Predictability Research Articles

The associations of insulin resistance, obesity, and lifestyle with the risk of developing hyperuricaemia in adolescents

BackgroundHyperuricaemia is common among obese children and adolescents, and is closely related to insulin resistance. The aim of this study was to explore the relationships between youth insulin resistance and hyperuricaemia, as well as their relationships with lifestyle factors in youths, to provide early guidance on the risk factors for hyperuricaemia in adolescents.MethodsThis study included 233 adolescents aged 10 to 20 years. Insulin resistance was evaluated via the homeostasis model assessment-insulin resistance (HOMA-IR) method. Binary logistic regression analysis was used to assess the associations of HOMA-IR with hyperuricaemia status and serum uric acid (UA) levels. The participants were subsequently divided into two groups, the noninsulin resistant group (HOMA-IR ≤ 3.2) and the insulin resistant group (HOMA-IR > 3.2), to further explore the factors that may affect the serum UA level. Finally, the predictive ability of different indicators of hyperuricaemia was evaluated via the ROC curve.ResultsBinary logistic regression analysis revealed a significant increase in the risk of developing hyperuricaemia for individuals with elevated HOMA-IR (p < 0.001) and insulin resistance (p < 0.01). Spearman’s correlation analysis revealed a significant positive linear correlation between HOMA-IR and serum UA levels (r = 0.4652, p < 0.001). Among insulin-resistant adolescents, UA levels were positively correlated with weight ratings, frequency of staying up late, and sugary beverages intake. Notably, individuals who engaged in 1–3 h of weekly exercise had the lowest UA levels. The area under the ROC curve for HOMA-IR was 0.847 (cut-off value = 2.165, p < 0.001), and the optimal prediction model included HOMA-IR, BMI z-score, and other lifestyle factors (AUC: 0.870, p < 0.001)).ConclusionHOMA-IR was identified as an independent risk factor for the development of hyperuricaemia and could be used as a sensitive indicator for the prediction its development in adolescents. In insulin-resistant adolescents with hyperuricaemia, maintaining normal weight, engaging in physical exercise for 1–3 h per week, avoiding staying up late and limiting sugary beverages intake are recommended to reduce the prevalence of hyperuricaemia among adolescents.

Predictive Ability of the Pedal Medial Arterial Calcification Score for Major Adverse Limb Events Among Patients with Chronic Limb-Threatening Ischemia

ObjectivePedal medial arterial calcification (pMAC) is a potential predictor of major adverse limb events (MALE) among patients with critical limb-threatening ischemia (CLTI). This study aims to validate the prognostic value of the pMAC score in predicting major lower limb amputation (MLLA) and need for re-do revascularisation. MethodsA single centre study involving 196 patients diagnosed with CLTI, reviewed between 2012 and 2022. Patients’ pMAC scores were obtained through X-ray assessments. Correlations between pMAC scores and the incidence of MALE and re-do revascularisation, were analysed. A power analysis using G* Power 3 software determined a needed sample size of 157 participants. Statistical analysis employed SPSS 26, utilising descriptive statistics, Chi Square, Mann-Whitney, Kaplan-Meier with log-rank test, and Cox regression for predictor identification. ResultsThe cohort was predominantly male (79.1%) and diabetic (59.2%). Re-do revascularisation need was 15.3%, increasing with pMAC scores: 3.3% (low), 24.5% (medium), and 50% (high) (p<0.001). MLLA frequency also rose with pMAC scores: 6.7%, 27.5%, and 50% respectively (p<0.001). Higher pMAC scores correlated with a higher incidence of MALE (p=0.029). Multivariate analysis showed medium pMAC (HR 4.159, p=0.002), high pMAC (HR 7.366, p=0.015), and GLASS stage 3 (HR 3.529, p=0.011) as independent MLLA predictors. Re-do revascularisation predictors included female sex (HR 2.106, p=0.063), Black race (HR 64.498, p=0.001), medium pMAC (HR 6.812, p=0.002), high pMAC (HR 11.455, p=0.008), and GLASS stage 3 (HR 2.899, p=0.037). ConclusionThe pMAC score effectively predicts MALE in CLTI patients and enhances risk stratification. Using both, pMAC scores and GLASS stages enhances predictive accuracy, identifying patients at higher risk for MLLA, Informing clinical decision-making and therapeutic strategies.

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.

To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Comprehensive malnutritional index for predicting clinical outcomes in locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy

The objective of this investigation was to assess the prognostic significance of the comprehensive malnutritional index (CNI) in patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant chemoradiotherapy (nCRT) followed by surgery. A total of 240 LARC patients were recruited. The CNI was calculated using principal components analysis based on hemoglobin (Hb), total lymphocyte count (TLC), albumin (ALB), body mass index (BMI), and usual body weight percentage (UBW%). The patients were then categorized into two groups based on the median CNI value. Cox regression and survival analyses were performed. The CNI-low (120 cases) and CNI-high (120 cases) groups were classified based on the median CNI value. The results indicated that the CNI demonstrated superior predictive ability for disease-free survival (DFS) and overall survival (OS) compared to other malnutritional indexes. LARC patients in the CNI-high group had significantly longer DFS and OS compared to those in the CNI-low group. Multivariate analysis revealed that the CNI was an independent prognostic factor for DFS (hazard ratio [HR] = 0.49; 95% confidence interval[CI], 0.29–0.83; P = 0.008) and OS (HR = 0.30; 95% CI, 0.16-0.58; P &lt; 0.001). Additionally, the CNI-high group benefited from postoperative chemotherapy (DFS: P = 0.029, OS: P = 0.024), while the CNI-low group did not show such benefits (DFS: P = 0.448, OS: P = 0.468). These findings suggest that the CNI could serve as a valuable prognostic indicator for LARC patients who undergo nCRT followed by surgery. Preoperative nutrition optimization is important for LARC patients.

Non-Invasive Detection of Nitrogen Deficiency in Cannabis sativa Using Hand-Held Raman Spectroscopy

Proper crop management requires rapid detection methods for abiotic and biotic stresses to ensure plant health and yield. Hemp (Cannabis sativa L.) is an emerging economically and environmentally sustainable crop capable of yielding high biomass. Nitrogen deficiency significantly reduces hemp plant growth, affecting photosynthetic capacity and ultimately decreasing yield. When symptoms of nitrogen deficiency are visible to humans, there is often already lost yield. A real-time, non-destructive detection method, such as Raman spectroscopy, is therefore critical to identify nitrogen deficiency in living hemp plant tissue for fast, precise crop remediation. A two-part experiment was conducted to investigate portable Raman spectroscopy as a viable hemp nitrogen deficiency detection method and to compare the technique’s predictive ability against a handheld SPAD (chlorophyll index) meter. Raman spectra and SPAD readings were used to train separate nitrogen deficiency discrimination models. Raman scans displayed characteristic spectral markers indicative of nitrogen deficiency corresponding to vibrational modes of carotenoids, essential pigments for photosynthesis. The Raman-based model consistently predicted nitrogen deficiency in hemp prior to the onset of visible stress symptoms across both experiments, while SPAD only differentiated nitrogen deficiency in the second experiment when the stress was more pronounced. Our findings add to the repertoire of plant stresses that hand-held Raman spectroscopy can detect by demonstrating the ability to provide assessments of nitrogen deficiency. This method can be implemented at the point of cultivation, allowing for timely interventions and efficient resource use.

Experimental study of the surface finishing of CNC magnetic abrasive finishing based on ANN

ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.

Poder diagnóstico de infección bacteriana de LIAISON MeMed BV® en los pacientes adultos atendidos en urgencias por sospecha de infección

To analyze the diagnostic accuracy of the new MeMed® test to predict bacterial infection in adult patients seen in emergency departments (ED) with clinical suspicion of infection, as well as to compare its performance with other commonly used biomarkers (protein C reactive-PCR-, procalcitonin -PCT-). A prospective, observational and analytical study was carried out on adult patients who were treated in an ED with the clinical diagnosis of an infectious process. Follow-up was carried out for 30 days. The diagnosis of bacterial infection (BI) was considered as the dependent variable. The predictive ability was analyzed with the area under the curve (AUC) of the receiver operating characteristic (COR) and the values of sensitivity (Se), specificity (Es), positive predictive value (PPV) and negative predictive value (NPV) of the PCR, PCT, leukocyte count and the LIAISON® MeMed® test. The study included 258 patients, 54 (15.6%) of whom died within 30 days of visiting the ED. The mean age was 68.28 (SD 19.53) years, 57.4% (148) were men. At 30 days, the group with the IB diagnosis had 137 patients, the viral infection group 68 cases and 17 in the indeterminate group. The AUC-COR achieved by MeMed® in the group that analyzes all patients was 0.920 (95% CI: 0.877-0.962) and the PCT was 0.811 (95% CI: 0.754-0.867). With a cut-off point (PC) > 65 points of the MeMed® test, achieves a Se: 79.2% and Es: 91.2% and with PC > 90 points a Se: 57% and Es: 95.9%. Applying the Youden index, the PC > 50 points achieves Se:84.1% and Es:88.2%. In adult patients treated with clinical suspicion of infection in the ED, the LIAISON MeMed® test has a great ability to diagnose its bacterial origin and achieves better performance than PCT, PCR and leukocyte count.

Plastic Constitutive Training Method for Steel Based on a Recurrent Neural Network

The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively.

SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

The value of systemic immune inflammation index, white blood cell to platelet ratio, and homocysteine in predicting the instability of small saccular intracranial aneurysms

Inflammation has a destructive effect on the homeostasis of the vascular wall, which is involved in the formation, growth, and rupture of human intracranial aneurysms (IAs) disease progression. However, inflammation-related markers have not been well studied in the risk stratification of unruptured IAs. The purpose of this study was to investigate the predictive value of serum inflammatory markers in the unstable progression of small saccular intracranial aneurysms (SIAs). This study retrospectively included 275 patients with small SIAs (aneurysm diameter less than or equal to 7 mm), to compare the level difference of serum inflammatory complex marker systemic immune-inflammatory index (SII), white blood cell to platelet ratio (WPR), and homocysteine (Hcy) in patients with stable (asymptomatic unruptured) and unstable (symptomatic unruptured, ruptured) small SIAs. 187 patients (68%) had aneurysm-related compression symptoms and rupture outcomes. In the multivariate logistic regression after adjusting for baseline differences, SII, WPR, and Hcy were independent risk factors for the instability of small SIAs, the prediction model combined with other risk factors (previous stroke history, aneurysm irregularity) showed good predictive ability for the instability of small SIAs, with an area under the curve of 0.905. In addition, correlation analysis showed that SII, WPR, and Hcy also had significant differences in patients with symptomatic unruptured and ruptured small SIAs, and higher inflammation levels often promoted the disease progression of small SIAs. Higher levels of SII, WPR and Hcy can be used as independent predictors of instability of small SIAs. As an economical and convenient biomarker, it is crucial for clinical treatment strategies of stable small SIAs.

Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features

ObjectiveWe aim to develop a predictive model for lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC), using magnetic resonance imaging (MRI)-based radiomics features.MethodsA total of 204 patients with IBC admitted to our hospital were included in this retrospective study. The data was split into training and validation sets at a 7:3 ratio. Feature normalization was conducted, followed by feature selection using ANOVA, correlation analysis, and LASSO in the training set. The final step involved building a logistic regression model. The LVI prediction models were established by single sequence image and combined different sequence images as follows: A: prediction model based on the optimal sequence in the 7-phase enhanced MRI scans; B: prediction model based on the optimal sequences in the sequences T1WI, T2WI, and DWI; and C: the combined model based on the optimal sequences selected from A and B. Subjects' work characteristic curves (ROC) and decision curves (DCA) were plotted to determine the extent to which they predicted LVI performance in the training and validation sets. Simultaneously, nomogram models were constructed by integrating radiomics features and independent risk factors. In addition, an additional 16 patients from the center between January and August 2024 were collected as the Nomogram external validation set. The ROC and DCA were used to evaluate the performance of the model.ResultsIn the enhanced images, Model A built based on the enhanced 2-phase achieved the best average AUC, with a validation set of 0.764. Model B built based on the T2WI had better results, with a validation set of 0.693. Model C built by combining enhanced 2-phase and T2WI sequences had a mean AUC of 0.705 in the validation set. In addition, the tumor size, whether the tumor boundary was clear or not, and whether there was a coelom in the tumor tissue had a statistically significant effect on the LVI of IBC, and a clinical-radiomics nomogram was established. DCAs as well as Nomogram also indicate that Model A has good clinical utility. The AUC of the nomogram in the training set, internal validation set, and external validation set were 0.703, 0.615, and 0.609, respectively. The DCA also showed that the radiomics nomogram combined with clinical factors had good predictive ability for LVI.ConclusionIn IBC, MRI radiomics can serve as a noninvasive predictor of LVI. The clinical-MRI radiomics model, as an efficient visual prognostic tool, shows promise in forecasting LVI. This highlights the significant potential of pre-radiomics prediction in enhancing treatment strategies.

Self-rated health in late adolescence as a predictor for mortality between 46 and 70 years of age

Self-rated health is a common assessment used in epidemiological research and an independent predictor of morbidity and mortality. We investigate if a single measure of self-rated health in late adolescence predict mortality between 46 and 70 years of age. This study was based on 47 286 Swedish men that conscribed 1969–1970 at age 18–20 and that were still alive in 1997. Self-rated health and data on potential explanatory factors (psychological factors, health status markers and health behaviors) were collected at conscription. Adult socioeconomic position in 1990 was derived from registries. Death and cause of death (cancer, cardiovascular disease, violent and alcohol abuse related disorders) were derived from the Causes of Death Register between 1997 and 2019. Conscripts that rated their health as fair or poor/very poor had significantly higher hazard of all-cause mortality than conscript that rated their health as very good (HRfair = 1.27, 95%CI:1.18–1.37 and HRpoor = 1.25, 95%CI:1.11–1.41) and disease-specific mortality. Adjusting for all explanatory factors attenuated the risk estimates by 9–100%. In conclusion, poor self-rated health reported in late adolescence predicts all-cause and disease-specific mortality between the ages of 46 and 70 years. Psychological factors and health behaviors measured at conscription may serve as potential explanatory factors underlying the predictive ability of self-rated health in late adolescence.

The Weight of Nutrition on Post-Resection Oncologic Morbidity and Mortality: A Systematic Review and Meta-Analysis of Nutritional Indices

Abstract Context Nutritional status is a critical factor in the selection of patients for solid tumor resection. A variety of indices have been developed to quantify nutritional status, and they have differing degrees of predictive power for various postoperative outcomes. Objective This study aimed to comprehensively evaluate the predictive ability of commonly used nutritional indices in relation to postoperative complications (POCs), recurrence-free survival (RFS), and OS. Data Sources We performed a systematic review of 14 established nutritional indices from January 2015 to July 2022: Data Extraction The primary end point was OS, while the secondary end points were POCs and RFS. A subsequent meta-analysis was performed to further assess the predictive ability of these indices for OS based on general index type, primary tumor site, and the patient’s index status. Data Analysis In this evaluation, 38 articles reporting data on 23 970 patients were analyzed, focusing on 14 nutritional indices. The indices were categorized into phenotypic, metabolic, immunologic, and combined types. Patients within the cut-off range of any index were predicted to have lower OS (hazard ratio [HR] 2.14, 95% CI 1.84–2.49, P &amp;lt; .01). Lower gastrointestinal (GI) and “other” sites were less predictive than upper GI primary tumors (HR 1.63, HR 1.82, and HR 2.54, respectively; all with P &amp;lt; .01). Phenotypic indices were less predictive than combined indices (HR 1.73 vs HR 2.47, P &amp;lt; .01). Within the combined category, there was no significant difference in the predictive ability of Prognostic Nutritional Index (PNI) vs Geriatric Nutritional Risk Index (GNRI) vs Controlling Nutritional Index (CONUT) (HR 2.63 vs HR 2.42 vs HR 2.07, P = .07). Conclusion The predictive efficacy of a nutritional index was found to be highly dependent on the index type, the primary tumor site, and the outcome of interest. In the context of upper GI resections, nutritional status appeared to be more of a significant predictor of OS, compared with cases involving lower GI and hepatic malignancies. Indices that integrate phenotypic, metabolic, and immunologic patient factors potentially offer greater clinical utility in forecasting OS.

The Integrated Transcriptome Bioinformatics Analysis of Energy Metabolism-Related Profiles for Dorsal Root Ganglion of Neuropathic Pain.

Neuropathic pain (NP) is a debilitating disease and is associated with energy metabolism alterations. This study aimed to identify energy metabolism-related differentially expressed genes (EMRDEGs) in NP, construct a diagnostic model, and analyze immune cell infiltration and single-cell gene expression characteristics of NP. GSE89224, GSE123919, and GSE134003 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) analysis and an intersection with highly energy metabolism-related modules in weighted gene co-expression network analysis (WGCNA) was performed in GSE89224. Least absolute shrinkage and selection operator (LASSO), random forest, and logistic regression were used for model genes selection. NP samples were divided into high- and low-risk groups and different disease subtypes based on risk score of LASSO algorithm and consensus clustering analysis, respectively. Immune cell composition was estimated in different risk groups and NP subtypes. Datasets 134,003 were performed for identification of single-cell DEGs and functional enrichment. Cell-cell communications and pseudo-time analysis to reveal the expression profile of NP. A total of 38 EMRDEGs were obtained and are majorly enriched in metabolism about glioma and inflammation. LASSO, random forest, and logistic regression identified 6 model genes, which were Itpr1, Gng8, Socs3, Fscn1, Cckbr, and Camk1. The nomogram, based on six model genes, had a good predictive ability, concordance, and diagnostic value. The comparisons between different risk groups and NP subtypes identified important pathways and different immune cells component. The immune infiltration results majorly associated with inflammation and energy metabolism. Single-cell analysis revealed cell-cell communications and cells differentiation characteristics of NP. In conclusion, our results not only elucidate the involvement of energy metabolism in NP but also provides a robust diagnostic tool with six model genes. These findings might give insight into the pathogenesis of NP and provide effective therapeutic regimens for the treatment of NP.

Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning.

Predicting the toxicity of nanoparticles plays an important role in biomedical nanotechnologies, in particular in the creation of new drugs. Safety analysis of nanoparticles can identify potentially harmful effects on living organisms and the environment. Advanced machine learning models are used to predict the toxicity of nanoparticles in a nutrient solution. In this article, we performed a comparative analysis of the current state of research in the field of nanoparticle toxicity analysis using machine learning methods; we trained a regression model for predicting the quantitative toxicity of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of MSE = 2.19 and RMSE = 1.48; we trained a multi-class classification model for predicting the toxicity class of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of Accuracy = 0.9756, Recall = 0.9623, F1-Score = 0.9640, and Log Loss = 0.1855. As a result of the analysis, we concluded the good predictive ability of the trained models. The optimal dosages for the nanoparticles under study were determined as follows: ZnO = 9.5 × 10-5 mg/mL; Fe3O4 = 0.1 mg/mL; SiO2 = 1 mg/mL. The most significant features of predictive models are the diameter of the nanoparticle and the nanoparticle concentration in the nutrient solution.

Impact of Sex, Age and Body Habitus on RBD-specific Antibody Response After Two Doses of Sinopharm BBIBP-CorV Vaccine

Background: Mongolia started its nationwide vaccination campaign against COVID-19 on 23 February 2021 after receiving the first batch of the inactivated BBIBP-CorV. Age and body habitus of vaccinees may be associated with the immune effecter functions. Several systematic reviews and meta-analyses suggested lower immunogenicity of vaccines against SARS-CoV-2 infection in the older adult population and obese individuals. We aimed to establish a possible relationship between post-vaccine seroconversion rate and some biometric characteristics in the Mongolian cohort of vaccinees after two shots of the Sinopharm BBIBP-CorV vaccine. Materials and Methods: We collected serum samples from 846 eligible vaccinees before the first dose of the BBIBP-CorV vaccine and 21-28 days after the second dose of the same vaccine. Anti-SARS-CoV-2 Receptor Binding Domain (RBD) Immunoglobulin class G (IgG) and M (IgM) titer were measured in all samples. Results: 686 (81.1%) of 846 vaccinees received the Sinopharm BBIBP-CorV vaccine demonstrated seroconversion. Vaccinees with seroconversion had a younger mean age and lower mean body-mass index (BMI) than non-responders. Seroconversion rate according to age of vaccinees tends to decrease and obese vaccinees demonstrated a greater portion among non-responders. The Optimal Cut-Point (OCP) of age for seroconversion was found to differ significantly in male and female vaccinees. BMI has shown prediction ability for seroconversion only in male vaccinees but not in females. Using Receiver Operating Characteristics (ROC) analysis, we found 39 years for males and 41 years for females as a crucial milestone increasing the probability of seronegativity 2.5 times on average. A BMI higher than 29.1kg/m2 in male vaccinees was considered an acceptable predictor for seroconversion increasing the probability of seronegativity 2.4 times on average. Conclusion: The above findings allow us to summarize the sex-adjusted approach to biometric characteristics has an improved predictiveness for post-vaccine antibody response.

Comparison of Hounsfield Unit, Vertebral Bone Quality, and Dual-Energy X-Ray Absorptiometry T-Score for Predicting Cage Subsidence After Posterior Lumbar Interbody Fusion.

Retrospective cohort study. To compare Hounsfield unit (HU), vertebral bone quality (VBQ), and dual-energy X-ray absorptiometry (DEXA) T-score in predicting cage subsidence (CS) after posterior lumbar interbody fusion. We retrospectively reviewed patients undergoing PLIF and measured L4 HU and VBQ. Baseline characteristics between CS and non-CS groups were compared. Multiple logistic regression analysis was used to identify risk factors for CS. Receiver operating characteristic (ROC) curves were used to explore the predictive value of variables for CS. Subgroup analysis was conducted for patients with available DEXA results. Eighty-four patients were analyzed. The subsidence group was older, with lower L4 HU (92.6 (35.4) vs 130.8 (47.8), P < 0.001) and higher VBQ (3.30 (0.64) vs 2.99 (0.63), P = 0.041) compared to the non-CS group. Low L4 HU (OR 0.979, P = 0.022) was an independent risk factor for subsidence, while VBQ was not. ROC analysis indicated that L4 HU (AUC = 0.733, P < 0.001) exhibited superior predictive performance compared to VBQ (AUC = 0.643, P = 0.032). Subgroup analysis in patients with DEXA showed that the femoral neck T-score (AUC = 0.67, P = 0.037) could differentiate CS, with L4 HU exhibiting the highest predictive ability (AUC = 0.778, P < 0.001), both outperforming VBQ (AUC = 0.645, P = 0.075). Among the three BMD parameters, only low L4 HU could be an independent risk factor for CS. Preoperative HU is mandatory in patients at risk for osteoporosis when undergoing spine surgery.

Study on risk factor analysis and model prediction of hyperuricemia in different populations.

The purpose of the present study was to explore the influencing factors of hyperuricemia (HUA) in different populations in Shandong Province based on clinical biochemical indicators. A prediction model for HUA was constructed to aid in the early prevention and screening of HUA. In total, 705 cases were collected from five hospitals, and the risk factors were analyzed by Pearson correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve in the gender and age groups. All data were divided into a training set and test set (7:3). The training set included age, gender, total protein (TP), low-density lipoprotein cholesterol (LDL-C), and 15 other indicators. The random forest (RF) and support vector machine (SVM) methods were used to build the HUA model, and model performances were evaluated through 10-fold cross-validation to select the optimal method. Finally, features were extracted, and the ROC curve of the test set was generated. TP, LDL-C, and glucose (GLU) were risk factors for HUA, and the area under the curve (AUC) value of the SVM validation set was 0.875. The SVM model based on clinical biochemical indicators has good predictive ability for HUA, thus providing a reference for the diagnosis of HUA and the development of an HUA prediction model.

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

ContextThe invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity.ObjectivesTo provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems.MethodsWe calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR).ResultsThe metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass.ConclusionsOur results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.