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Background: Extracorporeal cardiopulmonary resuscitation (ECPR) improves the prognosis of in-hospital cardiac arrest (IHCA). The six-factor RESCUE-IHCA score (resuscitation using ECPR during IHCA) was developed to predict outcomes of post-IHCA ECPR-treated adult patients. Our goal was to validate the score in an Asian medical center with a high volume and experience of ECPR performance and to compare the differences in patient characteristics between the current study and the original cohort in a 2022 observational study.Method: For this single-center, retrospective cohort study we enrolled 324 ECPR-treated adult IHCA patients. The primary outcome was in-hospital mortality. We used the area under the receiver operating curve (AUROC) to externally validate the RESCUE-IHCA score. The calibration of the model was tested by the decile calibration plot as well as Hosmer–Lemeshow goodness-of-fit with an associated P-value.Results: Of the 324 participants, 231 (71%) died before hospital discharge. The discriminative performance of the RESCUE-IHCA score was comparable with the originally validated cohort, with an AUC of 0.63. A prolonged duration of cardiac arrest was associated with an increased risk of mortality (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.01–1.03, P = .006). An initial rhythm of ventricular tachycardia (OR 0.14, 95% CI 0.04–0.51, P = .003), ventricular fibrillation (OR 0.11, 95% CI 0.03–0.46, P = .003), and palpable pulse (OR 0.26, 95% CI 0.07–0.92, P = 0.04) were associated with a reduced mortality risk compared to asystole or pulseless electrical activity. In contrast to the original study, age (P = 0.28), resuscitation timing (P = 0.14), disease category (P = 0.18), and pre-existing renal insufficiency (P = 0.12) were not associated with in-hospital death.Conclusion: In external validation, the RESCUE-IHCA score exhibited performance comparable to its original validation within the single-center population. Further investigation on hospital experience, time-of-day effect, and specific disease categories is warranted to improve the selection criteria for ECPR candidates during IHCA.

BackgroundSystematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.ObjectiveTo develop a prediction model for maternal tobacco smoking in late pregnancy based on birth certificate information using a combination of self- or provider-reported smoking and biomarkers (smoking metabolites) in neonatal blood spots as the alloyed gold standard.MethodsWe designed a case-control study where childhood cancer cases were identified from the California Cancer Registry and controls were from the California birth rolls between 1983 and 2011 who were cancer-free by the age of six. In this analysis, we included 894 control participants and performed high-resolution metabolomics analyses in their neonatal dried blood spots, where we extracted cotinine [mass-to-charge ratio (m/z) = 177.1023] and hydroxycotinine (m/z = 193.0973). Potential predictors of smoking were selected from California birth certificates. Logistic regression with stepwise backward selection was used to build a prediction model. Model performance was evaluated in a training sample, a bootstrapped sample, and an external validation sample.ResultsOut of seven predictor variables entered into the logistic model, five were selected by the stepwise procedure: maternal race/ethnicity, maternal education, child's birth year, parity, and child's birth weight. We calculated an overall discrimination accuracy of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval [CI] 0.77, 0.84) in the training set. Similar accuracies were achieved in the internal (AUC 0.81, 95% CI 0.77, 0.84) and external (AUC 0.69, 95% CI 0.64, 0.74) validation sets.ConclusionsThis easy-to-apply model may benefit future birth registry-based studies when there is missing maternal smoking information; however, some smoking status misclassification remains a concern when only variables from the birth certificate are used to predict maternal smoking.

ABSTRACT Aims and Objectives Background Design Methods Results Conclusions Relevance to Clinical Practice Reporting Method Patient or Public Contribution To develop and validate a prediction model for high‐flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF).AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non‐invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction.Multicentre observational study.This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)‐based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models.The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis.This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF.Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC.TRIPOD checklist for prediction model studies was followed in this study.Patients were involved in the sample of the study. [ABSTRACT FROM AUTHOR]

BACKGROUND: The high rate of postoperative hypoproteinemia in patients undergoing hip revision is associated with severe trauma, which affects the rapid recovery of patients. OBJECTIVE: To investigate the risk factors of perioperative hypoproteinemia in patients with hip revision, and to provide guidance for early screening of highrisk patients with postoperative hypoproteinemia. METHODS: According to the inclusion and exclusion criteria, 161 patients who underwent hip revision were divided into hypoproteinemia group (76 cases) and normal group (85 cases). The rate of hypoproteinemia was 47.2%. Data such as age, gender, body mass index, osteoporosis, operation time, preoperative erythrocytes, preoperative hemoglobin, preoperative leukocytes, preoperative platelets, preoperative fibrinogen, preoperative C-reaction protein, preoperative sedimentation rate, preoperative blood calcium, preoperative albumin, postoperative drainage tube placement, American Society of Anesthesiologists score, and postoperative hypoproteinemia were collected. SPSS software was used to analyze the independent risk factors of hypoproteinemia after hip revision using multivariate binary logistic regression analysis. R software was used to construct the nomogram prediction model. Receiver operating characteristic curve and calibration curve and decision curve were drawn to evaluate the model. RESULTS AND CONCLUSION: (1) Univariate analysis results showed that body mass index, preoperative erythrocytes, preoperative hemoglobin, preoperative platelets, preoperative fibrinogen, preoperative C-reaction protein, and operation time were significantly different between the two groups (P < 0.05). (2) Multivariate binary Logistic regression analysis results showed that body mass index (OR=0.859, P=0.021), operation time (OR=1.010, P=0.002), preoperative erythrocytes (OR=0.424, P=0.036), and preoperative C-reaction protein (OR=1.043, P=0.032) levels were independent risk factors for postoperative hypoproteinemia in patients with hip revision. (3) Based on four independent risk factors: body mass index, operation time, preoperative erythrocytes and preoperative C-reaction protein, the nomogram can effectively predict the risk of hypoproteinemia after hip revision. This nomogram prediction model has good differentiation and accuracy, and may lead to better clinical net benefits for patients. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is a common complication of mid–to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers. Methods: Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves. Results: Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband– wife relationship, family history, and parental relationships as predictors. The Hosmer–Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ2 = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89–0.97), 0.72 (95% CI: 0.62–0.81), and 0.68 (95% CI: 0.53–0.83), respectively. Conclusion: In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information. [ABSTRACT FROM AUTHOR]

Background: INCbase is an international, multicenter prospective observational study using a customizable web-based modular registry to study the clinical, biological and electrophysiological variation and boundaries of chronic inflammatory demyelinating polyneuropathy (CIDP). The primary objective of INCbase is to develop and validate a clinical prediction model for treatment response. Methods: All patients meeting clinical criteria for CIDP can be included in INCbase. Collected data include demographics, clinical history, diagnostics and various domains of clinical outcomes. Data is collected at a minimum of every 6 months for two years, and more frequently at the discretion of the investigational site to allow for assessment of unexpected changes in treatment response or clinical status. Participants can be enrolled in various sub-studies designed to capture data relevant to specific groups of interest. Data is entered directly into the web-based data entry system by local investigators and/or participants. Collection and local storage of biomaterial is optional. To develop a clinical prediction model for treatment response, newly diagnosed patients with active disease warranting start of first-line treatment will be included. The study population will be split into a development and validation cohort. Univariate and multivariate logistic regression analysis will be used to identify and combine predictors at start of treatment for treatment response at six months. Model performance will be assessed through discrimination and calibration in an external validation cohort. The externally validated prediction model will be made available to researchers and clinicians on the INCbase website. Discussion: With this study, we aim to create a clinically relevant and implementable prediction model for treatment response to first line treatments in CIDP. INCbase enrollment started in April 2021, with 29 centers across 8 countries and 303 patients participating to date. This collaborative effort between academia, patient advocacy organizations and pharmaceutical industry will deepen our understanding of how to diagnose and treat CIDP. [ABSTRACT FROM AUTHOR]

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging global infectious disease with a high mortality rate. Clinicians lack a convenient tool for early identification of critically ill SFTS patients. The aim of this study was to construct a simple and accurate nomogarm to predict the prognosis of SFTS patients. Methods: We retrospectively analyzed the clinical data of 372 SFTS patients collected between May 2015 and June 2023, which were divided 7:3 into a training set and an internal validation set. We used LASSO regression to select predictor variables and multivariable logistic regression to identify independent predictor variables. Prognostic nomograms for SFTS were constructed based on these factors and analysed for concordance index, calibration curves and area under the curve (AUC) to determine the predictive accuracy and consistency of the model. Results: In the training set, LASSO and multivariate logistic regression analyses showed that age, SFTSV RNA, maximum body temperature, pancreatitis, gastrointestinal bleeding, pulmonary fungal infection (PFI), BUN, and PT were independent risk factors for death in SFTS patients. There was a strong correlation between neurological symptoms and mortality (P < 0.001, OR = 108.92). Excluding neurological symptoms, nomograms constructed based on the other eight variables had AUCs of 0.937 and 0.943 for the training and validation sets, respectively. Furthermore, we found that age, gastrointestinal bleeding, PFI, bacteraemia, SFTSV RNA, platelets, and PT were the independent risk factors for neurological symptoms, with SFTSV RNA having the highest diagnostic value (AUC = 0.785). Conclusions: The nomogram constructed on the basis of eight common clinical variables can easily and accurately predict the prognosis of SFTS patients. Moreover, the diagnostic value of neurological symptoms far exceeded that of other predictors, and SFTSV RNA was the strongest independent risk factor for neurological symptoms, but these need to be further verified by external data. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aims Methods Results Conclusions Early identification of hepatitis B virus‐related acute‐on‐chronic liver failure (HBV‐ACLF) holds crucial importance in guiding clinical management and reducing mortality. However, existing scoring systems often overlook patient's underlying clinical condition, which significantly impacts prognosis.Use the age‐adjusted Charlson comorbidity index (aCCI) to evaluate the patient's complications to develop a more precise model for predicting transplant‐free mortality in HBV‐ACLF patients.Nine hundred and six patients were included for investigation and were segregated into a training cohort and a temporal validation cohort according to the chronological order of admission in a ratio of 7:3. In the training cohort, univariate analysis, logistic regression analysis and LASSO regression analysis were used to construct a prognostic model and it was subsequently validated in a temporal validation cohort and an external validation cohort.We found total bilirubin, neutrophils, international normalised ratio and aCCI exhibited significant associations with 28‐day transplant‐free mortality and established a novel prognostic model, named aCCI‐HBV‐ACLF. The model demonstrated strong predictive performance, with area under the receiver operating characteristic curve (ROC) values of 0.859 for 28‐day mortality, 0.822 for 90‐day mortality. In the temporal validation cohort, aCCI‐HBV‐ACLF achieved area under the ROC values of 0.869 for 28‐day mortality and 0.850 for 90‐day mortality. In the external validation cohort, aCCI‐HBV‐ACLF had area under the ROC values of 0.868 for 28‐day mortality and 0.888 for 90‐day mortality.This study proposes a new prognostic model, which achieved excellent predictive ability for 28−/90‐day transplant‐free mortality rates among patients with HBV‐ACLF. [ABSTRACT FROM AUTHOR]

Background: The purpose of this systematic review and meta-analysis is to assess the efficacy of various machine learning (ML) techniques in predicting preoperative lymph node metastasis (LNM) in patients diagnosed with papillary thyroid carcinoma (PTC). Although prior studies have investigated the potential of ML in this context, the current evidence is not sufficiently strong. Hence, we undertook a thorough analysis to ascertain the predictive accuracy of different ML models and their practical relevance in predicting preoperative LNM in PTC patients. Materials and methods: In our search, we thoroughly examined PubMed, Cochrane Library, Embase, and Web of Science, encompassing their complete database history until December 3rd, 2022. To evaluate the potential bias in the machine learning models documented in the included studies, we employed the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of 107 studies, involving 136,245 patients, were included. Among them, 21,231 patients showed central LNM (CLNM) and 4,637 had lateral LNM (LLNM). The meta-analysis results revealed that the c-index for predicting LNM, CLNM, and LLNM were 0.762 (95% CI: 0.747–0.777), 0.762 (95% CI: 0.747–0.777), and 0.803 (95% CI: 0.773–0.834) in the training set, and 0.773 (95% CI: 0.754–0.791), 0.762 (95% CI: 0.747–0.777), and 0.829 (95% CI: 0.779–0.879) in the validation set, respectively. A total of 134 machine learning-based prediction models were included, covering 10 different types. Logistic Regression (LR) was the most commonly used model, accounting for 81.34% (109/134) of the included models. Conclusions: Machine learning methods have shown a certain level of accuracy in predicting preoperative LNM in PTC patients, indicating their potential as a predictive tool. Their use in the clinical management of PTC holds great promise. Among the various ML models investigated, the performance of logistic regression-based nomograms was deemed satisfactory. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis. [ABSTRACT FROM AUTHOR]

Background Objectives Animals Methods Results Conclusions and Clinical Importance Sepsis is a main contributor to calf mortality, but diagnosis is difficult.Develop and validate a predictive model for bacteremia in critically ill calves (CIC).A total of 334 CIC, sampled for blood culture.Cross‐sectional study. Multivariable logistic regression and classification tree analysis on clinical, ultrasonographic, and laboratory variables were performed on a dataset including all animals. Model validation was done on 30% of the dataset. Similar statistics (except validation) were performed on a subset of the database (n = 143), in which presumed contaminants were excluded.The best performing model to predict bacteremia, taking all detected bacteria into account, included tachypnea, tachycardia, acidemia, hypoglycemia, venous hypoxemia, and hypoproteinemia. Sensitivity and specificity of this model were 70.6% and 98.0%, respectively, but decreased to 61.5% and 91.7% during model validation. The best‐performing model, excluding presumed contaminants, included abnormal temperature, heart rate, absence of enteritis, hypocalcemia, and hyperlactatemia as risk factors for bacteremia. Sensitivity and specificity of this model were 71.4% and 93.9%, respectively. Both classification trees performed less well in comparison to logistic regression. The classification tree excluding presumed contaminants, featured hypoglycemia, absence of diarrhea, and hyperlactatemia as risk factors for bacteremia. Sensitivity and specificity were 39.4% and 92.7%, respectively.Hypoglycemia, hyperlactatemia, and hypoproteinemia seem relevant in assessing bacteremia in CIC. The performance of these models based on basic clinical and blood variables remains insufficient to predict bacteremia. [ABSTRACT FROM AUTHOR]

Background: We evaluated the diagnostic value of homocysteine (Hcy) levels combined with the Wells score and established a prediction model for venous thromboembolism (VTE) occurrence in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) based on the Hcy level and the Wells score. Patients and methods: Clinical information from 914 patients with AECOPD was retrospectively collected in our hospital from June 2020 to October 2023. Receiver operating characteristic curves were plotted to evaluate the diagnostic ability of Hcy concentrations combined with Wells scores and the prediction ability of the model. Univariate and multivariate logistic regressions were used to explore the effects of Hcy levels and the Wells score on VTE occurrence. A nomogram was established for individual risk evaluation. Results: Hcy levels and Wells scores were significantly greater in the VTE group than in the non-VTE group (P < 0.001). The diagnostic ability of Hcy levels combined with the Wells score was greater than that Hcy levels or the Wells score alone. The AUC of the combined parameters was 0.935, with a sensitivity of 0.864 and a specificity of 0.855. Multivariate logistic regression indicated that elevated Hcy levels (OR:5.17, 95%CI: 3.76–7.09, P < 0.001) and Wells score (OR: 5.26, 95%CI: 3.22–8.59, P < 0.001) were independently associated with the risk of developing VTE in AECOPD patients. Decision curve analysis indicated that the net benefit of the nonadherence prediction nomogram was greater than that of the models adjusted for no or all variables, with a threshold of approximately 0.1-1.0. Conclusions: The established prediction model can be used to evaluate an individual's risk of developing VTE on the basis of the Hcy level, Wells score, and clinical parameters. AECOPD patients may benefit from the early intervention based on estimated risk probability. [ABSTRACT FROM AUTHOR]

The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model. Objective: Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage IIIII gastric cancer. Methods: A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies--Least absolute shrinkage and selection operator (LASSO) and random forest (RF)--we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogrammodel. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output. Results: Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy. Conclusion: In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making. [ABSTRACT FROM AUTHOR]

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F] Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice. [ABSTRACT FROM AUTHOR]

The rupture of vulnerable plaque (VP) are significant pathogenic factors leading to cardiovascular and cerebrovascular diseases. This study aims to construct a vulnerable plaque prediction model (VPPM) by combining multimodal vascular ultrasound parameters and clinical risk factors, and to validate it. A total of 196 atherosclerotic patients who underwent carotid endarterectomy (CEA) from January 2017 to December 2023 were collected and divided into a modeling group (n = 137) and a validation group (n = 59). Clinical information including: hypertension, diabetes, smoking history, and body mass index (BMI) was included in the analysis. All patients underwent carotid ultrasound and contrast-enhanced ultrasound (CEUS) examination after admission, with main ultrasound parameters including thickness, echogenicity types, stenosis degree, and CEUS neovascularization grading of plaques. Independent risk factors for VP in CEA patients were screened through binary Logistic regression analysis, and a prediction model was established along with a nomogram. The calibration curve, receiver-operating characteristic curve (ROC), and decision curve analysis (DCA) were employed to assess the calibration, diagnostic efficacy, and clinical utility of the VPPM model. There were no significant statistical differences in multimodal vascular ultrasound parameters and clinical risk factors between the modeling and validation groups (P > 0.05). Binary Logistic regression analysis identified plaque thickness, echo type, CEUS neovascularization grading, BMI, and smoking history as 5 variables entering the prediction model. The VPPM model showed good diagnostic efficacy, with an area under the ROC curve of 0.959 (95% CI 0.915–0.999). Using the nomogram with a VPPM risk assessment score of 135.42 as the diagnostic cutoff value in the modeling group, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were 88.1%, 94.1%, 14.98, 0.126, and 82.2%, respectively. In the DCA curve, the VPPM model curve was significantly better than two extreme lines, indicating good clinical utility. The VPPM model constructed by integrating multimodal ultrasound parameters and clinical key risk factors has high diagnostic efficacy and is expected to be an auxiliary tool for clinical diagnosis of vulnerable plaques. [ABSTRACT FROM AUTHOR]

&supSoft clay in the offshore area of the Yangtze River estuary has been investigated considering its basic physical properties. Forty-five unconfined compressive strength tests were conducted on the remolded marine soft clay to investigate the impacts of curing time T, water content w, plasticity index IP, and clay particle content; ρc on the thixotropic static shear strength ratio As of the marine soft clay from the Yangtze River estuary. Results show that the stress-strain curves were primarily strain hardening and strain softening curve types. Unconfined compressive strength qu increased with an increase in T. All specimens with different basic physical properties were capable of thixotropic strength recovery. When T = 0-28 days, As increased rapidly, while for T > 28 days, As of most specimens increased slightly or tended to stabilize. The impacts of w, IP and ρc on As do not follow a consistent pattern, but there is a strong correlation between As and w/wL (wL is the liquid-limit water content). For w/wL < 0.75, As increased with increasing w/wL, whereas for w/wL =0.75, As decreased with increasing w/wL. We proposed a simple and widely applicable power function prediction model for the As of the soft clay from the Yangtze River estuary. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Methods Results Conclusion This study aims to evaluate the predictive performance and methodological quality of post‐stroke readmission prediction models, identify key predictors associated with readmission, and provide guidance for selecting appropriate risk assessment tools.A comprehensive literature search was conducted from inception to February 1, 2024. Two independent researchers screened the literature and extracted relevant data using the CHARMS checklist.Eleven studies and 16 prediction models were included, with sample sizes ranging from 108 to 803,124 cases and outcome event incidences between 5.2% and 50.0%. The four most frequently included predictors in the models were length of stay, hypertension, age, and functional disability. Twelve models reported an area under the curve (AUC) ranging from 0.520 to 0.940, and five models provided calibration metrics. Only one model included both internal and external validation, while six models had internal validation. Eleven studies were found to have a high risk of bias (ROB), predominantly in the area of data analysis.This systematic review included 16 readmission prediction models for stroke, which generally exhibited good predictive performance and can effectively identify high‐risk patients likely to be readmitted. However, the generalizability of these models remains uncertain due to methodological limitations. Rather than developing new readmission prediction models for stroke, the focus should shift toward external validation and the iterative adaptation of existing models. These models should be tailored to local settings, extended with new predictors if necessary, and presented in an interactive graphical user interface. [ABSTRACT FROM AUTHOR]

Shot peening is a mechanical surface treatment process that enhances the fatigue performance of metallic components. Numerous studies have demonstrated the significant influence of peening particles' motion on the quality of treatment. However, previous research has not established a connection between particle motion characteristics and nozzle structure. The nozzle structure directly affects both airflow characteristics and shot movement, making it crucial to examine how it influences particle motion to optimize both nozzle design and process parameters. To accurately determine the motion characteristics of particles, this study constructs a high-speed camera system for capturing their movements and uses the Particle Image Velocimetry (PIV) method for analysis. This study thoroughly investigates how factors such as jet pressure, nozzle diameter, and length affect shot motion behavior. The results indicate positive correlations between particle velocity with jet pressure and nozzle length while showing a negative correlation with nozzle diameter. Modifying structural parameters can effectively regulate both the velocity and dispersion angles of particles during peening operations under specified jet pressure conditions. Furthermore, based on experimental findings, a new prediction model for particle velocity considering structural parameters associated with nozzles was proposed. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) syndrome is a prevalent form of respiratory sleep disorder, with an increasing prevalence among children. The consequences of OSA include obesity, diabetes, cardiovascular disease, and neuropsychological diseases. Despite its pervasive impact, a significant proportion of individuals especially children remain unaware that they suffer from OSA. Consequently, there is an urgent need for an accessible diagnostic approach. In this study, we conducted a bioinformatic analysis to identify potential biomarkers from a proteomics dataset comprising serum samples from children with OSA in the progression stage. In the Gene Set Enrichment Analysis (GSEA), we observed that the complement and immune response pathways persisted throughout the development of OSA and could be detected in the early stages. Subsequent to soft clustering and WGCNA analysis, it was revealed that the Hippo pathway, including ITGAL and FERMT3, plays a role in mild OSA. The analysis revealed a significant alteration of the complement and coagulation pathways, including TFPI and MLB2, in moderate OSA. In severe OSA, there was an association between hypoxia and the extracellular matrix (ECM) receptor interaction and collagen binding. In summary, it can be posited that the systemic inflammation may persist throughout the progression of OSA. Furthermore, severe OSA is characterized by abnormal vascular endothelial function, which may be attributed to chronic hypoxia. Finally, four potential biomarkers (ITGAL, TFPI, TTR, ANTXR1) were identified based on LASSO regression, and a prediction model for OSA progression was constructed based on the biomarkers. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to develop and validate a machine learningbased model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study. Methods: This study was a retrospective cohort study. The study population was 1855 participants who underwent consecutive physical examinations at the First Affiliated Hospital of Soochow University between 2018 and 2022. The dataset included medical history, physical examination, and biochemical index test results. The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. The performance of these models is evaluated by an area under the receiver operating characteristic (ROC) curves (AUC), calibration curves and decision curve analysis (DCA). To interpret the best-performing model, the Shapley Additive exPlanation (SHAP) Plots was used in this study. Results: The training/validation dataset consists of 1,855 individuals from the First Affiliated Hospital of Soochow University, yielded significant variables following selection by the Boruta algorithm and logistic multivariate regression analysis. These significant variables included systolic blood pressure (SBP), fatty liver, waist circumference (WC) and serum creatinine (Scr). The XGBoost model outperformed the other models, demonstrating an AUC of 0.7391 in the validation set. Conclusions: The XGBoost model was composed of SBP, fatty liver, WC and Scr may assist doctors with the early identification of IFG in middle-aged and elderly people. [ABSTRACT FROM AUTHOR]

Background: Osteonecrosis of the jaw (ONJ) stands as a severe complication linked to the use of bisphosphonates, particularly zoledronic acid, which is widely prescribed for managing conditions like osteoporosis and bone metastasis. This study is geared towards the development and validation of a clinical prediction model for ONJ in patients undergoing zoledronic acid treatment. Methods: We harnessed data from the FDA Adverse Event Reporting System (FAERS) as our training dataset, while the Canada Vigilance Adverse Reaction (CVAR) database served as the testing dataset. The study encompassed patients treated with zoledronic acid and subsequently diagnosed with ONJ. We analysed a range of predictive factors, including breast cancer, bone metastasis, osteoporosis, vitamin D and calcium levels, comorbidities, the number of concomitant medications, dosage, age, weight, and gender. Logistic regression and nomogram analysis were the chosen methodologies for constructing the predictive model. To evaluate the model's performance, we utilized receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The study encompassed a total of 2,126 patients in the training cohort, 911 patients in the internal test cohort from the FAERS database, and 121 patients in the external test cohort from the CVAR database. Notable predictors for ONJ included bone metastasis (OR: 1.65, 95% CI: 1.22-2.24), osteoporosis (OR: 0.33, 95% CI: 0.21-0.52), the number of concomitant medications (OR: 1.07, 95% CI: 1.05-1.09), and the dosage of zoledronic acid (OR: 1.24, 95% CI: 1.10-1.39). The nomogram exhibited robust discriminatory power, evidenced by an area under the curve (AUC) of 0.77 in the training cohort, 0.76 in the internal test cohort, and 0.90 in the external test cohort. Calibration plots demonstrated a strong alignment between observed and predicted probabilities. Furthermore, DCA highlighted the prediction model's significant net benefit across various threshold probabilities. Conclusion: By leveraging data from both the FAERS and Canadian databases, this study has successfully developed and validated a clinical prediction model for ONJ in patients receiving zoledronic acid. This model stands as a valuable tool for clinicians, enabling them to pinpoint high-risk patients and make evidence-based treatment decisions to minimize the risk of ONJ. [ABSTRACT FROM AUTHOR]

Introduction: Predicting TCR-peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR-peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR-peptide complexes, which provide much more information, could potentially lead to better prediction outcomes. Methods: In this study, we developed TCRcost, a deep learning method, to predict TCR-peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR-peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR-peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR-peptide binding. Results: Test results demonstrated that predicting TCR-peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures. Discussion: Thus, utilizing structural information of TCR-peptide complexes is a promising approach to improve the accuracy of binding predictions. [ABSTRACT FROM AUTHOR]

Objective To investigate the determinants of linezolid-associated neurological adverse reactions in patients with multidrug-resistant tuberculosis and develop a risk prediction model for such adverse events. Methods A prospective cohort study design was employed to select 120 patients with drug-resistant pulmonary tuberculosis who received a chemotherapy regimen containing linezolid at Guangzhou Chest Hospital from April 2023 to January 2024 as the study population. Clinical data, adverse reactions, and plasma concentration of linezolid were collected during fasting and at 2 hours post-medication. Univariate analysis and multivariate logistic regression were conducted to identify factors influencing linezolid-related neurological adverse reactions. Furthermore, a prediction model for such adverse reactions was developed, and its predictive efficacy and calibration ability were evaluated using ROC analysis. Results Re-treatment (OR = 2.540, P =0.028), coexistence of cavities (OR = 4.092, P = 0.021), anemia (OR = 10.921,P = 0.005), and Cmin 5 0.7665 mg/L (OR = 6.813,P < 0.001) are independent risk factors for the occurrence of linezolid-related neurological adverse reactions. The prediction model, based on these four factors, exhibits an AUC of 0.851 (95% CI:0.774 ~ 0.929), accompanied by a Youden index of 0.590, a sensitivity of 66.7%, and a specificity of 92.3%. Moreover, the prediction model demonstrates excellent calibration ability. (Hosmer-lemeshow X² = 8.719, P = 0.273). Conclusion In MDR/RR-TB patients, the presence of cavitation, retreatment, and anemia may confer a heightened risk of linezolid-related neurological adverse reactions. A risk prediction model incorporating these four indicators demonstrates significant predictive value for the occurrence of such adverse events. [ABSTRACT FROM AUTHOR]

Background and Purpose Methods Results Conclusions Elevated intracranial pressure (ICP) resulting from severe head injury or stroke poses a risk of secondary brain injury that requires neurosurgical intervention. However, currently available noninvasive monitoring techniques for predicting ICP are not sufficiently advanced. We aimed to develop a minimally invasive ICP prediction model using simple CT images to prevent secondary brain injury caused by elevated ICP.We used the following three methods to determine the presence or absence of elevated ICP using midbrain‐level CT images: (1) a deep learning model created using the Python (PY) programming language; (2) a model based on cistern narrowing and scaling of brainstem deformities and presence of hydrocephalus, analyzed using the statistical tool Prediction One (PO); and (3) identification of ICP by senior residents (SRs). We compared the accuracy of the validation and test data using fivefold cross‐validation and visualized or quantified the areas of interest in the models.The accuracy of the validation data for the PY, PO, and SR methods was 83.68% (83.42%‐85.13%), 85.71% (73.81%‐88.10%), and 66.67% (55.96%‐72.62%), respectively. Significant differences in accuracy were observed between the PY and SR methods. Test data accuracy was 77.27% (70.45%‐77.2%), 84.09% (75.00%‐85.23%), and 61.36% (56.82%‐68.18%), respectively.Overall, the outcomes suggest that these newly developed models may be valuable tools for the rapid and accurate detection of elevated ICP in clinical practice. These models can easily be applied to other sites, as a single CT image at the midbrain level can provide a highly accurate diagnosis. [ABSTRACT FROM AUTHOR]

Background: Quality caregiver-infant interaction is crucial for infant growth, health, and development. Traditional methods for evaluating the quality of caregiver-infant interaction have predominantly relied on rating scales or observational techniques. However, rating scales are prone to inaccuracies, while observational techniques are resource-intensive. The utilization of easily collected medical records in conjunction with machine learning techniques offers a promising and viable strategy for accurate and efficient assessment of caregiver-infant interaction quality. Methods: This study was conducted at a follow-up outpatient clinic at two tertiary maternal and infant health centers located in Shanghai, China. 68 caregivers and their 3-15-month-old infants were videotaped for 3–5 min during playing interactions in non-threatening environment. Two trained experts utilized the Infant CARE-Index (ICI) procedure to assess whether the caregivers were sensitive or not in a dyadic context. This served as the gold standard. Predictors were collected through Health Information Systems (HIS) and questionnaires, which included accessible features such as demographic information, parental coping ability, infant neuropsychological development, maternal depression, parent-infant interaction, and infant temperament. Four classification models with fivefold cross-validation and grid search hyperparameter tuning techniques were employed to yield prediction metrics. Interpretable analyses were conducted to explain the results. Results: The score of sensitive caregiver-infant interaction was 6.34 ± 2.62. The Random Forest model gave the best accuracy (83.85%±6.93%). Convergent findings identified infant age, care skills of infants, mother age, infant temperament-regulatory capacity, birth weight, positive coping, health-care-knowledge-of-infants, type of caregiver, MABIS-bonding issues, ASQ-Fine Motor as the strongest predictors of interaction sensitivity between infants and their caregiver. Conclusions: The proposed method presents a promising and efficient approach that synergistically combines rating scales and artificial technology to detect important features of caregiver-infant interactions. This novel approach holds several implications for the development of automatic computational assessment tools in the field of nursing studies. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes. Objective: This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data. Methods: A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation. Results: A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events. Conclusions: This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required. [ABSTRACT FROM AUTHOR]

Objective: Submucosal infiltration of less than 200 μm is considered an indication for endoscopic surgery in cases of superficial esophageal cancer and precancerous lesions. This study aims to identify the risk factors associated with submucosal infiltration exceeding 200 micrometers in early esophageal cancer and precancerous lesions, as well as to establish and validate an accompanying predictive model. Methods: Risk factors were identified through least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Various machine learning (ML) classification models were tested to develop and evaluate the most effective predictive model, with Shapley Additive Explanations (SHAP) employed for model visualization. Results: Predictive factors for early esophageal invasion into the submucosa included endoscopic ultrasonography or magnifying endoscopy> SM1(P<0.001,OR = 3.972,95%CI 2.161–7.478), esophageal wall thickening(P<0.001,OR = 12.924,95%CI,5.299–33.96), intake of pickled foods(P=0.04,OR = 1.837,95%CI,1.03–3.307), platelet-lymphocyte ratio(P<0.001,OR = 0.284,95%CI,0.137–0.556), tumor size(P<0.027,OR = 2.369,95%CI,1.128–5.267), the percentage of circumferential mucosal defect(P<0.001,OR = 5.286,95%CI,2.671–10.723), and preoperative pathological type(P<0.001,OR = 4.079,95%CI,2.254–7.476). The logistic regression model constructed from the identified risk factors was found to be the optimal model, demonstrating high efficacy with an area under the curve (AUC) of 0.922 in the training set, 0.899 in the validation set, and 0.850 in the test set. Conclusion: A logistic regression model complemented by SHAP visualizations effectively identifies early esophageal cancer reaching 200 micrometers into the submucosa. [ABSTRACT FROM AUTHOR]

BACKGROUND: It has been hypothesized that PANoptosis may be involved in the pathologic process of osteoporosis, but there have been no studies addressing the mechanisms of PANoptosis genes in osteoporosis. OBJECTIVE: To analyze the biological mechanism of PANoptosis regulators in the occurrence and development of osteoporosis. METHODS: The GSE56815 dataset was obtained from the Gene Expression Omnibus database and PANoptosis genes were extracted for differential analysis. The key genes of PANoptosis were screened by random forest tree model to construct a disease risk prediction model. Consensus clustering algorithm, single sample genome enrichment analysis and immune infiltration analysis were used to explore the differences between different PANoptosis molecular subtypes. Herbal drugs that regulate the key genes of PANoptosis were predicted through Coremine medical database, a medical ontology information retrieval platform. RESULTS AND CONCLUSION: Based on the four PANoptosis key genes (CASP1, CASP10, MEFV, and TNF), the diagnostic markers of osteoporosis were determined, and the risk prediction model was constructed and verified. Osteoporosis was divided into two different PANoptosis subtypes (clusters A, B and gene clusters A, B), and the PANoptosis scores of cluster B and gene cluster B were higher than those of cluster A and gene cluster A, respectively. Traditional Chinese drugs such as ginseng which can regulate the key genes of PANoptosis were predicted by the Coremine medical database. [ABSTRACT FROM AUTHOR]

Background: Migraine is a debilitating neurological disorder that presents significant management challenges, resulting in underdiagnosis and inappropriate treatments, leaving patients at risk of medication overuse (MO). MO contributes to disease progression and the development of medication overuse headache (MOH). Predicting which migraine patients are at risk of MO/MOH is crucial for effective management. Thus, this systematic review aims to review and critique available prediction models for MO/MOH in migraine patients. Methods: A systematic search was conducted using Embase, Scopus, Medline/PubMed, ACM Digital Library, and IEEE databases from inception to April 22, 2024. The risk of bias was assessed using the prediction model risk of bias assessment tool. Results: Out of 1,579 articles, six studies with nine models met the inclusion criteria. Three studies developed new prediction models, while the remaining validated existing scores. Most studies utilized cross-sectional and prospective data collection in specific headache settings and migraine types. The models included up to 53 predictors, with sample sizes from 17 to 1,419 participants. Traditional statistical models (logistic regression and least absolute shrinkage and selection operator regression) were used in two studies, while one utilized a machine learning (ML) technique (support vector machines). Receiver operating characteristic analysis was employed to validate existing scores. The area under the receiver operating characteristic (AUROC) for the ML model (0.83) outperformed the traditional statistical model (0.62) in internal validation. The AUROCs ranged from 0.84 to 0.85 for the validation of existing scores. Common predictors included age and gender; genetic data and questionnaire evaluations were also included. All studies demonstrated a high risk of bias in model construction and high concerns regarding applicability to participants. Conclusion: This review identified promising results for MO/MOH prediction models in migraine patients, although the field remains limited. Future research should incorporate important risk factors, assess discrimination and calibration, and perform external validation. Further studies with robust designs, appropriate settings, high-quality and quantity data, and rigorous methodologies are necessary to advance this field. [ABSTRACT FROM AUTHOR]

Background: The use of dermatoscopes is constantly increasing globally, but to date, there are no studies on the use of dermatoscopes by Chinese dermatologists. Objective: To determine factors influencing the use of dermatoscopes in China. Methods: A web‐based questionnaire was designed by the Department of Dermatology at Huashan Hospital affiliated with Fudan University and was published online via the Shanghai Wheat Color Intelligent Technology Company, China. In 2019 and 2022, 1581 and 1507 dermatologists, respectively, were recruited and completed the questionnaire online. Results: In China, the application rate of dermatoscopy is higher in eastern provinces than in western and remote areas. The proportion of doctors from public tertiary hospitals is the highest, with females being the majority. The age range of 30–40 years has the highest proportion, the proportion of doctors with professional titles of attending physician or above is the highest, and the proportion of doctors with a bachelor's degree or above is the highest. Conclusions: By improving the education and professional standards of doctors, providing more training opportunities, simplifying access, and promoting dermatoscopy in grassroots hospitals, we can increase the confidence of dermatologists in the use of dermatoscopy. [ABSTRACT FROM AUTHOR]

Al–Mg–Si (6xxx series) aluminum alloys are widely employed in the automotive industry for lightweight applications, but crack formation during thermal forming remains a common issue. To address the problem and enhance the mechanical properties of these materials, an in-depth study of the flow stress is crucial. This work introduces the active target particle swarm optimization (ATPSO) algorithm into the improvement of the support vector machine (SVM), forming the ATPSO-SVM prediction model. The flow stress of two extensively used lightweight materials, specifically 6181H18 and 6016H18 aluminum alloys, are predicted and comparatively analyzed. Numerical results indicate that both the SVM and ATPSO-SVM models are effective in predicting the flow stress. Error analysis results show that the ATPSO-SVM model improves prediction accuracy by over 24% compared to the original model, demonstrating its superior accuracy. This investigation reflects the impact of various thermal deformation parameters on flow stress, providing significant technical references for the development of lightweight materials. [ABSTRACT FROM AUTHOR]

To establish a predictive model to evaluate the risk of adjacent vertebral refracture (VRF) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF) based on perioperative imaging data. This study was a retrospective cohort study which established a predictive model of VRF after PKP for OVCF. Patients who underwent PKP for OVCF in our hospital between January 2018 and December 2020 were enrolled and divided into a refracture group and normal group. Perioperative imaging data including preoperative bone mineral density (BMD), fatty infiltration (FI%) of paravertebral muscle, sagittal parameters of the spine and pelvis, and recovery rate of vertebral height were collected. The prediction model is obtained by multifactor logistic regression analysis. A total of 242 patients were included, including 23 cases in the VRF group and 219 cases in the normal group. There were statistical differences in BMD, FI%, recovery rate of vertebral height, and sagittal imbalance between the 2 groups. Receiver operating characteristic curve analysis of continuous variables showed that BMD ≤−2.80, FI% ≥40%, and recovery rate of vertebral height ≥ 10% were the cutoff values. Logistic regression analysis showed that BMD ≤−2.80, FI% ≥40%, and sagittal imbalance were independent risk factors for VRF. The area under the curve according to the predicted probability was 0.85 (P < 0.05). After simplifying the model, the total point of the model was 7 points, with a cutoff value of 5 points. The prediction model obtained in this study can predict refracture after PKP for OVCF early and effectively. It has an excellent predictive effect which is suitable for clinicians. [ABSTRACT FROM AUTHOR]

Background: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status Purpose: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. Material and Methods: We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. Results: The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. Conclusion: The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: Nutritional composition of rapeseed cake varies considerably due to differences in cultivars, growing conditions, harvesting time, and processing techniques, making it difficult to formulate precise diets. Therefore, we conducted a study to determine the chemical composition and standardized ileal digestibility (SID) of the amino acids (AA) in rapeseed cake and to test the hypothesis that their nutritional value is influenced by their origin. At the same time, we attempted to establish a predictive equation for SID based on the chemical composition of AA in RSC fed to pigs. Objective: The experiment was conducted to determine the apparent or standardized ileal digestibility (AID or SID) of crude protein (CP) and amino acids (AA) in 10 rapeseed cake samples fed to pigs, and to construct predictive models for the SID of CP and AA based on the chemical composition of rapeseed cakes. Methods: Twenty-two cannulated pigs (initial body weight: 39.8 ± 1.2 kg) were assigned to two 11 × 3 incomplete Latin square designs, including an N-free diet and 10 diets containing rapeseed cake. Each experimental period included 5 days of adaptation and 2 days of ileal digesta collection. Titanium dioxide (TiO2) was added at 0.3% to all the diets as an indigestible marker for calculating the ileal CP and AA digestibility. Results: The coefficients of variation (CV) of the content of crude fat (EE), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), and total glucosinolates (TGS) in 10 samples of rapeseed cake were greater than 10%. The standardized ileal digestibility (SID) of crude protein (CP), lysine (Lys), methionine (Met), threonine (Thr), and tryptophan (Trp) in rapeseed cake was 73.34% (61.49 to 81.12%), 63.01% (41.41 to 73.10%), 69.47% (50.55 to 88.16%), 79.61% (74.41 to 87.58%), and 94.43% (91.34 to 97.20%), respectively. The best prediction equations for SIDCP, SIDLys, and SIDVal were as follows: SIDCP = 90.124 − 0.54NDF (R2 = 0.58), SIDLys = 100.107 − 1.229NDF (R2 = 0.94), and SIDVal = 151.012 − 2.990TGS (R2 = 0.57). Conclusion: Overall, great variation exists among the 10 rapeseed cakes, and the NDF, TGS, and heating temperature can be used as the key predictors for the SID of CP and AA. [ABSTRACT FROM AUTHOR]

The electrical conductivity of soil is closely associated with various physical properties of the soil, and accurately establishing the interrelationship between them has long been a critical challenge limiting its widespread application. Traditional approaches in geotechnical engineering have relied on specific conduction mechanisms and simplifying assumptions to construct theoretical models for electrical conductivity. This paper adopts a different approach by using machine learning methods to predict the electrical conductivity of clay materials. A reliable dataset was generated using the quartet structure generation set to create random clay microstructures and calculate their electrical conductivity. Based on this dataset, machine learning methods such as least squares support vector machine and backpropagation neural networks outperform theoretical models in terms of prediction accuracy and resistance to interference, with a coefficient of determination (R2) exceeding 0.995 when unaffected by disturbances. The computation of Shapley values for input features aids in explicating the machine learning model. The results reveal that saturation is a key feature in predicting electrical conductivity, while porosity and constrained diameter are relatively less important. Finally, an already trained model is applied to the one-dimensional electroosmosis-surcharge preloading consolidation theory. The results of the calculations demonstrate that neglecting changes in soil electrical conductivity during electroosmosis can lead to an overestimation of the absolute values of anode excess pore water pressure and soil settlement. [ABSTRACT FROM AUTHOR]

Objectives: Randomized controlled trials (RCTs) have shown that attention-deficit/hyperactivity disorder (ADHD) medications significantly reduce symptomatology at a group level, but individual response to ADHD medication is variable. Thus, developing prediction models to stratify treatment according to individual baseline clinicodemographic characteristics is crucial to support clinical practice. A potential valuable source of data to develop accurate prediction models is real-world clinical data extracted from electronic healthcare records (EHRs). Yet, systematic information regarding EHR data on ADHD is lacking. Methods: We conducted a comprehensive review of studies that included EHR reporting data regarding individuals with ADHD, with a specific focus on treatment-related data. Relevant studies were identified from PubMed, Ovid, and Web of Science databases up to February 24, 2024. Results: We identified 103 studies reporting EHR data for individuals with ADHD. Among these, 83 studies provided information on the type of prescribed medication. However, dosage, duration of treatment, and ADHD symptom ratings before and after treatment initiation were only reported by a minority of studies. Conclusion: This review supports the potential use of EHRs to develop treatment response prediction models but emphasizes the need for more comprehensive reporting of treatment-related data, such as changes in ADHD symptom ratings and other possible baseline clinical predictors of treatment response. [ABSTRACT FROM AUTHOR]

The equilibrium swelling test was employed to determine the swelling response of Nitrile Butadiene Rubber (NBR) with various acrylonitrile (ACN) contents, and the three-dimensional solubility parameter (HSP) and modified Flory–Huggins interaction parameter (χHSP) were used to establish the prediction model of the oil-resistant property. The results indicate that the energy difference (Ra) between NBR and solvents calculated by HSP values can be correlated with the swelling response qualitatively with an inversed "S-shape", and high swelling response occurs at Ra < 8 MPa1/2 for NBR. For the purpose of establishing the prediction model, the new modified χHSP value has been calculated and fitted with the swelling response using exponential and logarithmic fittings, respectively. Two prediction models considering all the possible influencing factors have been obtained to determine the swelling response and oil resistance of NBR-based rubber products in bio-fuels, represented by the bio-diesel and IRM 903 test oil in this work. The swelling response of NBR can be evaluated precisely, and high swelling regions can be predicted and avoided in the new emerging fuels through the prediction models. Thus, the oil resistance of NBR-based rubber products, such as seals, holes and gaskets can be well predicted now. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOxs) are some of the most important hazardous air pollutants from industry. In China, the annual NOx emission in the waste gas of industrial sources is about 8.957 million tons, while power plants remain the largest anthropogenic source of NOx emissions, and the precise control of NOx in power plants is crucial. However, due to inherent issues with measurement and pipelines in coal-fired power plants, there is typically a delay of about three minutes in NOx measurements, bringing mismatch between its control and measurement. Measuring delays in NOx from power plants can lead to excessive ammonia injection or failure to meet environmental standards for NOx emissions. To address the issue of NOx measurement delays, this study introduced a hybrid boosting model suitable for on-site implementation. The model could serve as a feedforward signal in SCR control, compensating for NOx measurement delays and enabling precise ammonia injection for accurate denitrification in power plants. The model combines generation mechanism and data-driven approaches, enhancing its prediction accuracy through the categorization of time-series data into linear, nonlinear, and exogenous regression components. In this study, a time-based method was proposed for analyzing the correlations between variables in denitration systems and NOx concentrations. This study also introduced a new evaluation indicator, part of R2 (PR2), which focused on the prediction effect at turning points. Finally, the proposed model was applied to actual data from a 330 MW power plant, showing excellent predictive accuracy, particularly for one-minute forecasts. For 3 min prediction, compared to predictions made by ARIMA, the R-squared (R2) and PR2 were increased by 3.6% and 30.6%, respectively, and the mean absolute error (MAE) and mean absolute percentage error (MAPE) were decreased by 9.4% and 9.1%, respectively. These results confirmed the accuracy and applicability of the integrated model for on-site implementation as a 3 min advanced prediction soft sensor in power plants. [ABSTRACT FROM AUTHOR]

The initial gap (IG) is frequently occurring in the process of resistance spot welding (RSW) for automotive body-in-white structures. It is an inevitable challenge that the RSW with IG can negatively impact the welding quality, subsequently reducing the structural integrity and safety of the vehicle. This research aims to study the influence of the IG on RSW mechanical behaviors based on the refined finite element model (FEM) of RSW with different IGs under tensile shear load. The influence of six types of IGs on the peak load and fracture modes of RSW of plates with similar thicknesses of 1.0 mm and 1.5 mm is investigated through FEM and experiments. To quantify the influence of the IG on the RSW's deformational behavior, a prediction model is introduced to predict the peak load of RSW with different IGs. The prediction model is locally optimized through the Limited memory Broyden Fletcher Goldfarb Shanno (L-BFGS-B) optimization algorithm. Based on the prediction model, the relationship among the peak load values of the tensile shear specimens, the IG, and the mechanical behavior of the RSW is revealed. The results show that the IG has an obvious influence on the peak load values of RSW under tensile shear load, and the fracture modes are both the pull-out fracture (PF) mode. The peak load values of the RSW's tensile shear specimens are decreased with the increment in the IG. Finally, the prediction model can accurately predict the peak load for various IGs, with errors of no more than 3%. [ABSTRACT FROM AUTHOR]

Objective To explore the application value of ultrasound radiomics technology based on grayscale ultrasound images in the differential diagnosis of histological classification of glomerulonephritis. Methods A total of 204 patients with renal biopsy were selected from our hospital, and according to pathological results, they were divided into the membranous nephropathy group (n=133) and the mesangial proliferative glomerulonephritis group (n=71). The ultrasound images were sketched and the image omics features were extracted by two physicians. The pathological results and ultrasound data of renal biopsy were collected from the two groups, and the ultrasound radiomics features were preliminarily screened by the maximum correlation and minimum redundancy algorithm (mRMR) algorithm for all the obtained omics feature data. Then the optimal effective features were selected from the screened features by minimum absolute shrinkage and selection operator (LASSO) algorithm, and random forest (RF), support vector machine (SVM), logistic regression (LR), four kinds of classifiers of K-nearest neighbor (KNN) method were used to establish a prediction model. All cases were randomly divided into the training set and the validation set according to the ratio of 7∶3, and the four models were trained by the training set, and then validated in the validation set, and the best prediction model was selected by comparing the receiver operating characteristic (ROC) curve, Delong test and GiViTI calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Results The radiomics method was used to extract 837 radiomics features per image, and 16 meaningful features were finally screened out by using the mRMR + LASSO algorithm. Among the four prediction models of RF, SVM, LR and KNN, the best performing model was LR model, with the AUC of 0.944, the specificity of 0.867 and the sensitivity of 0.878. The GiViTI calibration curve showed that the model had good accuracy (P＞ 0.05), and the decision curve showed that the prediction model had good clinical practical value. Conclusion Ultrasound radiomics has a good ability to distinguish the more common histological types of glomerulonephritis, and is a non-invasive method with good application prospects. [ABSTRACT FROM AUTHOR]

Purpose: Early dissemination of primary pancreatic ductal adenocarcinoma (PDAC) is the main cause of dismal prognosis as it highly limits possible treatment options. A number of PDAC patients experience distant metastasis even after treatment due to the metastatic clones. We aimed to demonstrate the molecular architecture of borderline resectable PDAC manifests cancer dissemination of PDAC. Methods: Here, 36 organoids isolated from primary tumor masses of PDAC patients with diverse metastatic statues are presented. Whole-exome sequencing and RNA sequencing were performed and drug responses to clinically relevant 18 compounds were assessed. Results: Our results revealed that borderline resectable PDAC organoids exhibited distinct patterns according to their metastatic potency highlighted by multiple genetic and transcriptional factors and strong variances in drug responses. Conclusions: These data suggest that the presence of metastatic PDAC can be identified by integrating molecular compositions and drug responses of borderline resectable PDAC. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approach for primary care screening of depression among adolescents. Methods: The data were from a large cross‐sectional study conducted in China from July to September 2021, enrolling 8635 adolescents aged 10–17 with their parents. We used the Patient health questionnaire (PHQ‐9) to rate adolescent depression symptoms, using scales and single‐item questions to collect demographic information and other variables. Initial model variables screening used the RF importance assessment, followed by building prediction model using the screened variables through the ANN. Results: The rate of depression symptoms in adolescents was 24.6%, and the depression risk prediction model was built based on 70% of the training set and 30% of the test set. Ten variables were included in the final prediction model with a model accuracy of 85.03%, AUC of 0.892, specificity of 89.79%, and sensitivity of 70.81%. The top 10 significant factors of depression risk were adolescent rumination, adolescent self‐esteem, adolescent mobile phone addiction, peer victimization, care in parenting styles, overprotection in parenting styles, academic pressure, conflict in parent–child relationship, parental rumination, and relationship between parents. Conclusions: The ANN model based on the RF effectively identifies depression risk in adolescents and provides a methodological reference for large‐scale primary screening. Cross‐sectional studies and single‐item scales limit further improvements in model accuracy. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Could the risk of subsequent pregnancy loss be predicted based on the risk factors of recurrent pregnancy loss (RPL) patients? SUMMARY ANSWER A nomogram, constructed from independent risk factors identified through multivariate logistic regression, serves as a reliable tool for predicting the likelihood of subsequent pregnancy loss in RPL patients. WHAT IS KNOWN ALREADY Approximately 1–3% of fertile couples experience RPL, with over half lacking a clear etiological factor. Assessing the subsequent pregnancy loss rate in RPL patients and identifying high-risk groups for early intervention is essential for pregnancy counseling. Previous prediction models have mainly focused on unexplained RPL, incorporating baseline characteristics such as age and the number of previous pregnancy losses, with limited inclusion of laboratory and ultrasound indicators. STUDY DESIGN, SIZE, DURATION The retrospective study involved 3387 RPL patients who initially sought treatment at the Reproductive Immunology Clinic of Renji Hospital, Shanghai Jiao Tong University School of Medicine, between 1 January 2020 and 31 December 2022. Of these, 1153 RPL patients met the inclusion criteria and were included in the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS RPL was defined as two or more pregnancy losses (including biochemical pregnancy loss) with the same partner before 28 weeks of gestation. Data encompassing basic demographics, laboratory indicators (autoantibodies, peripheral immunity coagulation, and endocrine factors), uterine and endometrial ultrasound results, and subsequent pregnancy outcomes were collected from enrolled patients through initial questionnaires, post-pregnancy visits fortnightly, medical data retrieval, and telephone follow-up for lost patients. R software was utilized for data cleaning, dividing the data into a training cohort (n = 808) and a validation cohort (n = 345) in a 7:3 ratio according to pregnancy success and pregnancy loss. Independent predictors were identified through multivariate logistic regression. A nomogram was developed, evaluated by 10-fold cross-validation, and compared with the model incorporating solely age and the number of previous pregnancy losses. The constructed nomogram was evaluated using the AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Patients were then categorized into low- and high-risk subgroups. MAIN RESULTS AND THE ROLE OF CHANCE We included age, number of previous pregnancy losses, lupus anticoagulant, anticardiolipin IgM, anti-phosphatidylserine/prothrombin complex IgM, anti-double-stranded DNA antibody, arachidonic acid-induced platelet aggregation, thrombin time and the sum of bilateral uterine artery systolic/diastolic ratios in the nomogram. The AUCs of the nomogram were 0.808 (95% CI: 0.770–0.846) in the training cohort and 0.731 (95% CI: 0.660–0.802) in the validation cohort, respectively. The 10-fold cross-validated AUC ranged from 0.714 to 0.925, with a mean AUC of 0.795 (95% CI: 0.750–0.839). The AUC of the nomogram was superior compared to the model incorporating solely age and the number of previous pregnancy losses. Calibration curves, DCAs, and CICAs showed good concordance and clinical applicability. Significant differences in pregnancy loss rates were observed between the low- and high-risk groups (P  <   0.001). LIMITATIONS, REASONS FOR CAUTION This study was retrospective and focused on patients from a single reproductive immunology clinic, lacking external validation data. The potential impact of embryonic chromosomal abnormalities on pregnancy loss could not be excluded, and the administration of medication to all cases impacted the investigation of risk factors for pregnancy loss and the model's predictive efficacy. WIDER IMPLICATIONS OF THE FINDINGS This study signifies a pioneering effort in developing and validating a risk prediction nomogram for subsequent pregnancy loss in RPL patients to effectively stratify their risk. We have integrated the nomogram into an online web tool for clinical applications. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the National Natural Science Foundation of China (82071725). All authors have no competing interests to declare. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Objectives PsA is the most prevalent coexisting condition associated with psoriasis. Early-stage PsA patients always present unspecific and subtle clinical manifestations causing delayed diagnosis and leading to unfavourable health outcomes. The application of US enables precise identification of inflammatory changes in musculoskeletal structures. Hence, we constructed US models to aid early diagnosis of PsA. Methods This was a cross-sectional study carried out in the Department of Dermatology at West China Hospital (October 2018–April 2021). All participants underwent thorough US examinations. Participants were classified into the under 45 group (18 ≤ age ≤ 45 years) and over 45 (age >45 years) group and then randomly grouped into derivation and test cohort (7:3). Univariable logistic regression, least absolute shrinkage and selection operator, and multivariable logistic regression visualized by nomogram were conducted in order. Receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were performed for model verification. Results A total of 1256 participants were included, with 767 participants in the under 45 group and 489 in the over 45 group. Eleven and 16 independent ultrasonic variables were finally selected to construct the under 45 and over 45 model with the area under the ROC of 0.83 (95% CI 0.78–0.87) and 0.83 (95% CI 0.78–0.88) in derivation cohort, respectively. The DCA and CICA analyses showed good clinical utility of the two models. Conclusion The implementation of the US models could streamline the diagnostic process for PsA in psoriasis patients, leading to expedited evaluations while maintaining diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) demonstrates considerable heterogeneity in the manifestation of clinical symptoms and disease progression. Recently, six clinical milestones have been proposed to evaluate disease severity in PD. However, the identification of PD progression subtypes based on these milestone events has not yet been performed. Methods: Latent class analysis (LCA) was employed to identify subtypes of PD progression based on the timing of the first occurrence of six milestones within a 6-year follow-up period in Parkinson's Progression Markers Initiative (PPMI) database. Results: The study cohort consisted of 354 early PD patients, of whom 42.9% experienced at least one milestone within six years. LCA identified two distinct subtypes of PD progression: slow progression (83%) and rapid progression (17%). The total number of milestones over six years was significantly higher in the rapid progression subtype compared to the slow progression subtype (median: 3.00 vs. 0.00, p < 0.001). At baseline, the rapid progression subtype, compared to the slow progression subtype, was characterized by an older age at onset and more severe motor and non-motor symptoms. On biomarkers, the rapid progression subtype demonstrated elevated CSF p-tau and serum NFL, but decreased mean striatal DAT uptake. Five clinical variables (age, SDMT score, MDS-UPDRS I score, MDS-UPDRS II + III scores, and RBD) were selected to construct the predictive model. The original predictive model achieved an AUC of 0.82. In internal validation using bootstrap resampling, the model achieved an AUC of 0.82, with a 95%CI ranging from 0.76 to 0.87. The model's performance was acceptable regarding both calibration and clinical utility. Conclusion: Approximately 17% of early PD patients exhibited the rapid progression subtype, characterized by the occurrence of more and earlier-onset milestones. The nomogram predictive model, incorporating five baseline clinical variables (age, SDMT score, MDS-UPDRS I score, MDS-UPDRS II + III scores, RBD), serves as a valuable tool for prognostic counseling and patient selection in PD clinical trials. [ABSTRACT FROM AUTHOR]

Background: Hypertension resolution following adrenalectomy in patients with primary aldosteronism (PA) remains a critical clinical challenge. Identifying preoperatively which patients will become normotensive is both a priority and a point of contention. In this narrative review, we explore the controversies and unresolved issues surrounding the prediction of hypertension resolution after adrenalectomy in PA. Methods: A comprehensive literature review was conducted, focusing on studies published between 1954 and 2024 that evaluated all studies that discussed predictive models for hypertension resolution post-adrenalectomy in PA patients. Databases searched included MEDLINE®, Ovid Embase, and Web of Science databases. Results: The review identified several predictors and predictive models of hypertension resolution, including female sex, duration of hypertension, antihypertensive medication, and BMI. However, inconsistencies in study designs and patient populations led to varied conclusions. Conclusions: Although certain predictors and predictive models of hypertension resolution post-adrenalectomy in PA patients are supported by evidence, significant controversies and unresolved issues remain. While the current predictive models provide valuable insights, there is a clear need for further research in this area. Future studies should focus on validating and refining these models. [ABSTRACT FROM AUTHOR]