Mechanistic insight into the effects of interaction between biochar and soil with different properties on phenanthrene sorption.

Soil composition varies considerably in nature, so it is vital to investigate the mechanism of the effect of various soil parameters on biochar sorption capacity. In this study, two biochars (W4 and W7) were derived from wheat straw at temperatures of 400 and 700 °C and were incubated with three different soils. Changes in biochar surface features by aging in the soils and the consequent impact on phenanthrene sorption were examined. The results showed that the effect of adding biochar on phenanthrene sorption capacity (Koc) varied by soil. When biochar was freshly mixed with soil, the Koc value in soil with higher clay content was more dramatically altered by biochar, which is due to clay particles adhering to the biochar surface. Moreover, the Koc value was significantly decreased by the addition of W4 but increased by the addition of W7 in general. After aging, most of the Koc value decreased. The greatest decrease in Koc value was observed in biochar and soil composed with the highest clay content for W4 (24-63%), as well as soil composed with the highest organic matter content for W7 (46-64%). This is because the surface polarity and micropores of biochar dropped the most rapidly in these mixes, resulting in a significant decrease in hydrophobic and pore-filling properties. The results revealed that the impact of biochar-soil interactions on phenanthrene sorption is related to not only biochar properties but also soil clay particles, soil organic matter content and pH. The findings of the study can be utilized to assess the efficacy of biochar application in soil remediation for various features.

Effects of biodegradable microplastics coexistence with biochars produced at low and high temperatures on bacterial community structure and phenanthrene degradation in soil.

The increasing use of biodegradable plastics may result in more serious pollution of microplastics which often coexist with biochar in soil, this will affect how organic pollutants move and transform in the soil. This work investigated the effect of biodegradable polybutylene adipate-co-terephthalate (PBAT) coexistence with biochars produced at temperatures of 400 and 700 °C (W4 and W7) on soil bacterial communities and phenanthrene degradation. The results showed that coexistence of PBAT and biochar paticles greatly boosted the relative abundance of Nocardioides while decreased the relative abundance of Sphingomonas as compared to soils with a single addition of PBAT or biochar. Changes in soil Eh values were the most influential factor in bacterial communities (more than 40% contribution). The degradation ratio of phenanthrene when PBAT coexisted with W7 (39.6 ± 3.6%) was not significantly different from the treatment with a single W7 addition (35.0 ± 2.3%, P＞0.05), and was related to phenanthrene degradation in the adsorbed state of W7 in soil. In contrast, the degradation ratio of phenanthrene in PBAT coexisting with W4 (35.1 ± 3.5%) was intermediate between that of single PBAT (49.8 ± 0.9%) and W4 (13.7 ± 5.8%) treatments. This was primarily due to changes in the experiment's initial bioavailable phenanthrene content. Furthermore, after the introduction of earthworms, phenanthrene degradation ratio in coexistence treatments were very similar to that described above in the absence of earthworms. Except for two treatments that contain W7, phenanthrene degradation ratio in the other treatments was increased by the presence of earthworms (up to 23%), which is related to the enhanced relative abundance of polycyclic aromatic hydrocarbon-degraders. Our findings indicated that PBAT coexistence with high-temperature or low-temperature biochar had a completely different impact on bacterial communities and phenanthrene degradation in soil.

Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23-45.

BACKGROUND: The relationship between sleep duration and physical fitness is one aspect of sleep health. Potential factors associated with sleep duration interfere with physical fitness performance, but the impact trends on physical fitness indicators remain unclear. METHODS: This study examined associations between sleep duration and physical fitness among young to middle-aged adults in Taiwan. A total of 42,781 Taiwanese adults aged 23-45 participated in the National Physical Fitness Examination Survey 2013 (NPFES-2013) in Taiwan between October 2013 and March 2014. A standardized structural questionnaire was used to record participants' sleep duration, which was stratified as short (< 6 h/day (h/d)), moderate (6-7 h/d; 7-8 h/d; 8-9 h), and long (≥ 9 h/d) sleep duration groups. Physical fitness was assessed based on four components: body composition (body mass index [BMI], waist-to-height ratio [WHtR], and waist-to-hip ratio [WHR]), muscle strength and endurance (1-min bent-leg sit-up test [BS]), flexibility (sit-and-reach test [SR]), and cardiorespiratory endurance index (3-min step test [CEI]). RESULTS: By using analysis of covariance (ANCOVA), after sex grouping and age adjustment, we observed that sleep duration was significantly associated with obesity, functional fitness, and self-perception of health. The sleep duration for low obesity-related values (BMI, WHtR, and WHR) for men was 7-9 h/d, and that for women was 7-8 h/d. Sleeping more than 8 h/d showed poor functional fitness performances (BS and SR). For both sexes, sleep duration of 8-9 h/d was the optimal sleep duration for self-perceptions of health. CONCLUSIONS: Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23-45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness.

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association.

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018-2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.

Removal of lead from two polluted soils by magnetic wheat straw biochars.

Due to high sorption capacity for heavy metals, magnetic biochar (MBC) has the potential to adsorb heavy metals in soils, which are then removed together with MBC from soils by a magnetic field. In this study, two magnetic biochars (MBC300 and MBC700) were derived from the magnetization of wheat straw biochars pyrolyzed at 300 and 700 °C. Strong binding of Pb with iron oxide particles deposited on biochar was observed. After the MBCs (7.5%, w/w) were applied to two naturally Pb-polluted soils (named as He-soil and Hu-soil) for 720 h, the removal efficiency of Pb from the soil by MBC300 (26.8-40.1%) was similar (p > 0.05) to that by MBC700 (25.1-42.1%). This is because MBC300 has lower sorption capacity for Pb but higher recovery percentage from soils as a result of lower saturation magnetization. The removal efficiencies of Pb by the two MBCs were 13-17% higher for He-soil than for Hu-soil, which was due to higher proportion of mobile forms of Pb in He-soil (82.3%) than in Hu-soil (51.5%). Spectroscopic analysis indicated that Pb in soils tended to bind onto the surface of MBC in more stable forms. Moreover, removing Pb from soils by MBC could decrease Pb concentration in ryegrass by about 30%. Therefore, it might be a potential method to remedy Pb-polluted soils.

Interaction between three marine microalgae and two phthalate acid esters.

Three marine microalgae were exposed to diethyl phthalate (DEP) and di-n-butyl phthalate (DBP) to investigate interactions between the algae and the pollutants. The 96-h 50% effect concentration (EC50) and no observed effect concentration (NOEC) based on cell density or chlorophyll a were in the order of Chaetoceros muelleri >Cylindrotheca closterium > Dunaliella salina for DEP, and C. closterium > C. muelleri > D. salina for DBP, respectively. Besides, EC50 value for C. closterium based on cell density increased over time in general, exhibiting strong adaptive ability to the pollutants. When singly existed, DEP was degraded more quickly at environmental relevant concentration (0.1 mg L-1) than at concentration of ≥EC20; DBP was degraded more quickly by C. closterium than by C. muelleri and D. salina. C. closterium was the most effective species to degrade the pollutants. When they were coexisted, degradation was inhibited by each other at environmental relevant concentration, while bioconcentration percentages of the pollutants increased, indicating that DEP-DBP mixture would be more problematic to marine organisms.

Phytoremediation of PAH-contaminated sediments with different organic matter contents by Potamogeton crispus L.

In this study, phytoremediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sediments with different contents of sediment organic matter (SOM) by Potamogeton crispus L. was investigated to find out the key factor controlling PAH dissipation enhancements. After a 36-day experiment, dissipation ratios of phenanthrene and pyrene in sediments were improved by P. crispus but decreased with increasing SOM content, no matter with or without P. crispus. Furthermore, the results of polyphenol oxidase activity and PAH bioavailability showed that they were both higher in planted treatments than in unplanted treatments. Finally, the enhancements of rapidly desorbing fraction (i.e., the main portion to be readily biodegradable) in sediments with SOM contents of 1.20%, 3.14%, and 5.08% by plants were 20.0%, 40.7%, and 66.7% for phenanthrene, 22.2%, 36.8%, and 58.8% for pyrene, respectively, which is consistent with the change trends of dissipation enhancements of the contaminants in sediments. However, there was no significant correlation between the enhancement of polyphenol oxidase activity and the dissipation enhancement. These results suggested that the improved bioavailability by P. crispus should be the key factor leading to the dissipation enhancements of the contaminants in sediments with different SOM contents, which provides essential information for phytoremediation of PAH-contaminated sediments.

[Fingerprint and spectrum-effect relationships on Tripterygium glycosides preparation].

Tripterygium glycosides preparation which extracted from the traditional Chinese herb Tripterygium wilfordii (TWHY), was widely used to treat the autoimmune diseases. Previous works demonstrated that TWHF had potent anti-inflammatory and immunosuppressive properties. But the different quality and high incident rate of side effects of different manufactures inhibited its clinical application. Since TWHF had been generally known to play a therapeutical effect by synergism of multiple constituents, it was necessary to build the relationship between the HPLC fingerprint and bioactivity so as to ensure the quality safety and efficacy. The HPLC fingerprint showed that description and content of peaks from different manufactures were diverse. Only 11 common peaks were found. In this study, mice spleen cells stimulated by Con A were used to test the proliferation inhibition bioactivity of TWHF preparations, which were incubated with 30, 15, 7.5, 3.75, 1.88 and 0.94 mg x L(-1) TWHF preparations for 48 h. The results showed that mice spleen cells proliferation was inhibited by all TWHF preparations significantly compared with the control group, which suggested the TWHF preparations showed immune suppress activity. The TWHF preparations from 7 manufacture showed different IC50 value, which might belong to different contents which showed in the HPLC fingerprint. Moreover, a relationship between the HPLC fingerprint and the bioactivity were established to identify important constituents by grey relational analysis (GRA). The result showed that all the contents were relative with the IC50, especially No. 5 and 10 peaks, but No. 1 peak, which was proved to be triptolide, had few contribute to the inhibition of mice spleen cells proliferation. The study of relationship between the HPLC fingerprint and the IC50 by GRA could help to investigate mechanism of bioactive and provide an evidence for the quantification of multi-constituents.

Head-to-head comparison of dipeptidyl peptidase-IV inhibitors and sulfonylureas - a meta-analysis from randomized clinical trials.

BACKGROUND: Dipeptidyl peptidase-IV (DPP-4) inhibitors and sulfonylureas are two important second-line anti-diabetic agents. The objective of this research was to evaluate the efficacy and safety of DPP-4 inhibitors compared with sulfonylureas by meta-analytic approach of available randomized studies. METHODS: We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials databases up to 30 June 2013, collecting all randomized clinical trials with a treatment duration of ≥18weeks. Data on glycated haemoglobin (HbA1c ), body weight, hypoglycaemia, total adverse events, and cardiovascular events were retrieved and analysed. RESULTS: The analysis included 12 randomized studies comprising 10 982 patients with type 2 diabetes mellitus. On the basis of meta-analysis, sulfonylureas lowered HbA1c significantly more than DPP-4 inhibitors with weighted mean difference (WMD) of 0.105 [95% confidence interval (CI) 0.103 to 0.107]. The results were consistent in trials with longer (>32 weeks) or shorter (≤32 weeks) duration; however, DPP-4 inhibitors showed greater reduction in HbA1c compared with the second-generation sulfonylureas and in patients with baseline eGFR < 50 mL/min/1.73 m(2) . Patients treated with DPP-4 inhibitors are less likely to achieve HbA1c < 7% compared with sulfonylureas [Mantel-Haenszel odds ratio (MH-OR) 0.91; 95% CI 0.84 to 0.99]. DPP-4 xinhibitors were associated with a reduction in body weight (WMD -1.652; 95% CI -1.658 to -1.646) and lower risk of hypoglycaemia (MH-OR, 0.13; 95% CI 0.11 to 0.16), total adverse events (MH-OR, 0.79; 95% CI 0.72 to 0.87), and cardiovascular events (MH-OR, 0.53; 95% CI 0.32 to 0.87) compared with sulfonylureas. CONCLUSION: Although DPP-4 inhibitors are less efficacious compared with sulfonylureas, they demonstrate a beneficial effect on body weight, episodes of hypoglycaemia, and total adverse events.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

A ratiometric fluorescence sensor with different responsive modes based on carbon dots-embedded Tb-MOFs for the determination of norfloxacin and levofloxacin.

Norfloxacin (NOR) and levofloxacin (LEV) are the two most frequently used fluoroquinolones (FQs) in clinic. Their residues seriously endanger the ecosystem and human health. Due to their similarity in structure and properties, it is urgent to develop an efficient and sensitive strategy for detection and differentiation. Herein, we synthesized a novel ratiometric fluorescent sensor for the first time by combining N, S co-doped carbon dots (CDs) and the precursors of Tb-MOFs through a facile one-pot method. The introduction of CDs effectively facilitated the energy transfer between Tb3+ and FQs, overcoming the limitation that single Tb-MOFs could not identify similar antibiotics. Specifically, the presence of NOR resulted in reverse signal response through the inner filter effect and antenna effect. The synergistic effect of these two mechanisms contributed to achieving signal amplification accompanied by a distinguishable color transition. The limit of detection (LOD) was 0.036 µM. Different from NOR, the addition of LEV reduced the electron density of the system, weakened the coordination ability of Tb3+ with LEV, and induced a single signal response with Tb3+ fluorescence intensity as a reference signal (LOD = 0.383 µM). Furthermore, the method proved to be rapid and visual, allowing for the straightforward analysis of FQs residues in water, food matrices, and biological samples with satisfactory precision. By integrating N, S-CDs@Tb-MOFs with flexible substrates, the paper-based sensor facilitated the visual quantitative determination of FQs by reading RGB values. The developed sensor presents a promising strategy for the identification and real-time monitoring of antibiotics.

Surge in Mycoplasma Pneumoniae infection and Respiratory Viruses Co-infection in Children With Community-Acquired Pneumonia in the Post-Pandemic.

Purpose: During the COVID-19 pandemic, multifaceted non-pharmaceutical interventions have not only reduced the transmission of SARS-CoV2 but also affected the prevalence of other respiratory pathogens. With the lifting of many restrictions, a surge in cases of pneumonia in children has been reported in many hospitals in China. The study assessed the changes in pathogen and symptoms of children with community-acquired pneumonia (CAP) before and after the adjustments of prevention and control measures of epidemic and provided recommendations for CAP in children. Patients and methods: Children diagnosed with CAP were enrolled in the study from 2022 to 2023. A cross-sectional retrospective study was conducted in a general hospital. We analyzed the data about demographic data, clinical symptoms, pathogens, and medical treatments. The Chi-square and Mann-Whitney U-test were used to assess the statistical significance of groups. Results: We studied 1103 children, 339 in 2022 and 764 in 2023. Compared with children in 2022, more children were diagnosed with CAP in 2023 and these children had a higher body temperature and levels of CRP and PCT, which indicated these children got severe inflammation. The positive rate of the pathogen was also higher in 2023, especially the detective rate of Mycoplasma pneumoniae. The number of children infected with more than two pathogens was higher in 2023, especially those co-infected with the virus and M. Pneumoniae. Concerning the medicine therapy, the usage of ß-lactam antibiotics, Macrolide antibiotics, and antiviral drugs kept rapid growth. Conclusion: After the adjustment of epidemic prevention and control policies in 2023, more children got CAP with severe clinical symptoms, and more antibiotics and antiviral drugs were used. Further study is needed to explore the reasons for the increase in children with CAP and to explore the rationality of treatment.

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.

[Preparation of fluorescent carbon dots by flow-assisted melt polymerization for tetracycline detection in medical wastewater].

Tetracycline (TC) is one of the most important therapeutic drugs that is widely used in hospitals. However, its harmful effects on human health and various ecosystems cannot be ignored. Owing to its poor metabolic activity and low biodegradability, TC commonly discharges as the parent compound and accumulates readily in sludges and soils by precipitation from wastewater, which can induce the evolution of antibiotic-resistant bacteria; therefore, it has been listed as one of the new pollutants with potential ecotoxicological risk. The control measures and environmental management of TC pollutants in environmental water samples require precise determination of TC pollutant concentrations. Carbon dots (CDs) are an emerging type of fluorescent material with numerous advantages such as easy preparation, low cost, low toxicity, and good biocompatibility. Consequently, they have attracted widespread attention in the field of TC detection. Herein, we synthesized TE-CDs with good blue-fluorescence performance via flow-assisted melt polymerization using tricarboxylic acid and ethylenediamine as raw precursors. The morphology and structure of the prepared TE-CDs were characterized. The transmission electron microscopy (TEM) results showed that the prepared TE-CDs were well dispersed, with an average diameter of (2.43±0.48) nm. The X-ray diffraction (XRD) results showed that the TE-CDs had an amorphous carbon structure. Infrared spectroscopy and X-ray photoelectron spectroscopy (XPS) characterizations showed that the surface of the TE-CDs was rich in hydrophilic groups, such as amino, hydroxyl, and carboxyl groups, which indicated that TE-CDs had good water solubility and were advantageous for detecting TC in medical wastewater. Subsequently, the optical properties of the TE-CDs were investigated. The fluorescence emission spectra of the TE-CDs were recorded at various excitation wavelengths. The emission spectra of the TE-CDs exhibited excitation wavelength dependence and when the excitation wavelength changes from 300 nm to 400 nm, their fluorescence intensity decreased to varying degrees. The TE-CDs exhibited optimal fluorescence intensity at an excitation wavelength of 368 nm, while the emission wavelength was 448 nm. TC could effectively quench the blue fluorescence of the CDs, and by utilizing this property, the detection of TC concentration could be achieved. After the addition of TC, the fluorescence of the system immediately reached an extreme value, and no significant change was observed within 10 min. An incubation time of 20 s was selected to obtain precise results. Additionally, the TE-CDs exhibited stable fluorescence intensity over a wide pH range. The fluorescence stability of the TE-CDs was investigated, and no significant change in fluorescence intensity was observed after standing for 10 d, indicating that the prepared TE-CDs had excellent fluorescence stability. The fluorescence intensity of the TE-CDs decreased to varying degrees within the range of 2-200 mg/L TC until complete quenching occurred. TC mass concentration in the range of 4-20 mg/L showed a good linear relationship (R2=0.9978) with the fluorescence quenching intensity of the TE-CDs. The limit of detection was 0.2 mg/L. A preliminary investigation was undertaken to explore the quenching mechanism of the TE-CDs fluorescence by TC. Upon addition of TC, a significant reduction in the fluorescence lifetime of the TE-CDs was observed. During the quenching process, no new substances were observed by UV absorption spectroscopy. Additionally, no significant changes in the 1H NMR spectra of the TE-CDs were noted before and after the addition of TC, indicating the absence of an interaction between the TE-CDs and TC. Therefore, the quenching mechanism may involve dynamic quenching. The selectivity and anti-interference ability of the developed method were evaluated; in the presence of interfering substances, TC quenched the fluorescence of the TE-CDs, indicating that the TE-CDs had good selectivity and anti-interference performance towards TC. The method was applied to the quantitative detection of TC in medical wastewater, with recoveries of 96.5%-119.8% and relative standard deviations of 0.8%-2.6%. In conclusion, the analytical performance of the proposed method is comparable with that of previously reported detection methods; moreover, the method has the advantages of low operational cost, simple preparation process, time-saving, and good repeatability. Therefore, the TE-CDs can be used as chemical sensors for the detection of TC in medical wastewater and have good practical applications.

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.

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.

Urchin-like NiCo-based bimetallic hydroxide decorated with DOPO as highly hydrophobic flame retardant for remarkably reducing fire hazard of poly (L-lactic acid).

Designing high-performance flame retardants for poly (L-lactic acid) (PLA) materials and exploring a simple and scalable strategy have been hot topics in research. In this work, a novel and highly efficient flame retardant, that is, 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) decorated urchin-like NiCo-based bimetallic hydroxide (NiCo-BH@DOPO), was synthesized and incorporated into PLA to prepare PLA and NiCo-BH@DOPO (PLA/NiCo-BH@DOPO) composite. Benefiting from the DOPO organic modification, NiCo-BH@DOPO had superb hydrophobicity and presented excellent dispersion in the PLA matrix. When 20 wt% NiCo-BH@DOPO was added, the LOI value of PLA/NiCo-BH@DOPO composites reached 33.2 %, passed the V-0 level of UL-94 grade, and its maximum peak heat release rate (PHRR) and total heat release (THR) were reduced by 13.2 % and 17.3 %, respectively, compared with PLA/NiCo-BH composites. Furthermore, the residue of PLA/NiCo-BH@DOPO at 800 °C reached 19.8 wt% and the T10% (temperature at 10 % weight loss) increased by 33 °C. More importantly, the residual PLA/NiCo-BH@DOPO char exhibits a significantly reduced presence of large cracks compared to PLA/NiCo-BH, indicating a more compact formation of residual char. NiCo-BH@DOPO endowed PLA with outstanding flame retardancy, thermal stability and carbonization properties, which were owing to the multi-coordinating effect transition metal (NiCo-BH) catalyzed the char formation to form a char layer barrier and DOPO free radicals captured to inhibit the combustion reaction chain. This investigation provided a facile strategy for the novel multi-function NiCo-based bimetallic hydroxide flame retardant, expanding NiCo-BH potential applications in PLA.

Efficient Biodegradation of the Neonicotinoid Insecticide Flonicamid by Pseudaminobacter salicylatoxidans CGMCC 1.17248: Kinetics, Pathways, and Enzyme Properties.

Nitrile-containing insecticides can be converted into their amide derivatives by Pseudaminobacter salicylatoxidans. N-(4-trifluoromethylnicotinoyl) glycinamide (TFNG-AM) is converted to 4-(trifluoromethyl) nicotinoyl glycine (TFNG) using nitrile hydratase/amidase. However, the amidase that catalyzes this bioconversion has not yet been fully elucidated. In this study, it was discovered that flonicamid (FLO) is degraded by P. salicylatoxidans into the acid metabolite TFNG via the intermediate TFNG-AM. A half-life of 18.7 h was observed for P. salicylatoxidans resting cells, which transformed 82.8% of the available FLO in 48 h. The resulting amide metabolite, TFNG-AM, was almost all converted to TFNG within 19 d. A novel amidase-encoding gene was cloned and overexpressed in Escherichia coli. The enzyme, PmsiA, hydrolyzed TFNG-AM to TFNG. Despite being categorized as a member of the amidase signature enzyme superfamily, PsmiA only shares 20-30% identity with the 14 previously identified members of this family, indicating that PsmiA represents a novel class of enzyme. Homology structural modeling and molecular docking analyses suggested that key residues Glu247 and Met242 may significantly impact the catalytic activity of PsmiA. This study contributes to our understanding of the biodegradation process of nitrile-containing insecticides and the relationship between the structure and function of metabolic enzymes.