Predictive risk score for isolated brain metastasis in non-small cell lung cancer.

Background: Brain metastasis is common with non-small cell lung cancer (NSCLC). Patients with some early-stage cancers don't benefit from routine brain imaging. Currently clinical stage alone is used to justify additional brain imaging. Other clinical and demographic characteristics may be associated with isolated brain metastasis (IBM). We aimed to define the most salient clinical features associated with synchronous IBM, hypothesizing that clinical and demographic factors could be used to determine the risk of brain metastasis. Methods: The National Cancer Database was used to identify patients with NSCLC from 2016-2020. Primary outcome was the presence of IBM relative to patients without evidence of any metastasis. Cohorts were divided into test and validation. The test cohort was used to identify risk factors for IBM using multivariable logistic regression. Using the regression, a scoring system was created to estimate the rate of synchronous IBM. The accuracy of the scoring system was evaluated with receiver operating characteristic (ROC) analysis using the validation cohort. Results: Study population consisted of 396,113 patients: 25,907 IBM and 370,206 without metastatic disease. IBM was associated with age, clinical T stage, clinical N stage, Charlson/Deyo comorbidity score, histology, and grade. A scoring system using these factors showed excellent accuracy in the test and validation cohort in ROC analysis (0.806 and 0.805, respectively). Conclusions: Clinical and demographic characteristics can be used to stratify the risk of IBM among patients with NSCLC and provide an evidence-based method to identify patients who require dedicated brain imaging in the absence of other metastatic disease.

Taper versus discontinuation of tocilizumab in patients with giant cell arteritis: Real-world experience from a tertiary center.

INTRODUCTION: Following the approval of tocilizumab (TCZ) for giant cell arteritis (GCA), recent studies have shown a high relapse frequency after abrupt discontinuation of TCZ. However, a thorough exploration of TCZ tapering compared to abrupt discontinuation has never been undertaken. Likewise, adverse events have only been scarcely investigated in routine care. This study aimed to compare the incidence of relapses in GCA patients undergoing TCZ tapering compared to abrupt discontinuation. METHODS: We performed a single-center retrospective cohort study from 2012 to 2022. Data from GCA patients treated with TCZ was obtained from the Electronic Patients Record. Relapse-free survival is reported in Kaplan-Meier plots and tapering versus abrupt discontinuation were compared using a Wilcoxon-Brewlos-Gehan test. RESULTS: We included 155 patients receiving TCZ treatment for GCA, of which 104 discontinued TCZ. Among the 104 patients discontinuing TCZ, 42 (40 %) experienced a relapse within the first year. A total of 57 patients underwent taper with 6/38 (16 %) and 2/19 (11 %) relapsing while receiving TCZ every second or third week, respectively. In comparison, 59 patients underwent abrupt discontinuation with 27 (46 %) relapsing during follow-up. The patients undergoing abrupt TCZ discontinuation demonstrated a significantly shorter time to relapse compared to all tapered patients (p = 0.02) as well as patients tapered from weekly TCZ treatment to every second week (p < 0.01). Furthermore, 15 % of patients discontinued TCZ due to adverse events. CONCLUSION: This is the first study indicating that TCZ taper induced longer relapse-free survival than abrupt discontinuation, implying that taper may be favored over discontinuation in patients with GCA.

Predicting bowel function after diverting stoma closure in patients with rectal cancer.

Background: Patients with rectal cancer undergoing laparoscopic anterior resection and diverting stomas often suffer from bowel dysfunction after stoma closure, impairing their quality of life. This study aims to develop a machine learning tool to predict bowel function after diverting stoma closure. Methods: Clinicopathological data and post-operative follow-up information from patients with mid-low rectal cancer after diverting stoma closure were collected and analyzed. Patients were randomly divided into training and test sets in a 7:3 ratio. A machine learning model was developed in the training set to predict major low anterior resection syndrome (LARS) and evaluated in the test set. Decision curve analysis (DCA) was used to assess clinical utility. Results: The study included 396 eligible patients who underwent laparoscopic anterior resection and diverting stoma in Tongji Hospital affiliated with Huazhong University of Science and Technology from 1 January 2012 to 31 December 2020. The interval between stoma creation and closure, neoadjuvant therapy, and body mass index were identified as the three most crucial characteristics associated with patients experiencing major LARS in our cohort. The machine learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.78 [95% confidence interval (CI): 0.74-0.83] in the training set (n=277) and 0.74 (95% CI: 0.70-0.79) in the test set (n=119), and area under the precision-recall curve (AUPRC) of 0.73 and 0.69, respectively, with sensitivity of 0.67 and specificity of 0.66 for the test set. DCA confirmed clinical applicability. Conclusions: This study developed a machine learning model to predict major LARS in rectal cancer patients after diverting stoma closure, aiding their decision-making and counseling.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy.

BACKGROUND: Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. METHODS: We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. RESULTS: We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. CONCLUSION: Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

Predicting the recurrence of spontaneous intracerebral hemorrhage using a machine learning model.

Background: Recurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence. Methods: We collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction. Results: The model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91. Conclusion: The machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort.

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database.

Purpose: The objective of this investigation was to construct and validate a nomogram for prognosticating cancer-specific survival (CSS) in patients afflicted with gastrointestinal stromal tumor (GIST) at 3-, 5-, and 8-years post-diagnosis. Methods: Data pertaining to patients diagnosed with GIST were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Through random selection, a training cohort (70%) and a validation cohort (30%) were established from the patient population. Employing a backward stepwise Cox regression model, independent prognostic factors were identified. Subsequently, these factors were incorporated into the nomogram to forecast CSS rates at 3-, 5-, and 8-years following diagnosis. The nomogram's performance was assessed using indicators such as the consistency index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis (DCA). Results: This investigation encompassed a cohort of 3,062 GIST patients. By analyzing the Cox regression model within the training cohort, nine prognostic factors were identified: age, sex, race, marital status, AJCC (American Joint Committee on Cancer) stage, surgical status, chemotherapy status, radiation status, and income status. The nomogram was subsequently developed and subjected to both internal and external validation. The nomogram exhibited favorable discrimination abilities, as evidenced by notably high C-indices and AUC values. Calibration curves confirmed the nomogram's reliability. Moreover, the nomogram outperformed the AJCC model, as demonstrated by enhanced NRI and IDI values. The DCA curves validated the clinical utility of the nomogram. Conclusion: The present study has successfully constructed and validated the initial nomogram for predicting prognosis in GIST patients. The nomogram's performance and practicality suggest its potential utility in clinical settings. Nevertheless, further external validation is warranted.

Application of the Nadaraya-Watson estimator based attention mechanism to the field of predictive maintenance.

Attention mechanism has recently gained immense importance in the natural language processing (NLP) world. This technique highlights parts of the input text that the NLP task (such as translation) must pay "attention" to. Inspired by this, some researchers have recently applied the NLP domain, deep-learning based, attention mechanism techniques to predictive maintenance. In contrast to the deep-learning based solutions, Industry 4.0 predictive maintenance solutions that often rely on edge-computing, demand lighter predictive models. With this objective, we have investigated the adaptation of a simpler, incredibly fast and compute-resource friendly, "Nadaraya-Watson estimator based" attention method. We develop a method to predict tool-wear of a milling machine using this attention mechanism and demonstrate, with the help of heat-maps, how the attention mechanism highlights regions that assist in predicting onset of tool-wear. We validate the effectiveness of this adaptation on the benchmark IEEEDataPort PHM Society dataset, by comparing against other comparatively "lighter" machine learning techniques - Bayesian Ridge, Gradient Boosting Regressor, SGD Regressor and Support Vector Regressor. Our experiments indicate that the proposed Nadaraya-Watson attention mechanism performed best with an MAE of 0.069, RMSE of 0.099 and R2 of 83.40 %, when compared to the next best technique Gradient Boosting Regressor with figures of 0.100, 0.138, 66.51 % respectively. Additionally, it produced a lighter and faster model as well.•We propose a Nadaraya-Watson estimator based "attention mechanism", applied to a predictive maintenance problem.•Unlike the deep-learning based attention mechanisms from the NLP domain, our method creates fast, light and high-performance models, suitable for edge computing devices and therefore supports the Industry 4.0 initiative.•Method validated on real tool-wear data of a milling machine.

Common hematological and biochemical parameters for predicting urinary tract infections in geriatric patients with hip fractures.

Background: This study aims to discern the significance of common hematological and biochemical parameters for predicting urinary tract infections in geriatric patients with hip fractures. Methods: Multivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for UTIs. The abilities of these parameters to predict UTIs were evaluated by receiver operating characteristic (ROC) curves. Dose-response relationships were assessed by categorizing hematological and biochemical parameters into quartiles. Subgroup analyses were further explored to investigate the relationship between these parameters and urinary tract infections. Results: Out of the 1,231 participants, 23.2% were diagnosed with UTIs. Hyperglycemia, hypoproteinemia and hyperglobulinemia were risk factors for UTIs in multivariate analysis. After propensity score matching, hyperglycemia (OR 2.14, 95% CI 1.50-3.05, p < 0.001), hypoproteinemia (OR 1.75, 95% CI 1.18-2.63, p = 0.006), and hyperglobulinemia (OR 1.38, 95% CI 0.97-1.97, p = 0.074) remained significantly associated with increased odds of urinary tract infections. ROC curve analyses showed moderate predictive accuracy of blood glucose, albumin and globulin for UTIs, with areas under the curves of 0.714, 0.633, and 0.596, respectively. Significant dose-response relationships were observed between these parameters and UTIs. The associations were consistent in subgroup analyses. Conclusion: Blood glucose, albumin and globulin levels can facilitate early identification of geriatric hip fracture patients at high risk of UTIs. These easily obtainable hematological and biochemical parameters provide a practical clinical prediction tool for individualized UTI prevention in this population.

Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm.

E-commerce provides a large selection of goods for sale and purchase, which promotes regular transactions and commodity flows. Efficient distribution of goods and precise estimation of customer wants are essential for cost reduction. In order to improve supply chain efficiency in the context of cross-border e-commerce, this article combines machine learning approaches with the Internet of Things. The suggested approach consists of two main stages. Order prediction is done in the first step to determine how many orders each merchant is expected to get in the future. In the second phase, allocation operations are conducted and resources required for each retailer are supplied depending on their needs and inventory, taking into account each store's inventory as well as the anticipated sales level. This suggested approach makes use of a weighted mixture of neural networks to anticipate sales orders. The Capuchin Search Algorithm (CapSA) is used in this weighted combination to concurrently enhance the learning and ensemble performance of models. This indicates that an effort is made to reduce the local error of the learning model at the model level via model weight adjustments and neural network configuration. To guarantee more accurate output from the ensemble model, the best weight for each individual component is found at the ensemble model level using the CapSA method. This method yields the ensemble model's final output in the form of weighted averages by choosing suitable weight values. With a Root Mean Squared Error of 2.27, the suggested technique has successfully predicted sales based on the acquired findings, showing a minimum decrease of 2.4 in comparison to the comparing methodologies. Additionally, the suggested method's strong performance is shown by the fact that it was able to minimize the Mean Absolute Percentage Error by 14.67 when compared to other comparison approaches.

C2c: Predicting Micro-C from Hi-C.

MOTIVATION: High-resolution Hi-C data, capable of detecting chromatin features below the level of Topologically Associating Domains (TADs), significantly enhance our understanding of gene regulation. Micro-C, a variant of Hi-C incorporating a micrococcal nuclease (MNase) digestion step to examine interactions between nucleosome pairs, has been developed to overcome the resolution limitations of Hi-C. However, Micro-C experiments pose greater technical challenges compared to Hi-C, owing to the need for precise MNase digestion control and higher-resolution sequencing. Therefore, developing computational methods to derive Micro-C data from existing Hi-C datasets could lead to better usage of a large amount of existing Hi-C data in the scientific community and cost savings. RESULTS: We developed C2c ("high" or upper case C to "micro" or lower case c), a computational tool based on a residual neural network to learn the mapping between Hi-C and Micro-C contact matrices and then predict Micro-C contact matrices based on Hi-C contact matrices. Our evaluation results show that the predicted Micro-C contact matrices reveal more chromatin loops than the input Hi-C contact matrices, and more of the loops detected from predicted Micro-C match the promoter-enhancer interactions. Furthermore, we found that the mutual loops from real and predicted Micro-C better match the ChIA-PET data compared to Hi-C and real Micro-C loops, and the predicted Micro-C leads to more TAD-boundaries detected compared to the Hi-C data. The website URL of C2c can be found in the Data Availability Statement.

Factors predicting acute kidney injury in patients after abdominal aortic aneurysm repair.

INTRODUCTION: Postoperative acute kidney injury (AKI) is one of the most frequent complications in abdominal aortic aneurysm (AAA) patients after open and endovascular aortic aneurysm repair. AKI decreases the efficiency of kidney function, allowing accumulation of waste products in the body, and an imbalance of water, acid and electrolytes in the body. As a result, the functioning of various organs throughout the body is affected. These effects may raise the cost of treatment, length of stay, and mortality rate. OBJECTIVE: This study aims to examine the predictive factors of AKI - preoperative of estimated glomerular filtration rate (eGFR), preoperative of hemoglobin level, types of abdominal aortic aneurysms repair, and intraoperative of cardiac arrhythmias - after open and endovascular aortic repair among AAA patients within 72 h. METHODS: This is a retrospective study of 196 patients with AAA after elective open and endovascular aortic aneurysm repair within the first 72 h who met the inclusion criteria recruited from a tertiary care hospital in Bangkok, Thailand. Postoperative AKI after elective open and endovascular aortic repair among AAA patients is defined by the 2012 Kidney Disease Improving Global Outcomes (KDIGO) Clinical Practice Guidelines. RESULTS: A total of 196 AAA patients, 75.5% were male with an average age of 75.12 years (SD = 8.45). Endovascular aortic aneurysm repair was used more frequently than open aortic aneurysm repair (64.8% vs 35.2%) and 37.2% of the AAA patients had intraoperative cardiac arrhythmias. The occurrence of AKI among the AAA patients after abdominal aortic aneurysm repair within 72 h was 54.1%. The AKI rate of EVAR patients was 69.8% while the AKI rate for OAR patients was 30.2%. The preoperative estimated glomerular filtration rate (eGFR) and hemoglobin level were found to jointly predict AKI and explain 32.2% of the variance (Nagelkerke R2 = 0.322, p < .05). However, the type of abdominal aortic aneurysms repair and intraoperative cardiac arrhythmias did not correlate with the incidence of AKI in AAA repair patients. The predictive factors for AKI among AAA patients after aortic aneurysm repair were preoperative eGFR < 60 mL/min/1.73 m2 (OR = 4.436, 95% CI: 2.202-8.928, p < .001) and preoperative hemoglobin level between 8.1-10.0 g/dL (OR = 4.496, 95% CI: 1.831-11.040, p = .001). CONCLUSION: Preoperative eGFR < 60 mL/min/1.73 m2 and preoperative hemoglobin level between 8.1-10.0 g/dL were the predictive factors for AKI among AAA patients after both open and endovascular AAA repair. Therefore, healthcare providers should be aware of and monitor signs of AKI after surgery in AAA patients, especially those undergoing EVAR with lower eGFR and hemoglobin levels.

Association between pathologic complete response and biochemical indicators after neoadjuvant therapy for HER2-positive breast cancer.

PURPOSE: This study investigated the changes in the fasting blood glucose (FBG), fasting triglyceride (FTG), and fasting total cholesterol (FTC) levels during neoadjuvant therapy (NAT) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) and the association with pathologic complete response (pCR). METHODS: Relevant data from Sichuan Cancer Hospital from June 2019 to June 2022 were collected and analyzed, and FBG, FTG, and FTC were divided into baseline, change, and process groups, which were grouped to analyze the changes after receiving NAT and the association with pCR. RESULTS: In the estrogen receptor (ER)-negative subgroup, patients with low levels of FTG in the process group were more likely to achieve pCR compared to high levels, and in the progesterone receptor (PR)-negative subgroup, patients with lower FTG compared to higher FTG after receiving NAT was more likely to achieve pCR. CONCLUSIONS: Patients with HER2-positive BC undergoing NAT develop varying degrees of abnormalities (elevated or decreased) in FBG, FTG, and FTC; moreover, the status of FTG levels during NAT may predict pCR in ER-negative or PR-negative HER2-positive BC.Early monitoring and timely intervention for FTG abnormalities may enable this subset of patients to increase the likelihood of obtaining a pCR along with management of abnormal markers.

How to Evaluate Reports of Intimate Partner Violence? Examining Interpartner Agreement in a Forensic Sample of Different-Sex Couples Where Men are Accused of Intimate Partner Violence.

Research about interpartner agreement on intimate partner violence (IPV) is mainly based on community and clinical samples, with forensic or court-related samples being overlooked. This study assesses interpartner agreement on IPV reports based on the Revised Conflict Tactic Scales, aiming to explore if the proxy method would be reliable in a court-related setting. The study sample comprised 62 different-sex couples identified in the Portuguese judicial system due to an IPV-related crime perpetrated by men. Agreement was assessed based on different indexes: percent agreement and Gwet's AC1 for occurrence, and Tau-b and intraclass correlations for frequency. Men's and women's perpetration were considered. Results showed that interpartner agreement on IPV occurrence (ranging from poor-to-very good) tended to be higher and more consistent among indexes than agreement on IPV frequency (ranging from non-existent to strong). This study highlights the need to collect both partners' reports in court-related settings.

Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability.

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

Anxiety and fear of COVID-19 as potential mechanisms to explain vaccine hesitancy among adults.

Background: Vaccine hesitancy is a significant global problem resulting from the interaction of multiple factors, including mental health factors. However, the association of COVID-19 vaccine hesitancy with mental health has not been well-examined, especially in Arab culture. This study aims to identify the correlation between anxiety/fear of COVID-19 and vaccine hesitancy among Saudi adults. Methods: An online-based survey was administered to 558 participants from all regions of Saudi Arabia using the snowball technique. However, this sample may not be representative of the Saudi adult population. Participants responded to the Questionnaire of Vaccine Hesitancy, the COVID-19-Anxiety Questionnaire (C-19-A), and the Fear of COVID-19 Scale (FCV-19S). Data were analyzed on vaccine uptake, vaccine hesitancy, coronavirus infection, and demographic variables. The predictive factors of vaccine hesitancy were examined in one model using multiple regression analysis by the Enter method (P= 0.05). Results: COVID-19 anxiety and fear have significant correlations with vaccine hesitancy (Phi=0.33, P=0.017; Phi=0.29, P=0.013, respectively). Anxiety and fear were higher among unhesitating participants (t =2.469, P=0.014; t=2.025, P=0.043, respectively). Participants who had previously been infected with coronavirus were more likely to be hesitant (X2 = 23.126, P=0.000). Participants who scored high in anxiety were more likely to be vaccinated (F=3.979, P=0.019) and have a secondary school or college education (F=4.903 P=0.002). COVID-19 anxiety, gender, and coronavirus infection significantly predicted vaccine hesitancy. Conclusion: Anxiety and fear of COVID-19 are among the most important factors correlated with vaccine hesitancy; unhesitant people are more likely to have anxiety and fear. COVID-19 anxiety significantly predicted vaccine hesitancy. We recommend integrating psychological care into vaccination plans to help increase the uptake rate during potential subsequent pandemics. Relevant intervention programs can be designed to help increase vaccine acceptance, deal with vaccine hesitancy, and relieve psychological symptoms during major pandemics. Psychologists can provide awareness messages, counselling seminars, online mentoring, or telemental health outreach.

Screening optimal candidates with operable, early-stage triple-negative breast cancer benefitting from capecitabine maintenance: A post-hoc analysis of the SYSUCC-001 study.

BACKGROUND: To explore whether specific clinicopathological covariates are predictive for a benefit from capecitabine maintenance in early-stage triple-negative breast cancer (TNBC) in the SYSUCC-001 phase III clinical trial. METHODS: Candidate covariates included age, menstrual status, type of surgery, postoperative chemotherapy regimen, Ki-67 percentage, histologic grade, primary tumor size, lymphovascular invasion, node status, and capecitabine medication. Their nonlinear effects were modeled by restricted cubic spline. The primary endpoint was disease-free survival (DFS). A survival prediction model was constructed using Cox proportional hazards regression analysis. RESULTS: All 434 participants (306 in development cohort and 128 in validation cohort) were analyzed. The estimated 5-year DFS in development and validation cohorts were 77.8 % (95 % CI, 72.9%-82.7 %) and 78.2 % (95 % CI, 70.9%-85.5 %), respectively. Age and node status had significant nonlinear effects on DFS. The prediction model constructed using four covariates (node status, lymphovascular invasion, capecitabine maintenance, and age) demonstrated satisfactory calibration and fair discrimination ability, with C-index of 0.722 (95 % CI, 0.662-0.781) and 0.764 (95 % CI, 0.668-0.859) in development and validation cohorts, respectively. Moreover, patient classification was conducted according to their risk scores calculated using our model, in which, notable survival benefits were reported in low-risk subpopulations. An easy-to-use online calculator for predicting benefit of capecitabine maintenance was also designed. CONCLUSIONS: The evidence-based prediction model can be readily assessed at baseline, which might help decision making in clinical practice and optimize patient stratification, especially for those with low-risk, capecitabine maintenance might be a potential strategy in the early-disease setting.

Random forest models of food safety behavior during the COVID-19 pandemic.

Machine learning approaches are increasingly being adopted as data analysis tools in scientific behavioral predictions. This paper utilizes a machine learning approach, Random Forest Model, to determine the top prediction variables of food safety behavioral changes during the pandemic. Data was collected among U.S. consumers on risk perception of COVID-19 and foodborne illness (FBI), food safety practice behaviors and demographics through online surveys at ten different time points from April 2020 through to May 2021; and post pandemic in May 2022. Random forest model was used to predict 14 food safety-related behaviors. The models for predicting Handwashing before cooking and Handwashing after eating had a good performance, with F-1 score of 0.93 and 0.88, respectively. Attitudes- related variables were determined to be important in predicting food safety behaviors. The importance ranking of the predicting variables were found to be changing over time.

2012 Briganti nomogram predict prostate cancer progression in EAU intermediate risk with unfavorable tumor grade: A single center experience.

BACKGROUND: To investigate the potential prognostic impact of Briganti's 2012 nomogram in EAU intermediate-risk patients presenting with an unfavorable tumor grade and treated with robot-assisted radical prostatectomy, eventually associated with extended pelvic lymph node dissection. MATERIALS AND METHODS: From January 2013 to December 2021, the study included 179 EAU intermediate-risk patients presenting with an unfavorable tumor grade (ISUP 3), eventually associated with a PSA of 10-20 ng/ml and/or cT-2b. Briganti's 2012 nomogram was assessed as both a continuous and dichotomous variable, categorized according to the median (risk score ⩾7% vs <7%). Disease progression, defined as biochemical recurrence and/or metastatic progression, was evaluated using Cox proportional hazards in both univariate and multivariate analyses. RESULTS: Disease progression occurred in 43 (24%) patients after a median (95% CI) follow-up of 78 (65.7-88.4) months. The nomogram risk score predicted disease progression, evaluated both as a continuous variable (hazard ratio, HR = 1.064; 95% CI: 1.035-1.093; p < 0.0001) and as a categorical variable (HR = 3.399; 95% CI: 1.740-6.638; p < 0.0001). This association was confirmed in multivariate analysis, where hazard ratios remained consistent even after adjusting for clinical and pathological factors. CONCLUSIONS: In EAU intermediate-risk PCa cases presenting with an unfavorable tumor grade and treated surgically, Briganti's 2012 nomogram was associated with disease progression after surgery. Consequently, as the nomogram risk score increased, patients were more likely to experience PCa progression, facilitating the stratification of the patient population into distinct prognostic subgroups.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis.

BACKGROUND: Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate. AIM: To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis. METHODS: We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models. RESULTS: Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value. CONCLUSION: The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.