were not significantly correlated with the patient′s age, menopausal status, HR status, Ki-67 index, molecular typing, TNM stage or pCR status. Likewise, no significant correlation was found between different PIK3CA mutation status and overall survival.Conclusions:This cohort study shows the overall mutation rate of PIK3CA in breast cancer and the mutation frequencies across different molecular subtypes. The findings reveal a significant correlation between PIK3CA mutations and HER2 status, which provides a new basis for the precise treatment of breast cancer with PIK3CA inhibitors.

Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective:To establish and validate a lipid parameter-based prognostic model for predicting recurrence free survival (RFS) in pancreatic cancer patients receiving postoperative adjuvant chemotherapy.Methods:A retrospective analysis was conducted on the clinical and pathological data of 155 patients who underwent pancreatic cancer resection followed by adjuvant chemotherapy at Affiliated Hospital of Qingdao University between January 2019 and December 2022. The patients were randomly divided into a training set ( n=108) and a validation set ( n=47) in a 7∶3 ratio. X-tile software was used to determine cutoff values for lipid parameters. Univariate and multivariate Cox regression analyses were performed to construct a model predicting RFS, which was then visualized using a nomogram. The model's predictive performance, accuracy and stability, and clinical application value were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. Individual risk scores for recurrence were calculated based on the nomogram model, and X-tile software was employed to identify optimal cutoff values for risk stratification, which was used to divide patients into low-risk and high-risk groups. Survival differences between two groups were analyzed using survival curves. Results:Among lipid parameters, patients with higher apolipoprotein A1 level had obviously longer RFS than those with low apolipoprotein A1 level (10.17 months vs 8.92 months, HR=0.397, 95% CI 0.237~0.664); patients with high total cholesterol level had obviously shorter RFS than those with low total cholesterol level (8.33 months vs 16.27months, HR=3.382, 95% CI 1.901~5.824) ; patients with high low-density lipoprotein level had obviously shorter RFS than those with low low-density lipoprotein level (8.53 months vs 11.43 months, HR=1.617, 95% CI 1.013~2.582) ; patients with high lipoprotein(a) had shorter RFS than those with low lipoprotein(a) (8.53 months vs 14.43 months, HR=2.640, 95% CI 1.514-4.604) ; and all the differences were statistical significant (all P value <0.05). Univariate Cox regression analysis identified advanced T stage, advanced N stage, high total cholesterol level, high low-density lipoprotein level, low apolipoprotein A1 level, high apolipoprotein B level, and high lipoprotein(a) level as risk factors for RFS. Multivariate Cox regression analysis revealed that tumors located in the pancreatic body or tail ( HR=0.63, 95% CI 0.36-0.86, P=0.042), advanced T stage ( HR=4.85, 95% CI 1.47-16.04, P=0.010), advanced N stage ( HR=0.48, 95% CI 0.26-0.87, P=0.015), elevated total cholesterol levels ( HR=3.61, 95% CI 1.46-8.91, P=0.005), high density lipoprotein levels ( HR=0.48, 95% CI 0.26-0.87, P=0.015), and elevated lipoprotein(a) levels ( HR=3.17, 95% CI 1.61-6.24, P<0.001) were independent risk factors for RFS. The nomogram model incorporating these six factors above demonstrated an AUC of 0.78 (95% CI 0.70-0.87) in the training set and 0.75 (95% CI 0.59-0.91) in the validation set. Calibration curves indicated a high degree of agreement between predicted and observed outcomes. DCA suggested that the model provides substantial clinical benefit. Kaplan-Meier survival curve analysis showed that patients in the high-recurrence risk group from training set and validation set both had significantly shorter RFS compared to those in the low-recurrence risk group (6.93 months vs 12.13 months, HR=4.024, 95% CI 2.594-6.243; 6.85 months vs 11.93 months, HR=2.314, 95% CI 1.227-4.362); and all the differences were statistical significant (all P value <0.05). Conclusions:The nomogram model based on lipid parameters can effectively predict recurrence free survival in patients undergoing adjuvant chemotherapy after pancreatic cancer surgery.