[Research progress of chemerin in polycystic ovary syndrome].

As a multifunctional adipokine, chemerin plays a crucial role in various pathophysiological processes through endocrine and paracrine manner. It can bind to three known receptors (ChemR23, GPR1 and CCRL2) and participate in energy metabolism, glucose and lipid metabolism, and inflammation, especially in metabolic diseases. Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases, which seriously affects the normal life of women of childbearing age. Patients with PCOS have significantly increased serum levels of chemerin and high expression of chemerin in their ovaries. More and more studies have shown that chemerin is involved in the occurrence and development of PCOS by affecting obesity, insulin resistance, hyperandrogenism, oxidative stress and inflammatory response. This article mainly reviews the production, subtypes, function and receptors of chemerin protein, summarizes and discusses the research status of chemerin protein in PCOS from the perspectives of metabolism, reproduction and inflammation, and provides theoretical basis and reference for the clinical diagnosis and treatment of PCOS.

Predictive Value of Neutrophil to High-Density Lipoprotein Ratio for Contrast-Induced Acute Kidney Injury for Patients with Acute Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention.

Background: To investigate the incidence of contrast-induced acute kidney injury (CI-AKI) in patients with acute myocardial infarction (AMI) undergoing primary percutaneous coronary intervention (PCI) in relation to the neutrophil to high-density lipoprotein cholesterol ratio (NHR), and to further compare the predictive value of NHR and the neutrophil to lymphocyte ratio (NLR) for CI-AKI. Methods: We retrospectively analyzed 1243 AMI patients undergoing PCI from January 2019 to December 2021, and collected creatinine within 72 h after PCI. All patients were divided into a CI-AKI group and non-CI-AKI group according to the definition of CI-AKI, and the clinical information of the two groups was compared. Potential risk factors for CI-AKI in AMI patients undergoing primary PCI were screened by using logistic regression analysis, and receiver operating characteristic (ROC) curves were used to compare the predictive value of NHR and NLR. Results: A high NHR and high NLR were correlated with a high incidence of CI-AKI in AMI patients undergoing primary PCI, and NHR (odds ratio (OR): 1.313, 95% confidence interval (CI): 1.199-1.438) and NLR (OR: 1.105, 95% CI: 1.041-1.174) were independent risk factors for CI-AKI (p < 0.05). Compared with NLR, the area under the curve (AUC) of NHR was larger (AUC = 0.668, 95% CI: 0.641-0.694 vs. AUC = 0.723, 95% CI: 0.697-0.748), and the difference was significant (p < 0.05), with higher sensitivity (61.67% vs. 70.83%) and specificity (64.91% vs. 66.10%). Conclusions: Compared with the NLR, the NHR is more valuable in predicting the incidence of CI-AKI in AMI patients undergoing primary PCI.

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.

BACKGROUND: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. OBJECTIVE: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. METHODS: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. RESULTS: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. CONCLUSIONS: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning.

BACKGROUND: With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources. METHODS: In this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model. RESULTS: Our proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R2 improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD. CONCLUSIONS: Our stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.

BACKGROUND: Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. METHODS: Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. RESULTS: Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. CONCLUSIONS: Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Gaseous air pollutants and hospitalizations for mental disorders in 17 Chinese cities: Association, morbidity burden and economic costs.

The short-term morbidity effects of gaseous air pollutants on mental disorders (MDs), and the corresponding morbidity and economic burdens have not been well studied. We aimed to explore the associations of ambient sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) with MDs hospitalizations in 17 Chinese cities during 2015-2018, and estimate the attributable risk and economic costs of MDs hospitalizations associated with gaseous pollutants. City-specific relationships between gaseous pollutants and MDs hospitalizations were evaluated using over-dispersed generalized additive models, then combined to obtain the pooled effect. Concentration-response (C-R) curves of gaseous pollutants with MDs from each city were pooled to allow regional estimates to be derived. The morbidity and economic burdens of MDs hospitalizations attributable to gaseous pollutants were further assessed. A total of 171,939 MDs hospitalizations were included. We observed insignificant association of O3 with MDs. An interquartile range increase in SO2 at lag0 (9.1 µg/m³), NO2 at lag0 (16.7 µg/m³) and CO at lag2 (0.4 mg/m³) corresponded to a 3.02% (95%CI: 0.72%, 5.38%), 5.03% (95%CI: 1.84%, 8.32%) and 2.18% (95%CI: 0.40%, 4.00%) increase in daily MDs hospitalizations, respectively. These effects were modified by sex, season and cause-specific MDs. The C-R curves of SO2 and NO2 with MDs indicated nonlinearity and the slops were steeper at lower concentrations. Overall, using current standards as reference concentrations, 0.27% (95%CI: 0.07%, 0.48%) and 0.06% (95%CI: 0.02%, 0.10%) of MDs hospitalizations could be attributable to extra SO2 and NO2 exposures, and the corresponding economic costs accounted for 0.34% (95%CI: 0.08%, 0.60%) and 0.07% (95%CI: 0.03%, 0.11%) of hospitalization expenses, respectively. Moreover, using threshold values detected from C-R curves as reference concentrations, the above mentioned morbidity and economic burdens increased a lot. These findings suggest more strict emission control regulations are needed to protect mental health from gaseous pollutants.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study.

BACKGROUND: Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed. OBJECTIVE: We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences. METHODS: We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends. RESULTS: The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community. CONCLUSIONS: Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.

Correction: Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study.

[This corrects the article DOI: 10.2196/27146.].

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately. METHODS: In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance. RESULTS: The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions. CONCLUSION: Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.

Differential effects of size-specific particulate matter on lower respiratory infections in children: A multi-city time-series analysis in Sichuan, China.

Evidence on the short-term effects of size-specific particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), and their difference (PMC) on children's Lower Respiratory Infections (LRI) is scare. This study aimed to estimate the differential effects of three size-specific PM on hospitalizations of children aged <18 years for pneumonia and bronchitis in 18 cities of southwestern China. The city-specific association was firstly estimated using the over-dispersed generalized additive model and then combined to obtain the regional average association. Further, to evaluate the robustness of the key findings, subgroup analyses and co-pollutant models were constructed. PM-related risks of LRI differed by PM fractions and cause-specific LRI. A 10 µg/m3 increment in PM2.5_lag03, PM10_lag06, and PMC_lag06 was associated with a 0.79% (95% CI: 0.29%, 1.29%), 0.77% (95% CI: 0.13%, 1.41%), and 2.33% (95% CI: 1.23%, 3.44%) increase in children's LRI hospitalizations, respectively. After adjustment for gaseous pollutants, adverse effects of the three types of size-specific PM on pneumonia hospitalizations were stable, ranging from 0.29% (95% CI: 0.05%, 0.54%) for PM2.5-2.50% (95% CI: 1.38%, 3.64%) for PMC. Additionally, PMC-related risk of bronchitis hospitalizations remained stable after adjustment for gaseous pollutants. Associations of pneumonia with PMC and PM10 in infants, bronchitis with PM2.5 in children aged 6-17 years, pneumonia and bronchitis with PM2.5, PMC, and PM10 in children aged 1-5 years were all statistical significant. Specifically, the effects of PM2.5 on LRI hospitalizations increased by age, with the highest effect of 1.72% (95%CI: 1.01%, 2.43%) in children aged 6-17 years. Our study provided evidence for short-term effects of different PM fractions on children LRI hospitalizations in Southwestern China, which will be useful for making and promoting policies on air quality standards in order to protect children's health.

Coarse particles (PM2.5-10) and cause-specific hospitalizations in southwestern China: Association, attributable risk and economic costs.

The short-term morbidity effects of the coarse particle (diameter in 2.5-10 µm, PM2.5-10), as well as the corresponding morbidity burden and economic costs, remain understudied, especially in developing countries. This study aimed to examine the associations of PM2.5-10 with cause-specific hospitalizations in a multi-city setting in southwestern China and assess the attributable risk and economic costs. City-specific associations were firstly estimated using generalized additive models with quasi-poisson distribution to handle over-dispersion, and then combined to obtain the regional average association. City-specific and pooled concentration-response (C-R) associations of PM2.5-10 with cause-specific hospitalizations were also modeled. Subgroup analyses were performed by age, sex, season and region. The health and economic burden of hospitalizations for multiple outcomes due to PM2.5-10 were further evaluated. A total of 4,407,601 non-accidental hospitalizations were collected from 678 hospitals. The estimates of percentage change in hospitalizations per 10 µg/m³ increase in PM2.5-10 at lag01 was 0.68% (95%CI: 0.33%-1.03%) for non-accidental causes, 0.86% (95% CI: 0.36%-1.37%) for circulatory diseases, 1.52% (95% CI: 1.00%-2.05%) for respiratory diseases, 1.08% (95% CI: 0.47%-1.69%) for endocrine diseases, 0.66% (95% CI: 0.12%-1.21%) for nervous system diseases, and 0.84% (95% CI: 0.42%-1.25%) for genitourinary diseases, respectively. The C-R associations of PM2.5-10 with cause-specific hospitalizations suggested some evidence of nonlinearity, except for endocrine diseases. Meanwhile, the adverse effects were modified by age and season. Overall, about 0.70% (95% CI: 0.35%-1.06%) of non-accidental hospitalizations and 0.78% (95% CI: 0.38%-1.17%) of total hospitalization expenses could be attributed to PM2.5-10. The largest morbidity burden and economic costs were observed in respiratory diseases. Our findings indicate that PM2.5-10 exposure may increase the risk of hospitalizations for multiple outcomes, and account for considerable morbidity and economic burden.

Alpha-lipoic acid ameliorates H2O2-induced human vein endothelial cells injury via suppression of inflammation and oxidative stress.

The study was aimed to investigate the effect of alpha-lipoic acid (ALA) on human umbilical vein endothelial cells (HUVECs) injury induced by hydrogen peroxide (H2O2) and to explore its possible mechanisms. We established the H2O2-induced HUVECs injury model and the ALA treatment groups in which HUVECs were co-incubated with H2O2 (250 µmol/L) and different final concentrations of ALA (100,200,400 µmol/L) for 48 h. Cell survival rate assay and LDH activity assay were carried out. The levels of related proteins were performed by Western Blot. We observed that H2O2 administration resulted in an increase in the LDH activity and a decrease in cell survival rate. The expression levels of Nox4, Bax, NF-κB p65, Caspase-9, Caspase-3, iNOS, VCAM-1 and ICAM-1 were up-regulated, while the expression level of Bcl-2 was down-regulated. All these factors were significantly improved by ALA treatment. In brief, ALA treatment ameliorates H2O2-induced HUVECs damage by inhibiting inflammation and oxidative stress.

Causal Effect of Honorary Titles on Physicians' Service Volumes in Online Health Communities: Retrospective Study.

BACKGROUND: An OHC online health community (OHC) is an interactive platform for virtual communication between patients and physicians. Patients can typically search, seek, and share their experience and rate physicians, who may be involved in giving advice. Some OHC providers provide incentives in form of honorary titles to encourage the web-based involvement of physicians, but it is unclear whether the award of honorary titles has an impact on their consultation volume in an OHC. OBJECTIVE: This study is designed to identify the differential treatment effect of the incentive policy on the service volumes for the subgroups of treatment and control in an OHC. This study aims to answer the following questions: Does an honorary title for physicians impact their service volumes in an OHC? During the period of discontinuity, can we identify the sharp effect of the incentive award on the outcomes of physicians' service volumes? METHODS: We acquired the targeted samples based on treatment, namely, physicians with an honorary title or not and outcomes measured before and after the award of the 2 subgroups. A regression discontinuity design was applied to investigate the impact of the honorary titles incentive as a treatment in an OHC. There was a sharply discontinuous effect of treatment on physicians' online health service performance. The experimental data set consisted of 346 physicians in the treatment group (with honorary titles). Applying the propensity score matching method, the same size of physicians (n=346) was matched and selected as the control group. RESULTS: A sharp discontinuity was found at the time of the physician receiving the honorary title. The results showed that the parametric estimates of the coefficient were significantly positively (P<.001) associated with monthly home page views. The jump in the monthly volumes of home page views was much sharper than that of the monthly consultations. CONCLUSIONS: The changes in the volumes of monthly consultations and home page views reflect the differential treatment effect of honorary titles on physicians' service volumes. The effect of the incentive policy with honorary titles is objectively estimated from both the perspective of online and offline medical services in an OHC. Being named with honorary titles significantly multiplied monthly home page views, yet it did not significantly impact monthly consultations. This may be because consultation capacity is limited by the physician's schedule for consultations.

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.

BACKGROUND: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017. METHODS: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models. RESULTS: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively. CONCLUSION: This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. METHODS: In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. RESULTS: The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). CONCLUSION: It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.

Attributable risk of hospital admissions for overall and specific mental disorders due to particulate matter pollution: A time-series study in Chengdu, China.

The associations of particulate matter (PM) pollution with the morbidity of overall and subtypes of mental disorders (MDs), as well as the corresponding morbidity burden, remain understudied, especially in developing countries. This study aimed to evaluate the short-term effects of PM2.5 (diameters ≤ 2.5 µm), PM10 (diameters ≤ 10 µm) and PMC (diameters between 2.5 and 10 µm) on hospital admissions (HAs) for MDs in Chengdu, China, during 2015-2016, and calculate corresponding attributable risks. A generalized additive model (GAM) with controlling for time trend, meteorological conditions, holidays and day of the week was used to estimate the associations. Stratified analyses were also performed by age, gender and season. We further estimated the burden of HAs for MDs attributable to PM exposure. During the study period, a total of 10,947 HAs for MDs were collected. PM2.5, PM10 and PMC were significantly associated with elevated risks of MDs hospitalizations. Each 10 µg/m3 increase in PM2.5, PM10 and PMC at lag06 corresponded to an increase of 2.89% (95% CI: 0.75-5.08%), 1.91% (95% CI: 0.57-3.28%) and 3.95% (95% CI: 0.84-7.15%) in daily HAs for MDs, respectively. The risk estimates of PM on MDs hospitalizations were generally robust after adjustment for gaseous pollutants in two-pollutant models. We found stronger associations between PM pollution and MDs in males and in cool seasons than in females and in warm seasons. For specific subtypes of MDs, significant associations of PM pollution with dementia,schizophrenia and depression were observed. Using WHO's air quality guidelines as the reference concentrations, 9.53% (95% CI: 2.67-15.58%), 9.17% (95% CI: 2.91-14.70%) and 6.10% (95% CI: 1.40-10.32%) of HAs for MDs could be attributable to PM2.5, PM10 and PMC, respectively. Our results suggested that PM exposure might be an important trigger of hospitalizations for MDs in Chengdu, China, and account for substantial morbidity burden.

The burden of overall and cause-specific respiratory morbidity due to ambient air pollution in Sichuan Basin, China: A multi-city time-series analysis.

Few studies have investigated the respiratory morbidity burden due to ambient air pollution in China, especially in a multi-city setting. This study aimed to estimate the short-term effects of ambient air pollutants (PM10, PM2.5, NO2 and SO2) on hospital admissions (HAs) for overall and cause-specific respiratory diseases, as well as the associated burden in 17 cities of Sichuan Basin, China during 2015-2016. Firstly, city-specific effect estimates for each pollutant on respiratory HAs were obtained using generalized additive model with quasi-Poisson link, and then random- or fixed-effects meta-analysis was applied to pool the effect estimates at the regional level. Subgroup analyses by sex, age, season and region were also performed. A total of 757,712 respiratory HAs were collected from all the tertiary and secondary hospitals located in the 17 cities. Risks of HAs for overall and cause-specific respiratory diseases were elevated following increased PM10, PM2.5, NO2 and SO2 exposure. An increase of 10 µg/m3 in PM10 at lag01, PM2.5 at lag01, NO2 at lag0 and SO2 at lag02 was associated with a 0.43% (95% CI: 0.33%, 0.53%), 0.53% (95% CI: 0.39%, 0.68%), 2.36% (95% CI: 1.75%, 2.98%) and 2.54% (95% CI: 1.51%, 3.59%) increases in total respiratory HAs, respectively. Children (≤ 14 years) and elderly (≥ 65 years) appeared to be more vulnerable to the effects of ambient air pollutants. Comparing to the WHO's air quality guidelines, we estimated that 1.84% (95%CI: 1.42%, 2.25%), 1.73% (95%CI: 1.27%, 2.19%) and 0.34% (95%CI: 0.21%, 0.48%) of respiratory HAs were due to PM10, PM2.5 and SO2 exposure, respectively. This study suggests that air pollution might be an important trigger of respiratory admissions, and result in substantial burden of HAs for respiratory diseases in Sichuan Basin.

SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients. METHODS: We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction. RESULTS: The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework. CONCLUSIONS: Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.

Picrasma quassioides leaves: Insights from chemical profiling and bioactivity comparison with stems.

BACKGROUND: In Chinese Pharmacopeia, Picrasma quassioides (PQ) stems and leaves are recorded as Kumu with antimicrobial, anti-cancer, anti-parasitic effects, etc. However, thick stems are predominantly utilized as medicine in many Asian countries, with leaves rarely used. By now, the phytochemistry and bioactivity of PQ leaves are not well investigated. METHODS: An Orbitrap Elite mass spectrometer was employed to comprehensively investigate PQ stems and leaves sourced from 7 different locations. Additionally, their bioactivities were evaluated against 5 fungi, 6 Gram-positive bacteria and 9 Gram-negative bacteria, a tumor cell line (A549), a non-tumor cell line (WI-26 VA4) and N2 wild-type Caenorhabditis elegans. RESULTS: Bioassay results demonstrated the efficacy of both leaves and stems against tumor cells, several bacteria and fungi, while only leaves exhibited anthelmintic activity against C. elegans. A total of 181 compounds were identified from PQ stems and leaves, including 43 ß-carbolines, 20 bis ß-carbolines, 8 canthinone alkaloids, 56 quassinoids, 12 triterpenoids, 13 terpenoid derivatives, 11 flavonoids, 7 coumarins, and 11 phenolic derivatives, from which 10 compounds were identified as indicator components for quality evaluation. Most alkaloids and triterpenoids were concentrated in PQ stems, while leaves exhibited higher levels of quassinoids and other carbohydrate (CHO) components. CONCLUSION: PQ leaves exhibit distinct chemical profiles and bioactivity with the stems, suggesting their suitability for medicinal purposes. So far, the antibacterial, antifungal, and anthelmintic activities of PQ leaves were first reported here, and considering PQ sustainability, the abundant leaves are recommended for increased utilization, particularly for their rich content of PQ quassinoids.

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study.

BACKGROUND: Patients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated. OBJECTIVE: This study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China. METHODS: In this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P<.025), which resulted in a total of 66 chronic diseases. Phenotypic comorbidity networks were constructed across all CRC patients and different subgroups by sex, age (18-59, 60-69, 70-79, and ≥80 years), area (urban and rural), and cancer site (colon and rectum), with comorbidity as a node and linkages representing significant correlations between multiple comorbidities. RESULTS: A total of 29,610 new CRC cases occurred in Sichuan, China in 2020. The mean patient age at diagnosis was 65.6 (SD 12.9) years, and 75.5% (22,369/29,610) had at least one comorbidity. The most prevalent comorbidities were hypertension (8581/29,610, 29.0%; 95% CI 28.5%-29.5%), hyperplasia of the prostate (3816/17,426, 21.9%; 95% CI 21.3%-22.5%), and chronic obstructive pulmonary disease (COPD; 4199/29,610, 14.2%; 95% CI 13.8%-14.6%). The prevalence of single comorbidities was different in each subgroup in most cases. Comorbidities were closely associated, with disorders of lipoprotein metabolism and hyperplasia of the prostate mediating correlations between other comorbidities. Males and females shared 58.3% (141/242) of disease pairs, whereas male-female disparities occurred primarily in diseases coexisting with COPD, cerebrovascular diseases, atherosclerosis, heart failure, or renal failure among males and with osteoporosis or gonarthrosis among females. Urban patients generally had more comorbidities with higher prevalence and more complex disease coexistence relationships, whereas rural patients were more likely to have co-existing severe diseases, such as heart failure comorbid with the sequelae of cerebrovascular disease or COPD. CONCLUSIONS: Male-female and urban-rural disparities in the prevalence of single comorbidities and their complex coexistence relationships in new CRC cases were not due to simple coincidence. The results reflect clinical practice in CRC patients and emphasize the importance of measuring comorbidity patterns in terms of individual and coexisting diseases in order to better understand comorbidity patterns.