Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Role of low-proportion, hydrophobic dissolved organic matter components in inhibiting methylmercury uptake by phytoplankton.

Uptake of methylmercury (MeHg), a potent neurotoxin, by phytoplankton is a major concern due to its role as the primary pathway for MeHg entry into aquatic food webs, thereby posing a significant risk to human health. While it is widely believed that the MeHg uptake by plankton is negatively correlated with the concentrations of dissolved organic matter (DOM) in the water, ongoing debates continue regarding the specific components of DOM that exerts the dominant influence on this process. In this study, we employed a widely-used resin fractionation approach to separate and classify DOM derived from algae (AOM) and natural rivers (NOM) into distinct components: strongly hydrophobic, weakly hydrophobic, and hydrophilic fractions. We conduct a comparative analysis of different DOM components using a combination of spectroscopy and mass spectrometry techniques, aiming to identify their impact on MeHg uptake by Microcystis elabens, a prevalent alga in freshwater environments. We found that the hydrophobic components had exhibited more pronounced spectral characteristics associated with the protein structures while protein-like compounds between hydrophobic and hydrophilic components displayed significant variations in both distributions and the values of m/z (mass-to-charge ratio) of the molecules. Regardless of DOM sources, the low-proportion hydrophobic components usually dominated inhibition of MeHg uptake by Microcystis elabens. Results inferred from the correlation analysis suggest that the uptake of MeHg by the phytoplankton was most strongly and negatively correlated with the presence of protein-like components. Our findings underscore the importance of considering the diverse impacts of different DOM fractions on inhibition of phytoplankton MeHg uptake. This information should be considered in future assessments and modeling endeavors aimed at understanding and predicting risks associated with aquatic Hg contamination.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.

PURPOSE: The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. METHODS: Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. RESULTS: The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. CONCLUSIONS: MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.

Fosfomycin Enhances the Inhibition Ability of Linezolid Against Biofilms of Vancomycin-Resistant Enterococcus faecium in vitro.

Purpose: We explored the inhibition ability of linezolid/fosfomycin combination against biofilms of vancomycin-resistant Enterococcus faecium (VREfm) and tried to provide a theoretical basis for the treatment of VREfm biofilm-associated infections. Methods: Four clinical isolates of VREfm (No.2, No.4, No.5, and No.6) were used for this study, which were collected from the First Affiliated Hospital of Anhui Medical University. The checkerboard method was used to assess the synergistic effect of linezolid and fosfomycin. The inhibition ability of biofilm biomass was evaluated by crystal violet staining, and the metabolic activity was tested by an Alamar blue cell viability assay. Changes in biofilm formation-related genes of the strains after incubating with drugs were investigated via the quantitative real-time polymerase chain reaction (RT-qPCR). Results: The fractional inhibitory concentration index (FICI) showed that linezolid combined with fosfomycin had a synergistic effect on all four VREfm isolates. Compared with linezolid monotherapy, linezolid combined with fosfomycin led to a significant decrease in biofilm biomass and metabolic activity, especially in the mature biofilm. The results of RT-qPCR showed linezolid combined with fosfomycin inhibition biofilm formation through the inhibition of cylA, ebpA, and gelE transcription in VREfm in the initial and mature stages. To the mature biofilm, the combination also reduced the expression of asa1, atlA, and esp. Conclusion: The combination of linezolid and fosfomycin represented stronger inhibitory effect on the biofilm formation of VREfm than linezolid alone.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.

Purpose: Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods: We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results: The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion: The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.

Background and objectives: Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5. Design: A retrospective cohort study. Setting and participants: A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021. Methods: This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM). Main outcome measurement: Subsequent eGFR level after three consecutive laboratory data assessments. Results: Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM. Conclusion: The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

A ratiometric fluorescent paper sensor based on dye-embedded MOF for high-sensitive detection of arginine.

Ratiometric fluorescent sensors can suppress the interference of factors unrelated to analysis due to their built-in self-calibration characteristics, which exhibit higher sensitivity and more obvious visual detection in the process of qualitative and quantitative analysis. Herein, we constructed a ratiometric fluorescence probe based on fluorescent/colorimetric dual-mode method for the determination of arginine by encapsulating rhodamine B in-situ into UiO-66-NH2 MOFs (UiO-66-NH2@RhB). The as-prepared probe showed dual-emission characteristics under a single excitation wavelength. The fluorescence intensity of UiO-66-NH2 was increased significantly by arginine, while the emission peak intensity of rhodamine B remained stable, resulting in a single-signal response with fixed reference. Furthermore, the practicality of the presented sensor was successfully validated by quantitative detection of arginine in human serum. More significantly, paper-based sensors for arginine detection were devised by using carboxymethyl cellulose modified filter papers. Under the irradiation of ultraviolet light, the paper-based sensors would produce obvious color variation from lightpink to bluish violet. This work provided a convenient and efficient method for on-site detection of arginine.

Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification.

Mammography is considered the gold standard for breast cancer screening. Multiple risk factors that affect breast cancer development have been identified; however, there is an ongoing debate regarding the significance of these factors. Machine learning (ML) models and Shapley Additive Explanation (SHAP) methodology can rank risk factors and provide explanatory model results. This study used ML algorithms with SHAP to analyze the risk factors between two different age groups and evaluate the impact of each factor in predicting positive mammography. The ML model was built using data from the risk factor questionnaires of women participating in a breast cancer screening program from 2017 to 2021. Three ML models, least absolute shrinkage and selection operator (lasso) logistic regression, extreme gradient boosting (XGBoost), and random forest (RF), were applied. RF generated the best performance. The SHAP values were then applied to the RF model for further analysis. The model identified age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. The differences between age groups ranked by reproductive lifespan and BMI were higher in the younger and older age groups, respectively. The use of SHAP frameworks allows us to understand the relationships between risk factors and generate individualized risk factor rankings. This study provides avenues for further research and individualized medicine.

Biodegradability of algal-derived dissolved organic matter and its influence on methylmercury uptake by phytoplankton.

Methylmercury (MeHg) uptake by phytoplankton represents a key step in determining the exposure risks of aquatic organisms and human beings to this potent neurotoxin. Phytoplankton uptake is believed to be negatively related to dissolved organic matter (DOM) concentration in water. However, microorganisms can rapidly change DOM concentration and composition and subsequent impact on MeHg uptake by phytoplankton has rarely been tested. Here, we explored the influences of microbial degradation on the concentrations and molecular compositions of DOM derived from three common algal sources and tested their subsequent impacts on MeHg uptake by the widespread phytoplankton species Microcystis elabens. Our results indicated that dissolved organic carbon was degraded by 64.3‒74.1% within 28 days of incubating water with microbial consortia from a natural meso­eutrophic river. Protein-like components in DOM were more readily degraded, while the numbers of molecular formula for peptides-like compounds had increased after 28 days' incubation, probably due to the production and release of bacterial metabolites. Microbial degradation made DOM more humic-like which was consistent with the positive correlations between changes in proportions of Peaks A and C and bacterial abundance in bacterial community structures as illustrated by 16S rRNA gene sequencing. Despite rapid losses of the bulk DOM during the incubation, we found that DOM degraded after 28 days still reduced the MeHg uptake by Microcystis elabens by 32.7‒52.7% relative to a control without microbial decomposers. Our findings emphasize that microbial degradation of DOM would not necessarily enhance the MeHg uptakes by phytoplankton and may become more powerful in inhibiting MeHg uptakes by phytoplankton. The potential roles of microbes in degrading DOM and changing the uptakes of MeHg at the base of food webs should now be incorporated into future risk assessments of aquatic Hg cycling.

The Role of Surgery in Stage I Small Cell Lung Cancer: A National VA Database Analysis.

BACKGROUND: Historically, limited stage Small Cell Lung Cancer (SCLC) has been treated with concurrent chemoradiation (CRT). While current NCCN guidelines recommend consideration of lobectomy in node-negative cT1-T2 SCLC, data regarding the role of surgery in very limited SCLC is lacking. METHODS: Data from the National VA Cancer Cube were compiled. A total of 1,028 patients with pathologically confirmed stage I SCLC were studied. Only 661 patients that either received surgery or CRT were included. Interval-censored Weibull and Cox proportional hazard regression models were used to estimate median overall survival (OS) and hazard ratio (HR), respectively. Two survival curves were compared by a Wald test. Subset analysis was performed based on the location of the tumor in the upper vs. lower lobe as delineated by ICD-10 codes C34.1 and C34.3. RESULTS: Four-hundred and forty-six patients received concurrent CRT; while 223 underwent treatment that contained surgery (93 surgery only, 87 surgery/chemo, 39 surgery/chemo/radiation and 4 surgery/radiation). The median OS for the surgery-inclusive treatment was 3.87 years (95% CI 3.21-4.48) while median OS for the CRT cohort was 2.45 years (95% CI 2.17-2.74). HR of death for surgery-inclusive treatment when compared to CRT is 0.67 (95% CI 0.55-0.81; P < .001). Subset analysis based on the location of the tumor in both the upper or lower lobes showed improved survival with surgery as compared to CRT regardless of the location. HR for the upper lobe was 0.63 (95% CI 0.50-0.80; P < .001) and lower lobe 0.61 (95% CI 0.42-0.87; P = .006). Multivariable regression analysis accounting for age and ECOG-PS shows a HR 0.60 (95% CI 0.43-0.83; P = .002) favoring surgery. CONCLUSIONS: Surgery was used in less than a third of patients with stage I SCLC who received treatment. Surgery-inclusive multimodality treatment was associated with a longer overall survival as compared to chemoradiation, independent of age, performance status or tumor location. Our study suggests a more expansive role for surgery in stage I SCLC.

A Review of Endocrine Therapy in Early-stage Breast Cancer: The Journey From Crudeness to Precision.

Endocrine therapy (ET) is the standard of care for hormone receptor-positive early-stage breast cancer in the adjuvant setting. However, response to ET can vary across patient subgroups. Historically, hormone receptor expression and clinical stage are the main predictors of the benefit of ET. A "window of opportunity" trials has raised significant interest in recent years as a means of assessing the sensitivity of a patient's cancer to short-term neoadjuvant ET, which provides important prognostic information, and helps in decision-making regarding treatment options in a time-efficient and cost-efficient manner. In the era of genomics, molecular profiling has led to the discovery and evaluation of the prognostic and predictive abilities of new molecular profiles. To realize the goal of personalized medicine, we are in urgent need to explore reliable biomarkers or genomic signatures to accurately predict the clinical response and long-term outcomes associated with ET. Validation of these biomarkers as reliable surrogate endpoints can also lead to a revolution in the clinical trial designs, and potentially avoid the need for repeated tissue biopsies in the surveillance of disease response. The clinical potential of tumor genomic profiling marks the beginning of a new era of precision medicine in breast cancer treatment.

Health Informatics: The Foundations of Public Health.

As technology continues to evolve, vast amounts of diverse digital data are becoming more easily generated and collected [...].

Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.

The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.

Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

Novel hierarchical carbon microspheres@layered double hydroxides@copper lignosulfonate architecture for polypropylene with enhanced flame retardant and mechanical performances.

Due to the inherent defect of flammability of polypropylene (PP), a novel and highly efficient carbon microspheres@layered double hydroxides@copper lignosulfonate (CMSs@LDHs@CLS) flame retardant was designed and prepared, which was attributed to the strong electrostatic interaction between carbon microspheres (CMSs), layered double hydroxides (LDHs) and lignosulfonate as well as the chelation effect of lignosulfonate on copper ions, and then it was incorporated into the PP matrix. Significantly, CMSs@LDHs@CLS not only observably improved its dispersibility in PP matrix, but also simultaneously achieved excellent flame retardant properties for composites. With the addition of 20.0 % CMSs@LDHs@CLS, the limit oxygen index of CMSs@LDHs@CLS and PP composites (PP/CMSs@LDHs@CLS) reached 29.3 % and achieved the UL-94 V-0 rating. Cone calorimeter tests indicated that the peak heat release rate, total heat release and total smoke production of PP/CMSs@LDHs@CLS composites exhibited declines of 28.8 %, 29.2 % and 11.5 %, respectively, compared with those of PP/CMSs@LDHs composites. These advancements were attributed to the better dispersibility of CMSs@LDHs@CLS in PP matrix and illustrated that CMSs@LDHs@CLS observably reduced fire hazards of PP. The flame retardant property of CMSs@LDHs@CLS might relate to condensed phase flame retardant effect of char layer and catalytic charring of copper oxides.

Impact of biochar coexistence with polar/nonpolar microplastics on phenanthrene sorption in soil.

Microplastics and biochar normally coexist in soil. In this study, two microplastics of different polarities (nonpolar polyethylene (PE) and polar polybutylene adipate-co-terephthalate (PBAT)) and two wheat straw biochars produced at 400 (W4) and 700 °C (W7) were selected to investigate the sorption behaviors of phenanthrene in soil where microplastics and biochar coexisted. The results showed that the presence of PE more significantly weakened the adhesion of soil particles onto biochar than the presence of PBAT. Meanwhile, the presence of biochar enhanced the soil particle attachment on the microplastic surface. As a result, the sorption behavior of phenanthrene was significantly different in soil where biochar coexisted with microplastics of different polarities. The Koc values of PE-biochar-soil mixtures at Ce= 0.005 Cs were up to 42 % lower than those of PBAT-biochar-soil mixtures, which is related to lower micropore area of particles isolated from the former. However, at Ce = 0.05 Cs and 0.5 Cs, the Koc values of PE-biochar-soil mixtures were up to 1.4 times higher than those of PBAT-biochar-soil mixtures because of a more significant reduction in biochar surface polarity when it coexisted with nonpolar PE.

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.