Development Of Predictive Models Research Articles

Development of a comprehensive risk prediction model for arterial stiffness assessment in individuals with obesity.

Obesity in adults is a known risk factor for cardiovascular events and is associated with a decline in arterial elasticity. This study aims to evaluate the utility of pulse wave analysis (PWA) parameters in routine clinical practice for the primary prevention of cardiovascular events by developing a prediction model for arterial stiffness among obese and overweight individuals. The study enrolled 84 adult patients, aged 18 to 85 years, with varying degrees of weight status, including optimal weight, overweight, and obesity. The lifestyle habits, the personal and family history of cardiometabolic diseases, as well the clinical evaluation that included BMI (body mass index), WHR (waist-to-hip ratio), WC (waist circumferance) were performed. PWA evaluation was conducted using the Mobil-O-Graph device, assessing the following parameters: pulse wave velocity (PWV), augmentation index (AIx), heart rate (HR), central pulse pressure (cPP), peripheral and central blood pressure (SBP, DBP, cSBP, cDBP). Body composition analysis was performed using the TANITA BC-418 body analyzer. Laboratory results from the past 3 months were also collected during initial nutritional consultations for each patient. Family history of cardiovascular events showed positive correlations with all PWA parameters, while diabetes history only with PWV and family history of obesity with PWV, DBP, and cSBP. Insufficient sleep duration showed positive associations with all arterial stiffness parameters except cDBP. Smoking status correlated with significantly elevated PWV and Aix values, while insufficient physical activity was associated solely with PWV. Positive correlations were showed between current weight and PWV, while WC demonstrated positive associations with PWV, SBP, and cSBP. Body composition analysis revealed significant associations between trunk adipose tissue mass (%) and PWV, SBP, and cSBP. Hydration status (%) emerged as an independent predictor for PWV, exhibiting an inverse relationship. HOMA-IR (Homeostatic Model Assessment for Insulin Resistance) showed a strong positive correlation with PWV. Negative associations were observed with HDL-c and vitamin D. Threshold values for age, cDBP and Cardiac Index providing positive diagnostic for vascular impairment. The assessment of arterial stiffness can be considered a reliable approach to prevent obesity-related cardiovascular events and facilitate the comprehensive management of such pathologies.

Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning

AbstractBackgroundShock is a life‐threatening condition that is associated with high mortality and morbidity. Therefore, the timely identification and management of this condition are important. We aimed to develop a prediction model for vasopressor use based on concise serial arterial blood pressure data.MethodsWe collected continuous arterial blood pressure from patients admitted to the emergency department (ED) resuscitation room. Patients with an initial systolic blood pressure lower than 90 mmHg were included in the study. We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. Discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC).ResultsA total of 120 patients were enrolled in the study. The CNN and LSTM models yielded AUROCs ranging from 0.731 to 0.834 for predicting the need for vasopressor infusion within different time frames (30 min, 1 h, and 6 h). LSTM outperformed the CNN in terms of predicting vasopressor infusion within 30 min and 1 h (p value < 0.05). The AUPRC values ranged from 0.200 to 0.252, and sensitivity–specificity analyses indicated that the models have potential for clinical applications.ConclusionThis study used serial arterial blood pressure data to construct a promising prediction model for the need for vasopressors in hypotensive ED patients. The simplicity and accuracy of the model provide valuable insights for developing a clinical decision support tool for resuscitation in emergency settings.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

An Experimentally Derived Prediction Model for Diffusion and Electrostatically Controlled Physical Interactions of Polyampholytes and Salts

AbstractThe interactions of salts with polyampholytes are well established using the Hofmeister series. However, the effect of these salts on the diffusivities of polyampholytes is unknown. This article discusses these electrostatically driven physical interactions using two developed prediction models. Sodium and Potassium salts are considered with 3 chaotropic anions and 2 kosmotropic anions. A dimensionless parameter has been developed to predict the trend for these salt interactions. Another model has been developed to predict these interactions for different salt mixtures. The effect of these salts and the mixtures on the second virial coefficient is also studied, and statistical clustering using the k‐means method has been defined, which can provide insight towards the changes in the solution properties of polyampholytes with these salt mixtures. At lower pH, the salt first screens the repulsions in the polyampholytes followed by a re‐expansion phenomenon.

Prediction of postoperative complications following transanal total mesorectal excision in middle and low rectal cancer: development and internal validation of a clinical prediction model

PurposeThe objective of this study is to develop a nomogram for the personalized prediction of postoperative complication risks in patients with middle and low rectal cancer who are undergoing transanal total mesorectal excision (taTME). This tool aims to assist clinicians in early identification of high-risk patients and in addressing preoperative risk factors to enhance surgical safety.MethodsIn this case–control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio.ResultsAmong the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model's accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis.ConclusionThe developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes.

Prediction model establishment for residential community occupancy considering urban environment

The accurate prediction of occupancy rates within the residential sector is pivotal for urban and energy planning and management. This study aims at addressing the often-neglected influence of regional variations within urban settings on occupancy rates by employing multi-source urban data and explainable machine learning algorithms. Utilizing mobile signaling data from January and July 2022 in Guangzhou, we developed prediction models using the CatBoost algorithm with hyperparameters finely tuned via Bayesian optimization. The SHapley Additive exPlanation was applied to elucidate the influence of different urban configurations. Our findings reveal significant seasonal variations and distinct differences between weekdays and weekends, showing higher occupancy rates in winter than in summer and elevated rates during weekends. Additionally, significant fluctuations were observed among different age groups on weekdays. The prediction models exhibited robust capabilities, achieving R2 values exceeding 0.95 for weekdays and 0.91 for weekends. Key findings highlight that the density of points of interest related to shopping and food, along with variability in building heights, significantly impact occupancy rates. The influence of diverse urban configurations varies across different hours of the day. By the comprehensive analysis of regional variations and the application of advanced machine learning techniques to predict and explain residential occupancy rates, this research offers tools to optimize the management and sustainability of built environments, enhances the understanding of urban operation patterns and contributes to advancing smart city technologies, providing valuable insights for urban and energy planning.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein

Introduction and ObjectivesThe increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. Patients and MethodsA total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. ResultsTwelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 μmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. ConclusionsThe proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

AI-based non-linear models for mechanical and toughness properties of sustainable fiber-reinforced geopolymer concrete (FRGPC)

The incorporation of fibers into geopolymer concrete (GPC) produces FRGPC which mitigates brittle failure and restrains macro crack propagation; however, research on predicting the mechanical and toughness properties of FRGPC remains limited. This study addresses this gap by developing prediction models for compressive strength (CS), flexural strength (FS), flexural toughness (FT), and fracture toughness (FR). A dataset of 600 data points was compiled from the published literature, encompassing various constituent proportions, fiber shapes, fiber dosages, aspect ratios, and curing conditions. Employing an artificial neural network (ANN) methodology, 10 independent variables (g1, g2, …., g10) were utilized to predict four dependent variables (CS, FS, FT, and FR), resulting in the development of eight non-linear ANN models for both straight fibers (SFs) and hooked fibers (HFs). Each model has shown a higher R 2 value and lower root mean square error (RMSE) for training (70%), testing (15%), and validation (15%) datasets. The prediction model for the CS of FRGPC with HFs (CS-HF) and SF (CS-SF) showed R 2 values of 0.983 and 0.973, and RMSE of 2.088 and 2.435 MPa highlighting the accuracy of the ANN models. Similarly, the comparative analysis for FR models exhibited R 2 values of 0.997 and 0.987, and RMSE values of 0.045 Mpa √m and 0.032 Mpa √m for FR-HF and FR-SF, highlighting that fiber addition strongly impacts improving toughness properties. To identify the most influential independent variable(s), sensitivity analysis revealed g10, g1, g8, and g9 as the most influential parameters for mechanical and toughness properties with SFs. For HFs, g1, g2, and g3 were most influential for mechanical properties, and g8, g9, and g10 for toughness. This study also presented the mathematical formulation of the developed models for better interpretability and to facilitate optimal and economical FRGPC mix design selection, highlighting the potential of AI-based models in advancing sustainable construction materials.

Sugarcane sustainability in a changing climate: a systematic review on pests, diseases, and adaptive strategies

The cultivation of sugarcane (Saccharum officinarum L.) in the face of climate change requires robust strategies for managing pests, diseases, and weeds. This systematic review exposes critical deficiencies in current practices and underscores the need for climate-adaptive strategies. Climate change differentially influences pest behaviour, disease progression, and weed growth across various regions, yet the lack of region-specific responses impairs effective management. The review emphasizes the necessity for localized approaches that consider specific climatic conditions and the development of predictive models to anticipate pest and disease outbreaks. These models include Decision Support Systems (DSS), Support Vector Machines (SVM), Susceptible-Exposed-Infectious-Recovered (SEIR) models, Geographic Information Systems (GIS), Species Distribution Models (SDMs), Agricultural Production Systems sIMulator (APSIM), and Integrated Pest Management (IPM). Crucial strategies encompass integrated pest and disease management, adaptive breeding, precision agriculture, and ongoing innovation. Precision agriculture technologies, such as remote sensing and drones, enable early detection and prompt interventions. By adopting these adaptive measures and addressing existing research gaps, the sugarcane industry can bolster its resilience and maintain productivity amidst evolving climatic conditions.Systematic review registrationhttps://www.bmj.com/content/372/bmj.n71.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Enhancing face-milling efficiency of wood–plastic composites through the application of genetic algorithm–back propagation neural network

ABSTRACT Wood–plastic composites (WPCs) have advanced physicochemical properties and are widely applied in various fields. How to achieve high-efficiency, high-quality machining of WPC is a pressing issue that WPC manufacturing enterprises need to address. To this end, this research focused primarily on improving the machinability of WPC with end milling experiments. In this work, a single-factor method was used to analyse the impact of axial milling depth, spindle speed, and tool rake angle on resultant forces and surface roughness. Main effects analysis was applied to explore the degree of influence of axial milling depth, spindle speed, and tool rake angle on cutting forces and surface roughness. Furthermore, three-dimensional characterisation techniques were utilised to analyse the surface morphology characteristics of WPC during end milling. Finally, a genetic algorithm–back propagation neural network was applied to develop prediction models for resultant force and surface roughness, and optimal milling conditions were identified as axial milling depth of 0.50 mm, spindle speed of 7841 r/min, and rake angle of 15°; these produced the lowest resultant force and surface roughness. The findings of this work are proposed as a guide for better cutting performance in the industrial production of WPC.

Development of a multi-modal learning-based lymph node metastasis prediction model for lung cancer

PurposeThis study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information. MethodsWe utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification. To further enhance classification performance, a soft-voting ensemble technique was applied to integrate the outcomes of multiple multi-modal models. ResultsA comparison of the classification performance revealed that the multi-modal model, which integrated CT images and clinical information, outperformed the single-modal models. Among the four multi-modal models, the Xception model demonstrated the highest classification performance, with an area under the curve (AUC) of 0.756 for the internal test dataset and 0.736 for the external validation dataset. The ensemble model (SEResNet50_DenseNet121_Xception) exhibited even better performance, with an AUC of 0.762 for the internal test dataset and 0.751 for the external validation dataset, surpassing the multi-modal model's performance. ConclusionsIntegrating CT images and clinical information improved the performance of the lymph node metastasis prediction models in patients with NSCLC. The proposed 3D multi-modal lymph node prediction model can serve as an auxiliary tool for evaluating lymph node metastasis in patients with non-pretreated NSCLC, aiding in patient screening and treatment planning.

In-Situ Liquid-Electrochemical X-Ray and Electron Microscopy for Multi-Modal Energy Materials Characterization

In-situ liquid cell electron and x-ray microscopy have enabled dynamic studies of electrochemical reactions in energy materials and revealed relationships between the performance, structure, and chemical composition of these material systems. Such fundamental relationships are critical to improving the performance of batteries, catalysts, and other energy materials. Growing research interest in energy materials systems has accelerated the development of in-situ liquid-electrochemical microscopy techniques into mature and robust characterization workflows using novel and versatile scientific hardware.Multiple characterization techniques or in-situ processing steps are often required to fully understand the mechanisms governing the behavior of energy materials for all relevant length scales and environmental conditions. A multi-modal workflow combining in-situ liquid-electrochemical transmission electron, X-ray synchrotron and scanning electron microscopy methods is presented. The breadth of research applications is discussed, including the study of chemical dynamics and structural changes to micron-scale LixFePO4 battery particles during lithium-ion insertion/extraction, electrocatalytic behavior of β-Co(OH)2 platelet particles, and electrochemical oxidation of copper nanoparticles under reductive electrolytic conditions. New insights into these materials systems provided by these experiments will directly inform the development of predictive models for material performance and guide improvement of material design and synthesis.New scientific hardware and method development has been critical to in-situ nano-scale liquid cell microscopy and spectroscopy of electrochemical systems. Therefore, best-practice hardware and method design and development for these in-situ liquid-electrochemical microscopy experiments are also discussed. The connections between potentiostat, holder, and on-chip leads must be carefully considered with respect to different ground potentials, and the incorporation of real bulk-scale reference electrodes in this hardware has yielded quantitatively higher fidelity data with less degradation from further electrochemical cycling. Heating the sample or illuminating with light during in-situ electrochemical data collection has begun to further expand the range of environmental conditions that can be incorporated into experiments.

Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches

BackgroundChildhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection of child stunting predictor variables is a critical step in the development and performance of any such prediction model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods.MethodsFirstly, we conducted a subjective review of the literature to identify determinants of child stunting in Sub-Saharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified predictors on stunting data among children aged 0–59 months in the Malawi Demographic Health Survey (MDHS 2015–16) data. Thirdly, several reduced multivariable logistic regression models were fitted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest, Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defined as the child propensity score based on derived coefficients, was calculated for each child. The prediction risk models were assessed using discrimination measures, including area under-receiver operator curve (AUROC), sensitivity and specificity.ResultsThe review identified 68 predictor variables of child stunting, of which 27 were available in the MDHS 2016–16 data. The common risk factors selected by all the variable selection models include household wealth index, age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-off point on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable selection method. The model’s accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test data. For children residing in urban areas, the corresponding AUROC was AUC = 67% (95% CI: 58–76%), as opposed to those in rural areas, AUC = 63% (95% CI: 59–67%).ConclusionThe derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions.

Development and validation of a risk prediction model for short-term progression of carotid atherosclerosis among early middle age adults

AimWe aimed at creating and validating a prognostic model incorporating easily accessible clinical and laboratory parameters to forecast the likelihood of short-term progression of carotid atherosclerosis. MethodsA prediction model was developed and validated for carotid plaque progression within 2 years in an early middle-age population in China. Progression was defined as the new appearance of carotid plaque or stenosis among participants who had normal carotid status at baseline. Leveraging data from a health check-up chain, predictors were identified using statistical methods including stepwise logistic regression, Markov Chain Monte Carlo (MCMC) simulation, random forest analysis and least absolute shrinkage selection operator (Lasso). Model performance was assessed. Bootstrap internal validation, validation on another check-up population and subgroup analysis were also conducted. ResultsAmong 7765 participants, predictors including age, diastolic blood pressure, uric acid levels, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were identified for carotid plaque progression in 2 years. The developed prediction model demonstrated good discrimination (AUC = 0.755, 95%CI:0.736–0.774) and calibration ability (slope = 0.922 and interception = 0.007) among development data, as well as among validation data (AUC = 0.759, 95%CI:0.674–0.770; slope = 1.076 and intercept = −0.014). Internal validation using bootstrap method yielded an adjusted AUC of 0.753. The model's performance remained consistent across different subgroups. ConclusionsOur study presents a validated risk prediction model for carotid plaque progression in an early middle age population, offering a valuable tool for early identification and monitoring of cardiovascular risks. The model's robustness and applicability across different subgroups highlight its potential utility in preemptive cerebrovascular and cardiovascular disease management.

Development of Volatile Fatty Acid and Methane Production Prediction Model Using Ruminant Nutrition Comparison of Algorithms

Development of Volatile Fatty Acid and Methane Production Prediction Model Using Ruminant Nutrition Comparison of Algorithms

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

MIXED DENTITION AMONG PATIENTS ATTENDING DENTAL OPDS IN TERTIARY CARE HOSPITALS IN RURAL SINDH

Objective:To evaluate the applicability of Moyers and Tanaka-Johnston mixed dentition prediction methods among patients attending dental outpatient departments in tertiary care hospitals in rural Sindh, Pakistan. Methods:A cross-sectional study was conducted over six months, involving 300 patients (150 males and 150 females) aged 8-12 years from Bhitai Dental and Medical College, Mirpurkhas. Patients selected had all sound erupted permanent teeth except second and third molars, with no history of orthodontic treatment. Mesiodistal widths of teeth were measured using Mitutoyo digimatic calipers. Data was analyzed using SPSS-16, employing Student’s t-test, paired t-tests, and simple linear regression to derive new prediction equations. Results:Significant discrepancies were found between the predicted and actual widths of unerupted canines and premolars using Moyers and Tanaka-Johnston methods, indicating these methods are unsuitable for this population. New regression equations were derived for the local demographic. Conclusion:The Moyers and Tanaka-Johnston prediction methods are ineffective for the population in rural Sindh. The study recommends the development of localized prediction models and further multicenter studies with larger, diverse samples to refine these equations. These findings are crucial for accurate orthodontic treatment planning and improving patient outcomes.

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis

BackgroundNosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to develop and validate two machine learning models for NIs and in-hospital mortality risk prediction.MethodsThe Prediction of Nosocomial Infection and Prognosis in Cirrhotic patients (PIPC) study included hospitalized patients with cirrhosis at the Qingchun Campus of the First Affiliated Hospital of Zhejiang University. We then assessed several machine learning algorithms to construct predictive models for NIs and prognosis. We validated the best-performing models with bootstrapping techniques and an external validation dataset. The accuracy of the predictions was evaluated through sensitivity, specificity, predictive values, and likelihood ratios, while predictive robustness was examined through subgroup analyses and comparisons between models.ResultsWe enrolled 1,297 patients into derivation cohort and 496 patients into external validation cohort. Among the six algorithms assessed, the Random Forest algorithm performed best. For NIs, the PIPC-NI model achieved an area under the curve (AUC) of 0.784 (95% confidence interval [CI] 0.741–0.826), a sensitivity of 0.712, and a specificity of 0.702. For in-hospital mortality, the PIPC- mortality model achieved an AUC of 0.793 (95% CI 0.749–0.836), a sensitivity of 0.769, and a specificity of 0.701. Moreover, our PIPC models demonstrated superior predictive performance compared to the existing MELD, MELD-Na, and Child-Pugh scores.ConclusionsThe PIPC models showed good predictive power and may facilitate healthcare providers in easily assessing the risk of NIs and prognosis among hospitalized patients with cirrhosis.

Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment.

The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms. In this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples. RRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs-THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients. THY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.