International Journal of General Medicine最新文献

Objective: Previous studies reported that low cholinesterase (ChE) levels were associated with poor prognosis in various cancers, including bladder, pancreatic, lung, and colorectal cancers. This study aimed to evaluate the clinical significance of serum ChE levels as a prognostic biomarker in ovarian cancer.

Materials and methods: A retrospective cohort analysis was conducted on 168 patients diagnosed with epithelial ovarian cancer at the Suzhou Ninth People's Hospital from 2019 to 2020. Serum ChE levels were measured before initiating treatment and stratified into low and high groups based on the median level (7600 U/L). Clinical and pathological data, such as FIGO stage, age, tumor histological type, and survival outcomes, were collected. Kaplan-Meier analysis and Cox proportional hazards regression were used to assess the relationship between ChE levels and overall survival and disease-free survival.

Results: ChE levels were significantly correlated with clinicopathological features of epithelial ovarian cancer, including FIGO stage (p < 0.001), surgery completeness (p = 0.001), and platinum-resistant (p = 0.001). Kaplan-Meier analysis demonstrated that patients in the low ChE group had significantly worse overall survival (p = 0.003) and disease-free survival (p = 0.005) than those in the high ChE group. Multivariate Cox regression analysis identified low serum ChE levels as an independent predictor of poor overall survival and disease-free survival.

Conclusion: Low serum ChE levels are independently associated with poor prognosis in ovarian cancer patients, reflecting systemic inflammation, malnutrition, and potential hepatic dysfunction. These findings suggest that ChE could serve as a cost-effective and non-invasive biomarker for risk stratification and prognosis in clinical practice.

Background and objective: Preeclampsia (PE) is a severe gestational disorder characterized by sudden hypertension and proteinuria, with substantial risks to both mother and fetus. This study aims to delineate the role of neprilysin, a metalloprotease known for its role in modulating vasoactive peptides, in the pathophysiology of PE.

Methods: We recruited a cohort of 57 participants, comprising 38 patients diagnosed with PE and 19 healthy controls, matched for demographic and clinical characteristics. Neprilysin expression was assessed in serum and placental tissues through quantitative RT-qPCR and Western blot analyses. Functional impacts of neprilysin modulation were explored via siRNA knockdown and overexpression in HTR8/SVneo cells, followed by assessments of oxidative stress, mitochondrial function, apoptosis, and trophoblast invasion using various biochemical assays including CCK-8, DCFH-DA, JC-1 staining, and flow cytometry.

Results: Our results demonstrate a marked overexpression of neprilysin in the serum and placental tissues of PE patients compared to healthy controls. Elevated neprilysin levels were positively correlated with increased systolic and diastolic blood pressures. In functional assays, neprilysin knockdown alleviated H₂O₂-induced oxidative stress, restored mitochondrial function, and improved cell invasion and migration in EVT cells. Conversely, the overexpression of IGF2BP1, a regulator of mRNA stability, exacerbated neprilysin expression and intensified cellular damage under oxidative stress conditions. The reciprocal regulation of neprilysin by IGF2BP1 highlights a critical molecular interplay impacting cellular resilience to oxidative stress in PE.

Conclusion: These findings establish neprilysin as a critical mediator in the pathogenesis of PE, where its aberrant overexpression is linked to exacerbated hypertensive symptoms and impaired trophoblast function. The interaction between neprilysin and IGF2BP1 provides a potential therapeutic target for mitigating the progression of PE, suggesting avenues for future intervention strategies.

Objective: This study aimed to develop a clinical early warning prediction model to evaluate the prognosis and response to chemoimmunotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC), thereby guiding clinical decision-making.

Methods: A retrospective analysis was conducted on the clinical data and radiomics parameters of 309 patients with ES-SCLC hospitalized at Baotou Cancer Hospital from February 2020 to September 2024. Patients were divided into reactive and non-reactive groups based on their response to chemoimmunotherapy.Machine learning algorithms (including random forests, decision trees, artificial neural networks, and generalized linear regression) were used to predict the combined treatment response. The model's predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). The prognostic evaluation of patients receiving combination therapy was based on the COX regression model, with predictive performance assessed through nomogram visualization and calibration curves.

Results: Out of 309 patients with ES-SCLC, 248 (80.26%) responded to combination therapy. Logistic regression and Least absolute shrinkage and selection operator (LASSO) regression analyses identified Energy, sum of squares(SOS), mean sum(MES), sum variance(SUV), sum entropy(SUE), difference variance(DIV), and pathomics score as independent risk factors for treatment response. The area under the ROC curve for predicting treatment response using machine learning were 0.764 (95% confidence interval [CI]: 0.707~0.821) and 0.901 (95% CI: 0.846~0.956) in the training and validation sets. The C-index of the radiomics and pathomics prognostic nomogram model based on the COX prognostic model was 0.766 and 0.812 in those sets, respectively.

Conclusion: We developed prediction model based on multi-omics demonstrated satisfactory performance in predicting chemoimmunotherapy response in patients with ES-SCLC. The random forest prediction model, in particular, provides accurate response and prognostic risk assessments, thereby assisting clinical decision-making.

Purpose: This study aims to develop a machine learning-based model for the objective assessment of CT pulmonary angiography (CTPA) image quality.

Patients and methods: A retrospective analysis was conducted using data from 99 patients who underwent CTPA between March 2022 and January 2023, alongside two public datasets, FUMPE (21 cases) and CAD-PE (30 cases). In total, 150 cases from multiple centers were included in this analysis. The dataset was randomly split into a training set (105 cases) and a testing set (45 cases) in a 7:3 ratio. CT values and their standard deviations (SD) were measured in 11 specific regions of interest, and two radiologists independently assigned anonymous random scores to the images. The average of their subjective scores was used as the target output for the model, which was the mean opinion score (MOS) for image quality. Feature selection was performed using the Lasso algorithm and Pearson correlation coefficient, and a random forest regression model was constructed. Model performance was evaluated using mean square error (MSE), coefficient of determination (R²), Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SRCC), and Kendall rank correlation coefficient (KRCC).

Results: After feature selection, three key features were retained: main pulmonary artery CT value, ascending aorta CT value, and the difference in noise values between the left and right main pulmonary arteries. The random forest regression model constructed achieved MSE, R2_score, PLCC, SRCC, and KRCC values of 0.2001, 0.6695, 0.8682, 0.8694, 0.7363, respectively, on the testing set.

Conclusion: This study successfully developed an interpretable machine learning-based model for the objective assessment of CTPA image quality. The model offers effective support for improving image quality control efficiency and precision. However, the limited sample size may affect the model's generalizability, so it's essential to conduct further research with larger datasets.

Background: Breathing control training programs have gained attention due to their potential to influence diagnostic procedures. However, the role of video-based tools to demonstrate breathing maneuvers during sonographic quantification of abdominal organs needs to be studied more. This study explores the impact of a video-based breathing training program on sonographic examination of abdominal organs.

Methodology: This quasi-experimental study recruited 49 healthy volunteers at the ultrasound lab of College of Health and Rehabilitation Sciences in Princess Nourah bint Abdul Rahman University. The study employed a video-based breathing control training program that includes diaphragmatic breathing exercises. Sonographic assessments of abdominal organs were conducted before and after the intervention period using standardized protocols. Data was analyzed using SPSS V 28.

Results: The right renal length (p-value <0.001), thickness (p-value 0.025), and volume measured (p-value <0.001) were significantly improved following the video training. Similarly, splenic length (p-value 0.015), width (p-value <0.001), and volume (p-value 0.001). Additionally, the operator time demonstrated a notable decrease from 13.14 ± 3.4 to 8.1 ± 3.5 minutes after the video training intervention (p-value <0.001).

Conclusion: The study provides evidence for the beneficial effects of a video-based breathing control training program on sonographic quantification of abdominal organs. These findings suggest potential clinical utility for integrating video-based breathing interventions into practice, although study limitations require further validation and generalization. Nonetheless, the study highlights the promising role of breathing control training in enhancing the diagnostic accuracy of abdominal sonography.

Objective: This research investigated the relationship between serum macrophage inhibitory cytokine-1 (MIC-1), vascular endothelial growth factor (VEGF), and transforming growth factor-β1 (TGF-β1) levels and clinicopathologic features, lymph node metastasis (LNM), and prognosis of gastric cancer (GC) patients.

Methods: The GC group (GC patients, 198 cases)) and healthy group (healthy people, 100 cases) were established. The relationship between serum MIC-1, VEGF, TGF-β1, and clinical and pathological features in GC patients was analyzed. GC patients were divided into a metastasis group (77 patients) and a non-metastasis group (121 patients) based on whether they had LNM. The factors influencing LNM in GC patients were identified. The predictive value of serum MIC-1, VEGF, and TGF-β1 for LNM in GC patients and the relationship between serum MIC-1, VEGF, TGF-β1 levels and prognosis were analyzed.

Results: MIC-1, VEGF, and TGF-β1 were higher in GC. Serum MIC-1, VEGF, and TGF-β1 levels were higher in GC patients with tumor diameter ≥ 3 cm, T stage of T3 and T4, low/moderate differentiation, and LNM. Multivariate Logistic regression analysis showed that TNM stage, tumor differentiation, and serum MIC-1, VEGF, and TGF-β1 levels were risk factors for LNM in GC patients. The ROC results indicated that the combination of serum MIC-1, VEGF, and TGF-β1 had the highest AUC for predicting LNM in GV patients. The median survival time of patients with low serum MIC-1, VEGF, and TGF-β1 was higher than that of patients with high serum MIC-1, VEGF, and TGF-β1 (26.13 months vs 19.24 months, 27.06 months vs 20.18 months, and 24.20 months vs 20.08 months).

Conclusion: The changes of serum MIC-1, VEGF and TGF-β1 levels are related to the clinicopathological characteristics of GC patients, and the elevated levels of these indices are independent risk factors affecting LNM and prognosis of GC patients.

Background: Low relative muscle mass was identified to be related to ascending risk of pre-cancerous polyps (adenoma) in recent cohort study. Our study aimed to dig out the correlation between muscle mass and different pathological types of colorectal polyps in Chinese asymptomatic population.

Methods: In all, 5923 adults were included. The effects of low skeletal muscle mass index (SMI) on colorectal polyp occurrence, including different pathological types, and the effects modification of age and BMI were analyzed using univariate and multivariate logistic regression.

Results: Lower SMI was connected with the lower occurrence of colorectal polyp (OR: 0.810, 95% CI: 0.683~0.960, p=0.015). Considering different pathological types of colorectal polyps, lower SMI was associated with lower occurrence of inflammatory polyp (OR: 0.633, 95% CI: 0.434~0.898, p=0.013), rather than conventional adenoma and serrated polyp (all p>0.05). Besides, SMI was positively related to the occurrence of 2 pathological types of colorectal polyp in males: inflammatory polyp (OR: 1.237, 95% CI: 1.058~1.444, p=0.007) and serrated polyp (OR: 1.288, 95% CI: 1.143~1.456, p<0.001). The interaction effect of BMI and SMI on occurrence of inflammatory polyp after adjusting age and smoking status was significant (p=0.015). For individuals with low SMI (compared with the normal SMI group), the incidence of inflammatory polyp was reduced from 8.95% to 3.50% in the low BMI quartile (Q1) in the adjusted model (OR of 0.332, 95% CI: 0.005-0.061, p<0.001). It was noticeable for males rather than females that individuals with colorectal polyps had higher levels of SMI (p=0.003). In addition, individuals with inflammatory polyps as well as serrated polyps possessed higher levels of SMI in males (all p<0.05).

Conclusion: Generally, especially in Chinese asymptomatic males, low SMI kept independent effect on the presence of inflammatory polyp and serrated polyp, rather than conventional adenoma.

Background: Identifying the risk factors for perioperative myocardial injury/infarction (PMI) is critical to prevent postoperative adverse cardiovascular events. However, whether reducing the preoperative LDL-C can mitigate the risk of PMI remains unclear. We therefore investigated the effect of the preoperative LDL-C level in patients with stable angina pectoris (SAP) or unstable angina pectoris (UAP) on perioperative myocardial injury/infarction (PMI) after elective percutaneous coronary intervention (PCI).

Methods: Patients with SAP or UAP who received PCI from January 2021 to June 2023 at one of the two institutions (Hebei Provincial People's Hospital, Handan Central Hospital) were reviewed. The occurrence of PMI was determined based on the elevation of cardiac troponin I (cTnI) after the operation. The preoperative low density lipoprotein cholesterol (LDL-C) level was divided into three grades: low, <1.4 mmol/L; medium, 1.4-1.8 mmol/L; high, >1.8 mmol/L. The relationship between PMI and preoperative LDL-C was analyzed.

Results: Of all 308 included patients, 226 did not have PMI and 82 experienced PMI. Positive correlation was found both between PMI and preoperative LDL-C level (r = 0.322, P < 0.05) and between PMI and preoperative LDL-C grade (r = 0.189, P < 0.05). According to the multivariate logistic regression analysis, the preoperative LDL-C grade (Medium vs Low, OR=3.994, P < 0.05; High vs Medium, OR=6.140, P < 0.05) and the number of stents implanted during PCI were independent risk factors for PMI (OR=1.940; P < 0.05).

Conclusion: For SAP and UAP patients, decreasing LDL-C to <1.4 mmol/L before elective PCI can reduce the incidence of PMI after the operation. We strongly recommend the practice of sufficiently reducing LDL-C level below 1.4 for patients with SAP or UAP who receive elective PCI.

Objective: The objective of this study was to analyze the latent categories of fatigue in patients with chronic heart failure (CHF), explore their characteristic differences, and identify the associated influencing factors.

Methods: This cross-sectional study included 289 patients with CHF who were enrolled at 2 tertiary-level hospitals in Shandong, China, from August to December 2023. The convenience sampling method was used to collect data. Furthermore, the level of fatigue, insomnia, anxiety, depression, and social support were evaluated using the Chinese version of the Multidimensional Fatigue Inventory-20, Insomnia Severity Index, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Multidimensional Scale of Perceived Social Support. Latent profile analysis was performed to elucidate the latent categories of fatigue in the patients. In addition, the risk factors associated with the different categories were assessed using multiple logistic regression analyses.

Results: The average fatigue score was 62.45 ± 13.55. The potential fatigue profile of CHF was divided into three categories: low fatigue group C1 (18.6%), moderate fatigue group C2 (47.4%), and high fatigue group C3 (34.0%). Multiple logistic regression analysis showed that C3 patients with CHF were mainly characterized by lower ejection fraction (OR = 0.01, p = 0.008), insomnia (OR = 1.19, p = 0.005), and anxiety (OR = 1.20, p = 0.034). C2 patients indicated lower ejection fraction (OR = 0.04, p = 0.040), and C1 patients had higher social support (OR = 0.91, p < 0.001; OR = 0.93, p < 0.001).

Conclusion: This study indicated that CHF patients had significantly heterogeneous levels of fatigue. Therefore, it is recommended that medical staff could adopt more precise interventions according to different category characteristics to improve the outcomes of patients with CHF.

Objective: To analyze the application value of novel inflammation indicators such as the lymphocyte/monocyte ratio (LMR), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) in patients with primary biliary cholangitis (PBC) undergoing ursodeoxycholic acid (UDCA) treatment. We plan to further seek simple and convenient methods to assess the response of patients to UDCA treatment.

Methods: We recorded routine blood tests, liver function, and vitamin D (VD) levels of PBC patients and healthy controls visiting the hospital between October 2022 and October 2023. LMR, NLR, and PLR were calculated, and differences between the two groups were analyzed. PBC patients were divided into good response and poor response groups according to the Paris I criteria, and differences in laboratory tests between the two groups were analyzed. The predictive value of novel inflammation indicators in UDCA treatment response was further analyzed using ROC analysis.

Results: LMR and VD levels were significantly lower in the PBC group compared to the control group (P=0.000, P=0.000). In PBC patients, the good response group had higher LMR than the poor response group (P=0.001) and lower NLR than the poor response group (P=0.015). The areas under the ROC curve for LMR and NLR were 0.682±0.049 and 0.630±0.052, respectively. There was a significant negative correlation between PLR and VD in PBC patients (r=-0.252, P=0.005).

Conclusion: Low LMR and high NLR may indicate poor treatment response. And PLR also have certain predictive values for treatment response.