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Background and Objectives: The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, but their long-term use raises concern about intraocular pressure (IOP). This study uses SHapley Additive exPlanations (SHAP) to improve the interpretability of machine learning (ML) model predicting end IOP, offering clinicians explainable insights for personalized patient management. Materials and Methods: This retrospective study analyzed data from 1191 individual eyes of 639 boys and 552 girls with myopia treated with atropine. The average age of the whole group was 10.6 ± 2.5 years old. The refractive error of spherical equivalent (SE) in myopia degree was base SE at 2.63D and end SE at 3.12D. Data were collected from clinical records, including demographic information, IOP measurements, and atropine treatment details. The patients were divided into two subgroups based on a baseline IOP of 14 mmHg. ML models, including Lasso, CART, XGB, and RF, were developed to predict the end IOP value. Then, the best-performing model was further interpreted using SHAP values. The SHAP module created a personalized and dynamic graphic to illustrate how various factors (e.g., age, sex, cumulative duration, and dosage of atropine treatment) affect the end IOP. Results : RF showed the best performance, with superior error metrics in both subgroups. The interpretation of RF with SHAP revealed that age and the recruitment duration of atropine consistently influenced IOP across subgroups, while other variables had varying effects. SHAP values also offer insights, helping clinicians understand how different factors contribute to predicted IOP value in individual children. Conclusions: SHAP provides an alternative approach to understand the factors affecting IOP in children with myopia treated with atropine. Its enhanced interpretability helps clinicians make informed decisions, improving the safety and efficacy of myopia management. This study demonstrates the potential of combining SHAP with ML models for personalized care in ophthalmology.

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 Tb 3+ 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 Tb 3+ with LEV, and induced a single signal response with Tb 3+ 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effective models using real-world data, which may include missing values and various integrated datasets., Method: This retrospective study analyzed data from 679 patients diagnosed with degenerative dementia at Fu Jen Catholic University Hospital, who were evaluated by neurologists, psychologists and followed for over two years. Predictive variables were categorized into demographic (D), clinical dementia rating (CDR), mini-mental state examination (MMSE), and laboratory data value (LV) groups. These categories were further integrated into three subgroups (D-CDR, D-CDR-MMSE, and D-CDR-MMSE-LV). We utilized the extreme gradient boosting (XGB) model to rank the importance of variables and identify the most effective feature combination via a step-wise approach., Result: The D-CDR-MMSE-LV model combination showed robust performance with an excellent area under the receiver operating characteristic curve (AUC) and the highest sensitivity value (84.66). Employing both demographic and neuropsychiatric variables, our prediction model achieved an AUC of 83.74. By incorporating additional clinical information from laboratory data and applying our proposed feature selection strategy, we constructed a model based on eight variables that achieved an AUC of 85.12 using the XGB technique., Conclusion: We established a machine-learning model to monitor the progression of dementia using a limited, real-world clinical dataset. The XGB technique identified eight critical variables across our integrated datasets, potentially providing clinicians with valuable guidance., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Human Investigation Committee (IRB) of Fu Jen Catholic University Hospital approved this study (IRB No. FJUH112280). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Informed consent: This research study was conducted retrospectively from electrical medical data of regularly clinical records. We consulted extensively with the IRB of Fu Jen Catholic University Hospital (IRB No. FJUH112280) who determined that our study did not need informed consent., (© 2024. The Author(s).)

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 ( R 2 =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 1 H 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.

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 T 10% (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., Competing Interests: Declaration of competing interest We would like to submit the enclosed manuscript entitled “Urchin-like NiCo-based bimetallic hydroxide decorated with DOPO as highly hydrophobic flame retardant for remarkably reducing fire hazard of polylactic acid”, which we wish to be considered for publication in “International Journal of Biological Macromolecules”. No conflict of interest exists in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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 β-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., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Chi et al.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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 (K oc ) varied by soil. When biochar was freshly mixed with soil, the K oc 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 K oc value was significantly decreased by the addition of W4 but increased by the addition of W7 in general. After aging, most of the K oc value decreased. The greatest decrease in K oc 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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.

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

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.

Purpose: We explored the inhibition ability of linezolid/fosfomycin combination against biofilms of vancomycin-resistant Enterococcus faecium (VRE fm ) and tried to provide a theoretical basis for the treatment of VRE fm biofilm-associated infections., Methods: Four clinical isolates of VRE fm (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 VRE fm 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 VRE fm 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 VRE fm than linezolid alone., Competing Interests: The authors report no conflicts of interest in this work., (© 2023 Chi et al.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CH declared a shared affiliation, with no collaboration with the authors to the handling editor at the time of the review. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Chang, Yeh, Chiu, Liu, Chen, Jhou and Lu.)

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-NH 2 MOFs (UiO-66-NH 2 @RhB). The as-prepared probe showed dual-emission characteristics under a single excitation wavelength. The fluorescence intensity of UiO-66-NH 2 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors report no conflicts of interest in this work., (© 2023 Huang et al.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tsai, Jhou, Liu, Fang and Lu.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chang, Liu, Lu, Chiu and Lin.)

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 meso‑eutrophic 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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 K oc values of PE-biochar-soil mixtures at C e = 0.005 C s 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 C e = 0.05 C s and 0.5 C s , the K oc 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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.

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.

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.

As the first step of methylmercury (MeHg) entry into the aquatic food webs, MeHg uptake by phytoplankton is crucial in determining the final human MeHg exposure risks. MeHg availability to plankton is regulated by dissolved organic matter (DOM) in the water, while the extent of the impacts can vary largely based on the sources of DOM. Here, we investigated impacts of DOM sources on MeHg bioconcentration by three freshwater phytoplankton species (i.e. S. quadricauda, Chlorella sp., Microcystis elabens) in the laboratory system. We found that algae-derived DOM would prohibited the cellular MeHg bioconcentration by a percent up to 77-93%, while the soil-derived DOM didn't show similar inhibition effects. DOM characterization by the excitation‒emission matrices, Fourier transform infrared spectrum, ultra‒high performance liquid chromatography‒tandem quadrupole time of flight mass spectrometry shown that the molecular size of S-containing compound, rather than thiol concentration, has played a crucial role in regulating the MeHg uptake by phytoplankton. Climate change and increasing nutrient loadings from human activities may affect plankton growth in the freshwater, ultimately changing the DOM compositions. Impacts of these changes on cellular MeHg uptakes by phytoplankton should be emphasized when exploring the aquatic Hg cycling and evaluating their risks to human beings and wild life., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Importance: The current standard of care for the treatment of small cell lung cancer (SCLC) is concurrent chemoradiation for patients with limited-stage SCLC (LS-SCLC) and chemoimmunotherapy for extensive-stage SCLC (ES-SCLC). The backbone of chemotherapy regimens in both is a platinum-etoposide doublet: cisplatin is traditionally the preferred platinum agent in the curative intent setting, whereas carboplatin is preferred in ES-SCLC because of its favorable toxicity profile., Objective: To determine whether cisplatin is associated with better survival outcomes than carboplatin in treating LS-SCLC and ES-SCLC., Design, Setting, and Participants: In this cohort study, data were compiled from the National Veterans Affairs Central Cancer Registry for patients with SCLC who received platinum-based multiagent chemotherapy between 2000 and 2020 for ES-SCLC and 2000 and 2021 for LS-SCLC. Only patients with pathologically confirmed cases of LS-SCLC who received concurrent chemoradiation and ES-SCLC who received chemotherapy were included., Main Outcomes and Measures: The primary end point was overall survival (OS). The secondary end points included OS by Eastern Cooperative Oncology Group performance status, age, and laterality. Interval-censored Weibull and Cox proportional hazard regression models were used to estimate median OS and hazard ratios (HRs), respectively. Survival curves were compared by a Wald test., Results: A total of 4408 SCLC cases were studied. Most patients were White (3589 patients [81.4%]), male (4252 [96.5%]), and non-Hispanic (4142 [94.0%]); 2262 patients (51.3%) were 60 to 69 years old, followed by 1476 patients (33.5%) aged 70 years or older, 631 patients (14.3%) aged 50 to 59 years, and 39 patients (0.9%) aged 30 to 49 years. Among 2652 patients with ES-SCLC, 2032 were treated with carboplatin-based therapy and 660 received cisplatin; the median OS was 8.45 months (95% CI, 7.75-9.20 months) for cisplatin and 8.51 months (95% CI, 8.07-8.97 months) for carboplatin (HR, 1.01; 95% CI, 0.91-1.12; P = .90). Subset analysis showed no survival difference between the 2 agents in different age or performance status groups except for patients aged 70 years and older, for whom the median OS was 6.36 months (95% CI, 5.31-7.56 months) for cisplatin and 8.47 months (95% CI, 7.79-9.19 months) for carboplatin (HR, 0.77; 95% CI, 0.61-0.96; P = .02). Multivariable analysis of performance status and age did not show a significant difference in survival between the 2 groups (HR, 0.96; 95% CI, 0.83-1.10; P = .54). Of 1756 patients with LS-SCLC, 801 received carboplatin, and 1018 received cisplatin. The median OS was 26.92 months (95% CI, 25.03-28.81 months) for cisplatin and 25.58 months (95% CI, 23.64-27.72 months) for carboplatin (HR, 1.04; 95% CI, 0.94-1.16; P = .46). The median OS was not significantly different between 2 agents according to cancer stage (I-III), performance status, and age groups. A multivariable analysis of factors associated with OS accounting for stage (I-III), performance status, and age did not demonstrate a significant difference in survival between carboplatin and cisplatin in patients with LS-SCLC (HR, 0.995; 95% CI, 0.86-1.15; P = .95)., Conclusions and Relevance: Cisplatin is not associated with a survival advantage over carboplatin among patients with either ES-SCLC or LS-SCLC, irrespective of performance status and age. The favorable toxicity profile of carboplatin and comparable OS support its use in both LS-SCLC and ES-SCLC in clinical practice and may allow more room for combination with novel treatment strategies in clinical trials.

Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline., Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods-stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)-to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range., Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension., Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research., Competing Interests: All authors declare that there are no conflict of interest.

This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.

The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

Objective: This study aimed to establish a prognostic stratified model of chemotherapy-based comprehensive treatment for patients with locoregional recurrent nasopharyngeal carcinoma (lrNPC), to help individualized treatment decision-making., Materials and Methods: This study retrospectively reviewed patients with lrNPC who received chemotherapy-based comprehensive treatment from January 1, 2010, to December 31, 2018. A total of 422 eligible patients were divided into test (n = 338) and validation (n = 84) cohorts. A LASSO cox regression model was used to identify significant prognostic factors for overall survival (OS) in the test cohort. A nomogram was then developed based on a combined consideration of clinically meaningful prognostic factors and statistically significant prognostic factors. The performance of the nomogram was assessed with Harrell's concordance index (C-index) and calibration plots., Results: Five significant factors were identified: age, albumin (ALB), T stage after recurrent (rT), neutrophil to lymphocyte ratio (NLR), and systematic immune-inflammation index (SII). The nomogram was established with these five factors. C-index was 0.636 in the test cohort and 0.610 in the validation cohort. The calibration curves for the OS rate at 3, and 5 years showed an excellent agreement in both cohorts. In addition, the corresponding risk classification system successfully classified patients into low- and high-risk groups and performed well in stratification (P < 0.001)., Conclusions: The nomogram shows well prognostic performance for lrNPC patients receiving chemotherapy-based comprehensive treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wei, Wang, Li, Wang, Liu, Huang, Wang, Tao and Xia.)

Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques-naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)-were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.

Objective: In this 3-year longitudinal cohort study, we aimed to evaluate the evolution of overactive bladder in female community residents aged 40 years and above in central Taiwan and identify its risk factors., Materials and Methods: Female community residents aged 40 years and above were invited to participate in this study and fill out a yearly Overactive Bladder Symptom Score (OABSS) questionnaire over a 3-year period. A woman was defined to have OAB if the total OABSS was ≧4 and urgency score was ≧2. At the end of the third year, the incidence, remission, persistence, and relapse of OAB in these community residents were calculated. A novel statistical analysis technique, machine learning with data mining, was applied to examine its use in this field. Five machine learning models were used to predict the risk factors associated with persistent OAB and the results were compared with the conventional logistic regression model., Results: In total, 1469 female residents were included in the first year and 1290 (87.8%) women completed the questionnaires for all 3 years. The prevalence of OAB was 20.2% (n = 260). The second- and third-year incidence rates of OAB were 13.5% and 7.1%. The remission rates were 39.6% and 44.3%. Twenty-two percent of the women reported relapse of OAB in the third year. The two-year OAB persistence rate was 43.8%. For the prediction of risk factors for persistent OAB, the multivariable logistic regression model had better predictive accuracy (AUC = 0.664) than the five machine learning models. Age ≧ 60 was associated with persistent OAB (OR 2.8; 95% CI: 1.34-5.89, P = 0.002)., Conclusion: The yearly incidence, remission, and persistence rates of OAB were high in female community residents aged 40 years and above in central Taiwan. Older women had a higher risk of persistent OAB symptoms in this 3-year longitudinal cohort study., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2022. Published by Elsevier B.V.)

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

Early detection of chronic kidney disease (CKD) for high-risk population adults is very important. It has a common risk factor and causal relationship with chronic diseases such as diabetes, hypertension and cardiovascular disease etc. The results of this study provide that for early high-risk factors detection in CKD healthy population can be used by home care to recommend adjuvant treatment.

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur.

Purpose: As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations., Methods: This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors., Results: The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods., Conclusion: The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research., Competing Interests: The authors report no conflicts of interest in this work., (© 2021 Chiu et al.)

By rational assembly of polytorsional-amide [ N , N '-bis(4-methylenepyridin-4-yl)-1,4-naphthalene dicarboxamide (L)] and polytorsional-carboxylates [H 2 ADI = adipic acid, H 2 PIM = pimelic acid, H 2 SUB = suberic acid], three new Cd-based coordination polymers (CPs) C 30 H 30 CdN 4 O 7 (1), C 31 H 32 CdN 4 O 7 (2) and C 31.03 H 30.55 CdCl 0.24 N 4 O 5.52 (3) were successfully synthesized. CPs 1-2 and 3 are 2D networks and a 3D framework, which all display 3,5-connected topologies with different structural details. The effects of carboxylates with different carbon chains on the structure of the complexes were studied. Fluorescence experiments show that CPs 1-3 have good multi-functional sensing ability for metal cations (Fe 3+ ), anions (MnO 4 - , CrO 4 2- , Cr 2 O 7 2- ) and organochlorine pesticides (2,6-dichloro-4-nitroamine) with good anti-interference and recyclable characteristics. The possible sensing mechanism is also investigated in detail., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients.

Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch's membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

Sweet syndrome (SS), also referred as acute febrile neutrophilic dermatosis, is an inflammatory process characterised by the abrupt appearance of erythematous papules or nodules with predominant neutrophilic infiltration in the dermis. Fever and neutrophilia are common presenting features. However, extracellular manifestations, including ocular and musculoskeletal, may occur. SS is divided into three subtypes: classical (or idiopathic), malignancy associated and drug induced. Medication-induced subtype accounts for up to 26% of cases. In recent years, emerging evidence has showed that SS may also occur in neutropenic patients who underwent induction for acute myeloid leukemia (AML). The identification of FMS-like tyrosine kinase 3 (FLT3) gene mutation in approximately 30% of patients with AML has promoted the targeted therapy with FLT3-internal tandem duplication (ITD) inhibitors. Midostaurin, a recently Food and Drug Administration-approved medication for FLT3-ITD-positive AML, was reported once as cause for SS. We report a midostaurin-induced SS with neutropenia in a patient following induction chemotherapy of AML., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Fludarabine 30 mg/m 2 /d × 5 and melphalan 140 mg/m 2  × 1 (Flu-Mel140) is a commonly used reduced-intensity conditioning regimen. We hypothesized that addition of 200cGy total body irradiation (TBI) to Flu-Mel140 may improve antitumor activity and transplant outcomes. Primary objectives was overall survival (OS) at 3 years. Secondary objectives were to assess the cumulative incidences of acute and chronic GVHD, relapse-free survival (RFS), relapse rate, and nonrelapse mortality (NRM). We retrospectively evaluated outcomes of patients receiving Flu-Mel140-TBI followed by HLA-matched donor allogeneic hematopoietic stem cell transplantation (alloSCT) using peripheral blood stem cells. Eighty-one patients (median age, 58 years) underwent alloSCT between January 2008 and December 2018. Thirty-one percent of patients had a prior transplant, 32% had high or very-high disease risk index, and the donor was unrelated in 70% of patients. Grade 3 to 4 regimen-related toxicities were mucositis (37%), cardiac toxicity (17%), and renal toxicity (10%). The cumulative incidence of grade III to IV acute GVHD at day +100 was 24.7% and chronic GVHD at 1 year was 51.3%. Median follow-up for survival was 6.1 years. At 3 years, OS was 39.81%, RFS was 31.47%, and relapse rate was 30.5%. One-year NRM was 29.9%. Patients undergoing first transplantation experienced improved OS compared with second or beyond (63.08% versus 42.31%, P = .02). After adjusting for disease subtypes, age (≤55 versus 55), comorbidity index (CI), number of transplant and GVHD prophylaxis, multivariable analysis did not demonstrate any survival difference among disease subtypes. High CI (≥3) was predictive of adverse OS and NRM, whereas older age (>55 years) was associated with high NRM. Our study shows that Flu-Mel140-TBI seems feasible and provides durable disease control. Addition of TBI did not appear to improve outcomes compared to previously published reports of Flu-Mel140. Considerable NRM could result from the inclusion of patients with older age and prior transplants., (Copyright © 2021 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Hemophagocytic lymphohistiocytosis (HLH) is a systemic inflammatory syndrome of inappropriate immune cell activation which can be rapidly fatal if not recognized and treated. Here we discuss a case of a 26-year-old male with HIV on antiretroviral therapy who presented with sepsis secondary to soft tissue infection and ultimately progressed to multi-organ dysfunction despite broad-spectrum antibiotics and an improvement in soft tissue infection. Continued fever and pancytopenia without an explanation found during additional infectious and rheumatologic testing eventually led to bone marrow biopsy and laboratory criteria consistent with HLH. Although pancytopenia is a common finding in patients with HIV, here it marked a more rapidly progressing and fatal disease, HLH. Here we highlight the difficulty in identifying and diagnosing this rare condition, including a discussion of the characteristics, outcomes, underlying etiologies, and treatment of HLH in patients with HIV., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Yazdanpanah et al.)

This study investigated the impact of wheat straw biochars pyrolyzed at temperatures of 100-700 ℃ (BC100-BC700) on biodegradation of phenanthrene in soils. During a 42-day experiment, biochar amendment reduced the biodegradation ratio of phenanthrene in soils by no change-77.0%. The biodegradation ratio decreased with the increase of pyrolysis temperature from 100 to 400 ℃ and then increased with the increase of pyrolysis temperature from 400 to 700 ℃, exhibiting a U-shape. Meanwhile, desorbing fraction of phenanthrene extracted by n-butanol declined with increasing pyrolysis temperature. Biochar-derived dissolved organic carbon (DOC) obviously influenced the soil DOC contents which were negatively correlated with the total relative abundances of dominant polycyclic aromatic hydrocarbon (PAH)-degraders. These results indicated that in soils amended with biochars pyrolyzed at low temperatures (i.e. 100-400 ℃), both the reduced bioavailability of phenanthrene and the reduced PAH-degrader abundance resulted in decreasing phenanthrene degradation with pyrolysis temperature. In soils amended with biochars pyrolyzed at high temperatures (i.e. 500-700 ℃; HT-biochars), two possible reasons contribute to increasing phenanthrene degradation with pyrolysis temperature: (1) high sorbed-phenanthrene concentration due to large specific surface area and high aromaticity of the biochars, and (2) the increased dominant PAH-degrader abundance for the removal of sorbed-phenanthrene due to the impact of HT-biochars on soil properties (mainly on DOC content)., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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.

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.