The effect of fine grits and fine debris concentrations on the MLVSS/MLSS ratio of an activated sludge system.

This study embarks on an explorative investigation into the effects of typical concentrations and varying particle sizes of fine grits (FG, the involatile portion of suspended solids) and fine debris (FD, the volatile yet unbiodegradable fraction of suspended solids) within the influent on the mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio of an activated sludge system. Through meticulous experimentation, it was discerned that the addition of FG or FD, the particle size of FG, and the concentration of FD bore no substantial impact on the pollutant removal efficiency (denoted by the removal rate of COD and ammonia nitrogen) under constant operational conditions. However, a notable decrease in the MLVSS/MLSS ratio was observed with a typical FG concentration of 20 mg/L, with smaller FG particle sizes exacerbating this reduction. Additionally, variations in FD concentrations influenced both MLSS and MLVSS/MLSS ratios; a higher FD concentration led to an increased MLSS and a reduced MLVSS/MLSS ratio, indicating FD accumulation in the system. A predictive model for MLVSS/MLSS was constructed based on quality balance calculations, offering a tool for foreseeing the MLVSS/MLSS ratio under stable long-term influent conditions of FG and FD. This model, validated using data from the BXH wastewater treatment plant (WWTP), showcased remarkable accuracy.

Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.

PURPOSE: This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD). METHODS: A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset. RESULTS: EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors. CONCLUSIONS: Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).

BACKGROUND: The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting. MATERIALS AND METHODS: This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). RESULTS: Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively. CONCLUSIONS: We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

Predictive modeling for post operative delirium in elderly.

Delirium, a complex neurocognitive syndrome, frequently emerges following surgery, presenting diverse manifestations and considerable obstacles, especially among the elderly. This editorial delves into the intricate phenomenon of postoperative delirium (POD), shedding light on a study that explores POD in elderly individuals undergoing abdominal malignancy surgery. The study examines pathophysiology and predictive determinants, offering valuable insights into this challenging clinical scenario. Employing the synthetic minority oversampling technique, a predictive model is developed, incorporating critical risk factors such as comorbidity index, anesthesia grade, and surgical duration. There is an urgent need for accurate risk factor identification to mitigate POD incidence. While specific to elderly patients with abdominal malignancies, the findings contribute significantly to understanding delirium pathophysiology and prediction. Further research is warranted to establish standardized predictive for enhanced generalizability.

Association between body composition and incisional surgical site infection after laparoscopic appendectomy for complicated appendicitis.

PURPOSE: Surgical site infection (SSI) is common after laparoscopic appendectomy, resulting in prolonged hospital stay and increased costs. This study examined the relationship between body composition parameters and risk of incisional SSI in patients with complicated appendicitis. METHODS: We included 411 patients who underwent laparoscopic surgery for complicated appendicitis at a single institution between March 2015 and October 2023. Body composition parameters were derived from preoperative computed tomography (CT). A nomogram was constructed based on the independent predictors of incisional SSI. RESULTS: Overall, 45 (10.9%) patients developed incisional SSI. Visceral fat area (VFA) was independently associated with risk of incisional SSI (hazard ratio 1.015, 95% confidence interval 1.010-1.020, P < 0.001). A nomogram integrating VFA and two other independent predictors (diabetes and conversion) demonstrated high discriminative (area under the curve = 0.793) and calibration abilities. CONCLUSIONS: CT-derived VFA could be a valuable predictor of incisional SSI in patients with complicated appendicitis undergoing laparoscopic surgery. A VFA-based nomogram may help in identifying patients at high risk of SSI.

Construction and validation of a predictive model for the return to work of patients with permanent colorectal stoma: a cross-sectional study.

BACKGROUND: The issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work. METHODS: This study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves. RESULTS: After surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P < 0.05). These factors were incorporated into a logistic regression model generated by R software, resulting in a ROC curve with an area under the curve (AUC) of 0.916 (95% CI: 0.884-0.947). The Youden index was 0.731, and the cutoff value was 0.228. Sensitivity and specificity were 0.920 and 0.811, respectively. The H-L test demonstrated good model fit (χ2 = 12.858, P = 0.117, P > 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities. CONCLUSIONS: The postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients' return to work. Caregivers should prioritize the early identification of various patient types for proactive intervention to enhance the rate of postoperative return to work.

Predictive Value of Current Perception Threshold for Prognosis of Pulsed Radiofrequency in Patients with Acute Herpetic Neuralgia.

Objective: This study aimed to evaluate the prognostic accuracy of the Current Perception Threshold (CPT) in Acute Herpetic Neuralgia (AHN) patients receiving Pulsed Radiofrequency (PRF) therapy and to develop a corresponding prognostic model. Methods: We retrospectively analyzed data from 106 AHN patients treated with PRF between January 2022 and May 2023. The occurrence of Postherpetic Neuralgia (PHN) after treatment categorized patients into non-PHN and PHN groups. The predictive role of CPT indices for PRF outcomes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC). Then the dataset was split into a training set (n=74) and a validation set (n=32). Factors associated with PHN development were identified using univariate and multivariate logistic regression. A nomogram model was developed using significant predictors and internal validation was performed using valid set data. Results: Among the 106 patients, 45 had a poor prognosis. Significant differences in age, preoperative Numerical Rating Scale (NRS) score, and 5Hz CPT ratio were observed between the groups (p<0.05). Logistic regression identified these factors as independent predictors for PRF prognosis (p<0.05). The 5Hz CPT ratio demonstrated predictive value (AUC= 0.764, 95% CI: 0.674-0.855). The nomogram model, incorporating these predictors, showed high AUC in both the training (0.863, 95% CI: 0.776-0.950) and validation sets (0.859, 95% CI: 0.721-0.998). Calibration curves indicated good model fit, and the Hosmer-Lemeshow test confirmed this (p>0.05). Decision Curve Analysis (DCA) highlighted the model's predictive advantage. Conclusion: The 5Hz CPT ratio can predict the prognosis of PRF in AHN patients. The nomogram model has high precision and clinical utility. It can help identify AHN patients with a poor PRF prognosis at an early stage and assist in clinical decision-making.

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.

Background: Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions. Methods: We built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases. Results: The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma. Conclusion: The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

Influencing factors and predictive model for left atrial appendage emptying velocity in nonvalvular AF patients.

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia, significantly increasing the risk of death and stroke. The left atrial appendage (LAA) plays a crucial role in the development of AF. Reduced left atrial appendage emptying velocity (LAAEV) is an important indicator of nonvalvular AF, associated with thrombosis and recurrence after catheter ablation. This study aims to identify factors influencing LAAEV and construct a predictive model for LAAEV in nonvalvular AF patients. Methods: This retrospective cohort study included 1,048 nonvalvular AF patients hospitalized at the Second Hospital of Hebei Medical University from January 1, 2015, to December 31, 2021. Patients underwent transthoracic and transesophageal echocardiography and had complete laboratory data. Statistical analyses included binary logistic regression and multiple linear regression to identify independent predictors of reduced LAAEV and construct a predictive model. Results: Patients were divided into two groups: reduced LAAEV (<40 cm/s) and normal LAAEV (≥40 cm/s). The reduced LAAEV group included 457 patients (43.61%), with significant differences in age, gender, alcohol consumption, heart failure (HF), ischemic stroke, AF type, resting heart rate, CHA2DS2-VASc score, serum creatinine (SCR), serum uric acid (SUA), estimated glomerular filtration rate (eGFR), glycated hemoglobin (HbA1C), ß2 macroglobulin (B2M), left atrial diameter (LAD), and left ventricular ejection fraction (LVEF) compared to the normal LAAEV group. Logistic regression analysis identified age (OR 0.974, 95% CI 0.951-0.997, P = 0.028), HF (OR 0.637, 95% CI 0.427-0.949, P = 0.027), AF type [Persistent AF vs. PAF (OR 0.063, 95% CI 0.041-0.095, P = 0) Long-standing Persistent AF vs. PAF (OR 0.077, 95% CI 0.043-0.139, P = 0)], LAD (OR 0.872, 95% CI 0.836-0.91, P < 0.001), and LVEF (OR 1.057, 95% CI 1.027-1.089, P = 0) as independent predictors of reduced LAAEV. Multiple linear regression analysis included age, AF type, LAD, and LVEF in the final predictive model, explaining 43.5% of the variance in LAAEV (adjusted R² = 0.435). Conclusion: Age, HF, type of AF, LAD, and LVEF are independent predictors of reduced LAAEV. The predictive model (LAAEV = 96.567-15.940 × AFtype-1.309 × LAD-0.18 × Age + 37.069 × LVEF) demonstrates good predictive value, aiding in the initial assessment and management of nonvalvular AF patients.

A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.

BACKGROUND: Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge. METHOD: Pre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA). RESULT: The total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively. CONCLUSION: The study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.

Construction and Validation of an Assistant Decision-Making Model for Platelet Transfusion Refractoriness in Patients with Acute Myeloid Leukemia.

Background: Platelet transfusion refractoriness (PTR) is a complication of multiple transfusions in patients with hematological malignancies. PTR may induce a series of adverse events, such as delaying the treatment of the primary disease and life-threatening bleeding. Early prediction of PTR holds promise in facilitating prompt adjustments to treatment strategies by clinicians. Methods: We collected the clinical data of 250 patients with acute myeloid leukemia (AML). Subsequently, the patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic-regression methods were used to select characteristic variables. Assessment of the model was conducted through the receiver operating characteristic (ROC), calibration curve and decision curve analysis (DCA). Results: Out of 250 patients with AML, 95 individuals (38.0%) experienced PTR. Among those with positive platelet associated antibodies (PAAs), the incidence of PTR was 66.7% (30/45), while among patients positive for human leukocyte antigen(HLA)-I antibodies, the PTR incidence was 56.5% (48/85). The final predictive model incorporated risk factors such as KIT mutations, splenomegaly, the number of HLA-I antibodies, and positive PAAs. A prediction nomogram model was constructed based on these four risk factors. The LASSO-logistic regression model demonstrated excellent discrimination, calibration, and clinical decision value. Conclusion: The LASSO-logistic regression model in the study can better predict the risk of PTR. The study includes both PAAs and HLA antibodies, expanding the field of work that has not been involved in the previous prediction model of PTR.

Prediction model for spontaneous preterm birth less than 32 weeks of gestation in low-risk women with mid-trimester short cervical length: a retrospective cohort study.

BACKGROUND: A short cervix in mid-trimester pregnancy is a risk factor for spontaneous preterm birth. However, there is currently a lack of predictive models and classification systems for predicting spontaneous preterm birth in these patients, especially those without additional risk factors for spontaneous preterm birth. METHODS: A retrospective observational cohort study of low-risk singleton pregnant women with a short cervix (≤ 25 mm) measured by transvaginal ultrasonography between 22 and 24 weeks was conducted. A multivariate logistic regression model for spontaneous preterm birth < 32 weeks in low-risk pregnant women with a short cervix was constructed. Moreover, we developed a nomogram to visualize the prediction model and stratified patients into three risk groups (low-, intermediate-, and high-risk groups) based on the total score obtained from the nomogram model. RESULTS: Between 2020 and 2022, 213 low-risk women with a short cervix in mid-trimester pregnancy were enrolled in the study. Univariate logistic analysis revealed that a high body mass index, a history of three or more miscarriages, multiparity, a short cervical length, leukocytosis, and an elevated C-reactive protein level were associated with spontaneous preterm birth < 32 weeks, but multivariate analysis revealed that multiparity (OR, 3.31; 95% CI, 1.13-9.68), leukocytosis (OR, 3.96; 95% CI, 1.24-12.61) and a short cervical length (OR, 0.88; 95% CI, 0.82-0.94) were independent predictors of sPTB < 32 weeks. The model incorporating these three predictors displayed good discrimination and calibration, and the area under the ROC curve of this model was as high as 0.815 (95% CI, 0.700-0.931). Patients were stratified into low- (195 patients), intermediate- (14 patients) and high-risk (4 patients) groups according to the model, corresponding to patients with scores ≤ 120, 121-146, and > 146, respectively. The predicted probabilities of spontaneous preterm birth < 32 weeks for these groups were 6.38, 40.62, and 71.88%, respectively. CONCLUSIONS: A noninvasive and efficient model to predict the occurrence of spontaneous preterm birth < 32 weeks in low-risk singleton pregnant women with a short cervix and a classification system were constructed in this study and can provide insight into the optimal management strategy for patients with different risk stratifications according to the score chart.

Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study.

BACKGROUND: Gallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients. METHODS: Patients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit. RESULTS: Eight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set. CONCLUSIONS: The novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making.

Machine learning as a tool predicting short-term postoperative complications in Crohn's disease patients undergoing intestinal resection: What frontiers?

The recent study, "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" investigated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn's disease (CD) patients. Employing a random forest analysis and Shapley Additive Explanations, the study prioritizes factors such as preoperative nutritional status, operative time, and CD activity index. Despite the retrospective design's limitations, the model's robustness, with area under the curve values surpassing 0.8, highlights its clinical potential. The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases, emphasizing the importance of comprehensive assessment and optimization. While a significant advancement, further research is crucial for refining preoperative strategies in CD patients.

Linking the neural signature of response time variability to Alzheimer's disease pathology and cognitive functioning.

Promising evidence has suggested potential links between mind-wandering and Alzheimer's disease (AD). Yet, older adults with diagnosable neurocognitive disorders show reduced meta-awareness, thus questioning the validity of probe-assessed mind-wandering in older adults. In prior work, we employed response time variability as an objective, albeit indirect, marker of mind-wandering to identify patterns of functional connectivity that predicted mind-wandering. In the current study, we evaluated the association of this connectome-based, mind-wandering model with cerebral spinal fluid (CSF) p-tau/Aß 42 ratio in 289 older adults from the Alzheimer's Disease NeuroImaging Initiative (ADNI). Moreover, we examined if this model was similarly associated with individual differences in composite measures of global cognition, episodic memory, and executive functioning. Edges from the high response time variability model were significantly associated with CSF p-tau/Aß ratio. Furthermore, connectivity strength within edges associated with high response time variability was negatively associated with global cognition and episodic memory functioning. This study provides the first empirical support for a link between an objective neuromarker of mind-wandering and AD pathophysiology. Given the observed association between mind-wandering and cognitive functioning in older adults, interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.

Response time variability is considered an objective, albeit indirect, marker of mind-wandering. In this study, we applied a previously-derived connectome-based model of response time variability to resting-state data obtained from 289 older adults in the Alzheimer's Disease NeuroImaging Initiative. The network strength of the high response time variability model was correlated with a cerebrospinal fluid (CSF)-based ratiometric measure of amyloid and tau pathology. Additionally, our results demonstrated that the network strength in the high response time variability model was also linked with global cognition and episodic memory. This study provides the first empirical support for the association between a neuromarker of response time variability­an indirect marker of mind-wandering­and AD pathophysiology.

Establishment and validation of a risk prediction model for sarcopenia in gastrointestinal cancer patients: A systematic review and meta-analysis-based approach.

OBJECTIVE: The study aimed to develop a model to predict the risk of sarcopenia in gastrointestinal cancer patients. The goal was to identify these patients early and classify them into different risk categories based on their likelihood of developing sarcopenia. METHODS: This study evaluated risk factors for sarcopenia in patients with gastrointestinal cancers through a systematic review and meta-analysis. The natural logarithm of the combined risk estimate for each factor was used as a coefficient to assign scores within the model for risk prediction. Data from 270 patients with gastrointestinal cancers, collected between October 2023 and April 2024, was used to assess the predictive performance of the scoring model. RESULTS: The analysis included 17 studies that included 9405 patients with gastrointestinal cancers, out of which 4361 had sarcopenia. The model identified several significant predictors of sarcopenia, including age (OR = 2.45), sex (OR = 1.15), combined diabetes (OR = 2.02), neutrophil-to-lymphocyte ratio (NLR) category (OR = 1.61), TNM stage (OR = 1.61), and weight change (OR = 1.60). Model validation was performed using an external cohort through logistic regression, resulting in an area under the curve (AUC) of 0.773. This model attained a sensitivity of 0.714 and a specificity of 0.688 and ultimately selected 16.5 as the ideal critical risk score. Furthermore, an AUC of 0.770 was obtained from Bayesian model validation; the optimal critical risk score was determined to be 19.0, which corresponds to a sensitivity of 0.658 and a specificity of 0.847. CONCLUSIONS: The model of risk prediction developed through systematic review and meta-analysis demonstrates substantial for sarcopenia in patients with gastrointestinal cancers. Its clinical usability facilitates the screening of patients at high risk for sarcopenia.

Downskin Surface Roughness Prediction with Machine Learning for As-Built CM247LC Fabricated Via Powder Bed Fusion with a Laser Beam.

Powder bed fusion with a laser beam (PBF-LB) is a widely used metal additive manufacturing method for fabricating complex three-dimensional components with a variety of metallic powders. However, metal parts fabricated by PBF-LB often present surface quality problems because of the layer-wise building process and the occurrence of partially unmelted powder particles. To reduce the surface roughness, surface post-processing is required, which incurs additional time and cost. In particular, the downskin surface generally has the worst surface roughness among the fabricated components. The rough surface reduces the lifetime and quality of the holed part owing to cracks, corrosion, and wear. In this study, for fast and efficient improvement of the downskin surface roughness of CM247LC fabricated by PBF-LB, machine learning algorithms, namely support vector regression (SVR), random forest (RF), and multilayer perceptron (MLP), were introduced to predict downskin surface roughness in the process parameter selection step. Three PBF-LB process parameters (laser power, scanning speed, and hatching distance) and the overhang angle were selected as the input variables for the machine learning models for predicting downskin surface roughness. Test samples were prepared and used for training and evaluation of the proposed machine learning algorithms, with RF showing the most promising results. Early results were confirmed when model predictions were compared to the actual measured roughness of a fabricated vane part, with average deviations of 13.7%, 4.3%, and 22.5% observed for SVR, RF, and MLP, respectively. The results showed that the proposed machine learning models could accurately predict the downskin surface roughness in the process parameter selection step without the use of any sensor, with RF showing the highest prediction accuracy.

Construction and Evaluation of a Predictive Model for Grassroots Nurses' Risk Perception of "Internet + Nursing Services": A Multicenter Cross-Sectional Study.

Purpose: The development of "Internet + nursing services" can effectively solve the problem of population aging, and grassroots nurses are the primary providers of such services in rural areas. This study aimed to analyze the factors affecting grassroots nurses' risk perception of "Internet + nursing services" and construct a predictive model. Patients and Methods: A multicenter cross-sectional study of 2220 nurses from 27 secondary hospitals and 36 community health centers in Hubei Province was conducted from August to December 2023 using a multi-stage cluster sampling method. Information was collected through a structured anonymous questionnaire. A Chi-square test, a Welch t-test, and binary logistic regression analyses were employed to determine independent risk factors for grassroots nurses' risk perception of "Internet + nursing services", and a nomogram was constructed. Receiver operating characteristic curves, calibration curves, and decision curves were plotted to evaluate the discrimination, calibration, and clinical effectiveness of the nomogram. Results: A total of 2050 valid questionnaires were collected, demonstrating that 51.95% of grassroots nurses thought that "Internet + nursing services" was a medium-high risk. Age, other sources of income, knowledge about "Internet + nursing services", personal safety, physical function, occupational exposure, social psychosocial, and time risk (P<0.05) were independent risk factors for grassroots nurses' risk perception. The area under the receiver operating characteristic curve of the nomogram was 0.939. The calibration and decision curve analyses demonstrated good calibration ability and clinical application values. Conclusion: The prediction model constructed in this study has good prediction ability. Most grassroots nurses believe that "Internet + nursing services" are risky and influenced by several factors. It is suggested that the government and hospitals should formulate a unified charging standard, improve the safety guarantee, and gradually eliminate the concerns of grassroots nurses.

Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning.

Objective: This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning. Methods: A total of 26,030 elderly aspirin users (aged over 65) were retrospective included in the study. Data on patient demographics, clinical features, underlying diseases, medical history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. Patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation, respectively. Least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and multivariate logistic regression were employed to develop prediction models. Model performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). Results: The XGBoost model exhibited the highest AUC among all models. It consisted of six clinical variables: HGB, PLT, previous bleeding, gastric ulcer, cerebral infarction, and tumor. A visual nomogram was developed based on these six variables. In the training dataset, the model achieved an AUC of 0.842 (95% CI: 0.829-0.855), while in the test dataset, it achieved an AUC of 0.820 (95% CI: 0.800-0.840), demonstrating good discriminatory performance. The calibration curve analysis revealed that the nomogram model closely approximated the ideal curve. Additionally, the DCA curve, CIC, and NRC demonstrated favorable clinical net benefit for the nomogram model. Conclusion: This study successfully developed a predictive model to estimate the risk of bleeding in elderly aspirin users. This model can serve as a potential useful tool for clinicians to estimate the risk of bleeding in elderly aspirin users and make informed decisions regarding their treatment and management.

Nomograms to Appraise The Risk of Chronic Kidney Disease After Radical Cystectomy: Shifting The Focus to Prevention.

INTRODUCTION: Our objectives were to analyse the incidence of changes in renal function after radical cystectomy (RC) and determine the factors responsible for those changes, as a basis for rethinking strategies to ensure early detection and development of a risk-adapted approach. PATIENTS AND METHODS: A single-centre retrospective study included 316 patients who underwent RC between 2010 and 2019. A competing risk Cox model, whereby death from any cause was treated as a censoring event, was used to establish nomograms to analyze the prognostic factors for CKD at 2 and 5 years. The nomograms were validated based on discrimination using the C-index, calibration plots and analysis of net benefit from decision curves. RESULTS: During a median follow-up of 48.73 months (0.13-156.67), 138 patients (43.7%) developed CKD. The probability of CKD development at 2 and 5 years was 41.3% (95% CI, 35.8-47.2) and 48.5% (95% CI, 42.8-54.6), respectively. Hypertension (HR 1.69, 95% CI, 1.23-2.34), prior hydronephrosis (HR 1.62, 95% CI, 1.17-2.25), acute kidney injury (AKI) during the immediate postoperative period (HR 1.88, 95% CI, 1.35-2.61) and readmission due to urinary tract infection (HR 1.41, 95% CI, 1.01-1.96) were predictors of 2-year CKD. Hydronephrosis at follow-up computed tomography (HR 2.21, 95% CI, 1.60-3.07), prior hydronephrosis (HR 1.54, 95% CI, 1.09-2.15), AKI during the immediate postoperative period (HR 1.77, 95% CI, 1.27-2.46) and hypertension (HR 1.60, 95% CI, 1.16-2.21) were predictors for 5-year CKD. Prior eGFR ≥ 90 mL/min/1.73 m2 was a protective factor (HR 0.50, 95% CI, 0.32-0.80 and HR 0.48, 95% CI, 0.30-0.78 for 2- and 5-year CKD, respectively). The resulting nomograms were based on these prognostic factors. CONCLUSION: Almost half of the patients had developed CKD at 5 years. Thus, it is crucial to identify patients at risk of developing CKD in order to initiate renal function-sparing measures and tailor follow-up protocols. The proposed nomograms effectively predicted CKD in these patients.