Objective To develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 (53.1%) females, with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated optimal performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.

Pan-immune inflammation value (PIV) is a comprehensive immune inflammatory biomarker based on complete blood cell counts, which has been proven to predict treatment response and survival outcomes for different types of tumors. However, the predictive value of the PIV varies in different strategies for tumor treatment. This paper aims to systematically review the latest progress of PIV in predicting survival outcomes and tumor prognosis for immunotherapy, radiotherapy, targeted therapy, endocrine therapy, surgical treatment and neoadjuvant therapy, and analyze its existing challenges and issues, as well as look forward to its future development direction and application prospects.

Objective To investigate the expression and clinical significance of microRNA-148a-3p (miR-148a-3p) in lung adenocarcinoma and analyze the effect and mechanism of miR-148a-3p on radiotherapy sensitivity of lung adenocarcinoma cells by targeting the protein core 1β13-galactosyltransferase 1(C1GALT1). Methods Seventy-six patients' tumor tissues from lung adenocarcinoma tissue microarrays and lung adenocarcinoma A549 cell line were selected for the study. The miR-148a-3p in situ hybridizations (ISH) and C1GALT1 immunohistochemical staining were performed on the tissue microarrays to analyze the correlations of miR-148a-3p expression with clinical pathology, prognosis and C1GALT1 expression in the tumor tissues of the 76 patients with lung adenocarcinoma. A549 cells were transfected with miR-148a-3p overexpression plasmid by using cell transfection technique; the clone formation assay was used to detect the sensitivity of the transfected cells for radiotherapy after receiving 2 Gy radiotherapy; the protein expression level of cellular C1GALT1 was detected by western blot; the targeted regulatory relationship between miR-148a-3p and C1GALT1 was verified by dual-luciferase reporter gene experiment; the mechanism of miR-148a-3p regulating the sensitivity of A549 cells to radiotherapy was analyzed by co-transfection technique. Results Low expression of miR-148a-3p in 76 cases of lung adenocarcinoma tissues was significantly associated with lymph node metastasis (P=0.012); the prognosis of patients with high expression of miR-148a-3p was significantly better than that of patients with low expression of miR-148a-3p (P=0.005); there was a significant negative correlation between the expression of miR-148a-3p and C1GALT1 in lung adenocarcinoma tissues (P=0.023). Multivariate analysis showed that low expression of miR-148a-3p, T staging and lymph node metastasis were the independent risk factors for prognosis. Clone formation experiment showed that the number of clone formation in the miR-148a-3p overexpression group was significantly lower than that in the control group (P < 0.001); western blot result showed that overexpression of miR-148a-3p was able to significantly down-regulate the level of C1GALT1 protein in cells (P < 0.001). Dual luciferase reporter gene experiment showed that miR-148a-3p was able to regulate the expression of C1GALT1 by binding to the 3'UTR of C1GALT1. Functional rescue experiment showed that C1GALT1 could partially offset the radiosensitization effect of miR-148a-3p. Conclusion Low expression of miR-148a-3p in lung adenocarcinoma is significantly associated with lymph node metastasis, high expression of C1GALT1 and poor prognosis, and miR-148a-3p can enhance the radiosensitivity of lung adenocarcinoma cells by negatively regulating the expression of C1GALT1.

Objective To validate the performance of droplet digital polymerase chain reaction (ddPCR) reagents using clinical and standard strains, and to evaluate the effectiveness and practicability of ddPCR technology in clinical applications. Methods The concordance rate, specificity, precision, and lower limit of detection of the ddPCR kit were validated using clinical and standard strains. Blood samples from 74 patients with suspected bloodstream infections were collected, and both ddPCR and blood culture methods were used to determine the pathogens in the patient's blood samples. Results The average detection time of ddPCR for pathogens of bloodstream infection was 3.5 hours, which was able to complete the detection of over a dozen common pathogens simultaneously. The concordance rate, specificity, precision, and lower limit of detection of the ddPCR kit for bloodstream infection pathogens all met clinical requirements. Among the 74 patients with suspected bloodstream infections, the positive detection rate using the ddPCR method was 64.86%, while was 40.54% using blood culture, with a statistically significant difference (P < 0.05). Conclusion The method of ddPCR can detect common pathogens of bloodstream infections rapidly, efficiently, and in large quantities, and its main detection performance can meet clinical needs. The combination of blood culture and ddPCR technology is conductive to early and rapid diagnosis of clinical bloodstream infection patients.

Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.

Objective To investigate the correlation between pre-treatment pan-immune inflammation value (PIV) and clinicopathological features in esophageal squamous cell carcinoma (ESCC) patients with postoperative adjuvant radiotherapy and evaluate its value in prognosis assessment combined with T stage. Methods A retrospective analysis was conducted on data of 85 ESCC patients with postoperative adjuvant radiotherapy in the Department of Radiation Oncology of the Affiliated Hospital of Yangzhou University from January 2019 to January 2023. The receiver operating characteristic (ROC) curve was drew to obtain the optimal cut-off value of PIV and other immune-inflammatory biomarkers. The area under the curve (AUC) and clinical applicability of PIV and other immune-inflammatory biomarkers were compared based on the ROC curve and decision curve analysis (DCA). According to the optimal cut-off value, patients were divided into high PIV group and low PIV group, and the correlation between PIV level and clinicopathological features of ESCC was evaluated. Kaplan-Meier method was used for survival analysis, the Cox proportional hazards model was used for multivariate analysis, and a risk stratification model combining PIV and T stage was established by recursive partitioning analysis (RPA). Results The optimal cut-off value of pre-treatment PIV was determined as 187.22 based on the ROC curve. The AUC of PIV was 0.679, which was greater than 0.640, 0.583, 0.656 and 0.644 of the other four immune-inflammatory biomarkers such as the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR). The 85 patients were divided into low PIV group (< 187.22, n=48) and high PIV group (≥187.22, n=37). The level of PIV was significantly correlated with tumor diameter (P < 0.05). The 3-year overall survival (OS) (75.6% versus 30.6%, P < 0.001) and 3-year disease-free survival (DFS) (56.1% versus 21.0%, P < 0.001) were significantly higher in the low PIV group than the high PIV group. Tumor diameter, T stage and PIV were independent factors affecting OS in ESCC patients (P < 0.05), and T stage and PIV were independent factors affecting DFS in ESCC patients (P < 0.05). A new staging system with three risk groups was established by the RPA stratification model based on T stage and PIV, which further improved the predictive value of prognosis compared with T stage or PIV alone. Conclusion Pre-treatment PIV is helpful in predicting the prognosis of ESCC patients with postoperative adjuvant radiotherapy, and the combination of PIV and T stage can improve the predictive value.

Early postoperative inflammatory small bowel obstruction is one of the complications after abdominal surgery. Most obstructions will resolve on their own after nutritional support. However, the recovery of intestinal function takes a long time, which leads to high costs and is prone to occur related complications. Early postoperative inflammatory small bowel obstruction is also one of the risk factors for secondary adhesive intestinal obstruction. A few patients with ineffective conservative treatment have to undergo high-risk surgical treatment. Cases of early postoperative inflammatory small bowel obstruction complicated by superior mesenteric artery syndrome are relatively rare. Comprehensive treatment including acupuncture, hormones, and water-soluble contrast agents as well as early placement of enteral nutrition tubes is the key to the treatment of such patients.

Objective: This study aims to survey the incidence of surgical site infection (SSI) in China and to analyze its risk factors, so as to prevent and control SSI after colorectal surgery. Methods: An observative study was conducted. Based on a program of Chinese SSI Surveillance from 2018 to 2020, the clinical data of all adult patients undergoing colorectal surgery during this time period were extracted. These included demographic characteristics and perioperative clinical parameters. Minors, pregnant women, obstetric or gynecological surgery, urological system surgery, retroperitoneal surgery, resection of superficial soft tissue masses, and mesh or other implants were excluded. A total of 2122 patients undergoing colorectal surgery from 50 hospitals were included, including 1252 males and 870 females. The median age was 63 (16) years and the median BMI was 23 (4.58) kg/m². The primary outcome was the incidence of SSI within 30 days after colorectal surgery. The secondary outcomes were mortality within 30 days postoperatively, length of ICU stays and postoperative hospital stays, and cost of hospitalization. Patients were divided into the SSI group and non-SSI group based on the occurrence of SSI. Multivariable logistic regression was performed to analyze risk factors of SSI after colorectal surgery, and subgroup analysis was conducted for open and laparoscopic surgery. Results: The incidence of SSI after colorectal surgery was 5.6% (119/2122), including 47 cases (47/119, 39.5%) with superficial incisional infections, 24 cases (24/119, 20.2%) with deep incisional infections, and 48 cases (48/119, 40.3%) with organ/space infections. The occurrence of SSI significantly increased mortality [2.5% (3/119) vs. 0.1%(3/2003), χ²=22.400, P=0.003], the length of ICU stay [0 (1) day vs. 0(0) day, U=131 339, P<0.001], postoperative hospital stay [18.5 (12.8) days vs. 9.0 (6.0) days, U=167 902, P<0.001], and medical expenses [75 000 (49 000) yuan vs. 60 000 (31 000) yuan, U=126 189, P<0.001] (P<0.05). Multivariate analysis revealed that hypertension (OR=1.782, 95%CI: 1.173-2.709, P=0.007), preoperative albumin level (OR=1.680, 95%CI: 1.089-2.592, P=0.019), a contaminated or infected incision (OR= 1.993, 95%CI: 1.076-3.689, P=0.028), emergency surgery (OR=2.067, 95%CI: 1.076-3.972, P=0.029), open surgery (OR=2.132, 95%CI: 1.396-3.255, P<0.001), and surgical duration (OR=1.804, 95%CI: 1.188-2.740, P=0.006) were risk factors for SSI, while preoperative skin preparation (OR=0.478, 95%CI: 0.310-0.737, P=0.001) was a protective factor for SSI. Subgroup analysis was performed on patients undergoing open or laparoscopic surgery. The incidence of SSI in the open surgery group was 10.2%, which was significantly higher than that in the laparoscopic or robotic group (3.5%, χ²=39.816, P<0.001). Subgroup analysis identified that a contaminated or infected incision (OR=2.168, 95%CI: 1.042-4.510, P=0.038) and surgical duration (OR=2.072, 95%CI: 1.171-3.664, P=0.012) were risk factors for SSI after open surgery, while mechanical bowel preparation (OR=0.428, 95%CI: 0.227-0.807, P=0.009) and preoperative skin preparation (OR=0.356, 95%CI: 0.199-0.634, P<0.001) were protective factors for SSI after open surgery. In laparoscopic surgery, diabetes mellitus (OR= 2.292, 95%CI: 1.138-4.617, P=0.020) and hypertension (OR=2.265, 95%CI: 1.234-4.159, P=0.008) were risk factors for SSI. Conclusions: The incidence of SSI after colorectal surgery is 5.6%. Minimally invasive surgery should be selected to reduce the occurrence of postoperative SSI. To prevent the occurrence of SSI after open surgery, skin preparation and mechanical bowel preparation should be performed before the operation, and the duration of the operation should be shortened as much as possible. In the perioperative period, care of patients with hypertension, diabetes, and contaminated or infected incisions should be given particular attention.
Adult ; Albumins ; China/epidemiology* ; Colorectal Surgery/adverse effects* ; Female ; Humans ; Hypertension/complications* ; Male ; Middle Aged ; Pregnancy ; Surgical Wound Infection/etiology*

Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; China/epidemiology* ; Heart Valve Prosthesis Implantation ; Humans ; Severity of Illness Index ; Treatment Outcome

BACKGROUNDWhite matter lesions (WMLs) are common findings in brain magnetic resonance imaging (MRI) and are strongly associated with stroke incidence, recurrence, and prognosis. However, the relationship between WMLs and transient ischemic attacks (TIAs) is not well established. This study aimed to determine the clinical significance of WMLs in patients with TIA.

METHODSA total of 181 consecutive inpatients with first-ever TIA were enrolled. Brain MRIs within 2 days of symptom onset were used to measure WML volumes. Recurrent vascular events within 1 year of TIA onset were assessed. The relationship between WMLs and recurrent risk of vascular events was determined by a multivariate logistic regression.

RESULTSWMLs were identified in 104 patients (57.5%). Age and ratio of hypertension were significantly different between patients with and without WMLs. The incidence of vascular events in patients with WMLs significantly increased in comparison to those without WMLs (21.15% vs. 5.19%, 95% confidence interval [CI]: 1.18-15.20, P = 0.027) after controlling for confounders. Furthermore, distributions of WML loads were found to be different between patients who developed vascular events and those who did not. WML volumes were demonstrated to be correlated with recurrent risks, and the fourth quartile of WML volumes led to an 8.5-fold elevation of recurrent risk of vascular events compared with the first quartile (95% CI: 1.52-47.65, P = 0.015) after adjusting for hyperlipidemia.

CONCLUSIONWMLs occur frequently in patients with TIA and are associated with the high risk of recurrent vascular events, suggesting a predictive neuroimaging marker for TIA outcomes.