Interventional effects of Pueraria oral liquid on T2DM rats and metabolomics analysis.

Pueraria lobata, commonly known as kudzu, is a medicinal and food plant widely used in the food, health food, and pharmaceutical industries. It has clinical pharmacological effects, including hypoglycemic, antiinflammatory, and antioxidant effects. However, its mechanism of hypoglycemic effect on type 2 diabetes mellitus (T2DM) has not yet been elucidated. In this study, we prepared a Pueraria lobata oral liquid (POL) and conducted a comparative study in a T2DM rat model to evaluate the hypoglycemic effect of different doses of Pueraria lobata oral liquid. Our objective was to investigate the hypoglycemic effect of Puerarin on T2DM rats and understand its mechanism from the perspective of metabolomics. In this study, we assessed the hypoglycemic effect of POL through measurements of FBG, fasting glucose tolerance test, plasma lipids, and liver injury levels. Furthermore, we examined the mechanism of action of POL using hepatic metabolomics. The study's findings demonstrated that POL intervention led to improvements in weight loss, blood glucose, insulin, and lipid levels in T2DM rats, while also providing a protective effect on the liver. Finally, POL significantly affected the types and amounts of hepatic metabolites enriched in metabolic pathways, providing an important basis for revealing the molecular mechanism of Pueraria lobata intervention in T2DM rats. These findings indicate that POL may regulate insulin levels, reduce liver damage, and improve metabolic uptake in the liver. This provides direction for new applications and research on Pueraria lobata to prevent or improve T2DM.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS: Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS: The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Single-cell transcriptomics reveals activation of endothelial cell and identifies LHPP as a potential target in ulcerative colitis.

This study delves into Ulcerative colitis (UC), a persistent gastrointestinal disorder marked by inflammation and ulcers, significantly elevating colorectal cancer risk. The emergence of single-cell RNA sequencing (scRNA-seq) technology has opened new avenues for dissecting the intricate cellular dynamics and molecular mechanisms at play in UC pathology. By analyzing scRNA-seq data from individuals with UC, our study has revealed a consistent enhancement of inflammatory response pathways throughout the course of the disease, alongside detailing the characteristics of endothelial cell damage within colitis environments. A noteworthy finding is the downregulation of Phospholysine Phosphohistidine Inorganic Pyrophosphate Phosphatase (LHPP), which exhibited a inversely correlate with STAT3 expression levels. The markedly reduced expression of LHPP in both the tissues and plasma of UC patients positions LHPP as a compelling target for therapeutic intervention. Our findings highlight the pivotal role LHPP could play in moderating inflammation, spotlighting its potential as a crucial molecular target in the quest to understand and treat UC.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma.

OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. CONCLUSION: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Podocyte-specific Nup160 knockout mice develop nephrotic syndrome and glomerulosclerosis.

More than 60 monogenic genes mutated in steroid-resistant nephrotic syndrome (SRNS) have been identified. Our previous study found that mutations in nucleoporin 160 kD (NUP160) are implicated in SRNS. The NUP160 gene encodes a component of the nuclear pore complex. Recently, two siblings with homozygous NUP160 mutations presented with SRNS and a nervous system disorder. However, replication of nephrotic syndrome (NS)-associated phenotypes in a mammalian model following loss of Nup160 is needed to prove that NUP160 mutations cause SRNS. Here, we generated a podocyte-specific Nup160 knockout (Nup160podKO) mouse model using CRISPR/Cas9 and Cre/loxP technologies. We investigated NS-associated phenotypes in these Nup160podKO mice. We verified efficient abrogation of Nup160 in Nup160podKO mice at both the DNA and protein levels. We showed that Nup160podKO mice develop typical signs of NS. Nup160podKO mice exhibited progression of proteinuria to average albumin/creatinine ratio (ACR) levels of 15.06 ± 2.71 mg/mg at 26 weeks, and had lower serum albumin levels of 13.13 ± 1.34 g/l at 30 weeks. Littermate control mice had urinary ACR mean values of 0.03 mg/mg and serum albumin values of 22.89 ± 0.34 g/l at the corresponding ages. Further, Nup160podKO mice exhibited glomerulosclerosis compared with littermate control mice. Podocyte-specific Nup160 knockout in mice led to NS and glomerulosclerosis. Thus, our findings strongly support that mutations in NUP160 cause SRNS. The newly generated Nup160podKO mice are a reliable mammalian model for future study of the pathogenesis of NUP160-associated SRNS.

Development and validation of a clinical prediction model for the risk of distal metastasis in intrahepatic cholangiocarcinoma: a real-world study.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. METHODS: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. RESULTS: This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. CONCLUSIONS: This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA.

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study.

Objective: Acute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm. Methods: We enrolled AIS patients in our hospital from August 2017 to July 2019, and divided them into the "prolonged LOS" group and the "no prolonged LOS" group. Prolonged LOS was defined as hospitalization for more than 7 days. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data. We compared the predictive capacity of extended LOS in eight different machine learning algorithms. SHapley Additive exPlanations (SHAP) values were used to interpret the outcome, and the most optimal model was assessed by discrimination, calibration, and clinical utility. Results: Prolonged LOS developed in 149 (22.0%) of the 677 eligible patients. In eight machine learning algorithms, prolonged LOS was best predicted by the Gaussian naive Bayes (GNB) model, which had a striking area under the curve (AUC) of 0.878 ± 0.007 in the training set and 0.857 ± 0.039 in the validation set. The variables sorted by the gap values showed that the strongest predictors were pneumonia, dysphagia, thrombectomy, and stroke severity. High net benefits were observed at 0%-76% threshold probabilities, while good agreement was found between the observed and predicted probabilities. Conclusions: The model using the GNB algorithm proved excellent for predicting prolonged LOS in AIS patients. This simple model of prolonged hospitalization could help adjust policies and better utilize resources.

An AMPK agonist suppresses the progress of colorectal cancer by regulating the polarization of TAM to M1 through inhibition of HIF-1α and mTOR signal pathway.

OBJECTIVE: This study aimed to evaluate the impact of an adenosine monophosphate-activated protein kinase (AMPK) agonist, metformin (MET), on the antitumor effects of macrophages and to determine the underlying mechanism involved in the process. MATERIALS AND METHODS: M0 macrophages were derived from phorbol-12-myristate-13-acetate-stimulated THP-1 cells. RESULTS: The levels of tumor necrosis factor-alpha (TNF-α) and human leukocyte antigen-DR (HLA-DR) were decreased in macrophages incubated with HCT116 cells, whereas those of arginase-1 (Arg-1), CD163, and CD206 were elevated; these effects were reversed by MET. The transfection of small interfering (si) RNA abrogated the influence of MET on the expression of the M1/M2 macrophage biomarkers. MET significantly suppressed the proliferation and migration abilities of HCT116 cells incubated with M0 macrophages; these actions were reversed by siRNA transfection against AMPK. The hypoxia-inducible factor 1-alpha (HIF-1α), phosphorylated protein kinase B (p-AKT), and phosphorylated mammalian target of rapamycin (p-mTOR) levels were reduced by the introduction of MET and promoted by siRNA transfection against AMPK. In addition, the levels of HIF-1α, p-AKT, and p-mTOR suppressed by MET were markedly increased following the transfection of siRNA against AMPK. CONCLUSION: These findings indicate that MET can repress the progression of colorectal cancer by transforming tumor-associated macrophages to the M1phenotype via inhibition of the HIF-1α and mTOR signaling pathways.

Deep Eutectic Solvent-Based Dispersive Liquid-Liquid Microextraction Coupled with LC-MS/MS for the Analysis of Two Ochratoxins in Capsicum.

Ochratoxins, a common class of mycotoxin in capsicum, and techniques and methods for the determination of mycotoxins in spices have been increasingly developed in recent years. An innovative and eco-friendly method of dispersive liquid-liquid microextraction (DLLME) was demonstrated in this study, based on a synthesized deep eutectic solvent (DES) combined with LC-MS/MS, for the quantification and analysis of two ochratoxins in capsicum. The DES-DLLME method parameters entail selecting the DES type (thymol:decanoic acid, molar ratio 1:1) and DES volume (100 µL). The volume of water (3 mL) and salt concentration (0 g) undergo optimization following a step-by-step approach to achieve optimal target substance extraction efficiency. The matrix effect associated with the direct detection of the target substance in capsicum was significantly reduced in this study by the addition of isotopic internal standards corresponding to the target substance. This facilitated optimal conditions wherein quantitative analysis using LC-MS/MS revealed a linear range of 0.50-250.00 µg/mL, with all two curves calibrated with internal standards showing correlation coefficients (r2) greater than 0.9995. The method's limits of detection (LODs) and limits of quantification (LOQs) fell in the ranges of 0.14-0.45 µg/kg and 0.45-1.45 µg/kg, respectively. The method's spiked recoveries ranged from 81.97 to 105.17%, indicating its sensitivity and accuracy. The environmental friendliness of the technique was assessed using two green assessment tools, AGREE and complexGAPI, and the results showed that the technique was more in line with the concept of sustainable development compared to other techniques for detecting ochratoxins in capsicum. Overall, this study provides a new approach for the determination of mycotoxins in a complex food matrix such as capsicum and other spices using DES and also contributes to the application of green analytical chemistry methods in the food industry.

Development and comparative analysis of initiation ability in large-scale Heuchera propagation using tissue culture versus cuttings.

The Heuchera genus, a member of the Saxifragaceae family, encompasses a wide array of varieties and hybrids, serving both traditional medicinal and ornamental purposes. However, a significant knowledge gap persists in achieving efficient mass propagation of diverse Heuchera cultivars creating a substantial market void. To address this, our study focuses on expedited seedling regeneration by investigating leaf cutting and tissue culture techniques to offer novel insights to cultivators. Herein, we successfully rooted thirteen distinct cultivars from the Heuchera and Heucherella (Heuchera × Tiarella) genera through cutting. Moreover, in vitro culture experiments led to the successful induction of calli and shoots from petiole samples. Notably, variations in measured parameters were observed across cultivars in both cutting and tissue culture methodologies. When petiole explants were exposed to cytokinin 6-benzylaminopurine (BA) at concentrations of 0.5, 1.0, and 2.0 mg/L along with auxin α-naphthaleneacetic acid (NAA) at 0.5 mg/L, shoots were produced either directly or indirectly during the primary culture. Exposure to darkness and the application of 2,4-dichlorophenoxyacetic acid (2,4-D) did not promote shoot formation but were beneficial for callus stimulation. Interestingly, a negative correlation was observed between the ease of initiating cutting recovery and inducting tissue culture regeneration, suggesting that cultivars that easily regenerate through cutting might encounter difficulties during induction by tissue culture. In light of these findings, we devised a streamlined and effective protocol for rapid Heuchera propagation. This protocol involves micropropagation, directly acquiring adventitious shoots from primary cultures supplemented by cutting-based propagation methods.

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke.

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100ß, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100ß, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100ß, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

Corrigendum: A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: a real-world retrospective study.

[This corrects the article DOI: 10.3389/fnins.2023.1130831.].

A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study.

Background and purpose: Recurrent stroke accounts for 25-30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS). Methods: A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator. Results: Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly. Conclusion: This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

The predictors of death within 1 year in acute ischemic stroke patients based on machine learning.

Objective: To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms. Methods: This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator. Results: Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year. Conclusions: The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.

Effect of ertugliflozin on renal function and cardiovascular outcomes in patients with type 2 diabetes mellitus: A systematic review and meta-analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) is growing yearly. The efficacy of ertugliflozin (ERT), a recently licensed anti-diabetic drug, has been widely reported. However, additional evidence-based data is required to ensure its safety. In particular, convincing evidence on the effects of ERT on renal function and cardiovascular outcomes is needed. METHODS: We searched PubMed, Cochrane Library, Embase, and Web of Science for randomized placebo-controlled trials of ERT for T2DM published up to August 11, 2022. Cardiovascular events here mainly refer to acute myocardial infarction and angina pectoris (AP) (including stable AP and unstable AP). The estimated glomerular filtration rate (eGFR) was used to measure renal function. The pooled results are risk ratios (RRs) and 95% confidence intervals (CIs). Two participants worked independently to extract data. RESULTS: We searched 1516 documents and filtered the titles, abstracts, and full text, 45 papers were left. Seven trials met the inclusion criteria and were ultimately included in the meta-analysis. The meta-analysis found that ERT reduced eGFR by 0.60 mL·min-1·1.733 m-2 (95% CI: -1.02--0.17, P = .006) in patients with T2DM when used for no more than 52 weeks and these differences were statistically significant. Compared with placebo, ERT did not increase the risk of acute myocardial infarction (RR 1.00, 95% CI: 0.83-1.20, P = .333) and AP (RR 0.85, 95% CI: 0.69-1.05, P = .497). However, the fact that these differences were not statistically significant. CONCLUSION: This meta-analysis shows that ERT reduces eGFR over time in people with T2DM but is safe in the incidence of specific cardiovascular events.

A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study.

Background: Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to predict the risk of LM using the nomogram in patients with osteosarcoma. Methods: A total of 1100 patients who were diagnosed as osteosarcoma between 2010 and 2019 in the Surveillance, Epidemiology and End Results (SEER) database were selected as the training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice. Results: A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model. Conclusions: In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.

Single-cell analysis identifies phospholysine phosphohistidine inorganic pyrophosphate phosphatase as a target in ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) is a chronic gastrointestinal disorder characterized by inflammation and ulceration, representing a significant predisposition to colorectal cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) technology offer a promising avenue for dissecting the complex cellular inter-actions and molecular signatures driving UC pathology. AIM: To utilize scRNA-seq technology to dissect the complex cellular interactions and molecular signatures that underlie UC pathology. METHODS: In this research, we integrated and analyzed the scRNA-seq data from UC patients. Moreover, we conducted mRNA and protein level assays as well as pathology-related staining tests on clinical patient samples. RESULTS: In this study, we identified the sustained upregulation of inflammatory response pathways during UC progression, characterized the features of damaged endo-thelial cells in colitis. Furthermore, we uncovered the downregulation of phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) has a negative correlation with signal transducer and activator of transcription 3. Significant downregulation of LHPP in UC patient tissues and plasma suggests that LHPP may serve as a potential therapeutic target for UC. This paper highlights the importance of LHPP as a potential key target in UC and unveils its potential role in inflammation regulation. CONCLUSION: The findings suggest that LHPP may serve as a potential therapeutic target for UC, emphasizing its importance as a potential key target in UC and unveiling its role in inflammation regulation.

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules.

Objective: This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms. Methods: A retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC). Results: The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost. Conclusions: Combining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Comparative effectiveness of first-line therapies for eradication of antibiotic-resistant Helicobacter pylori strains: A network meta-analysis.

BACKGROUND: As a first-line treatment regimen for Helicobacter pylori (H. pylori) infection, antibiotic therapy is widely used worldwide. However, the question of increasing antibiotic resistance must be considered. Given this issue, we need to find ways to reduce drug resistance. This study examined all currently available first-line regimens and compared them with standard triple treatment through a network meta-analysis of randomized controlled trials (RCTs). AIM: To compare first-line treatment regimens for eradication of antibiotic-resistant H. pylori strains. METHODS: To compare the effectiveness of the first-line regimens for treating H. pylori infection, a Bayesian network meta-analysis was applied to process data extracted from RCTs. The plausible ranking for each regimen was assessed by the surface under the cumulative ranking curve (SUCRA). In addition, we conducted a relevant search by reference citation analysis. RESULTS: Twenty-five RCTs involving 12029 participants [including 1602 infected with clarithromycin (CAM)-resistant strains and 1716 infected with metronidazole (MNZ)-resistant strains] were included, in which a total of seven regimens were used for H. pylori eradication. The results showed that dual therapy containing a high-dose proton pump inhibitor (HDDT) [odds ratio (OR): 4.20, 95% confidence interval (CI): 2.29-8.13] was superior to other therapies for all patients, including those with CAM/MNZ-resistant H. pylori infection. In the comparative effectiveness ranking, for CAM-resistant H. pylori, HDDT (OR: 96.80, 95%CI: 22.46-521.9) had the best results, whereas standard triple therapy ranked last (SUCRA: 98.7% vs 0.3%). In the subgroup of high cure rates (≥ 90%), HDDT was also generally better than other therapies. CONCLUSION: For the eradication of CAM- and MNZ-resistant H. pylori strains, HDDT exhibited considerable advantages. The studies of CAM-resistant H. pylori were based on small samples due to a lack of antibiotic sensitivity tests in many RCTs, but the results showed that all patients, including those with CAM-resistant H. pylori infection, had a concordant trend. Overall, HDDT may be a reference for RCTs and other studies of H. pylori eradication.