Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedures in specialised medical centres. This study aimed to explore the feasibility of utilising machine learning models to accurately screen for MASLD in large populations based on a combination of essential demographic and clinical characteristics. A total of 10,007 outpatients who underwent transient elastography at the First Affiliated Hospital of Gannan Medical University were enrolled to form a derivation cohort. Using eight demographic and clinical characteristics (age, educational level, height, weight, waist and hip circumference, and history of hypertension and diabetes), we built predictive models for MASLD (classified as none or mild: controlled attenuation parameter (CAP) ≤ 269 dB/m; moderate: 269–296 dB/m; severe: CAP > 296 dB/m) employing 10 machine learning algorithms: logistic regression (LR), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), bootstrap aggregating, decision tree, K-nearest neighbours, light gradient boosting machine, naive Bayes, random forest, and support vector machine. These models were externally validated using the National Health and Nutrition Examination Survey (NHANES) 2017–2023 datasets. In the hospital outpatient cohort, machine learning algorithms demonstrated robust predictive capabilities. Notably, LR achieved the highest accuracy (ACC) of 0.711 in the test cohort and 0.728 in the validation cohort, coupled with robust areas under the receiver operating characteristic curve (AUC) values of 0.798 and 0.806, respectively. Similarly, MLP and XGBoost showed promising results, with MLP achieving an ACC of 0.735 in the test cohort, and XGBoost registering an AUC of 0.798. External validation using the NHANES datasets yielded consistent AUC results, with LR (0.831), MLP (0.823), and XGBoost (0.784) performing robustly. This study demonstrated that machine learning models constructed using a combination of essential demographic and clinical characteristics can accurately screen for MASLD in the general population. This approach significantly enhances the feasibility, accessibility, and compliance of MASLD screening and provides an effective tool for large-scale health assessments and early intervention strategies.

The study was conducted to investigate the difference between Han Chinese and Caucasians on various parameters measured from responses to transcranial magnetic brain stimulation (TMS). Sixteen subjects were studied in each group. A circular coil at the vertex was used for stimulation, whilst recording surface electromyograms from right first dorsal interosseous. In the passive state, motor-evoked potential (MEP) threshold, MEP recruitment, short-interval intracortical inhibition (SICI) and intracortical facilitation were measured. The MEP threshold, recruitment and silent period were also measured in the active state. Chinese subjects showed significantly higher passive thresholds (P < 0.005), less inhibition of the motor response (SICI, P < 0.0005) and a shorter silent period (P < 0.05). Differences in SICI appeared to be a consequence of the differences in passive threshold and were not seen when active threshold was used to determine the conditioning stimulus intensity. Differences in silent period may also reflect differences in cortical excitability rather than inhibitory processes, as they were not seen when the silent-period duration was expressed as a function of MEP size, rather than TMS intensity. There appears to be a significant difference in some TMS parameters between Han Chinese and Caucasian subjects. This may reflect an underlying difference in cortical excitability.

Dysregulations in transcription factors (TFs) and their genetic products play important roles in tumorigenesis, tumor progression and metastasis. However, prognostic value of the transcriptional regulatory networks in different cancers has not been investigated in depth. The purpose of our study was to identify and validate a potential predictive signature that combines TFs and their regulatory products in eight solid tumors. We used bioinformatics analysis to identify MET Transcriptional Regulator (MACC1) and Serine Peptidase Inhibitor Kunitz Type 1 (SPINT1) as candidate TFs with the respective downstream regulatory proteins for patient prognosis in pan‐cancer. Subsequent molecular analysis of clinical gastric cancer tissue samples further verified the negative correlation between MACC1 and SPINT1. Further, we showed that mechanistically, MACC1/SPINT1 mediated the pro‐HGF proteolysis and c‐Met phosphorylation in HGF/c‐MET signaling pathway. Kaplan‐Meier plots and receiver operating characteristics analysis revealed that the two‐gene signature combining MACC1 with SPINT1 was effective in predicting survival in all eight cancer cohorts tested. In conclusion, our study clarified the regulatory relationship between MACC1 and SPINT1 in the context of the HGF/c‐MET signaling pathway and determined MACC1/SPINT1 panel as a valuable signature for the prediction of prognosis in patients for multiple solid cancer types.

Background: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of this investigation was to develop a sophisticated machine learning (ML) model, leveraging comprehensive clinical datasets, to forecast the likelihood of significant liver fibrosis in patients with retinopathy and to interpret the ML model by applying the SHapley Additive exPlanations (SHAP) method. Methods: This inquiry was based on data from the National Health and Nutrition Examination Survey 2005–2008 cohort. Utilizing the Fibrosis-4 index (FIB-4), liver fibrosis was stratified across a spectrum of grades (F0-F4). The severity of retinopathy was determined using retinal imaging and segmented into four discrete gradations. A ten-fold cross-validation approach was used to gauge the propensity towards liver fibrosis. Eight ML methodologies were used: Extreme Gradient Boosting, Random Forest, multilayer perceptron, Support Vector Machines, Logistic Regression (LR), Plain Bayes, Decision Tree, and k-nearest neighbors. The efficacy of these models was gauged using metrics, such as the area under the curve (AUC). The SHAP method was deployed to unravel the intricacies of feature importance and explicate the inner workings of the ML model. Results: The analysis included 5,364 participants, of whom 2,116 (39.45%) exhibited notable liver fibrosis. Following random allocation, 3,754 individuals were assigned to the training set and 1,610 were allocated to the validation cohort. Nine variables were curated for integration into the ML model. Among the eight ML models scrutinized, the LR model attained zenith in both AUC (0.867, 95% CI: 0.855–0.878) and F1 score (0.749, 95% CI: 0.732–0.767). In internal validation, this model sustained its superiority, with an AUC of 0.850 and an F1 score of 0.736, surpassing all other ML models. The SHAP methodology unveils the foremost factors through importance ranking. Conclusion: Sophisticated ML models were crafted using clinical data to discern the propensity for significant liver fibrosis in patients with retinopathy and to intervene early. Practice implications: Improved early detection of liver fibrosis risk in retinopathy patients enhances clinical intervention outcomes.

This study aims to evaluate the efficacy and safety of 3D printing technology in brachytherapy for cervical cancer, comparing its outcomes with conventional free hand implantation brachytherapy. A total of 50 cervical cancer patients treated at the First Affiliated Hospital of Gannan Medical College from January 2019 to July 2023 were included in this study. Patients were divided into two groups: 25 patients received intensity-modulated radiotherapy (IMRT) combined with 3D-printed brachytherapy, and 25 patients underwent IMRT combined with free hand brachytherapy implantation. Key indicators analyzed included short-term therapeutic effects, survival outcomes, operation times, the number of CT scans, the number of needles inserted, dosimetric parameters, and complications. The use of 3D-printed brachytherapy significantly improved the safety of radiation therapy operations, especially for large tumors (≥ 30 mm), by providing more precise dose distribution and reducing the radiation doses received by critical organs such as the bladder and rectum. Compared to the artificial implant group (88% prevalence), the 3D-printed brachytherapy group showed a significantly lower incidence of radiation enteritis (29.2% prevalence, p < 0.001). There were no significant differences in other complications between the two groups. For instance, the incidence of radiation cystitis was relatively high in the 3D-printed brachytherapy group (79.2% prevalence) compared to the artificial implant group (64% prevalence, p = 0.240). The median follow-up period in this study was 22.5 months [IQR 18–29]. Among the 49 patients included, 43 had cervical squamous carcinoma and 6 had cervical adenocarcinoma. Short-term therapeutic response rates were comparable, with no significant difference in overall survival observed between the two groups. 3D-printed brachytherapy offers a more effective and safer therapeutic option for patients with cervical cancer, particularly for those with large tumors or complex anatomical structures.