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.

A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics.

BACKGROUND: The prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored. METHODS: We Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model's performance. RESULTS: The enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram's good predictive performance and clinical utility. CONCLUSION: Integrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC.

Advancing Precise Syphilis Diagnosis: A Nontreponemal IgM Antibody-Based Model for Latent Syphilis Staging.

Purpose: Accurate differentiation between early and late latent syphilis stages is pivotal for patient management and treatment strategies. Nontreponemal IgM antibodies have shown potential in discriminating latent syphilis staging by differentiating syphilis activity. This study aimed to develop a predictive nomogram model for latent syphilis staging based on nontreponemal IgM antibodies. Patients and Methods: We explored the correlation between nontreponemal IgM antibodies and latent syphilis staging and developed a nomogram model to predict latent syphilis staging based on 352 latent syphilis patients. Model performance was assessed using AUC, calibration curve, Hosmer-Lemeshow χ2 statistics, C-index, Brier score, decision curve analysis, and clinical impact curve. Additionally, an external validation set was used to further assess the model's stability. Results: Nontreponemal IgM antibodies correlated with latent syphilis staging. The constructed model demonstrated a strong discriminative capability with an AUC of 0.743. The calibration curve displayed a strong fit, key statistics including Hosmer-Lemeshow χ² at 2.440 (P=0.486), a C-index score of 0.743, and a Brier score of 0.054, all suggesting favorable model calibration performance. Decision curve analysis and clinical impact curve highlighted the model's robust clinical applicability. The external validation set yielded an AUC of 0.776, Hosmer-Lemeshow χ² statistics of 2.440 (P=0.486), a C-index score of 0.767, and a Brier score of 0.054, further underscored the reliability of the model. Conclusion: The nontreponemal IgM antibody-based predicted model could equip clinicians with a valuable tool for the precise staging of latent syphilis and enhancing clinical decision-making.

The predictive value of mBDNF for major adverse cardiovascular events in stable coronary artery disease patients with depressive symptoms: A single-center, 5-year follow-up study.

BACKGROUND: Myokines play vital roles in both stable coronary artery disease (SCAD) and depression. Meanwhile, there is a pressing necessity to find effective biomarkers for early predictor of major adverse cardiovascular events (MACE) in SCAD patients with depressive symptoms. METHODS: A single-center, 5-year follow-up study was investigated. MACE was defined as composite end points, including cardiovascular death, non-fatal stroke, non-fatal myocardial infarction, coronary artery revascularization, or hospitalization for unstable angina. RESULTS: A total of 116 SCAD patients were enrolled, consisting of 30 cases (25.9%) without depressive symptoms and 86 cases (74.1%) with depressive symptoms. During the follow-up, 3 patients (2.6%) were lost. Out of 113 patients, 51 (45.1%) experienced MACE. In the subgroup of 84 SCAD patients with depressive symptoms, 44 cases (52.4%) of MACE were observed. Finally, mature brain-derived neurotrophic factor (mBDNF), pro-brain-derived neurotrophic factor, receptor activator of nuclear factor-κB ligand, smoking history, hypertension and cystatin C were incorporated into the predictive model. CONCLUSIONS: Depressive symptoms represent an independent risk factor for MACE in patients with SCAD. Additionally, low mBDNF expression may be an important early predictor for MACE in SCAD patients with depressive symptoms. The predictive model may exhibit a commendable predictive performance for MACE in SCAD patients with depressive symptoms.

Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

Objective: Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients seeking the infertility treatment. However, there are no high-quality models and clinical applicable tools to predict the probability of natural conception after reproductive surgery. Methods: The eligible data involving 1013 patients who operated for infertility between June 2016 and June 2021 in Yantai Yuhuangding Hospital in China, were randomly divided into training and internal testing cohorts. 195 subjects from the Linyi People's Hospital in China were considered for external validation. Both univariate combining with multivariate logistic regression and the least absolute shrinkage and selection operator (LASSO) algorithm were performed to identify independent predictors. Multiple common machine learning algorithms, namely logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, and extreme gradient boosting, were employed to construct the predictive models. The optimal model was verified by evaluating the model performance in both the internal and external validation datasets. Results: Six clinical indicators, including female age, infertility type, duration of infertility, intraoperative diagnosis, ovulation monitoring, and anti-Müllerian hormone (AMH) level, were screened out. Based on the logistic regression model's superior clinical predictive value, as indicated by the area under the receiver operating characteristic curve (AUC) in both the internal (0.870) and external (0.880) validation sets, we ultimately selected it as the optimal model. Consequently, we utilized it to generate a web-based nomogram for predicting the probability of spontaneous pregnancy after reproductive surgery. Furthermore, the calibration curve, Hosmer-Lemeshow (H-L) test, the decision curve analysis (DCA) and clinical impact curve analysis (CIC) demonstrated that the model has superior calibration degree, clinical net benefit and generalization ability, which were confirmed by both internal and external validations. Conclusion: Overall, our developed first nomogram with online operation provides an early and accurate prediction for the probability of natural conception after reproductive surgery, which helps clinicians and infertile couples make sensible decision of choosing the mode of subsequent conception, natural or IVF, to further improve the clinical practices of infertility treatment.

Maternal diseases and congenital anomalies of the kidney and urinary tract in offspring: a cohort study.

BACKGROUND: Congenital anomalies of the kidneys and urinary tract (CAKUT) are the most common cause of prenatally diagnosed developmental malformation. This study aimed to assess the relationship between maternal diseases and CAKUT in offspring. METHODS: This retrospective study enrolled all pregnant women registered from January 2020 to December 2022 at one medical center. Medical information on maternal noncommunicable diseases, including obesity, hypertension, diabetes mellitus, kidney disease, hyperthyroidism, hypothyroidism, psychiatric disease, epilepsy, cancer, and autoimmune disease was collected. Based on the records of ultrasound scanning during the third trimester, the diagnosis was classified as isolated urinary tract dilation (UTD) or kidney anomalies. Multivariate logistic regression was performed to establish models to predict antenatal CAKUT. RESULTS: Among the 19,656 pregnant women, perinatal ultrasound detected suspicious CAKUT in 114 (5.8/1000) fetuses, comprising 89 cases with isolated UTD and 25 cases with kidney anomalies. The risk of antenatal CAKUT was increased in the fetuses of mothers who experienced gestational diabetes, thyroid dysfunction, neuropsychiatric disease, anemia, ovarian and uterine disorders. A prediction model for isolated UTD was developed utilizing four confounding factors, namely gestational diabetes, gestational hypertension, maternal thyroid dysfunction, and hepatic disease. Similarly, a separate prediction model for kidney anomalies was established based on four distinct confounding factors, namely maternal thyroid dysfunction, gestational diabetes, disorders of ovarian/uterine, and kidney disease. CONCLUSIONS: Isolated UTD and kidney anomalies were associated with different maternal diseases. The results may inform the clinical management of pregnancy and highlight potential differences in the genesis of various subtypes of CAKUT.

ProsTAV, a clinically useful test in prostate cancer: an extension study.

PURPOSE: To assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa). METHODS: Preliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3-10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center's routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined. RESULTS: The mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85-0.95) and specificity of 0.27 (95% CI, 0.19-0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46-0.60) and 0.74 (95% CI, 0.62-0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI. CONCLUSIONS: ProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

External validation of the barcelona magnetic resonance imaging predictive model for detecting significant prostate cancer including men receiving 5-alpha reductase inhibitors.

PURPOSE: To validate the Barcelona-magnetic resonance imaging predictive model (BCN-MRI PM) for clinically significant prostate cancer (csPCa) in Catalonia, a Spanish region with 7.9 million inhabitants. Additionally, the BCN-MRI PM is validated in men receiving 5-alpha reductase inhibitors (5-ARI). MATERIALS AND METHODS: A population of 2,212 men with prostate-specific antigen serum level > 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and targeted and/or systematic biopsies in the year 2022, at ten participant centers of the Catalonian csPCa early detection program, were selected. 120 individuals (5.7%) were identified as receiving 5-ARI treatment for longer than a year. The risk of csPCa was retrospectively assessed with the Barcelona-risk calculator 2 (BCN-RC 2). Men undergoing 5-ARI treatment for less than a year were excluded. CsPCa was defined when the grade group was ≥ 2. RESULTS: The area under the curve of the BCN-MRI PM in 5-ARI naïve men was 0.824 (95% CI 0.783-0.842) and 0.849 (0.806-0.916) in those receiving 5-ARI treatment, p 0.475. Specificities at 100, 97.5, and 95% sensitivity thresholds were to 2.7, 29.3, and 39% in 5-ARI naïve men, while 43.5, 46.4, and 47.8%, respectively in 5-ARI users. The application of BCN-MRI PM would result in a reduction of 23.8% of prostate biopsies missing 5% of csPCa in 5-ARI naïve men, while reducing 25% of prostate biopsies without missing csPCa in 5-ARI users. CONCLUSIONS: The BCN-MRI PM has achieved successful validation in Catalonia and, notably, for the first time, in men undergoing 5-ARI treatment.

Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma.

PURPOSE: To develop and validate a model for predicting suboptimal debulking surgery (SDS) of serous ovarian carcinoma (SOC) using radiomics method, clinical and MRI features. METHODS: 228 patients eligible from institution A (randomly divided into the training and internal validation cohorts) and 45 patients from institution B (external validation cohort) were collected and retrospectively analyzed. All patients underwent abdominal pelvic enhanced MRI scan, including T2-weighted imaging fat-suppressed fast spin-echo (T2FSE), T1-weighted dual-echo magnetic resonance imaging (T1DEI), diffusion weighted imaging (DWI), and T1 with contrast enhancement (T1CE). We extracted, selected and eliminated highly correlated radiomic features for each sequence. Then, Radiomic models were made by each single sequence, dual-sequence (T1CE + T2FSE), and all-sequence, respectively. Univariate and multivariate analyses were performed to screen the clinical and MRI independent predictors. The radiomic model with the highest area under the curve (AUC) was used to combine the independent predictors as a combined model. RESULTS: The optimal radiomic model was based on dual sequences (T2FSE + T1CE) among the five radiomic models (AUC = 0.720, P < 0.05). Serum carbohydrate antigen 125, the relationship between sigmoid colon/rectum and ovarian mass or mass implanted in Douglas' pouch, diaphragm nodules, and peritoneum/mesentery nodules were considered independent predictors. The AUC of the radiomic-clinical-radiological model was higher than either the optimal radiomic model or the clinical-radiological model in the training cohort (AUC = 0.908 vs. 0.720/0.854). CONCLUSIONS: The radiomic-clinical-radiological model has an overall algorithm reproducibility and may help create individualized treatment programs and improve the prognosis of patients with SOC.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma.

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Predicting Inadequate Weight Loss After Bariatric Surgery: Derivation and Validation of a Four Factor Model.

INTRODUCTION: Weight loss following bariatric surgery is variable and predicting inadequate weight loss is required to help select patients for bariatric surgery. The aim of the present study was to determine variables associated with inadequate weight loss and to derive and validate a predictive model. METHODS: All patients who underwent laparoscopic sleeve gastrectomy and Roux-en-Y gastrectomy (2008-2022) in a tertiary referral centre were followed up prospectively. Inadequate weight loss was defined as excess weight loss (EWL) < 50% by 24 months. A top-down approach was performed using multivariate logistic regression and then internally validated using bootstrapping. Patients were categorised into risk groups. RESULTS: A total of 280 patients (median age, 49 years; M:F, 69:211) were included (146 LSG; 134 LRYGB). At 24 months, the median total weight loss was 30.9% and 80.0% achieved EWL ≥ 50% by 24 months. Variables associated with inadequate weight loss were T2DM (OR 2.42; p = 0.042), age 51-60 (OR 1.93, p = 0.006), age > 60 (OR 4.93, p < 0.001), starting BMI > 50 kg/m² (OR 1.93, p = 0.037) and pre-operative weight loss (OR 3.51; p = 0.036). The validation C-index was 0.75 (slope = 0.89). Low, medium and high-risk groups had a 4.9%, 16.7% and 44.6% risk of inadequate weight loss, respectively. CONCLUSIONS: Inadequate weight loss can be predicted using a four factor model which could help patients and clinicians in decision-making for bariatric surgery.

Interleukin-6 in Sepsis-Promising but Yet to Be Proven.

How to cite this article: Sinha S. Interleukin-6 in Sepsis-Promising but Yet to Be Proven. Indian J Crit Care Med 2024;28(7):629-631.

Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis.

Background: Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Methods: Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested cross-validation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. Results: XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUC-ROC of 0.84). Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. Conclusions: These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.

Emergence and Potential Spread of Rust Disease on Wisteria floribunda and Corydalis incisa Influenced by Climate Change in Korea.

Global climate change influences the emergence, spread, and severity of rust diseases that affect crops and forests. In Korea, the rust diseases that affect Wisteria floribunda and its alternate host Corydalis incisa are rapidly spreading northwards. Through morphological, molecular, phylogenetic, and pathogenicity approaches, Neophysopella kraunhiae was identified as the causal agent, alternating between the two host plants to complete its life cycle. Using the maximum entropy model (Maxent) under shared socioeconomic pathways (SSPs), the results of this study suggest that by the 2050s, C. incisa is likely to extend its range into central Korea owing to climate shifts, whereas the distribution of W. floribunda is expected to remain unchanged nationwide. The generalized additive model revealed a significant positive correlation between the presence of C. incisa and the incidence of rust disease, highlighting the role that climate-driven expansion of this alternate host plays in the spread of N. kraunhiae. These findings highlight the profound influence of climate change on both the distribution of a specific plant and the disease a rust fungus causes, raising concerns about the potential emergence and spread of other rust pathogens with similar host dynamics.

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study.

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization. Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm. Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97-0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model. Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients' clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

Development and validation of a nomogram for breast cancer-related lymphedema.

To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.

Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.

Purpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed. Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts. Results: Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986-0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945-0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915-0.955), 82.0%, and 90.3% in the replication cohort. Conclusion: Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.

LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis.

Introduction: This study aims to establish a comprehensive, multi-level approach for tackling tropical diseases by proactively anticipating and managing Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) within the initial 14 days of Intensive Care Unit (ICU) admission. The primary objective is to amalgamate a diverse array of indicators and pathogenic microbial data to pinpoint pivotal predictive variables, enabling effective intervention specifically tailored to the context of tropical diseases. Methods: A focused analysis was conducted on 1733 patients admitted to the ICU between December 2016 and July 2019. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, disease severity and laboratory indices were scrutinized. The identified variables served as the foundation for constructing a predictive model designed to forecast the occurrence of PICS. Results: Among the subjects, 13.79% met the diagnostic criteria for PICS, correlating with a mortality rate of 38.08%. Key variables, including red-cell distribution width coefficient of variation (RDW-CV), hemofiltration (HF), mechanical ventilation (MV), Norepinephrine (NE), lactic acidosis, and multiple-drug resistant bacteria (MDR) infection, were identified through LASSO regression. The resulting predictive model exhibited a robust performance with an Area Under the Curve (AUC) of 0.828, an accuracy of 0.862, and a specificity of 0.977. Subsequent validation in an independent cohort yielded an AUC of 0.848. Discussion: The acquisition of RDW-CV, HF requirement, MV requirement, NE requirement, lactic acidosis, and MDR upon ICU admission emerges as a pivotal factor for prognosticating PICS onset in the context of tropical diseases. This study highlights the potential for significant improvements in clinical outcomes through the implementation of timely and targeted interventions tailored specifically to the challenges posed by tropical diseases.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is a common gynecological malignancy that typically requires prompt surgical intervention; however, the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes. Previous studies have highlighted the prognostic potential of circulating tumor DNA (ctDNA) monitoring for minimal residual disease in patients with EC. AIM: To develop and validate an optimized ctDNA-based model for predicting short-term postoperative EC recurrence. METHODS: We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model, which was validated on 143 EC patients operated between 2020 and 2021. Prognostic factors were identified using univariate Cox, Lasso, and multivariate Cox regressions. A nomogram was created to predict the 1, 1.5, and 2-year recurrence-free survival (RFS). Model performance was assessed via receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA), leading to a recurrence risk stratification system. RESULTS: Based on the regression analysis and the nomogram created, patients with postoperative ctDNA-negativity, postoperative carcinoembryonic antigen 125 (CA125) levels of < 19 U/mL, and grade G1 tumors had improved RFS after surgery. The nomogram's efficacy for recurrence prediction was confirmed through ROC analysis, calibration curves, and DCA methods, highlighting its high accuracy and clinical utility. Furthermore, using the nomogram, the patients were successfully classified into three risk subgroups. CONCLUSION: The nomogram accurately predicted RFS after EC surgery at 1, 1.5, and 2 years. This model will help clinicians personalize treatments, stratify risks, and enhance clinical outcomes for patients with EC.