Strong Discrimination Research Articles

Epidemiology of food addiction among nursing and health technician students in Agadir, Morocco

ABSTRACT This study investigates food addiction among nursing students at the Higher Institute of Nursing and Technical Health in Agadir, Morocco, using a cross-sectional design. It employs the Yale Food Addiction Scale and a self-administered survey, selecting participants through proportional sampling across study levels and random selection within each level. Analysis includes univariate and multivariate logistic regressions, evaluating model using area under the curve (AUC). Among 451 participants (mean age 20.89 ± 3.71 years; 64.7% female), 19.3% exhibited food addiction. Physical activity (OR = 0.447; 95% CI [0.264–0.757]) and being underweight (OR = 0.336; 95% CI [0.124–0.915]) reduced risk, while anxiety and depression increased it (Yes; OR = 1.904; 95% CI [1.158–3.129]). The predictive model achieved an AUC of 0.6985, indicating strong discrimination. The study underscores the importance of psychological support and regular physical activity during students’ academic and professional training. Future studies should encompass diverse locations and institutions to enhance the findings’ generalisability beyond Agadir, Morocco.

Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model

BackgroundTo identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients.MethodsA retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model’s predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method.ResultsLASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (p < 0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715–0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model’s robustness.ConclusionThe nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.

Alteration of Serum MLKL Levels and Their Association with Severity and Clinical Outcomes in Human Severe Traumatic Brain Injury: A Prospective Cohort Study.

Mixed lineage kinase domain-like protein (MLKL), which modulates necroptosis, has been implicated in pathophysiological processes following acute brain injury. Here, serum MLKL was quantified to determine its prognostic significance in severe traumatic brain injury (sTBI). This prospective cohort study enrolled 155 patients with sTBI and 155healthy volunteers. The severity metrics included the Glasgow Coma Scale (GCS) score and Rotterdam computed tomography (CT) classification. The extended Glasgow outcome scale (GOSE) at posttraumatic 180 days was considered as a prognostic parameter, with a score of 1-4 as indicating poor prognosis. Univariate and subsequent multivariate analyses were used for independent factorial investigation. Compared to controls, patients displayed profoundly elevated serum MLKL levels. In the framework of restricted cubic spline analysis, serum MLKL levels were linearly correlated with the likelihood of mortality, overall survival, and poor prognosis. Serum MLKL levels were not only independently correlated with GCS, Rotterdam CT scores and GOSE scores, but were also independently predictive of death, overall survival, and poor prognosis. Subgroup analysis showed that serum MLKL levels exhibited negligible interactions with age, sex, hypertension, diabetes, smoking habits, and alcohol consumption to distinguish the possibility of death, overall survival, and poor prognosis. Within the context of receiver operating characteristic curve analysis, serum MLKL levels had strong discrimination effectiveness for death and poor prognosis and, in contrast to GCS and Rotterdam CT scores, were considered to have equivalent predictive ability. Extreme elevation of serum MLKL levels is intimately related to trauma severity, death, and neurological outcomes, suggesting that serum MLKL may act as a potential predictor for facilitating severity stratification and prognosis prediction of sTBI.

A nomogram for predicting neurogenic lower urinary tract dysfunction in patients with acute ischemic stroke: A retrospective study.

To investigate the risk factors for neurogenic lower urinary tract dysfunction (NLUTD) in patients with acute ischemic stroke (AIS), and develop an internally validated predictive nomogram. The study aims to offer insights for preventing AIS-NLUTD. We conducted a retrospective study on AIS patients in a Shenzhen Hospital from June 2021 to February 2023, categorizing them into non-NLUTD and NLUTD groups. The bivariate analysis identified factors for AIS-NLUTD (p < 0.05), integrated into a least absolute shrinkage and selection operator (LASSO) regression model. Significant variables from LASSO were used in a multivariate logistic regression for the predictive model, resulting in a nomogram. Nomogram performance and clinical utility were evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Internal validation used 1000 bootstrap resamplings. A total of 373 participants were included in this study, with an NLUTD incidence rate of 17.7% (66/373). NIHSS score (OR = 1.254), pneumonia (OR = 6.631), GLU (OR = 1.240), HGB (OR = 0.970), and hCRP (OR = 1.021) were used to construct a predictive model for NLUTD in AIS patients. The model exhibited good performance (AUC = 0.899, calibration curve p = 0.953). Internal validation of the model demonstrated strong discrimination and calibration abilities (AUC = 0.898). Results from DCA and CIC curves indicated that the prediction model had high clinical utility. We developed a predictive model for AIS-NLUTD and created a nomogram with strong predictive capabilities, assisting healthcare professionals in evaluating NLUTD risk among AIS patients and facilitating early intervention.

Triglyceride and Glucose Index as an Optimal Predictor of Metabolic Syndrome in Lebanese Adults.

Background: Globally, the prevalence of metabolic syndrome (MetS) is on the rise, especially in Arab countries, which emphasizes the need for reliable ethnic-specific biochemical screening parameters. Methods: Two hundred twenty-one Lebanese adults were enrolled in this cross-sectional study. Biochemical parameters including Homeostasis Model Assessment (HOMA), Triglyceride and Glucose index (TyG), ratio of Triglycerides to High-Density Lipoprotein Cholesterol (TG/HDL-C), Atherogenic Index of Plasma (AIP), and Visceral Adiposity Index (VAI) were assessed for their prediction of MetS. Analysis of covariance, logistic regression, expected-versus-observed case ratio were used to determine model calibration, concordance statistic, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs), sensitivity, specificity, and negative and positive predictive values (PPV, NPV). Results: The prevalence of MetS was 44.3%. All biochemical parameters were significantly associated with MetS, with a strong model discrimination (c-statistic between 0.77 and 0.94). In both sex categories, TyG best predicted MetS (females: cut-off value, 8.34; males: cut-off value, 8.43) and showed good estimation among females, but overestimation among males. HOMA had the lowest discriminatory power in both sex categories. Conclusions: This study suggests that TyG best predicts MetS, while HOMA has the lowest predictive power. Future larger studies need to focus on harmonizing ethnic specific cut-offs and further validating our results.

The Detection of Peripheral Neuropathy by Clinical Scales in Patients Diagnosed With Alcohol Use Disorder.

Introduction Peripheral neuropathy is a well-known manifestation of alcohol overconsumption, but neurophysiological confirmation of peripheral nerve damage is costly and sometimes involves invasive procedures. The aim of this study was to investigate the ability of commonly used clinical scales to detect the presence of neuropathy in patients with alcohol use disorder (AUD). Methods Data were collected retrospectively on 116 patients diagnosed with AUD and treated voluntarily in a detoxification special unit. Ninety-eight age and gender-matched healthy subjects without a diagnosis of AUD were used as the control group. The five tested clinical neuropathy scales were the Neuropathy Symptoms Score (NSS), the Neuropathy Disability Score, the Neuropathy Impairment Score (NIS), the Neuropathy Impairment Score of the Lower Limbs, and the modified Toronto Clinical Neuropathy Scale. Results The mean values of all tested clinical scales of the patients with AUD were significantly higher than the control group. All examined clinical scales were determined to be useful in discriminating between patients with neuropathy from patients without neuropathy. The strongest discrimination was seen with the NIS, including the best sensitivity and specificity for the range of scores obtained. All scales, except NSS, showed a stronger correlation with measures of large (LFN) than small fiber neuropathy (SFN). Conclusion Our study suggests that clinical scales could be used to detect peripheral neuropathy in patients with AUDwhen neurophysiological testing is not available. Moreover, we suggest that the NIS and the NSS scales would be most helpful in assessing LFN and SFN, respectively, in patients with AUD.

Rethinking self-supervised learning for time series forecasting: A temporal perspective

Self-supervised learning has garnered significant attention for its ability to learn meaningful representations. Recent advancements have introduced self-supervised methods for time series forecasting. However, these efforts have faced limitations due to two primary drawbacks. Firstly, these approaches often borrow techniques from vision and language domains without adequately addressing the unique temporal dependencies inherent in time series data. Secondly, time series often show that the distribution shifts over time, which makes accurate forecasting challenging. In response to these issues, we propose TempSSL, a self-supervised learning framework designed for time series forecasting. TempSSL divides the time series data into context (history data) and target (future data), employing two pre-training strategies: (1) Temporal Masked Modeling (TMM) designed to capture temporal dependencies by reconstructing future time series based on historical context; (2) Temporal Contrastive Learning (TCL) employs context and target as positive samples to enhance discriminative representations and mitigate distribution shifts within the time series. TempSSL’s innovation lies in two key aspects. Firstly, it underscores the importance of temporal dependencies for time series forecasting by designing specific pre-training tasks. Secondly, it effectively integrates contrastive learning and masked modeling, leveraging their respective strengths to develop time series representation with strong instance discriminability and local perceptibility. Extensive experiments across seven widely used benchmark datasets demonstrate that TempSSL consistently outperforms existing self-supervised and end-to-end forecasting methods, achieving improvements ranging from 1.92% ∼ 78.12%. Additionally, TempSSL’s practical effectiveness is further demonstrated through successful application in natural gas demand forecasting.

Status and Trend of General Education, Technical Education, and Vocational Education by Persons with Disabilities in India: Reflection of three NSS rounds

Abstract : The present study attempts to estimate the status and trend in educational achievement, particularly in technical and vocational education among persons with disabilities in India across gender and social groups. 47th, 58th, and 76th round unitlevel National Sample Survey data were analyzed using the gender parity index, compound annual growth rate, ordered logit model, and binary logit model. Result shows that though the literacy rate among persons with disabilities in India gradually increases over time, still half of the persons with disabilities were illiterate compared to one-fifth of non-disabled peers. A large section of children with disabilities drops out. The transition of education from school level to higher education, technical and vocational education by persons with disabilities was very slow. Only 4.31 percent, 3.08 percent, and 1.08 percent of persons with disabilities received higher education, vocational education, and technical training in 2018. There was a gradual decrease in gender disparity against women with disabilities and turns in favor of women at all levels of general education. However, gender disparity persisted against females with disabilities, in the attainment of technical and vocational training. The attainment of technical education by males with disabilities was three times higher compared to females with disabilities. Strong caste discrimination still exists in all levels of educational attainment against scheduled castes and scheduled tribes. Educational achievement is negatively related to the severity of the disability, household size, and rural-urban areas, but positively to households' economic status. The study draws important policy implications to improve the education of persons with disabilities. Keywords: Caste discrimination, Gender discrimination, General education, Persons with disabilities, Technical Education, Vocational Education

Predictive Value of Machine Learning Model Based on CT Values for Urinary Tract Infection Stones

Preoperative diagnosis of infection stones presents a significant clinical challenge. We developed a machine learning model to predict urinary infection stones using Computed Tomography (CT) values, enabling in vivo preoperative identification. In this study, we included 1209 patients who underwent urinary lithotripsy at our hospital. Seven machine learning algorithms along with eleven preoperative variables, were used to construct the prediction model. Subsequently, Model performance was evaluated by calculating AUC and AUPR for subjects in the validation set. On the validation set, all seven machine learning models demonstrated strong discrimination (AUC: 0.687-0.947). Additionally, the XGBoost model was identified as the optimal model significantly outperforming the traditional LR model. Taken together, the XGBoost model is the first machine learning model for preoperative prediction of infection stones based on CT values. It can rapidly and accurately identify infection stones in vitro, providing valuable guidance for urologists in managing these stones.

Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study

BackgroundYouths face significant mental health challenges exacerbated by stressful life events, particularly in the context of the COVID-19 pandemic. Immature coping strategies can worsen mental health outcomes. MethodsThis study utilised a two-wave cross-sectional survey design with data collected from Chinese youth aged 14–25 years. Wave 1 (N = 3038) and Wave 2 (N = 539) datasets were used for model development and external validation, respectively. Twenty-five features, encompassing dimensions related to demographic information, stressful life events, social support, coping strategies, and emotional intelligence, were input into the model to predict the mental health status of youth, which was considered their coping outcome. Shapley additive explanation (SHAP) was used to determine the importance of each risk factor in the feature selection. The intersection of top 10 features identified by random forest and XGBoost were considered the most influential predictors of mental health during the feature selection process, and was then taken as the final set of features for model development. Machine learning models, including logistic regression, AdaBoost, and a backpropagation neural network (BPNN), were trained to predict the outcomes. The optimum model was selected according to the performance in both internal and external validation. ResultsThis study identified six key features that were significantly associated with mental health outcomes: punishment, adaptation issues, self-regulation of emotions, learning pressure, use of social support, and recognition of others' emotions. The BPNN model, optimized through feature selection methods like SHAP, demonstrated superior performance in internal validation (C-index [95 % CI] = 0.9120 [0.9111, 0.9129], F-score [95 % CI] = 0.8861 [0.8853, 0.8869]). Additionally, external validation showed the model had strong discrimination (C-index = 0.9749, F-score = 0.8442) and calibration (Brier score = 0.029) capabilities. LimitationsAlthough the clinical prediction model performed well, the study it still limited by self-reported data and representativeness of samples. Causal relationships need to be established to interpret the coping mechanism from multiple perspectives. Also, the limited data on minority groups may lead to algorithmic unfairness. ConclusionsMachine learning models effectively identified and predicted mental health outcomes among youths, with the SHAP+BPNN model showing promising clinical applicability. These findings emphasise the importance and effectiveness of targeted interventions with the help of clinical prediction model.

Enrichment of EPA and DHA in glycerides by selective enzymatic ethanolysis

It has been reported that eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in glycerides have various biological functions. This study presents an effective method for enriching glycerides rich in EPA and DHA through lipase-catalyzed alcoholysis. The results showed that Eversa® Transform 2.0 had the strongest discrimination against DHA and EPA in alcoholysis, which was verified by molecular docking. Additionally, selectivity of the lipase and ratio of DHA and EPA in glyceride products were significantly affected by alcohol type. Under the optimal conditions, the contents of EPA and DHA in glycerides after ethanolysis reached 12.91 % and 55.40 %, respectively, with a DHA yield of 79.22 %. In this study, an interesting finding was that Eversa® Transform 2.0 could effectively differentiate EPA and DHA during alcoholysis to allow us to prepare DHA-enriched glycerides and EPA-enriched ethyl esters after removing saturated and monounsaturated ethyl esters.

Unsupervised anomaly detection using inverse generative adversarial networks

Unsupervised anomaly detection (UAD) holds considerable promise for a myriad of real-world applications related to information sciences. Recently, generative models, such as Generative Adversarial Networks (GANs), have shown their potential in addressing UAD tasks. Despite their potential, current GAN-based UAD methods frequently employ intricate network architectures. Some even necessitate solving an iterative optimization problem for each test sample, making them both time-consuming and impractical for large-scale datasets or time-sensitive industrial applications. In this work, we introduce a novel generative adversarial learning method for UAD. Diverging from existing GAN-based methods, our method incorporates a distribution converter and a distribution discriminator. The converter transforms normal data into a compact target distribution such as a truncated Gaussian while the discriminator endeavors to distinguish the transformed data from the data in the target distribution. After an adversarial training process, the learned converter becomes proficient in transforming normal data into the target distribution but cannot accurately transform anomalous samples to the target distribution, which can be easily identified by the strong discriminator. Extensive experiments on nine publicly available anomaly detection benchmark datasets and a real-world, large-scale logistics dataset demonstrate that our method achieves superior detection accuracy and lower inference time than many strong baselines. Our code is publicly available in https://github.com/xiaofeng-github/IGAN.

Evaluating the performance of the SORG machine learning algorithm for predicting discharge disposition in lumbar surgery patients

PurposeProtracted admissions following lumbar surgeries are rising, often stemming from inefficient identification of patients requiring nonhome discharge for rehabilitation. The SORG Orthopaedic Research Group at Harvard Medical School have developed a machine learning algorithm for predicting discharge following lumbar surgery. This study assessed its predictive performance on an independent tertiary centre patient cohort. MethodsMedical records were retrospectively reviewed for all elective adult lumbar disc degeneration or herniation surgeries performed between July 2017–2021 at a tertiary neurosurgical centre in the United Kingdom. Preoperative variables were collated and discharge destinations noted. Algorithm predictions were analysed using the concordance (c) statistic, Brier score and calibration plot. Positive and negative predictive values (PPV, NPV) were calculated, and a decision curve analysis (DCA) plotted. Results251 subjects were included (48.2 % female, mean age 55.3 years). 2.8 % underwent nonhome discharge. Most had surgery at 1/2 spinal levels (98.4 %) and were functionally independent (84.5 %). Algorithm predictions yielded a 0.88 c-statistic and 0.029 Brier score. The algorithm was miscalibrated to the data (calibration plot slope 1.31 and intercept -1.12). At a 0.25 threshold for nonroutine discharge risk, the PPV was 0.19 and NPV 0.98. DCA revealed limited clinical utility. ConclusionsAlgorithm predictive performance was mixed for this cohort, displaying strong discrimination but poor calibration and overestimation of nonroutine discharges. Differences in patient management practices and the low nonhome discharge rate may explain this. Larger validation studies across different healthcare systems, alongside geographically specific algorithm development, will improve predictive accuracy prior to clinical application.

Non-invasive assessment of severe liver fibrosis in patients with Fontan-associated liver disease: The VALDIG-EASL FONLIVER cohort

Background & AimsFontan-type surgery is used as a palliation for congenital heart disease with univentricular physiology but may, in the long term, lead to advanced chronic liver disease. This study assessed the accuracy of conventional non-invasive models in assessing liver fibrosis and introduces a new risk score employing non-invasive tools. MethodsA prospective, cross-sectional, observational study was conducted across five European centers and encompassing all consecutive adult patients with Fontan circulation, liver biopsy and non-invasive tests (elastography, APRI, FIB-4, Fibrosis score, Doha, GUCI, and AAR). The primary outcome was the identification of severe liver fibrosis on biopsy. Multivariable logistic regression identified non-invasive predictors of severe fibrosis, leading to the development and internal validation of a new scoring model named the FonLiver risk score. ResultsIn total, 217 patients (mean [standard deviation] age, 27.9 [8.9] years; 50.7% males) were included. Severe liver fibrosis was present in 47.9% (95% CI 41.2%–54.5%) and correlated with a lower functional class, protein-losing enteropathy, and compromised cardiopulmonary and systemic hemodynamics. The final FonLiver risk score incorporated liver stiffness measurement using transient elastography and platelet count and demonstrated strong discrimination and calibration (area under the receiver operating curve [AUROC] of 0.81). The FonLiver risk score outperformed conventional prediction models (APRI, FIB-4, Fibrosis score, Doha, GUCI, and AAR), which all exhibited worse performance in our cohort (AUROC < 0.70 for all). ConclusionSevere liver fibrosis is prevalent in adults following Fontan-type palliation and can be effectively estimated using with the novel FonLiver risk score. This scoring system can be easily incorporated into the routine assessment of patients with Fontan circulation.

Development and validation of a nomogram for predicting the placement of nasointestinal tubes in critically ill patients based on abdominal radiography: A single-center, retrospective study

BackgroundEnteral nutrition administered via the nasointestinal tube (NET) is a prevalent nutritional modality among critically ill patients, and abdominal radiographs hold significant value in accurately ascertaining the precise positioning of the NET subsequent to its placement. Therefore, we propose an innovative approach to construct a clinical prediction model based on NET's configuration within the gastrointestinal tract in abdominal radiography. This model aims to enhance the accuracy of determining the position of NETs after their placement. MethodsPatients admitted to the intensive care unit of Zhejiang Provincial People's Hospital between October 2017 and October 2021 were included to constitute the training cohort for retrospective analysis, and nomogram was constructed. Consecutively enrolled patients admitted to the same hospital from October 2021 to October 2023 were included as the validation cohort. The training cohort underwent a univariate analysis initially, followed by a multivariate logistic regression approach to analyze and identify the most appropriate model. Subsequently, nomogram was generated along with receiver operator characteristic curves, calibration curves, and decision curves for both the training and validation cohorts to evaluate the predictive performance of the model. ResultsThe training and validation cohorts comprised 574 and 249 patients, respectively, with successful tube placement observed in 60.1 % and 76.3 % of patients, correspondingly. The predictors incorporated in the prediction maps encompass the “C-shape,” the height of “inverse C-shape,” showing the duodenojejunal flexure, and the location of the head end of the NET. The model demonstrated excellent predictive efficacy, achieving an AUC of 0.883 (95 % CI 0.855–0.911) and good calibration. Furthermore, when applied to the validation cohort, the nomogram exhibited strong discrimination with an AUC of 0.815 (95 % CI 0.750–0.880) and good calibration. ConclusionThe combination of abdominal radiography and NET's configuration within the gastrointestinal tract enables accurate determination of NET placement in critically ill patients.

Percentage area reduction at week 4 as a prognostic indicator of complete healing in patients treated with standard of care: a post hoc analysis.

Early indicators of healing provide valuable information on the potential benefit of treatment. In patients with hard-to-heal (chronic) diabetic foot ulcers (DFUs), timely intervention is critical. Ulcers that fail to show measurable progress within four weeks of treatment are considered recalcitrant. These ulcers increase the risk of soft tissue infection, osteomyelitis and lower extremity amputation. A prognostic indicator or surrogate marker allows for rapid evaluation of treatment efficacy and safety. An inverse correlation between a percentage area reduction (PAR) of ≤50% at week 4 and complete healing by week 12 has been previously established; however, the data were derived from a standard of care (SoC) arm of clinical trials that are over a decade old. In this post hoc analysis, data from a large multicentre prospective randomised controlled trial were reviewed to assess PAR at week 4 as a prognostic indicator in patients treated with SoC. Overall, 65.4% (17/26) of patients with PAR >50% at week 4 achieved complete closure at week 12. The receiver operating characteristic (ROC) curve for area reduction by week 4 showed strong discrimination for predicting non-healing (area under the ROC curve: 0.92; p<0.001; positive predictive value: 70.6%; negative predictive value: 87.2%). These findings are consistent with previous studies and support the use of four-week PAR as a prognostic indicator.

Nomogram for predicting intolerable postoperative early enteral nutrition following definitive surgery for small intestinal fistula: a cohort study.

This study was designed to develop and validate a nomogram for predicting intolerable early enteral nutrition (EEN) following definitive surgery (DS) for small intestinal fistula. A total of 377 patients, recruited from January 2016 to September 2023, was randomly allocated into development ( n =251) and validation ( n =126) groups in a 2:1 ratio. Risk factors were identified using the nomogram. Its performance was assessed based on calibration, discrimination, and clinical utility, with validation confirming its effectiveness. Of the 377 patients, 87 (23.1%) were intolerant to EEN, including 59 (23.1%) in the development cohort and 28 (22.1%) in the validation cohort ( P =0.84). Four factors were identified as predictive of intolerable EEN: severe abdominal adhesion, deciliter of blood loss during DS, human serum albumin (Alb) input >40g during and within 48h post-DS, and the visceral fat area (VFA)/total abdominal muscle area index (TAMAI) ratio. The model demonstrated excellent discrimination, with a C-index of 0.79 (95% CI: 0.74-0.87, including internal validation) and robust calibration. In the validation cohort, the nomogram showed strong discrimination (C-index=0.77; 95% CI: 0.64-0.87) and solid calibration. Decision curve analysis affirmed the nomogram's clinical utility. This research introduces a nomogram that enables the individualized prediction of intolerable EEN following DS for small intestinal fistula, demonstrating a possible clinical utility.

Mandibular dose-volume predicts time-to-osteoradionecrosis in an actuarial normal-tissue complication probability (NTCP) model: External validation of right-censored clinico-dosimetric and competing risk application across international multi-institutional observational cohorts and online graphical user interface clinical support tool assessment.

Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors. Demographic, clinical and dosimetric data were retrospectively obtained for a clinical observational cohort of 1129 patients with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. ORNJ was diagnosed in 198 patients (18%). A multivariable logistic regression analysis with forward stepwise variable selection identified significant predictors for ORNJ. These predictors were then used to train a Weibull Accelerated Failure Time (AFT) model, which was externally validated using an independent cohort of 265 patients (92 ORNJ cases and 173 controls) treated at Guy's and St. Thomas' Hospitals. Our model identified that each unit increase in D25% is significantly associated with a 12% shorter time to ORNJ (Adjusted Time Ratio [ATR] 0·88, p<0·005); pre-RT dental extractions was associated to a 27% faster (ATR 0·73, p=0·13) onset of ORNJ; male patients experienced a 38% shorter time to ORNJ (ATR 0·62, p = 0·11). The model demonstrated strong internal calibration (integrated Brier score of 0·133, D-calibration p-value 0.998) and optimal discrimination at 72 months (Harrell's C-index of 0·72). The model also showed good generalization to the independent cohort, despite a slight drop in performance. This study is the first to demonstrate a direct relationship between radiation dose and the time to ORNJ onset, providing a novel characterization of the impact of delivered dose not only on the probability of a late effect (ORNJ), but the conditional risk during survivorship. This work was supported by various funding sources including NIH, NIDCR, NCI, NAPT, NASA, BCM, Affirmed Pharma, CRUK, KWF Dutch Cancer Society, NWO ZonMw, and the Apache Corporation.

TAKAGI–SUGENO–KANG FUZZY SYSTEM MODELING BASED ON LOW-RANK SPARSE SUBSPACE LEARNING FOR MOTOR IMAGERY ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION

The classification of electroencephalogram (EEG) signals derived from motor imagery (MI) has always been a hot topic in the field of brain–computer interfaces. Due to its ability to handle the nonstationary and uncertain information contained in EEG signals, the Takagi–Sugeno–Kang fuzzy system (TSK-FS) has become an advantageous classification algorithm. To train a fuzzy system with strong discrimination capabilities from EEG data interspersed with redundant information, this paper proposes a TSK-FS modeling method based on low-rank sparse subspace learning (TSK-LSSL). This method focuses on consequent parameter learning, which transforms the traditional consequent parameter learning strategy into low-rank subspace and sparse subspace learning processes. Low-rank subspace learning is used to mine the global structural information of data and effectively reduce the number of fuzzy rules. During sparse subspace learning, [Formula: see text]-norm regularization is used to constrain the consequent parameters and causes the number of redundant consequent parameters to be zero, thereby simplifying the fuzzy rules. In addition, a local boundary term based on graph matrices is embedded into the objective function to mine the local structural information of the given data. TSK-LSSL simplifies the number of rules and the consequent part of the fuzzy rules. It exhibits good classification performance on two BCI Competition databases.

Predictive Factors for Tissue Inflammation in Patients with Benign Prostatic Hyperplasia: A Clinical Prediction Study

Introduction: Benign prostatic hyperplasia (BPH) is a common condition in older men, marked by the noncancerous enlargement of the prostate gland. Inflammation of the prostate plays a significant role in the progression of BPH and the symptoms it causes. The objective of this study was to create a predictive model for prostatic inflammation in men with BPH based on important clinical factors. Methods: A retrospective cohort study was conducted with 137 patients diagnosed with BPH. Data collected included various factors such as age, prostate volume (PV), preoperative international prostate symptom score, preoperative maximum urine flow rate (Qmax), preoperative post-void residue, weight of the excised tissue, body mass index, fasting blood glucose (FBG), cholesterol levels, prostate-specific antigen, blood calcium, blood phosphorus, blood uric acid, triglycerides, hypertension status, and presence of prostate calcifications. Multivariate logistic regression and LASSO regression analyses were performed to identify significant predictors and develop a nomogram. The model’s performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Results: Among the patients, 9.49% showed no signs of prostatic inflammation, while 22.63% had mild, 47.45% had moderate, and 20.44% had severe inflammation. Factors such as PV, FBG, and prostate calcification were identified as important predictors of prostatic inflammation. The predictive model developed exhibited strong discrimination and calibration, as evidenced by a high area under the curve value, indicating reliable predictive accuracy. DCA further validated the clinical usefulness of the nomogram. Conclusion: The developed nomogram, incorporating PV, FBG, and prostate calcification, effectively predicts prostatic inflammation in men with BPH. This tool can aid in early intervention and targeted treatment, potentially improving patient outcomes. Further validation in diverse populations is recommended to enhance its generalizability and clinical applicability.