Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.

Development and evaluation of the model for acute kidney injury in patients with cardiac arrest after successful resuscitation.

BACKGROUND: This study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, the study aims to validate the clinical predictive accuracy of the developed model. METHODS: Data were retrieved from the Dryad database, and publicly shared data were downloaded. This retrospective cohort study included 347 successfully resuscitated patients post-cardiac arrest from the Dryad database. Demographic and clinical data of patients in the database, along with their renal function during hospitalization, were included. Through data analysis, the study aimed to explore the relevant influencing factors of acute kidney injury (AKI) in patients after cardiopulmonary resuscitation. The study constructed a line chart prediction model using multivariate logistic regression analysis with post-resuscitation shock status (Post-resuscitation shock refers to the condition where, following successful cardiopulmonary resuscitation after cardiac arrest, some patients develop cardiogenic shock.), C reactive protein (CRP), Lactate dehydrogenase (LDH), and Alkaline phosphatase (ALP) identified as predictive factors. The predictive efficiency of the fitted model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Multivariate logistic regression analysis showed that post-resuscitation shock status, CRP, LDH, and PAL were the influencing factors of AKI after resuscitation in CA patients. The calibration curve test indicated that the prediction model was well-calibrated, and the results of the Decision Curve Analysis (DCA) demonstrated the clinical utility of the model constructed in this study. CONCLUSION: Post-resuscitation shock status, CRP, LDH, and ALPare the influencing factors for AKI after resuscitation in CA patients. The clinical prediction model constructed based on the above indicators has good clinical discriminability and practicality.

VlMYB4 and VlCDF3 co-targeted the VlLOG11 promoter to regulate fruit setting in grape (Vitis vinifera L).

KEY MESSAGE: The VlLOG11 mediates the cytokinin signaling pathway to regulate grape fruit setting. Fruit set, as an accepted agronomic trait, is inextricably linked with fruit quality and yield. Previous studies have demonstrated that exogenous treatment with the synthetic cytokinin analog, forchlorfenuron (CPPU), significantly enhances fruit set. In this study, a significant reduction in endogenous cytokinins was found by measuring the content of cytokinins in young grape berries after CPPU treatment. LONELY GUYs (VlLOGs), a key cytokinin-activating enzyme working in the biosynthesis pathway of cytokinins, exhibited differential expression. Some differentially expressed VlLOGs genes were presented by RNA seq data and their functions and regulation patterns were further investigated. The results showed that VlLOG11 was differentially expressed in young grape berries after CPPU treatment. Overexpression of VlLOG11 in tomato increases the amount of fruit set, and upregulated the expression of genes associated with cytokinin signaling including SlHK4, SlHK5, SlHP3, SlHP4, SlPHP1, SlPHP2. VlMYB4 and VlCDF3 could regulate the expression of VlLOG11 by directly binding to its promoter in young grape berries during fruit set. These results strongly demonstrated that VlMYB4/VlCDF3-VlLOG11 regulatory module plays a key role in the process of fruit setting in grape. This provided a basis for the molecular mechanism of VlLOG11-mediated cytokinin biosynthesis in young grape fruit set.

CircTMC5 promotes gastric cancer progression and metastasis by targeting miR-361-3p/RABL6.

BACKGROUND: Gastric cancer (GC) is common in East Asia, yet its molecular and pathogenic mechanisms remain unclear. Circular RNAs (circRNAs) are differentially expressed in GC and may be promising biomarkers. Here, we investigated the role and regulatory mechanism of circTMC5 in GC. METHODS: CircTMC5 expression was detected in human GC and adjacent tissues using microarray assays and qRT-PCR, while the clinicopathological characteristics of patients with GC were used to assess its diagnostic and prognostic value. The circTMC5/miR-361-3p/RABL6 axis was examined in vitro and vivo, and the immune roles of RABL6 were evaluated using bioinformatics analyses and immunohistochemistry (IHC). RESULTS: CircTMC5 was highly expressed in GC tissues, plasma, and cell lines, and was closely related to histological grade, pathological stage, and T classification in patients with GC. CircTMC5 expression was also an independent prognostic factor for GC and its combined detection with carcinoembryonic antigen may improve GC diagnosis. Low circTMC5 expression correlated with good prognosis, inhibited GC cell proliferation, and promoted apoptosis. Mechanistically, circTMC5 overexpression promoted GC cell proliferation, invasion, and metastasis but inhibited apoptosis by sponging miR-361-3p and up-regulating RABL6 in vitro and vivo, whereas miR-361-3p up-regulation had the opposite effects. RABL6 was highly expressed in GC and was involved in immune regulation and infiltration in GC. CONCLUSIONS: CircTMC5 promotes GC and sponges miR-361-3p to up-regulate RABL6 expression, thus may have diagnostic and prognostic value in GC. RABL6 also displays therapeutic promise due to its role in the immune regulation of GC.

CPS analysis: self-contained validation of biomedical data clustering.

MOTIVATION: Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. RESULTS: We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. AVAILABILITY AND IMPLEMENTATION: The method is implemented in an R package called OTclust, available on CRAN. CONTACT: lzz46@psu.edu or jiali@psu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Self-calibration method for temperature errors in multi-axis rotational inertial navigation system.

Rotation modulation technology of inertial navigation system brings navigation performance increasement without any new requirement on inertial sensors. However, device errors still make significant influence on navigation precision. Traditional temperature model identification methods cost plenty of time which reduce production efficiency. Therefore, it is necessary to study an effective solution decreasing temperature resulted errors for engineering application. The paper proposes a fast-self-calibration method for temperature errors. A continuous rotation scheme is designed to excite 21 errors inside of 10 minutes. Kalman Filter algorithm is applied to estimate 21 errors taking velocity errors and position errors as measurements. In order to identify temperature model, the rotation scheme is repeated ten times to estimate error parameters under different temperature. Due to the fast rotation scheme, temperature rising rate can be higher than traditional methods and calibration time is shortened. Finally, the method is verified by simulations and experiments.

A capillary-based fluorimetric platform for the evaluation of glucose in blood using gold nanoclusters and glucose oxidase in the ZIF-8 matrix.

A capillary-based fluorimetric analysis method was developed for probing glucose (Glu) in blood using Glu oxidase-anchored gold nanoclusters (GOD-AuNCs) and the ZIF-8 matrix. AuNCs were attached with GOD to be further encapsulated into the ZIF-8 matrix through the protein-mediated formation route. The resulting GOD-AuNCs@ZIF-8 nanocomposites could present the AuNC-improved catalysis of GOD and ZIF-8-improved environmental stability. The ZIF-8-enhanced fluorescence intensity of AuNCs could also be expected. Moreover, a capillary-based fluorometric platform was constructed for sensing Glu by coating the capillaries first with GOD-AuNCs and then the ZIF-8 matrix. Herein, Glu was introduced through the self-driven sampling to trigger the GOD-catalyzed production of hydrogen peroxide, which could induce the fluorescence quenching rationally depending on the Glu concentrations. The developed fluorimetric method could allow for the rapid and simple detection of Glu with the concentrations linearly ranging from 5.0 µM to 2.5 mM. Besides, the feasibility of practical applications was demonstrated by the evaluation of Glu in blood showing the recoveries of 96.2%-103.4%. Importantly, the proposed design of the capillary-based fluorimetric platform by the synergetic combination of catalysis-specific recognition and fluorescence signaling may open a new door toward extensive applications in the biological sensing, catalysis, and imaging fields.

Correction: A capillary-based fluorimetric platform for the evaluation of glucose in blood using gold nanoclusters and glucose oxidase in the ZIF-8 matrix.

Correction for 'A capillary-based fluorimetric platform for the evaluation of glucose in blood using gold nanoclusters and glucose oxidase in the ZIF-8 matrix' by Luping Feng et al., Analyst, 2020, 145, 5273-5279, DOI: 10.1039/D0AN01090A.

The prognostic significance of serum gamma-glutamyltransferase levels and AST/ALT in primary hepatic carcinoma.

BACKGROUND: Blood counting and the liver function tests, as the routine examinations, can reflect the immune and nutritional status of the body, our aim is to assess the prognostic significance of serum gamma-glutamyltransferase (GGT) levels and AST/ALT in primary hepatic carcinoma. METHODS: Clinico-pathological data of 414 patients with primary hepatic carcinoma in the 1st Affiliated Hospital of Anhui Medical College between January 2007 to January 2014 was analyzed retrospectively in this study. Survival curves were described by Kaplan-Meier method and compared by Log-rank test, univariate and multivariate analysis were used to identify the prognostic factors. RESULTS: GGT was positively correlated with the tumor size(P = 0.000), tumor volume (P = 0.000), tumor volume percent (P = 0.004), TNM stage(P = 0.009), 1-year survival rate (P = 0.000), 3- years survival rate (P = 0.000) and 5-years survival rate(P = 0.000). The serum ALT/AST was significantly correlated with age (P = 0.047), tumor size(P = 0.002), tumor volume (P = 0.010), tumor volume percent (P = 0.005), TNM stage(P = 0.006), liver cirrhosis(P = 0.003), 3- years survival rate (P = 0.032) and 5-years survival rate(P = 0.000). The Kaplan-Meier curves showed that the patients with primary hepatic carcinoma had a longer time in the low GGT group and low AST/ALT group, showing a significant difference (P < 0.05). The univariate and multivariate analyses showed that TNM stage, differentiation grade, tumor volume, GGT and AST/ALT were independent factors for predicting overall survival rate of primary hepatic carcinoma patients. CONCLUSIONS: GGT and AST/ALT were independent factors for predicting overall survival rate of primary hepatic carcinoma patients.

Perchlorate adsorption onto orange peel modified by cross-linking amine groups from aqueous solutions.

Orange peel was made into a highly efficient bio-sorbent by modification with cross-linking amine groups for perchlorate removal. Bench-scale experiments were performed to explore the factors affecting the perchlorate adsorption onto the modified orange peel (MOP). Perchlorate could be removed effectively at a wide range of pH (from 1.5 to 11). The maximum adsorption capacity of MOP for perchlorate was calculated as 154.1 mg/g within 15 min. The Redlich-Peterson model was fitted to the adsorption isotherm very well (R2>0.99). The adsorption process was spontaneous and exothermic, which was proved by thermodynamic parameters (Gibbs energy and enthalpy). The pseudo-second-order kinetic model could provide satisfactory fitting of the experimental data (R2>0.99). The scanning electron microscopy and energy-dispersive X-ray analysis indicated that the surface of MOP became smooth and the contents of N and Cl in MOP were increased during the modification process. Elemental analysis results showed that the nitrogen content in MOP was increased to 5.5%, while it was 1.06% in orange peel. The adsorption mechanism was also explored using zeta potential and Fourier transform infrared spectroscopy analysis. Ion exchange was the primary mechanism responsible for uptake of perchlorate onto MOP.

[The impact of ZnS/CdS composite window layer on the quantun efficiency of CdTe solar cell in short wavelength].

ZnS/CdS composite window layer was prepared by magnetron sputtering method and then applied to CdTe solar cell. The morphology and structure of films were measured. The data of I-V in light and the quantum efficiency of CdTe solar cells with different window layers were also measured. The effect of ZnS films prepared in different conditions on the performance of CdTe solar cells was researched. The effects of both CdS thickness and ZnS/CdS composite layer on the transmission in short wavelength were studied. Particularly, the quantum efficiency of CdTe solar cells with ZnS/CdS window layer was measured. The results show as follows. With the thickness of CdS window layer reducing from 100 to 50 nm, the transmission increase 18.3% averagely in short wavelength and the quantum efficiency of CdTe solar cells increase 27.6% averagely. The grain size of ZnS prepared in 250 degrees C is smaller than prepared at room temperature. The performance of CdTe solar cells with ZnS/CdS window layer is much better if ZnS deposited at 250 degrees C. This indicates grain size has some effect on the electron transportation. When the CdS holds the same thickness, the transmission of ZnS/CdS window layer was improved about 2% in short wavelength compared with CdS window layer. The quantum efficiency of CdTe solar cells with ZnS/CdS window layer was also improved about 2% in short wavelength compared with that based on CdS window layer. These indicate ZnS/CdS composite window layer can increase the photon transmission in short wavelength so that more photons can be absorbed by the absorbent layer of CdTe solar cells.

Establishment and validation of a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning.

Objective: To develop and validate a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning methods. Methods: A retrospective cohort study was conducted to select 303 elderly patients with severe coronary calcification as the study subjects. According to the occurrence of postoperative heart failure, the study subjects were divided into the heart failure group (n = 53) and the non-heart failure group (n = 250). Retrospective collection of clinical data from the study subjects during hospitalization. After processing the missing values in the original data and addressing sample imbalance using Adaptive Synthetic Sampling (ADASYN) method, the final dataset consists of 502 samples: 250 negative samples (i.e., patients not suffering from heart failure) and 252 positive samples (i.e., patients with heart failure). According to a 7:3 ratio, the datasets of 502 patients were randomly divided into a training set (n = 351) and a validation set (n = 151). On the training set, logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), and lightweight gradient boosting machine (LightGBM) algorithms were used to construct heart failure risk prediction models; Evaluate model performance on the validation set by calculating the area under the receiver operating characteristic curve (ROC) curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and prediction accuracy. Result: A total of 17.49% of 303 patients occured postoperative heart failure. The AUC of LR, XGBoost, SVM, and LightGBM models in the training set were 0.872, 1.000, 0.699, and 1.000, respectively. After 10 fold cross validation, the AUC was 0.863, 0.972, 0.696, and 0.963 in the training set, respectively. Among them, XGBoost had the highest AUC and better predictive performance, while SVM models had the worst performance. The XGBoost model also showed good predictive performance in the validation set (AUC = 0.972, 95% CI [0.951-0.994]). The Shapley additive explanation (SHAP) method suggested that the six characteristic variables of blood cholesterol, serum creatinine, fasting blood glucose, age, triglyceride and NT-proBNP were important positive factors for the occurrence of heart failure, and LVEF was important negative factors for the occurrence of heart failure. Conclusion: The seven characteristic variables of blood cholesterol, blood creatinine, fasting blood glucose, NT-proBNP, age, triglyceride and LVEF are all important factors affecting the occurrence of heart failure. The prediction model of heart failure risk for elderly patients after CRA based on the XGBoost algorithm is superior to SVM, LightGBM and the traditional LR model. This model could be used to assist clinical decision-making and improve the adverse outcomes of patients after CRA.

Development and validation of a prediction model for hyperuricemia risk in hypertensive patients.

OBJECTIVE: This study aimed to create a predictive model for hyperuricemia (HUA) in patients diagnosed with hypertension and evaluate its predictive accuracy. METHODS: Employing a retrospective cohort design, this study investigated HUA incidence and clinical data among 228 patients with essential hypertension selected from the Department of Cardiology at a tertiary A-level hospital in Anhui Province, China, between January 2018 and June 2021. The patients were divided randomly into a training group (168 cases) and a validation group (60 cases) at a 7:3 ratio. The training group underwent univariate and multivariate logistic regression analyses to identify risk factors for HUA. Additionally, an R software-generated nomogram model estimated HUA risk in hypertensive patients. The validation group assessed the nomogram model's discriminatory power and calibration using receiver operating characteristic curve analysis and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The study found a 29.39% prevalence of HUA among the 228 participants. Logistic regression analyses identified age, body mass index, and concomitant coronary heart disease as independent HUA risk factors (odds ratio [OR] > 1 and P < 0.05). Conversely, high-density lipoprotein cholesterol emerged as an independent protective factor against HUA in hypertensive patients (OR < 1 and P < 0.05). Using these factors, a nomogram model was constructed to assess HUA risk, with an AUC of 0.873 (95% confidence interval [CI]: 0.818-0.928) in the training group and 0.841 (95% CI: 0.735-0.946) in the validation group, indicating a strong discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test showed no significant deviation between predicted and actual HUA frequency in both groups (χ2 = 5.980, 9.780, P = 0.649, 0.281), supporting the nomogram's reliability. CONCLUSION: The developed nomogram model, utilizing independent risk factors for HUA in hypertensive patients, exhibits strong discrimination and calibration. It holds promise as a valuable tool for cardiovascular professionals in clinical decision-making.

Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium. Timely medical contact is critical for successful AMI treatment, and delays increase the risk of death for patients. Pre-hospital delay time (PDT) is a significant challenge for reducing treatment times, as identifying high-risk patients with AMI remains difficult. This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care, ultimately reducing PDT and improving treatment outcomes. AIM: To construct a nomogram model for forecasting pre-hospital delay (PHD) likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk. METHODS: A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022. The study included 252 patients, with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio. Independent risk factors influencing PHD were identified in the development group, leading to the establishment of a nomogram model for predicting PHD in patients with AMI. The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups. RESULTS: Independent risk factors for PHD in patients with AMI included living alone, hyperlipidemia, age, diabetes mellitus, and digestive system diseases (P < 0.05). A nomogram model incorporating these five predictors accurately predicted PHD occurrence. The receiver operating characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787 (95% confidence interval: 0.716-0.858) and 0.770 (95% confidence interval: 0.660-0.879) in the development and validation groups, respectively, demonstrating the model's good discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts (P > 0.05), indicating satisfactory model calibration. CONCLUSION: The nomogram model, developed with independent risk factors, accurately forecasts PHD likelihood in AMI individuals, enabling efficient identification of PHD risk in these patients.

Coptisaustrogaoligongensis (Ranunculaceae), a new species from West Yunnan, China.

Based on morphological and plastid data, we have described and confirmed that Coptisaustrogaoligongensis distributed in Tongbiguan Provincial Nature Reserve, Yingjiang County, Yunnan Province, is a new species of Coptis. It is distinctly different from C.teetasubsp.teeta and C.teetasubsp.lohitensis with differences mainly reflected in the following features: former leaf segment lobes contiguous to each other, and lateral segments equal to central one; plants without developed stolons; inflorescences with only 1-3 flowers; petals have short claws. Phylogenetic analysis indicated that C.austrogaoligongensis is a sister to C.teetasubsp.teeta and C.teetasubsp.lohitensis.

Development and validation of a nomogram for predicting the risk of poor prognosis in patients with cerebral infarction.

Objective: To identify factors related to poor prognosis in patients with cerebral infarction (CI) and to construct and validate a personalized prediction model based on these factors. Methods: A retrospective analysis was conducted on the clinical and follow-up data of 857 patients with CI who were diagnosed in the neurology department of a tertiary A hospital in Anhui Province, China from April 2020 to March 2022. Based on follow-up data and the Modified Rankin Scale (mRS) score one year after discharge, patients were divided into a good prognosis group (793 cases, mRS ≤2) and a poor prognosis group (64 cases, mRS >2). Multivariate logistic regression analysis was used to identify independent risk factors, which were then used to establish a nomogram model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (ROC, AUC), and the calibration curve was used to evaluate the calibration of the nomogram. Results: There was a statistically significant difference in the distribution of eight variables between the groups, including post-discharge use of biguanide hypoglycemic drugs, insulin, systolic blood pressure, exercise status, alcohol consumption, smoking status, age, and gender (P < 0.05). Multivariate logistic regression analysis suggested that gender, smoking after discharge, alcohol consumption, lack of exercise, and oral administration of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI (P < 0.05). The personalized poor prognosis nomogram constructed based on these five predictive factors showed good discriminative ability and predictive stability, with AUCs of 0.768 (95 % CI: 0.712-0.825) and 0.775 (95 % CI: 0.725-0.836) before and after internal validation, respectively. The calibration curve confirmed the accuracy and consistency of the nomogram (P = 0.956). Conclusion: Female gender, smoking, alcohol consumption, lack of exercise, and post-discharge use of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI. The constructed nomogram shows good predictive efficiency for post-discharge prognosis and can help in clinical decision-making.

A risk predictive model for determining the severity of coronary artery lesions in older postmenopausal women with coronary heart disease.

OBJECTIVE: To determine the risk factors affecting the severity of coronary artery disease (CAD) in older postmenopausal women with coronary heart disease (CHD) and to construct a personalized risk predictive model. METHODS: In this cohort study, clinical records of 527 female patients aged ≥60 with CHD who were hospitalized in the First Affiliated Hospital of the University of Science and Technology of China from March 2018 to February 2019 were analyzed retrospectively. The severity of CAD was determined using the Gensini scores that are based on coronary angiography findings. Patients with Gensini scores ≥40 and <40 were divided into high-risk (n=277) and non-high-risk groups (n=250), respectively. Logistic regression analysis was used to assess independent predictors of CAD severity. The nomogram prediction model of CAD severity was plotted by the R software. The area under the receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive efficiency of the nomogram model, and the decision curve analysis (DCA) was used to assess the clinical applicability of the nomogram model. RESULTS: Multivariate analysis showed that high-sensitivity C-reactive protein, RBC count, WBC count, BMI, and diabetes mellitus were independent risk factors associated with CAD severity in older menopausal women (P<0.05); the area under the ROC curve of the nomogram constructed based on the independent risk factors was 0.846 (95% CI: 0.756-0.937). The area under the ROC curve after internal validation of the nomogram by the Bootstrap method after resampling 1000 times was 0.840 (95% CI: 0.741-0.923). The calibration curve suggested that the nomogram had an excellent predictive agreement, and the DCA curve indicated that the net benefit of applying the nomogram was significantly higher than that of the "no intervention" and "all intervention" methods when the risk probability of patients with high-risk CAD severity was 0.30-0.81. CONCLUSION: A personalized risk assessment model was constructed based on the risk factors of severe CAD in older menopausal women with CHD, which had good prediction efficiency based on discrimination, calibration, and clinical applicability evaluation indicators. This model could assist cardiology medical staff in screening older menopausal women with CHD who are at a high risk of severe CAD to implement targeted interventions.

Construction of direct Z-scheme Co9S8/CdS with tubular heterostructure through the simultaneous immobilization and in-situ reduction strategy for enhanced photocatalytic Cr(VI) reduction under visible light.

Tubular Co9S8/CdS heterostructures have been successfully synthesized by in-situ growing CdS onto Co9S8 nanotubes through a simultaneous immobilization and in-situ reduction strategy. It turned out that the so-obtained heterostructure with Co9S8/CdS molar ratio of 1/10 can display a broad light absorption edge and especially much enhanced capacity for photocatalytic reduction of Cr(VI) under visible light. The characterization analysis and experimental results suggested that an interfacial electrostatic field between Co9S8 and CdS elements in the heterostructure could be constructed due to their different Fermi levels, allowing for more quantities of highly reductive electrons to participate in the photocatalytic reaction. Therefore, the so-obtained Co9S8/CdS (1/10) heterostructures could achieve the photocatalytic reduction efficiency of 100% within 20 min, which was more than two and four times larger than that of pristine CdS and Co9S8, respectively. Moreover, the possible photocatalytic reaction mechanism for reducing Cr(VI) was investigated and found to follow the direct Z-scheme charge transfer pathway. This novel fabrication route for composite photocatalysts with tubular heterostructures could lead to the widespread implementations for the elimination of various harmful pollutants in the process of environmental governance.

Construction and Verification of Urinary Tract Infection Prediction Model for Hospitalized Rehabilitation Patients with Spinal Cord Injury.

OBJECTIVE: To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model. METHODS: This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected. Univariate analysis and multivariate logistic regression were used to analyze the influencing factors of UTI in hospitalized patients with spinal cord injuries. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic curve and calibration curve. RESULTS: Logistic regression analysis showed that the American Spinal Cord Injury Association (ASIA)-E grade (compared with ASIA-A grade) was an independent protective factor for UTI in hospitalized patients with spinal cord injury (odds ratio < 1, P < 0.05), while white blood cell count and indwelling catheter were independent risk factors for UTI in hospitalized patients with spinal cord injury (odds ratio > 1, P < 0.05). Based on this, a nomogram risk predictive model for predicting UTI in hospitalized rehabilitation patients with spinal cord injury was constructed, which proved to have good predictive efficiency. In the training group and the verification group, the area under the receiver operating characteristic curve of the nomogram model is 0.808 and 0.767, and the 95% confidence interval of the area under the receiver operating characteristic curve of the nomogram in the training group and the verification group is 0.760∼0.856 and 0.688∼0.845, respectively, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of UTI in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has a good calibration degree in both the training group and the verification group (P = 0.329, 0.067). CONCLUSIONS: ASIA classification level, white blood cell count, and indwelling catheter are independent influencing factors of UTI in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of UTI in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment, and nursing strategies in time.