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Purpose: The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels. Methods: Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability. Results: Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models' robustness and stability. Conclusion: This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community. Methods: In the study, 412 cognitively normal older adults aged over 65  years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline. Results: A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (p  <  0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, antisaccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC  =  0.969). Conclusion: The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community. [ABSTRACT FROM AUTHOR]

Background: Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. We aimed to develop an efficient, clinically applicable, and noninvasive method for the early screening of BCa by detecting specific serum miRNA levels. Methods: A mixed‐cohort (including BCa, 12 different other cancers, benign disease patients, and health population) study was conducted using a sample size of 16,189. Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. The performance of the model was evaluated using receiver operating characteristic curve and decision curve analysis on the testing dataset, and subsequently, the model with the best predictive power was selected. Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. Results: The BCaS3miR model, utilizing only three serum miRNAs (miR‐6087, miR‐1343‐3p, and miR‐5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miR consistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivity and specificity levels. The area under the curve was 0.990 (95% CI: 0.984–0.991), 0.964 (95% CI: 0.936–0.984), and 0.917 (95% CI: 0.836–0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accuracy in various clinical subgroups of BCa. In addition, we developed a BCa screening scoring model (BCaSS) based on the levels of miR‐1343‐3p/miR‐6087 and miR‐5100/miR‐6087. The screening effect of BCaSS is investigated and the findings indicate that it has predictability and distinct advantages. Conclusions: Using a mixed cohort with the largest known sample size to date, we have developed effective screening models for BCa, namely BCaS3miR and BCaSS. The models demonstrated remarkable screening accuracy, indicating potential for the early detection of BCa. [ABSTRACT FROM AUTHOR]

Purpose: To validate the Graves ophthalmopathy quality of life (GO-QOL) questionnaire in screening DON and to construct an effective model. Methods: A total of 194 GO patients were recruited and divided into DON and non-DON (mild and moderate-to-severe) groups. Eye examinations were performed, and quality of life was assessed by the GO-QOL questionnaire. The random forest, decision tree model, receiver operator characteristic (ROC) curve, accuracy and Brier score were determined by R software. Results: In GO-QOL, age, best corrected visual acuity (BCVA), exophthalmos, CAS, severity, and Gorman score were found to be factors related to visual function scores. On the appearance scale, gender, duration of GO, BCVA, exophthalmos, CAS and severity of GO were relevant. Both the visual function scores and appearance scores were significantly lower in DON groups than in non-DON groups (33.18 ± 24.54 versus 81.26 ± 17.39, 60.08 ± 24.82 versus 76.14 ± 27.56). The sensitivity, specificity, and AUC of the visual function scores were 91.1%, 81.7% and 0.939, respectively Visual function scores were used to construct a decision tree model. The sensitivity, specificity, and AUC of the model were 92.9%, 88.0% and 0.941, respectively, with an accuracy of 89.7% and a Brier score of 0.024. Conclusions: Visual function scores were qualified as a screening method for DON, with a cutoff point of 58. A multifactorial screening model based on visual function scores was constructed. Key messages: What is known • Generally, GO-QOL questionnaire is an indicator of treatment efficacy in GO and provides an evaluation of the impact of disease on daily lives in GO patients including both visual function and appearance. What is new • Visual function scores of GO-QOL questionnaire is valid in screening DON with a cut-off point as 58 according to ROC curve. On this basis, a simple model for DON was constructed by machine learning in our study. • This study may expand the clinical application of GO-QOL (especially for visual function scores) in DON and boost diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Background: The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China. Methods: We collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model. Results: The average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening. Conclusion: This study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD. [ABSTRACT FROM AUTHOR]

Abstract Background The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China. Methods We collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model. Results The average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening. Conclusion This study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD.

Objective To establish a simple model for arteriosclerosis (AS) screening to provide a viable tool for the timely identification of AS risk among residents aged 40-65 years. Methods Data were obtained from the Sleep and Chronic Diseases Program in Fuquan City. The original dataset was divided into a training subset and a validation subset (80%: 20%). LASSO and logistic regression models were used to screen variables, perform multivariate regression analyses. Internal validation was performed using the Bootstrap method. Nomogram Plot was constructed, and risk score thresholds were determined based on ROC curves to classify high-risk populations. Results RS Model was established to include age, gender, napping, sleep efficiency, sleep disorders, hypertension and diabetes, with AUC = 74.80% and a model risk score threshold = 84.20. PHC Model was established to include age, gender, napping, sleep efficiency, systolic blood pressure, fasting blood glucose, and pulse variables, with AUC = 82.80% and a risk score threshold of 78.00. Decision curves showed that both models performed well in terms of calibration and actual benefits for health management. Conclusion The two AS screening models exhibit acceptable accuracy and differentiation. Therefore, it can be applied in residents' self-health management and in primary care organizations' screening work in a large scale. [ABSTRACT FROM AUTHOR]

Ammonium is the preferred N nutrition over nitrate for some plant species, but it is toxic to many other plant species and induces senescence at high concentrations. The duckweed Wolffia microscopica (Griff.) Kurz is the smallest and fast-growing angiosperm. It is highly sensitive to ammonium and has a short lifespan on media containing 0.5 mM or higher ammonia. This feature makes it a potential model plant to screen for anti-aging microorganisms. By co-culturing W. microscopica with endophytic microorgainisms isolated from rubber tree, we screened out an Aspergillus sclerotiorum strain ITBB2-31 that significantly increased the lifespan and the biomass of W. microscopica. Interestingly, both filter-sterilized and autoclaved exudates of ITBB2-31 increased the lifespan of W. microscopica cultures from 1 month to at least 7 months. Meanwhile, the exudates also showed strong anti-aging effects on cassava and the rubber tree leaves and increased chlorophyll contents by 50% - 350%. However, high contents of filter-sterilized exudates inhibited the growth of W. microscopica while extending its lifespan, indicating that there were heat-sensitive growth-inhibiting agents in the exudates as well. Comparative metabolome analysis of the filter-sterilized and autoclaved exudates revealed multiple heat-stable anti-aging and heat-sensitive growth-inhibiting compounds. Our results suggest that W. microscopica can be served as a rapid and efficient model plant to screen for plant anti-aging microorganisms.

ABSTRACT Background Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. We aimed to develop an efficient, clinically applicable, and noninvasive method for the early screening of BCa by detecting specific serum miRNA levels. Methods A mixed‐cohort (including BCa, 12 different other cancers, benign disease patients, and health population) study was conducted using a sample size of 16,189. Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. The performance of the model was evaluated using receiver operating characteristic curve and decision curve analysis on the testing dataset, and subsequently, the model with the best predictive power was selected. Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. Results The BCaS3miR model, utilizing only three serum miRNAs (miR‐6087, miR‐1343‐3p, and miR‐5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miR consistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivity and specificity levels. The area under the curve was 0.990 (95% CI: 0.984–0.991), 0.964 (95% CI: 0.936–0.984), and 0.917 (95% CI: 0.836–0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accuracy in various clinical subgroups of BCa. In addition, we developed a BCa screening scoring model (BCaSS) based on the levels of miR‐1343‐3p/miR‐6087 and miR‐5100/miR‐6087. The screening effect of BCaSS is investigated and the findings indicate that it has predictability and distinct advantages. Conclusions Using a mixed cohort with the largest known sample size to date, we have developed effective screening models for BCa, namely BCaS3miR and BCaSS. The models demonstrated remarkable screening accuracy, indicating potential for the early detection of BCa.

Abstract Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC–MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann–Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P

Abstract Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others.

Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC–MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann–Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P < 0.05). Conversely, serum levels of TG, CA19-9, and CEA were elevated (P < 0.05). Among the measured parameters, ApoA2 stands out for its strong correlation with the presence of CRC, showcasing exceptional screening efficacy with an area under the curve (AUC) of 0.957, a sensitivity of 85.71%, and a specificity of 93.33%. The screening model, integrating ApoA1, ApoA2, lithocholic acid (LCA), and CEA, attained an impressive AUC of 0.995, surpassing the diagnostic accuracy of individual lipids, bile acids, and tumor markers. CRC patients manifest noteworthy alterations in both blood lipids and bile acid profiles. A screening model incorporating ApoA1, ApoA2, LCA, and CEA provides valuable insights for detecting CRC. [ABSTRACT FROM AUTHOR]

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others. [ABSTRACT FROM AUTHOR]

Donghao Wang,1,&ast; Yating Chen,1,&ast; Yutong Ding,1,&ast; Yongkang Tang,1,&ast; Xiaofen Su,1,&ast; Shiwei Li,1 Haojie Zhang,1,2 Yanyan Zhou,1 Zhiyang Zhuang,1 Qiming Gan,1 Jingcun Wang,1 Yuting Zhang,1 Dongxing Zhao,1 Nuofu Zhang1 1State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Sleep Medicine Center, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510160, People’s Republic of China; 2The Clinical Medicine Department, Henan University, Zhengzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Nuofu Zhang, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Sleep Medicine Center, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510160, People’s Republic of China, Tel +86-13600460056, Email nfzhanggird@163.comBackground: Obstructive sleep apnea (OSA) is a common chronic disease with various comorbidities. The cardiometabolic index (CMI) reflects visceral fat tissue distribution and function, assessing the risk of obesity-related conditions such as metabolic syndrome (MetS) and stroke, which are strongly connected to OSA. The relationship between CMI with OSA and OSA combined with MetS (OMS) remains unclear. This study aims to evaluate the screening value of CMI for OSA and OMS, compared to the lipid accumulation product (LAP).Methods: A total of 280 participants who underwent polysomnography were finally included, with the measurements of metabolic-related laboratory test results such as total cholesterol and triglyceride. Receiver operating curve (ROC) analysis and calculation of the area under the curve (AUC) were conducted to assess the screening potential of CMI, LAP, and the logistic regression models established based on them for OSA and OMS. The Youden index, sensitivity, and specificity were used to determine the optimal cutoff points.Results: ROC curve analysis revealed that the AUCs for CMI in screening OSA and OMS were 0.808 and 0.797, and the optimal cutoff values were 0.71 (sensitivity 0.797, specificity 0.776) and 0.89 (sensitivity 0.830, specificity 0.662), respectively, showing higher Youden index than LAP. The AUCs of screening models based on CMI for OSA and OMS were 0.887 and 0.824, respectively.Conclusion: CMI and LAP can effectively screen for OSA and OMS, while CMI has more practical cutoff values for identifying the diseased states. Screening models based on CMI demonstrate a high discriminatory ability for OSA and OMS, which needs verification in a large-scale population.Keywords: obstructive sleep apnea, metabolic syndrome, cardiometabolic index, lipid accumulation products, screening model

Background: There was evidence that significant bidirectional associations between psoriasis and inflammatory bowel diseases (IBDs), which influences management strategy of the patients, so the investigation on the mechanisms by which these two diseases co‐occur is important. Methods: The Gene Expression Omnibus (GEO) database was used to download gene expression profiles of psoriasis and IBD. The differentially expressed genes (DEGs) between disease and health control groups for each data set were calculated, and Venn diagram was used to obtain for intersection. We performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the intersection, followed by developing a protein–protein interaction network and module construction, and identified hub genes by cytoHubba. Thereafter, least absolute shrinkage and selection operator algorithms was used to identify the co‐biomarkers of psoriasis and IBD from the top 50 hub genes. The biomarkers were used to construct a screening model, the discriminatory capacity of which was verified by receiver operating characteristic (ROC) curves. CIBERSORT algorithm was utilized to estimate the compositional patterns of immune cell infiltration in biomarkers of psoriasis and IBD. Spearman rank correlation analysis was used to further evaluate the correlation between the identified biomarkers and immune cells. Results: A total of 271 shared DEGs were screened. The GO and KEGG enrichment analysis indicated that the shared DEGs were mainly enriched in response to lipopolysaccharide, secretory granule lumen, cytokine activity, and interleukin (IL)‐17 signaling pathway. Fifty genes such as IL1B, IL6, were identified as hub genes, based on which, FOS, IFI44, MMP9, MNDA, PTGS2, S100A9, and STAT1 were identified as biomarkers of psoriasis. CCL20, CD274, CTGF, CXCL1, CXCL10, CXCL2, CXCL9, FCGR3B, FOS, GBP1, GZMB, IFI27, IFI6, IL1RN, ISG15, ISG20, LCN2, LILRB2, MMP12, MMP7, S100A8, TLR8, and TNFSF13B were identified as biomarkers of IBD. FOS was the common biomarker of psoriasis and IBD. Screening models were validated in the validation data set (Psoriasis: area under the curve (AUC) = 1.000, IBD: AUC = 0.870). Immunocyte infiltration analysis showed the macrophages cells, mast cells resting, and T cells CD4 memory activated have the common characteristics in psoriasis and IBD. Conclusions: FOS may play a key role in the occurrence and development of psoriasis complicated with IBD and macrophages cells may be an entrance for treating this comorbidity. [ABSTRACT FROM AUTHOR]

Abstract Background Placenta accreta spectrum (PAS) disorder is a major cause of postpartum hemorrhage-associated maternal and fetal death, and novel methods for PAS screening are urgently needed for clinical application. Methods The purpose of this study was to develop new methods for PAS screening using serum biomarkers and clinical indicators. A total of 95 PAS cases and 137 controls were enrolled in a case–control study as cohort one, and 44 PAS cases and 35 controls in a prospective nested case–control study were enrolled as cohort two. All subjects were pregnant women of Chinese Han population. Biomarkers for PAS from maternal blood samples were screened based on high-throughput immunoassay and were further validated in three phases of cohort one. Screening models for PAS were generated using maternal serum biomarkers and clinical indicators, and were validated in two cohorts. The expression levels of biomarkers were analyzed using histopathological and immunohistochemical (IHC) techniques, and gene expression was examined by QPCR in the human placenta. Binary logistic regression models were built, and the area under the curve (AUC), sensitivity, specificity, and Youden index were calculated. Statistical analyses and model building were performed in SPSS and graphs were generated in GraphPad Prism. The independent-sample t test was used to compare numerical data between two groups. For nonparametric variables, a Mann–Whitney U test or a X 2 test was used. Results The results demonstrated that the serum levels of matrix metalloproteinase-1 (MMP-1), epidermal growth factor (EGF), and vascular endothelial growth factor-A (VEGF-A) were consistently higher, while the level of tissue-type plasminogen activator (tPA) was significantly lower in PAS patients compared with normal term controls and patients with pre-eclampsia (PE) and placenta previa (PP). IHC and QPCR analysis confirmed that the expression of the identified biomarkers significantly changed during the third trimester in human placenta. The generated screening model combining serum biomarkers and clinical indicators detected 87% of PAS cases with AUC of 0.94. Conclusions Serum biomarkers can be used for PAS screening with low expense and high clinical performance; therefore, it may help to develop a practicable method for clinical prenatal PAS screening.

Recent studies have revealed the potency of probiotics in alleviating metabolic diseases associated with intestinal barrier dysfunction. However, an efficient model for screening probiotic strains against specific metabolic diseases has not been well developed. In the present study, a Caco-2 cell monolayer membrane model treated with tumor necrosis factor (TNF)-α or alcohol was used to evaluate the effect of 139 Lactobacillus strains on intestinal barrier function in vitro. We then selected 11 Lactobacillus strains with different regulatory abilities on the gut barrier to determine their effect against ovariectomy-induced osteoporosis or chronic alcoholic liver injury in vivo. Our results showed that the Pearson coefficient between the data of cell and animal models were 0.82 and −0.97 for the protection of probiotics against osteoporosis and alcoholic liver disease, respectively, suggesting the reliability of the cell model to simulate the in vivo protective effects of probiotics. This study established a potential in vitro approach based on a Caco-2 cell monolayer membrane model for the efficient screening of potential probiotics against specific metabolic diseases such as osteoporosis and chronic alcoholic liver disease.

Copyright of Ozel Egitim Dergisi is the property of Ankara University, Faculty of Educational Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Placenta accreta spectrum (PAS) disorder is a major cause of postpartum hemorrhage-associated maternal and fetal death, and novel methods for PAS screening are urgently needed for clinical application. Methods: The purpose of this study was to develop new methods for PAS screening using serum biomarkers and clinical indicators. A total of 95 PAS cases and 137 controls were enrolled in a case–control study as cohort one, and 44 PAS cases and 35 controls in a prospective nested case–control study were enrolled as cohort two. All subjects were pregnant women of Chinese Han population. Biomarkers for PAS from maternal blood samples were screened based on high-throughput immunoassay and were further validated in three phases of cohort one. Screening models for PAS were generated using maternal serum biomarkers and clinical indicators, and were validated in two cohorts. The expression levels of biomarkers were analyzed using histopathological and immunohistochemical (IHC) techniques, and gene expression was examined by QPCR in the human placenta. Binary logistic regression models were built, and the area under the curve (AUC), sensitivity, specificity, and Youden index were calculated. Statistical analyses and model building were performed in SPSS and graphs were generated in GraphPad Prism. The independent-sample t test was used to compare numerical data between two groups. For nonparametric variables, a Mann–Whitney U test or a X2 test was used. Results: The results demonstrated that the serum levels of matrix metalloproteinase-1 (MMP-1), epidermal growth factor (EGF), and vascular endothelial growth factor-A (VEGF-A) were consistently higher, while the level of tissue-type plasminogen activator (tPA) was significantly lower in PAS patients compared with normal term controls and patients with pre-eclampsia (PE) and placenta previa (PP). IHC and QPCR analysis confirmed that the expression of the identified biomarkers significantly changed during the third trimester in human placenta. The generated screening model combining serum biomarkers and clinical indicators detected 87% of PAS cases with AUC of 0.94. Conclusions: Serum biomarkers can be used for PAS screening with low expense and high clinical performance; therefore, it may help to develop a practicable method for clinical prenatal PAS screening. [ABSTRACT FROM AUTHOR]

With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment. [ABSTRACT FROM AUTHOR]

Melanin has an increasing market demand in cosmetics, food, medicine as well as aerospace due to its unique properties. Heterologous expression of 4-hydroxyphenylpyruvate dioxygenase (HPPD) from the melanin-producing strain Streptomyces fungicidicus NW-EN1 in Escherichia coli shortened the fermentation cycle of melanin. HPPD catalyzed 4-hydrophenylpyruvate (HPP) to form homologous acid (HGA) and finally form melanin. The purified melanin had the highest absorption peak at 460 nm. Fourier transform infrared spectroscopy and scanning electron microscope scanning showed that the pigment had universal characteristic peaks. The presence of HGA, a predictor of pyomelanin, was identified by high-performance liquid chromatography analysis. The recombinant E. coli produced 804.4 ± 5.9 mg/L pyomelanin within 48 h. Metal ions had a great influence on the production of pyomelanin. Pyomelanin was stable in response to light intensity and had a protective effect against bacteria under UV irradiation. Meanwhile, we utilized the chromogenic effect after whole-cell catalysis to reflect the inhibition of the HPPD inhibitors (mesotrione and isoxaflutole) on HPPD by observing the color change. As a rapid method to test the action of inhibitors, this method is expected to be useful for the development of HPPD-inhibiting herbicides. [Display omitted] • Engineered strains for melanin production have promising industrial applications. • Enables real-time detection of melanin production. • It provides a new idea for the screening of novel herbicides. [ABSTRACT FROM AUTHOR]

Abstract Background The coordination between different levels of care is essential for the management of obstructive sleep apnea (OSA). The objective of this multicenter project was to develop a screening model for OSA in the primary care setting. Methods Anthropometric data, clinical history, and symptoms of OSA were recorded in randomly selected primary care patients, who also underwent a home sleep apnea test (HSAT). Respiratory polygraphy or polysomnography were performed at the sleep unit to establish definite indication for continuous positive airway pressure (CPAP). By means of cross-validation, a logistic regression model (CPAP yes/no) was designed, and with the clinical variables included in the model, a scoring system was established using the β coefficients (PASHOS Test). In a second stage, results of HSAT were added, and the final accuracy of the model was assessed. Results 194 patients completed the study. The clinical test included the body mass index, neck circumference and observed apneas during sleep (AUC 0.824, 95% CI 0.763–0.886, P

The man-made gas sulfur hexafluoride (SF 6 ) is an excellent and stable insulating medium. However, some insulation defects can cause SF 6 to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF 6 in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO 2 , SOF 2 , SO 2 F 2 , H 2 S, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material properties and discover new materials. However, it has become a major challenge to establish a common model between material properties derived from various types of calculations and intelligent algorithms. In order to make some progress in addressing this challenge. In this work, 250 data sets were extracted from 52 publications exploring the detection of SF 6 decomposition products by nanocomposites based on relevant work over the past 10 years, and the adsorption behavior of SF 6 decomposition products can be predictively analyzed. By comparing six different algorithmic models, the best model for predicting the adsorption distance (XGBoost: R 2 = 91.94 %) and adsorption energy (GBR: R 2 = 78.63 %) of SF 6 decomposed gas was identified. Subsequently, the importance of each of the selected feature descriptors in predicting the gas adsorption effect was explained. This work combines first-principles computational results and machine-learning algorithms with each other to provide a new research idea for evaluating the gas sensing capability of nanocomposites., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Pengfei Jia reports financial support was provided by Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province. Changyou Ma reports financial support was provided by Neijiang basic research and applied basic research project. Pengfei Jia reports financial support was provided by National Natural Science Foundation of China. Pengfei Jia reports financial support was provided by Sichuan Province Science and Technology Support Program. Dachang Chen reports financial support was provided by Natural Science Foundation of Hubei Province., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community., Methods: In the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline., Results: A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups ( p  < 0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, anti-saccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC = 0.969)., Conclusion: The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community., 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 © 2024 Hao, Zhang, An, Bao, Yang, Chen, Hou, Wang, Du, Zhao, Wang, Min and Li.)

BackgroundHypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80–90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening.ObjectiveWe aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM.MethodsBetween April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model.ResultsAmong 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818–0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812–0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825–0.841]. A browser-based calculator was generated accordingly.ConclusionThe pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening.

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

Background: The coordination between different levels of care is essential for the management of obstructive sleep apnea (OSA). The objective of this multicenter project was to develop a screening model for OSA in the primary care setting.Methods: Anthropometric data, clinical history, and symptoms of OSA were recorded in randomly selected primary care patients, who also underwent a home sleep apnea test (HSAT). Respiratory polygraphy or polysomnography were performed at the sleep unit to establish definite indication for continuous positive airway pressure (CPAP). By means of cross-validation, a logistic regression model (CPAP yes/no) was designed, and with the clinical variables included in the model, a scoring system was established using the β coefficients (PASHOS Test). In a second stage, results of HSAT were added, and the final accuracy of the model was assessed.Results: 194 patients completed the study. The clinical test included the body mass index, neck circumference and observed apneas during sleep (AUC 0.824, 95% CI 0.763-0.886, P < 0.001). In a second stage, the oxygen desaturation index (ODI) of 3% (ODI3% ≥ 15%) from the HSAT was added (AUC 0.911, 95% CI 0.863-0.960, P < 0.001), with a sensitivity of 85.5% (95% CI 74.7-92.1) and specificity of 67.8% (95% CI 55.1-78.3).Conclusions: The use of this model would prevent referral to the sleep unit for 55.1% of the patients. The two-stage PASHOS model is a useful and practical screening tool for OSA in primary care for detecting candidates for CPAP treatment. Clinical Trial Registration Registry: ClinicalTrials.gov; Name: PASHOS Project: Advanced Platform for Sleep Apnea Syndrome Assessment; URL: https://clinicaltrials.gov/ct2/show/NCT02591979 ; Identifier: NCT02591979. Date of registration: October 30, 2015. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) and diabetic retinopathy (DR) are important issues in the district health system (DHS) of South Africa (SA). Guidelines for the management of DR in SA were developed more than a decade ago but not effectively implemented. Aim: The aim of this study was to develop a suitable model for DR that could be effectively implemented by a team of healthcare practitioners (HCPs) to co-manage DM and DR in the DHS of SA. Setting: The study was conducted through Voortrekker District Hospital, Limpopo Province, SA. Methods: A saturated strategy sample study was employed, and questionnaires were distributed to 24 endocrinologists in both private and public practices in Gauteng Province and to three ophthalmologists and 10 medical officers (MOs) in ophthalmology in health institutions in Waterberg and Capricorn districts of Limpopo Province. The questionnaires distributed included questions relating to the recommended roles of primary healthcare (PHC) nurses, MOs in general practice, MOs in ophthalmology, ophthalmic nurses, optometrists, and ophthalmologists to manage patients with DM in the public sector. The Delphi technique was employed requiring experts to comment qualitatively and quantitatively to elicit the required information. Results: At PHC level, PHC nurses are to document a comprehensive patient case history and assess vitals before referring to MOs in general practice. Medical officers in general practice are to assess DM control and screen for target organ disease. All patients with DM are to be referred to optometrists for retinal photography. Optometrists and ophthalmic nurses are to detect, grade and monitor pre-proliferative stages of DR, and refer to MOs in ophthalmology or ophthalmologists at district or tertiary hospitals for surgical intervention or treatment. Conclusion: Based on the expertise of the endocrinologists and ophthalmologists concerned, a DR screening model for a DHS was proposed, reflecting the role of HCPs in the management of DM and DR in the DHS of Limpopo Province, SA. [ABSTRACT FROM AUTHOR]

Human cytomegalovirus (HCMV) infection is a common viral disease. In view of the lack of effective vaccines against HCMV currently, the screening of antiviral drugs for treatment of HCMV are of great significance. In order to construct a rapid screening model of anti-cytomegalovirus drugs in vitro, in this study, a murine cytomegalovirus bacterial artificial chromosome with a luciferase tag (Luc-MCMV-BAC) was transfected into mouse fibroblast NIH-3T3 cells. The recombinant virus Luc-MCMV was successfully obtained, and it can stably emit light in cultured cells. The virus growth curves determined by the plaque method showed that Luc-MCMV displayed similar growth kinetics comparable to those of wild-type virus Wt-MCMV, indicating that Luc-MCMV can be used to screen drugs and evaluate the antiviral activity. Furthermore, using Ganciclovir as a positive control drug, six kinds of traditional Chinese medicine monomers were screened using this system. Results showed that Baicalein and Astragaloside IV had anti-MCMV activity, which laid the foundation for the screening of anti-HCMV drugs. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) and diabetic retinopathy (DR) are important issues in the district health system (DHS) of South Africa (SA). Guidelines for the management of DR in SA were developed more than a decade ago but not effectively implemented. Aim: The aim of this study was to develop a suitable model for DR that could be effectively implemented by a team of healthcare practitioners (HCPs) to co-manage DM and DR in the DHS of SA. Setting: The study was conducted through Voortrekker District Hospital, Limpopo Province, SA. Methods: A saturated strategy sample study was employed, and questionnaires were distributed to 24 endocrinologists in both private and public practices in Gauteng Province and to three ophthalmologists and 10 medical officers (MOs) in ophthalmology in health institutions in Waterberg and Capricorn districts of Limpopo Province. The questionnaires distributed included questions relating to the recommended roles of primary healthcare (PHC) nurses, MOs in general practice, MOs in ophthalmology, ophthalmic nurses, optometrists, and ophthalmologists to manage patients with DM in the public sector. The Delphi technique was employed requiring experts to comment qualitatively and quantitatively to elicit the required information. Results: At PHC level, PHC nurses are to document a comprehensive patient case history and assess vitals before referring to MOs in general practice. Medical officers in general practice are to assess DM control and screen for target organ disease. All patients with DM are to be referred to optometrists for retinal photography. Optometrists and ophthalmic nurses are to detect, grade and monitor pre-proliferative stages of DR, and refer to MOs in ophthalmology or ophthalmologists at district or tertiary hospitals for surgical intervention or treatment. Conclusion: Based on the expertise of the endocrinologists and ophthalmologists concerned, a DR screening model for a DHS was proposed, reflecting the role of HCPs in the management of DM and DR in the DHS of Limpopo Province, SA.

Spontaneous preterm birth (sPTB) is a complex and clinically heterogeneous condition that remains incompletely understood, leading to insufficient interventions to effectively prevent it from occurring. Cell-free ribonucleic acid signatures in the maternal circulation have the potential to identify biologically relevant subtypes of sPTB. These could one day be used to predict and prevent sPTB in asymptomatic individuals, and to aid in prognosis and management for individuals presenting with threatened preterm labor and preterm prelabor rupture of membranes., Competing Interests: Disclosure A.D. Cowan, M. Rasmussen, and M. Jain are all employees of and hold equity in Mirvie, Inc., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective·To establish and evaluate a screening model of liver cirrhosis patients complicated with minimal hepatic encephalopathy (MHE). Methods·A total of 404 patients with liver cirrhosis who were hospitalized from June 2017 to November 2019 were selected as the research subjects, and the clinical data of them were collected. Based on Logistic regression analysis and artificial neural network (ANN), the MHE screening models were established, and the discriminant ability of the two models was evaluated and compared. Results·The Logistic regression model showed that age, history of diabetes mellitus, infection, renal insufficiency, nutritional risk, total bilirubin>24 μmol/L, blood ammonia>47 μmol/L and international normalized ratio (INR)≥1.5 were the significant risk factors (all P<0.05). The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) of ANN model and Logistic regression model were 0.814 and 0.737 (Z=4.208, P=0.000), respectively. The sensitivities were 72.4% and 69.9%, and the specificities were 76.7% and 67.8%, respectively. Conclusion·The ANN model is more effective than the Logistic regression model in MHE screening. [ABSTRACT FROM AUTHOR]

This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.

Background: Nowadays with advanced improvement in NICUs, more preterm infants are surviving with more risks related to ROP. Objectives: The aim of the present study was to collect ROP risk factors and design data mining techniques to suggest a predictive ROP treatment-requiring model. Methods: A cross-sectional study was carried out in an Iranian hospital (2014 - 2018). The population study consisted of 76 preterm neonates with ROP diagnosis. Of all, retinopathy was treated in 35 cases and others had not received any treatment associated with retinopathy. The pre-set questionnaire was used to extract the risk factors leading to treatment-requiring retinopathy. Then specific software models were designed for predictingROPtreatment-requiring model. In order tocomparethe performance of data mining methods, several performance metrics such as accuracy, precision, sensitivity, specificity, and F-measure have been used. Results: Seventy neonates with ROP entered the study. Results have shown thatamongfour models, Naive Bayes had the best performance with the highest accuracy (87.14), precision (96.43), sensitivity (77.14) and F-measure (85.71). Confusion matrix for Naive Bayes classifier showed that positive predictive value and negative predictive value were 0.7714 and 0.9714, respectively. Overall 87.14% of all data were correctly classified. Moreover, of all data mining techniques, decision tree model could indicate understandable findings as follow; if oxygen therapy continues more than 16 days or blood infusion is > 6 units of packed cells then patients need treatment. Conclusions: The results of the present study have demonstrated that data mining techniques could be effectively implemented in ROP screening programs. [ABSTRACT FROM AUTHOR]

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO after adsorbing 24 kinds of gases were calculated. These computed features served as the basis for training a machine learning model. Subsequently, various machine learning and evaluation algorithms were utilized to train the fast screening model. The importance of feature values was evaluated by the AdaBoost, Random Forest, and Extra Trees models. Specifically, charge transfer was assigned importance values of 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closely was the d-band center, which was presumed to exert influence on electrical conductivity and, consequently, adsorption properties. With 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an accuracy of 78% using multilayer perceptron after 5-fold cross-validation, with both data sets exhibiting low standard deviations. This verified the accuracy and reliability of the strategy, showcasing its potential for rapidly screening a material's responsiveness to the target gas.

Background and objective: ACO is a syndrome with high prevalence. However, a pragmatic diagnostic criterion to differentiate ACO is non‐existent. We aimed to establish an effective model for screening ACO. Methods: A multicentre survey was developed to assess the clinical criteria considered important and applicable by pulmonologists for screening ACO. These experts were asked to take the surveys twice. The expert grading method, analytic hierarchy process and ROC curve were used to establish the model, which was then validated by a cross‐sectional study of 1066 patients. The GINA/GOLD document was the gold standard in assessing this model. Results: Increased variability of symptoms, paroxysmal wheezing, dyspnoea, historical diagnosis of COPD or asthma, allergic constitution, exposure to risk factors, the FEV1/FVC < 70% and a positive BDT were important for screening ACO. According to the weight of each criterion, we confirmed that patients meeting six or more of these eight criteria should be considered to have ACO. We called this Chinese screening model for ACO 'CSMA'. It differentiated patients with ACO with a sensitivity of 83.33%, while the sensitivity of clinician‐driven diagnosis had a sensitivity of only 42.73%. Conclusion: CSMA is a workable model for screening ACO and provides a simple tool for clinicians to efficiently diagnose ACO. We established a screening model for ACO. It is as effective as the GINA/GOLD document to distinguish between ACO, asthma and COPD patients but much simpler to perform. See related Editorial [ABSTRACT FROM AUTHOR]

The objective of the present work is the studying of air quality that contained pollutant gases (SO2, NOx, CO), and PM released from the Shuaibah III (IWPP) plant as a case study. Also, we tried to evaluate the effect of Flue gas desulfurization byproduct (FGDB) on SO2 that turned into carried out within the studied place. For the determination of dispersion, the source of pollution was taken into consideration to be in a rural area. The screening model was used to calculate concentrations dispersion of gas pollutants at different Pasquill-Gifford stability classes' conditions. The levels Cmax (maximum concentrations) decreased from A-class to F-class, and the influence distance Dmax (maximum distance of downwind concentrations) quickly grows. The SO2 dispersion became affected by the FGDB system. The results confirmed that the Cmax of air pollutants released from the stack may additionally decreases than the Saudi Arabian standard. [ABSTRACT FROM AUTHOR]

In this paper, we not only develop a Stackelberg game to capture the unique characteristics of the ocean freight transportation, but also employ a screening model to address the contracting issue between one carrier and one freight forwarder under asymmetric information. The freight forwarder faces random demand from multiple shippers. In our framework, the spot price is positively correlated with the shippers' demand. We first derive the forwarder's optimal strategy, and then formulate the carrier's contract design problem under symmetric and asymmetric information. Subsequently, we fully characterize the equilibrium parameters for a two-part tariff contract. The numerical experiments conducted thereafter reveal that (i) under a higher correlation between the shippers' demand and the spot price, the carrier prefers a lower purchase price and the forwarder orders more through a contract in the peak seasons; (ii) when the market demand is extremely volatile, the carrier should raise the purchase price and the forwarder should order more through a contract; and (iii) with a higher degree of information asymmetry, the carrier prefers a higher purchase price while the forwarder relies more on the spot market than the contract market. [ABSTRACT FROM AUTHOR]

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care. [ABSTRACT FROM AUTHOR]

Plant virus disease is one of the major causes of biological disasters in agriculture worldwide. Given the complexity of transmission media and plant disease infection mechanisms, the prevention and control of plant viral diseases is a great challenge, and an efficient green pesticide is urgently needed. For this reason, when developing candidate drug leads to regulate plant viruses, pesticide experts have focused on characteristics such as low pesticide resistance, eco-friendliness, and novel mechanism. Researchers have also theoretically investigated the molecular targets of viruses infecting agricultural crops. Antiviral screening models have been constructed based on these molecular targets, and the mechanisms of commercial drugs and high-activity compounds have been extensively investigated. After screening, some compounds have been applied in the field and found to have good commercial prospects; these drugs may be used to create new green antiviral pesticides to control plant viruses. This paper reviews the screening, mode of action, development and application of recently used plant-based antiviral agents.

Organic pesticides are one of the main environmental pollutants, and how to reduce their environmental risks is an important issue. In this contribution, we disclose the molecular basis for the resistance of American sloughgrass to aryloxyphenoxypropionic acid pesticides using site-directed mutagenesis and molecular modeling and then construct an effective screening model. The results indicated that the target-site mutation (Trp-1999-Leu) in acetyl-coenzyme A carboxylase (ACCase) can affect the effectiveness of the pesticides (clodinafop, fenoxaprop, cyhalofop, and metamifop), and the plant resistance to fenoxaprop, clodinafop, cyhalofop, and metamifop was found to be 564, 19.5, 10, and 0.19 times, respectively. The established computational models (i.e. wild-type/mutant ACCase models) could be used for rational screening and evaluation of the resistance to pesticides. The resistance induced by target gene mutation can markedly reduce the bioreactivity of the ACCase-clodinafop/fenoxaprop adducts, and the magnitudes are 10 and 102, respectively. Such event will seriously aggravate environmental pollution. However, the biological issue has no distinct effect on cyhalofop (RI＝10), and meanwhile it may markedly increase the bioefficacy of metamifop (RI＝0.19). We could selectively adopt the two chemicals so as to decrease the residual pesticides in the environment. Significantly, research findings from the computational screening models were found to be negatively correlated with the resistance level derived from the bioassay testing, suggesting that the screening models can be used to guide the usage of pesticides. Obviously, this story may shed novel insight on the reduction of environmental risks of pesticides and other organic pollutants. Image 1 • Target-site mutation in American sloughgrass will aggravate pesticide pollution. • The changes in enzyme bioaffinity can greatly affect the pesticides usage. • Computational findings are negatively related to the results of bioassay testing. • Computational screening models could be used for reducing environmental stress. [ABSTRACT FROM AUTHOR]

Objective To construct a rapid, visual and quantifiable method for the screening and evaluation of compounds for bidirectional intervention of angiogenesis with quail chorioallantoic membrane (qCAM) as the carrier. Methods By comparing the results of gelatin sponge, molecular sieve, rice grain, macroporous resin, silica gel particles and methyl cellulose, the conventional drug carriers were determined; by observing the development of blood vessels on qCAM afterwindow opening test, the incubation time after administration was set; by comparing the development of microvasculature in different batches, the continuous stability of the model was tested. In the reliability test of the model, DG-15 and BA-12 were selected as model drugs, and the effects of diiferent doses of these drugs on the macromorphology and vascular count of qCAM were analyzed evaluate to eudu ate the reliabity of tho model. Results The gelatin sponge was chosen as the drug delivery carrier as it could accurately locate the drug delivery point with exact quantity and liitle influence on the qCAM or the drug. 36 hr after the window opening test was chosen as the best time for administration as the integrity of samples would be affected when qCAM began to gradually sink into the embryo to provide nutrition on the second day after the window opening test. By comparing the microvessel counts (grade 3 + grade 4) in qCAM in different months, the development of blood vessels in qCAM was basically stable. In the reliability test, the model could be adopted to preliminarily evaluate the activity of DG-15(20 g) in promoting angiogenesis (P < 0.05),and the activity of BA-12 (20 ug) in inhibiting angiogenesis (P < 0.05). Conclusion The method established in this study could possibly be used to evaluate the bidirectional intervention effect of drugs on angiogenesis with relative reliability, and provide a screening method and basis for the research and development of active compounds on angiogenesis. [ABSTRACT FROM AUTHOR]

Highlights • Animal models are critical for screening new therapies. • Amphetamine-induced hyperactivity (AIH) is a screening model for mood stabilizers. • The validity of screening models depends on their response to established treatments. • We show that in ICR and black Swiss mice, chronic lithium does not ameliorate AIH. • The findings cast doubt on the validity of the model to screen mood-stabilizing drugs. Abstract Animal models are critical for the study of disease mechanisms and the screening of potential novel treatments. In the context of bipolar disorder, amphetamine-induced hyperactivity (AIH) is a frequently used screening model for antimanic effects. Yet, the utility of screening models depends on their predictive (or pharmacological) validity and it is expected that such models will respond to effective treatments. Lithium is the prototypic mood stabilizer but previous data regarding the effects of lithium in the AIH model are not clear and most data comes from studies using acute lithium administration that is not relevant to the therapeutic regimen in patients. To evaluate the pharmacological validity of AIH as a model for mania-like behavior we tested the interaction between chronic oral administration of lithium and amphetamine in ICR (CD-1®) mice and in black Swiss mice. We conducted 4 different experiments where chronic lithium was followed by an acute injection of amphetamine and one experiment where chronic amphetamine was combined with chronic lithium. The results show that amphetamine result in hyperactivity (experiments 1–4) and that lithium has no effects. Moreover, chronic amphetamine (experiment 5) result in sensitization that is not attenuated by lithium. The results clearly show that the predictive validity of the AIH model in ICR or black Swiss mice is problematic and possibly cast doubt on the utilization of the AIH as a screening model for novel mood stabilizers in other strains of mice. [ABSTRACT FROM AUTHOR]

Abstract Retired cells from electric vehicles could provide considerable economic benefits through secondary uses such as energy storage. However, the screening and regrouping of large-scale cells is facing the problem of low efficiency and low accuracy. To address this problem, two rapid and accurate screening approaches are proposed in this study. Firstly, the characteristics of the series-charging curve of large-scale retired cells are investigated. Then, two novel screening models, namely the neural network model and the piecewise linear fitting model, are thus constructed using the capacity and voltage profiles of a small number of sample cells, and the capacity of a large number of cells can be estimated in batches. Moreover, a device for fast switching between series and parallel is designed to improve efficiency, and a regrouping approach for different second-use applications is introduced. Finally, the proposed approaches are verified by simulations and experiments. The main results are as follow: (1) The piecewise linear fitting model should be used in the small sample case, and the neural network model should be adopted in the large sample case for higher estimation accuracy; (2) The proposed approaches are feasible and effective, and several cases illustrate that the capacity estimation error of the proposed approaches is less than 4%; (3) The screening efficiency of the proposed approaches increases with the increase in the number of cells, and screening results of 5000 cells indicate that the screening efficiency of our proposed approach is at least 5 times higher than that of a traditional approach. Highlights • Two rapid and accurate screening approaches for the secondary applications of retired cells are proposed. • Simulation and experimental results show that the proposed approaches are feasible and effective. • A device for fast-switching between series and parallel is designed to improve screening efficiency. • A regrouping approach based on screening results for different second-use applications is introduced. [ABSTRACT FROM AUTHOR]

With the mortality rate of invasive aspergillosis caused by Aspergillus fumigatus reaching almost 100% among some groups of patients, and with the rapidly increasing resistance of A. fumigatus to available antifungal drugs, new antifungal agents have never been more desirable than now. Numerous bioactive compounds were isolated and characterized from marine resources. However, only a few exhibited a potent activity against A. fumigatus when compared to the multitude that did against some other pathogens. Here, we review the marine bioactive compounds that display a bioactivity against A. fumigatus. The challenges hampering the discovery of antifungal agents from this rich habitat are also critically analyzed. Further, we propose strategies that could speed up an efficient discovery and broaden the dimensions of screening in order to obtain promising in vivo antifungal agents with new modes of action.

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013−2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013−2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

Hair disorders such as hair loss (alopecia) and androgen dependent, excessive hair growth (hirsutism, hypertrichosis) may impact the social and psychological well‐being of an individual. Recent advances in understanding the biology of hair have accelerated the research and development of novel therapeutic and cosmetic hair growth agents. Preclinical models aid in dermocosmetic efficacy testing and claim substantiation of hair growth modulators. The in vitro models to investigate hair growth utilize the hair follicle Dermal Papilla cells (DPCs), specialized mesenchymal cells located at the base of hair follicle that play essential roles in hair follicular morphogenesis and postnatal hair growth cycles. In this review, we have compiled and discussed the extensively reported literature citing DPCs as in vitro model to study hair growth promoting and inhibitory effects. A variety of agents such as herbal and natural extracts, growth factors and cytokines, platelet‐rich plasma, placental extract, stem cells and conditioned medium, peptides, hormones, lipid‐nanocarrier, light, electrical and electromagnetic field stimulation, androgens and their analogs, stress‐serum and chemotherapeutic agents etc. have been examined for their hair growth modulating effects in DPCs. Effects on DPCs' activity were determined from untreated (basal) or stress induced levels. Cell proliferation, apoptosis and secretion of growth factors were included as primary end‐point markers. Effects on a wide range of biomolecules and mechanistic pathways that play key role in the biology of hair growth were also investigated. This consolidated and comprehensive review summarizes the up‐to‐date information and understanding regarding DPCs based screening models for hair growth and may be helpful for researchers to select the appropriate assay system and biomarkers. This review highlights the pivotal role of DPCs in the forefront of hair research as screening platforms by providing insights into mechanistic action at cellular level, which may further direct the development of novel hair growth modulators. [ABSTRACT FROM AUTHOR]