Strong Discrimination Research Articles

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values

This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and provide valuable guidance for urologists in the early diagnosis and intervention of post-operative urosepsis. This study included 833 patients who underwent retrograde intrarenal surgery (RIRS) or percutaneous nephrolithotomy (PCNL) for kidney stones. Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. The SHapley Additive exPlanations (SHAP) method was used to explain the distribution of feature importance in the model’s predictions. Among the 833 patients, 126 (15.1%) developed SIRS postoperatively. All five machine learning models demonstrated strong discrimination on the validation set (AUC: 0.690–0.858). The eXtreme Gradient Boosting (XGBoost) model was the best performer [AUC: 0.858; sensitivity: 0.877; specificity: 0.981; accuracy: 0.841; positive predictive value: 0.629; negative predictive value: 0.851]. The characteristic importance of the Machine Learning model (ML model) and SHAP results indicated Hounsfield Unit (HU), Urinary protein, Stone burden, and Serum uric acid as important predictors for the model. A machine learning model utilizing CT values was developed to predict postoperative SIRS in endoscopic kidney stone surgery. The model demonstrates strong predictive performance and can assist in assessing the risk of urosepsis in postoperative patients.

OPLS-Based Multiclass Classification and Data-Driven Interclass Relationship Discovery.

Multiclass data sets and large-scale studies are increasingly common in omics sciences, drug discovery, and clinical research due to advancements in analytical platforms. Efficiently handling these data sets and discerning subtle differences across multiple classes remains a significant challenge. In metabolomics, two-class orthogonal projection to latent structures discriminant analysis (OPLS-DA) models are widely used due to their strong discrimination capabilities and ability to provide interpretable information on class differences. However, these models face challenges in multiclass settings. A common solution is to transform the multiclass comparison into multiple two-class comparisons, which, while more effective than a global multiclass OPLS-DA model, unfortunately results in a manual, time-consuming model-building process with complicated interpretation. Here, we introduce an extension of OPLS-DA for data-driven multiclass classification: orthogonal partial least squares-hierarchical discriminant analysis (OPLS-HDA). OPLS-HDA integrates hierarchical cluster analysis (HCA) with the OPLS-DA framework to create a decision tree, addressing multiclass classification challenges and providing intuitive visualization of interclass relationships. To avoid overfitting and ensure reliable predictions, we use cross-validation during model building. Benchmark results show that OPLS-HDA performs competitively across diverse data sets compared to eight established methods. This method represents a significant advancement, offering a powerful tool to dissect complex multiclass data sets. With its versatility, interpretability, and ease of use, OPLS-HDA is an efficient approach to multiclass data analysis applicable across various fields.

Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis.

Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation. This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023. Different ML algorithms were tested using 24 routine laboratory parameters. The Shapley Additive exPlanations (SHAP) analysis was used to evaluate the contribution of each parameter in the ML model. A total of 2554 patients (1750 for DILI and 804 for AIH) were included. Using Gradient Boost Decision Tree algorithm, five key parameters-aspartate transaminase, globulin, prealbumin, creatinine and platelet count-were selected to construct the ML model. Consequently, a web-based tool named Beijing-AID (BJ-AID) was developed (http://43.143.153.225:5000/). The BJ-AID model demonstrated excellent discrimination performance, with an area under the receiver operating characteristic curve (AUROC) of 0.94 (95% CI, 0.902-0.975) in the development set, 0.91 (95% CI, 0.900-0.928) in all external validation sets and 0.93 (95% CI, 0.889-0.974) in a prospective validation set. Notably, the BJ-AID model also effectively discriminated atypical cases, including drug-induced autoimmune-like hepatitis and AIH with the history of drug consumption, achieving an AUROC = 0.85 (95% CI, 0.742-0.949). We successfully developed and validated a machine learning-based model, BJ-AID, which exhibits a strong discrimination performance. BJ-AID can assist practitioners and hepatologists in diagnosing both typical and atypical cases of DILI and AIH. ClinicalTrials.gov identifier: NCT05532345.

Development of an Ultrasound-Based Radiomics Nomogram for Preoperative Prediction of HER-2 Status in Invasive Breast Cancer.

This study aimed to create a radiomics nomogram using grayscale ultrasound (US) to predict human epidermal growth factor receptor 2 (HER-2) expression status preoperatively in invasive breast cancer (IBC) patients. The study population was randomly divided into a training dataset (360 patients, 99 HER-2-positive) and a validation dataset (155 patients, 42 HER-2-positive). Clinical data, including US features, were collected. Radiomics features were extracted from grayscale US images, followed by feature selection to establish a radiomics score (Radscore) model. Univariate and multivariate logistic regression analyses identified independent risk factors for the clinical and radiomics nomogram models. Model performance was evaluated using receiver operating characteristic curves, calibration curves, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. 16 radiomics features were selected for the Radscore model. Tumor margin and calcification emerged as significant preoperative risk factors for HER-2 status, forming the basis of a clinical prediction model. The integrated radiomics nomogram, combining tumor margin, calcification, and Radscore, demonstrated strong discrimination with area under the curvevalues of 0.810 in the training dataset and 0.807 in the validation dataset. The US-based radiomics nomogram shows substantial promise for preoperatively predicting HER-2 status in IBC patients.

Enhancing Mental Health Disorder Detection: A Hybrid Classifier System Using Soft Voting and Ensemble Methods

Early detection and diagnosis of mental health disorders based on patient-reported symptoms is vital in mental health care. This research proposes an ensemble learning pipeline for predicting mental health disorders using symptoms and demographic data. The pipeline incorporates preprocessing techniques such as standardization, one-hot encoding, and Term Frequency - Inverse Document Frequency transformation, and addresses class imbalance with the Synthetic Minority Over-sampling Technique to enhance predictions of rare disorders. Multiple classifiers, including Random Forest, Gradient Boosting, XGBoost, CatBoost, and LightGBM, are combined in a voting classifier, with each model optimized using Grid Search. The pipeline's performance is evaluated on its ability to predict mental health disorders, showing potential for supporting clinical diagnosis. CatBoost proves effective in certain models, while LightGBM delivers strong performance across most. The models achieve around 95% accuracy, demonstrating strong discrimination between disorders and other conditions.

Prediction of scleroderma renal crisis in patients of systemic sclerosis: insight from the CRDC cohort study.

Early personalized identification of systemic sclerosis (SSc) patients at risk for scleroderma renal crisis (SRC) can help provide better treatment and improve outcomes. This study aimed to create and validate a new multi-predictor nomogram to predict SRC risk and compare it to an existing model. A retrospective multicentre observational study was conducted using clinical data from SSc patients with SRC registered in the Chinese Rheumatism Data Center (CRDC) database. Each SSc patient with SRC was matched with four SSc patients without SRC, registered consecutively afterward, as controls. Differences between the two groups were analyzed using Student's t test, Mann-Whitney U test, χ2 test, or Fisher's exact test. Key risk factors were identified using univariate and multivariate logistic regression, as well as LASSO regression. The Nomogram's performance was assessed with ROC curves, calibration plots, decision curve analysis, and Bootstrap resampling for internal and external validation. NRI and IDI were used to compare models. The nomogram incorporated predictive factors such as myocardial involvement, SSc subtype, anaemia, platelet count, and disease duration. The AUC showed strong discrimination in both the training and validation datasets. Calibration curves and the Hosmer-Lemeshow test indicated good agreement between predicted and actual outcomes. Decision curve analysis demonstrated greater clinical net benefit. The NRI and IDI results showed significant improvement over the previous model. A Nomogram with improved predictive performance compared with the previous one was developed in a larger sample size in China.

Predicting 90-day return to the emergency department in orthopaedic trauma patients in the Southeastern USA: a machine-learning approach

IntroductionReturn-to-acute-care metrics, such as early emergency department (ED) visits, are key indicators of healthcare quality, with ED returns following surgery often considered avoidable and costly events. Proactively identifying patients at...

A nomogram for the prediction of co-infection in MDA5 dermatomyositis: A rapid clinical assessment model.

Patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5 DM) are prone to infections, but there is a lack of rapid methods to assess infection risk, which greatly affects patient prognosis. This study aims to analyze the clinical features of MDA5 DM patients systematically and develop a predictive model for infections. Retrospective analysis was performed on clinical data from 118 hospitalized patients with MDA5 DM. According to the results of pathogen detection and clinical manifestations, the patients were divided into infected group and non-infected group. LASSO analysis and multivariate logistic regression were used to establish the prediction model of infection in MAD5 DM patients. The resulting model was visualized using a Nomogram. We used methods such as Receiver Operating Characteristic (ROC) curve analysis, Area Under the Curve (AUC) calculation to evaluate the model. The Cough, interstitial lung disease, moist rales, positive anti-RO-52, carcinoembryonic antigen, triglyceride, hydroxybutyrate dehydrogenase and erythrocyte sedimentation rate were significantly associated with infection risk in MDA5 DM patients. A prediction model was developed using these eight risk factors, achieving an AUC of 0.851 in determining co-infection status. Further analysis based on infection site and pathogen classification demonstrated strong discrimination performance of the model in identifying pulmonary infection (AUC: 0.844) and fungal infection (AUC: 0.822). This study aimed to develop a clinical prediction model and visualize it using Nomogram to assess the risk of infection in MDA5 DM. The model provides an effective tool for determining infection status in patients and serves as a reference for formulating clinical medication regimens.

Can we diagnose noninvasive follicular thyroid neoplasm with papillary-like nuclear features before surgery?

Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) can be differentiated from invasive encapsulated follicular variant of papillary thyroid carcinoma (eFV-PTC) by the presence of a tumor capsule or blood vessel invasion in histological examination. The objective of this study was to investigate whether it is possible to distinguish between NIFTP and invasive eFV-PTC before surgery. Patients diagnosed with NIFTP and invasive eFV-PTC from 2017 to 2023 were analyzed for biochemical, ultrasonographic, and cytological features. No differences were found in thyroid function tests, thyroid autoantibody levels, tumor size, or ultrasonographic characteristics. However, patients with invasive eFV-PTC had higher preoperative neutrophil-to-lymphocyte ratio (NLR) values and a higher Bethesda cytology category compared to those with NIFTP. In the multivariable analysis, NLR was the only significant predictor of invasive eFV-PTC. Nevertheless, there was no reliable NLR cutoff for distinguishing between the two entities. Overall, this study substantiates considerable overlap in demographic and clinical data between NIFTP and invasive eFV-PTC. Although the higher NLR observed in thyroid cancer does not provide strong discrimination, it suggests that the invasive nature of tumor cells might elicit a more profound systemic inflammatory or immune response.

Up-front resection for hepatocellular carcinoma: Assessing futility in the preoperative setting.

We sought to develop a predictive model to preoperatively identify patients with hepatocellular carcinoma (HCC) at risk of undergoing futile upfront liver resection (LR). Patients undergoing curative-intent LR for HCC were identified from a large multi-institutional database. Futile LR was defined by death or disease recurrence within six months postoperatively. Backward logistic regression was performed to identify factors associated with futility. Additionally, binary criteria were established for surgical candidacy, aiming to keep the likelihood of futility below 20%. Among 1633 patients with HCC, 264 (16.2%) underwent futile upfront LR. Tumor burden score (TBS) (coefficient: 0.083, 95%CI: 0.067-0.099), alpha-fetoprotein (AFP) (coefficient: 0.254, 95%CI: 0.195-0.310), and albumin-bilirubin (ALBI) grade 2/3 (coefficient: 0.566, 95%CI: 0.420-0.718) were independently associated with an increased risk of futile LR. The model demonstrated strong discrimination and calibration in both derivation and validation cohorts. Low, intermediate, and high-risk groups were determined based on the risk model, each with an escalating likelihood of futility, worse histological features, and worse survival outcomes. Six distinct conditions based on AFP-adjusted-to-TBS criteria were established, all with a futility likelihood of less than 20%. Patients fulfilling these criteria had significantly better long-term recurrence-free and overall survival. The futility risk model was made available online for wide clinical applicability: (https://altaf-pawlik-hcc-futilityofsurgery-calculator.streamlit.app/). A preoperative risk model and AFP-adjusted-to-TBS criteria were developed and validated to predict the likelihood of futile LR among patients with HCC. This pragmatic clinical tool may assist clinicians in preoperative decision-making, helping them avoid futile surgery unlikely to offer long-term benefits.

Development and validation of a risk prediction model for acute kidney injury in coronary artery disease

BackgroundAcute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. This study focuses on developing accurate predictive models to improve the early detection and prognosis of AKI in CAD patients.MethodsWe used Electronic Health Records (EHRs) from a nationwide CAD registry including 54 429 patients. Initially, univariate analysis identified potential predictors. Subsequently, a stepwise multivariate logistic model integrated clinical significance and data distribution. To refine predictor selection, we applied a random forest algorithm. The top 10 variables, including admission to the surgical department, EGFR, hemoglobin, and others, were incorporated into a logistic regression-based prediction model. Model performance was assessed using the area under the curve (AUC) and calibration analysis, and a nomogram was developed for practical application.ResultsDuring hospitalization, 2,112 (3.88%) patients in the overall population of both the development and validation groups experienced AKI within 30 days. The final prediction model exhibited strong discrimination with an AUC of 0.867 (95% CI: 0.858 to 0.876) and well calibration capability in both the development and validation groups. Key predictors included surgical department admission, eGFR, hemoglobin, chronic kidney disease history, male sex, white blood cell count, age, left ventricular ejection fraction, acute myocardial infarction at admission, and congestive heart failure history. Bootstrap resampling confirmed model stability (Harrell's optimism-correct AUC = 0.866). The nomogram provided a practical tool for AKI risk assessment.ConclusionThis study introduced a refined AKI risk prediction model for CAD patients. This model showed adaptability to subgroups and held the potential for early AKI alerts and personalized interventions, thereby enhancing patient care.

Development of novel nomograms for predicting prostate cancer in biopsy-naive patients with PSA < 10 ng/ml and PI-RADS ≤ 3 lesions.

To develop novel nomograms for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with prostate-specific antigen (PSA) < 10 ng/ml and PI-RADS v2.1 score ≤ 3. We retrospectively collected data from 327 men with PSA < 10 ng/ml and PI-RADS score ≤ 3 from June 2020 to June 2024 in our hospital. Clinical data were compared among the PI-RADS scores 1-3 population, PI-RADS scores 1-2 population, and PI-RADS score 3 population. Logistic regression analyses were conducted to identify independent risk factors for PCa or csPCa, and nomograms were subsequently developed. The nomograms were evaluated via receiver operating curves (ROC), calibration curves, and decision curve analysis (DCA). Internal validation was conducted using bootstrap methods. Among the 327 patients, 224 (68.50%) were diagnosed with benign, 65 (19.87%) with csPCa, and 38 (11.62%) with clinically insignificant prostate cancer (cisPCa). Prostate-specific antigen density (PSAD), lesion volume (LV), lesion location, and apparent diffusion coefficient (ADC) were found to be independent risk factors for PCa and csPCa in PI-RADS scores 1-3 population. PSAD and lesion location were independent risk factors for PCa in the PI-RADS scores 1-2 population, while PSAD, lesion location and ADC were independent risk factors for PCa in the PI-RADS score 3 population. Four nomograms were established based on these variables. For the population with PI-RADS scores 1-3, the area under the ROC (AUC) for predicting PCa and csPCa was 0.78 and 0.79, respectively. For patients with PI-RADS scores 1-2, the AUC for predicting PCa was 0.75. For patients with PI-RADS score 3, the AUC for predicting PCa was 0.78. The calibration curves revealed good concordance between the predicted probability and the actual probability. DCA demonstrated the net benefit of nomograms. Internal validation revealed strong discrimination of the nomograms. We developed novel nomograms with acceptable discriminability for predicting PCa and csPCa in patients with PSA < 10 ng/ml and PI-RADS score ≤ 3. These models can assist urologists in determining the necessity of prostate biopsy.

Development and validation of a knowledge questionnaire regarding the relationship between bacterial resistance and cancer among oncologists.

Bacterial resistance poses a significant threat to cancer patients due to their compromised immune systems and frequent use of antibiotics. Understanding oncologists' knowledge of this issue is essential for improving patient care. This study aimed to develop and validate a questionnaire assessing oncologists' knowledge of the relationship between bacterial resistance and cancer. A cross-sectional study involving 421 participants was conducted in Iraq from November 2023 to March 2024 across various cancer centers and oncology hospitals. A 41-item questionnaire covering eight domains-Risk Factors, Infections and Antibiotic Use, Mechanisms of Resistance, Impact on Drug Efficacy and Toxicity, Genetic Factors, Microbial Adaptation, Impact of Gut Microbiota, and Antimicrobial Stewardship-was developed. Content validity was evaluated by five experts. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to assess construct validity, while Item Response Theory (IRT) evaluated item performance. Reliability was measured using Cronbach's Alpha and Composite Reliability (CR). The questionnaire showed strong content validity, with most items achieving a Content Validity Index (CVI) of ≥0.8. EFA identified eight factors, confirmed by CFA with acceptable model fit (CFI = 0.96, TLI = 0.95, RMSEA = 0.05). IRT indicated strong item discrimination and acceptable difficulty. Cronbach's Alpha was 0.88, with CR at 0.89. This validated 41-item questionnaire provides a reliable tool for assessing oncologists' knowledge of bacterial resistance among cancer patients. It helps identify knowledge gaps and guide interventions to improve oncology practice outcomes.

Emerging Implications of Serum Resolvin D2 as a Biochemical Marker for Severity Assessment and Prognosis Prediction Following Moderate-Severe Traumatic Brain Injury: A Prospective Cohort Study.

Resolvin D2 (RvD2), which exhibits anti-inflammatory properties, is neuroprotective. This study aimed to ascertain the potential of serum RvD2 level as a prognostic predictor of moderate-to-severe traumatic brain injury (msTBI). In this prospective cohort study, serum RvD2 levels were measured in 136 patients with msTBI and 100healthy controls. The severity scoring systems encompassed the Rotterdam computed tomography classification and Glasgow Coma Scale (GCS). Post-trauma six-month Glasgow outcome scale (GOS) was deemed an outcome indicator, with GOS scores below 4 indicating poor prognosis. Sequential univariate and multivariate analyses were used to determine the correlative factors of changeable serum RvD2 levels and the predictors of adverse prognosis. Patients displayed a marked decline in serum RvD2 levels compared to controls (median, 95.2 versus 252.8 pg/mL; P<0.001). Serum RvD2 levels were independently correlated with GCS scores (beta, 8.989; 95% confidence interval (CI), 3.678-14.280; P=0.001) and Rotterdam scores (beta, -14.676; 95% CI, -25.885--3.468; P=0.011), and were independently associated with continuous GOS scores (beta, 0.004; 95% CI, 0.002-0.007; P=0.003), ordinal GOS scores (odds ratio, 1.008; 95% CI, 1.002-1.015; P=0.015), and poor prognosis (odds ratio, 0.991; 95% CI, 0.983-0.999; P=0.037) at the six-month mark. A linear correlation was observed between serum RvD2 levels and the likelihood of poor prognosis (P for nonlinear = 0.090). Serum RvD2 levels exhibited strong discrimination efficiency for the probability of poor prognosis (P<0.001), with similar ability as Rotterdam scores (P=0.337) and GCS scores (P=0.300). The integrative model encompassing serum RvD2, Rotterdam scores and GCS scores performed well using a series of statistical methods. A significant decrease in serum RvD2 levels after msTBI may accurately indicate trauma severity and efficiently distinguish the possibility of poor neurological outcomes in msTBI, signifying that serum RvD2 may be of clinical significance in the prognostic prediction of TBI.

Temperature alters bacterial community structure in sediment of mountain stream

Temperature and nutrients are known as crucial drivers for the variations of bacterial community structure and functions in oceans and lakes. However, their significance and mechanisms in influencing the bacterial community structure and function in mountain stream remain unclear. In this study, we investigated the spatiotemporal patterns of the bacterial communities and the main environmental factors in the Taizicheng River, a high-latitude mountainous stream, to reveal the main driving factors for sedimental bacterial communities. Our findings identified that the phyla Acidobacteriota and Bacteroidota served as the strong discriminant for sedimental bacterial community in the non-freezing and freezing periods, respectively. In contrast, no significant difference was detected in bacterial functional composition. Mantel test and Redundancy analysis showed that temperature and nutrients played significant roles in determining the bacterial community structure and temperature critically influenced the bacterial metabolic processes. The results of partial least squares path model demonstrated that temperature affected the bacterial community structure in both directly (43.70%) and indirectly pathways (41.10%) by affecting the sediment parameters. However, the functional composition was not significantly affected by temperature and nutrients. Our results highlight that the change of temperature can significantly alters the structure of bacterial communities rather than functional composition and provides new insights into the response mechanisms of bacterial communities to environmental factors which contributes to the deep understanding of the driving factors as well as the protection strategies for microbial communities in mountain stream and aquatic ecosystem.

Predictive nomogram of ultrasound indicators for the termination outcome of caesarean scar pregnancy

To develop and validate a nomogram for predicting the risk of adverse events (intraoperative massive haemorrhage or retained products of conception) associated with the termination of Caesarean scar pregnancy (CSP). Data were retrospectively collected from patients diagnosed with CSP who underwent Dilation and Curettage (D&C) at two hospitals. This data was divided into internal and external cohorts for analysis. The internal cohort was randomly split, with 70% of the data designated for a training set and 30% for an internal validation set. The external cohort served exclusively as the external validation set. LASSO and logistic regression techniques were employed to select variables and construct the nomogram. The performance of the nomogram was evaluated using various methods, including C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA), to assess its identification, calibration, and clinical effectiveness. The prediction nomogram included several predictors, such as scar thickness, type of CSP, gestational sac diameter, and blood flow. It demonstrated strong discrimination, with a C-index of 0.83 (95% confidence interval: 0.77–0.89). Furthermore, in the internal validation set, a high C-index of 0.78 was achieved, while in the external validation set, it reached 0.83. Additional assessments using calibration curve analysis, DCA, and CICA indicated robust agreement between the nomogram’s predictions and actual observations, highlighting its utility and reliability. The developed nomogram shows excellent discriminative ability and calibration, with the potential for effective local prediction of adverse events in CSP.

Pathomics-based machine learning models for predicting pathological complete response and prognosis in locally advanced rectal cancer patients post-neoadjuvant chemoradiotherapy: insights from two independent institutional studies

BackgroundAccurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT) is essential for formulating effective treatment plans. This study aimed to construct and validate the machine learning (ML) models to predict pCR and DFS using pathomics.MethodA retrospective analysis was conducted on 294 patients who received NCRT from two independent institutions. Pathomics from pre-NCRT H&E stains were extracted, and five ML models were developed and validated across two centers using ROC, Kaplan-Meier, time-dependent ROC, and nomogram analyses.ResultAmong the five ML models, the Xgboost (XGB) model demonstrated superior performance in predicting pCR, achieving an AUC of 1.000 (p < 0.001) on the internal data-set and an AUC of 0.950 (p = 0.001) on the external data-set.The XGB model effectively differentiated between high-risk and low-risk prognosis patients across all five centers: internal dataset (DFS, p = 0.002; OS, p = 0.004) and external dataset (DFS, p = 0.074; OS, p = 0.224).Furthermore, the COX regression demonstrated that the tumor length (HR = 1.230, 95%CI: 1.050–1.440, p = 0.010), post-NCRT CEA (HR = 1.716, 95%CI: 1.031– 2.858, p = 0.038), and XGB model score (HR = 0.128, 95%CI: 0.026–0.636, p = 0.012) were independent predictors of DFS after NCRT in the internal data-set.Using COX regression, the nomogram model and time-dependent AUC analysis demonstrated strong predictive discrimination for DFS in LARC patients across two independent institutions.ConclusionThe ML model based on pathomics demonstrated effective prediction of pCR and prognosis in LARC patients. Further validation in larger cohorts is warranted to confirm the findings of this study.

Photosynthetic traits, water use and the yield of maize are influenced by soil water stability

BackgroundThe aim of this study is to investigate the effect of soil water stability on maize (Zea mays L.) yield, water use, and its photosynthetic physiological mechanisms, and to innovate the relationship between maize and soil water, which currently only considers soil water content and neglects soil water stability.MethodsAn organized water experiment was conducted on maize. The effects of stable soil water (SW) at two water content levels were examined, with fluctuating soil water (FW) as a control. The assessed effects included leaf water, chlorophyll, gas exchange, leaf water use efficiency (WUE), stable carbon isotope ratio (δ13C), and yield of maize.ResultsSoil water stability had a significant effect on maize yield, yet it was slightly smaller than soil water content. Compared with FW, SW increased the maximum net photosynthetic rate, saturated light intensity, stomatal conductance, SPAD, leaf water content, and leaf WUE, and decreased δ13C, promoting dry matter assimilation and conversion into grain yield, ultimately increasing yield by 100.8%. Under the same soil water stability, 55% FC versus 75% FC weakened photosynthetic capacity and exacerbated stomatal limitation of maize leaves, making them more susceptible to light inhibition, which decreased photoassimilate accumulation, resulting in a significant decrease in yield. And the δ13C under 75% FC conditions decreased by 4.7–7.7% compared with 55% FC.ConclusionIn conclusion, SW exhibits a positive effect on maize leaf water content, photosynthetic carbon assimilation, and grain yields, regardless of soil water content. Compared to FW, SW increased leaf WUE and maize yield by enhancing photosynthesis, and SW has stronger discrimination against 13C during photosynthetic CO2 assimilation, thus decreasing leaf δ13C. This study fills a gap in understanding how soil water stability influences maize yield and gas exchange, and provides a fresh perspective on how to improve crop yield and WUE by managing soil water stability.

Initial Validation of the Mandarin Translation of the Stanford Social Dimensions Scale (SSDS).

This study aimed to validate the Mandarin translation of the Stanford Social Dimensions Scale (SSDS). The initial validation sample consisted of 480 children with Autism Spectrum Disorder (ASD) (Mage = 9.35). Discriminant validity was appraised by comparing relevant SSDS scores among samples of children with ASD, typical development (TD) (N = 160, Mage = 7.16), and non-ASD but Neurodevelopmental Disorders (NDDs) (N = 170, Mage = 5.08). Confirmatory application of the Exploratory Structural Equation Modelling indicated that a five-factor model encompassing Social Motivation (SM), Social Affiliation (SA), Expressive Social Communication (ESC), Social Recognition (SR) and Unusual Approach (UA) provided adequate to excellent fit to the data in ASD group ([CFI] = .908, [TLI] = .930, [RMSEA] = .052, [SRMR] = .028). The factor loadings of most items constituting SM, SA, ESC, and SR factors were aligned with the original factor structure in the US sample, except items on the UA factor that showed lower loadings. The internal consistency was .93-.96, and test-retest reliability was .86. Discriminate validity was excellent, with the ASD group showing significantly lower scores compared to non-ASD NDDs and TD groups. Convergent and divergent validity of the SSDS was strong, as indexed by the pattern of correlations with relevant Social Responsiveness Scale, second edition (SRS-2), and Social Communication Questionnaire (SCQ) domains. This study provided preliminary validation of the Mandarin translation of the SSDS by largely replicating the original factor structure and showing evidence for strong discriminant, convergent, and divergent validity. Potential areas where further refinement and potential expansion of the SSDS are needed were identified.

Biomarker Panels for Predicting Progression of Kidney Disease in Acute Kidney Injury Survivors.

Acute kidney injury (AKI) increases the risk for chronic kidney disease (CKD). We aimed to identify combinations of clinical variables and biomarkers that predict long-term kidney disease risk after AKI. We analyzed data from a prospective cohort of 723 hospitalized patients with AKI in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI (ASSESS-AKI) Study. Using machine learning, we investigated 75 candidate predictors including biomarkers measured at three-month post-discharge follow-up to predict major adverse kidney events (MAKE) within three years, defined as a decline in eGFR ≥40%, development of end-stage kidney disease (ESKD), or death. The mean age of study participants was 64 ± 13 years, 68% were men, and 79% were of White race. Two hundred and four (28%) patients developed MAKE over 3 years of follow-up. Random forest and LASSO penalized regression models using all 75 predictors yielded area under the receiver-operating characteristic curve (AUC) values of 0.80 (95% CI: 0.69-0.91) and 0.79 (95% CI: 0.68-0.90) respectively. The most consistently selected predictors were albuminuria, soluble tumor necrosis factor receptor 1 (sTNFR1), and diuretic use. A parsimonious model using the top eight predictor variables showed similarly strong discrimination for MAKE (AUC = 0.78; 95% CI: 0.66-0.90). Clinical impact utility analyses demonstrated that the eight-predictor model would have 55% higher efficiency of post-AKI care (number needed to screen/follow-up for a MAKE event decreased from 3.55 to 1.97). For a kidney-specific outcome of eGFR decline or ESKD, a four-predictor model showed strong discrimination (AUC = 0.82; 95% CI: 0.68-0.96). Combining clinical data and biomarkers can accurately identify high-risk AKI patients, enabling personalized post-AKI care and improved outcomes.