Gut microbiome and metabolism alterations in schizophrenia with metabolic syndrome severity.

BACKGROUND: Schizophrenia (SCZ) patients undergoing antipsychotic treatment demonstrated a high prevalence and harmful effects of metabolic syndrome (MetS), which acted as the major cause of cardiovascular disease. The major clinical challenge is the lack of biomarkers to identify MetS episodes and prevent further damage, while the mechanisms underlying these drug-induced MetS remain unknown. METHODS: This study divided 173 participants with SCZ into 3 groups (None, High risk, and MetS, consisting of 22, 88, and 63 participants, respectively). The potential biomarkers were searched based on 16S rRNA gene sequence together with metabolism analysis. Logistic regression was used to test the effects of the genus-metabolites panel on early MetS diagnoses. RESULTS: A genus-metabolites panel, consisting of Senegalimassilia, sphinganine, dihomo-gamma-linolenoylcholine, isodeoxycholic acid, and MG (0:0/22:5/0:0), which involved in sphigolipid metabolism, fatty acid metabolism, secondary bile acid biosynthesis and glycerolipid metabolism, has a great discrimination efficiency to MetS with an area under the curve (AUC) value of 0.911 compared to the None MetS group (P = 1.08E-8). Besides, Senegalimassilia, 3-Hydroxytetradecanoyl carnitine, isodeoxycholic acid, and DG(TXB2/0:0/2:0) distinguished between subgroups robustly and exhibited a potential correlation with the severity of MetS in patients with SCZ, and may act as the biomarkers for early MetS diagnosis. CONCLUSIONS: Our multi-omics study showed that one bacterial genus-five lipid metabolites panel is the potential risk factor for MetS in SCZ. Furthermore, Senegalimassilia, 3-Hydroxytetradecanoyl carnitine, isodeoxycholic acid, and DG(TXB2/0:0/2:0) could serve as novel diagnostic markers in the early stage. So, it is obvious that the combination of bacterial genus and metabolites yields excellent discriminatory power, and the lipid metabolism provide new understanding to the pathogenesis, prevention, and therapy for MetS in SCZ.

Prevalence and influencing factors of abnormal carotid artery intima-media thickness in Henan Province in China.

Background: Carotid intima-media thickness (CIMT) has been shown to be a valuable predictor of cardiovascular diseases (CVDs). The aim of this study was to investigate the influencing factors of CIMT among adults in Central China. Methods: A total of 2,578 participants who underwent physical examination in Henan Provincial People's Hospital between January 2018 and July 2018 were enrolled. The respondents were divided into two subgroups according to their CIMT value (CIMT ≥1.0 mm group and CIMT <1.0 mm group). Variables considered were age, gender, total cholesterol, triglycerides, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) triglycerides, fasting blood glucose, and uric acid levels, as well as hypertension, diabetes, body mass index (BMI), waist-to-hip ratio, body fat percentage, and visceral fat area (VFA). Logistic regression analysis was performed to explore the potential factors influencing CIMT. Results: The proportion of CIMT ≥1.0 mm among the physical examination population was 27.42% (707/2 578). The analysis of the two groups revealed significant differences in age, sex, hypertension, diabetes, total cholesterol, and HDL cholesterol. In the logistic regression analysis, age (OR=1.071, 95%CI=1.062-1.080), male sex (OR=2.012, 95%CI=1.251-2.238), hypertension (OR=1.337, 95%CI=1.031-1.735), diabetes (OR=1.535, 95%CI=1.045-2.255), HDL cholesterol (OR=0.540, 95%CI=0.366-0.796), and LDL cholesterol (OR=1.336, 95%CI=1.154-1.544) were significantly associated with abnormal CIMT. Conclusion: Early screening should be carried out among men, the older adults, and those with hypertension, diabetes, and dyslipidemia.

Predicting the formation of mixed pattern hemorrhages in ruptured middle cerebral artery aneurysms based on a decision tree model: A multicenter study.

OBJECTIVE: Mixed-pattern hemorrhages (MPH) commonly occur in ruptured middle cerebral artery (MCA) aneurysms and are associated with poor clinical outcomes. This study aimed to predict the formation of MPH in a multicenter database of MCA aneurysms using a decision tree model. METHODS: We retrospectively reviewed patients with ruptured MCA aneurysms between January 2009 and June 2020. The MPH was defined as subarachnoid hemorrhages with intracranial hematomas and/or intraventricular hemorrhages and/or subdural hematomas. Univariate and multivariate logistic regression analyses were used to explore the prediction factors of the formation of MPH. Based on these prediction factors, a decision tree model was developed to predict the formation of MPH. Additional independent datasets were used for external validation. RESULTS: We enrolled 436 patients with ruptured MCA aneurysms detected by computed tomography angiography; 285 patients had MPH (65.4%). A multivariate logistic regression analysis showed that age, aneurysm size, multiple aneurysms, and the presence of a daughter dome were the independent prediction factors of the formation of MPH. The areas under receiver operating characteristic curves of the decision tree model in the training, internal, and external validation cohorts were 0.951, 0.927, and 0.901, respectively. CONCLUSION: Age, aneurysm size, the presence of a daughter dome, and multiple aneurysms were the independent prediction factors of the formation of MPH. The decision tree model is a useful visual triage tool to predict the formation of MPH that could facilitate the management of unruptured aneurysms in routine clinical work.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer.

BACKGROUND: Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients. METHODS: We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated. RESULTS: The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

Study on the material basis and mechanism of Hemerocallis citrina Baroni on sleep-improvement using Drosophila activity monitoring, metabolomic, targeted screening and transcriptomic.

Daylily (Hemerocallis citrina Baroni, HC) is an edible plant and is traditionally considered with potential to improve sleep. Herein, based on the Drosophila activity monitoring, metabolome, targeted screening and transcriptome, the material basis and mechanism of HC on sleep-improvement was investigated. The results showed that the aqueous extracts of HC (HAE) as well as the ethanol extracts (HEE) all prolonged the total sleep time of insomnia fruit flies, especially HEE-60 and HEE-95 exhibited more significant effects. In addition, 539 of 728 found metabolites were screened as potential sleep-improved metabolites, and quercetin, linoleic acid, phenethyl caffeate, L-methionine and γ-aminobutyric acid were considered as core active metabolites. Meanwhile, 368 differentially expressed genes (DEGs) were revealed by transcriptomics analysis, and the neuroactive ligand-receptor interaction was deduced as the main pathway by KEGG pathway enrichment. Furthermore, nine DEGs located in this pathway, namely betaTry, deltaTry, gammaTry, epsilonTry, etaTry, iotaTry, lambdaTry, kappaTry and CG30031 were proven being up-regulated. All these results contribute to the development of HC-related functional foods.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Background: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

Quantifying the impacts of coal mining activities on topsoil using Hg stable isotope: A case study of Guqiao mining area, Huainan City.

The Hg released from coal mining activities can endanger soil ecosystems and pose a risk to human health. Understanding the accumulation characteristics of mercury (Hg) in coal mining soil is important for effectively controlling Hg emissions and developing measures for the prevention and control of Hg contamination. To identify the potential sources of Hg in soils, the Hg concentration and isotopic composition characteristics of raw coal and different topsoil types from the areas surrounding a coal mine were determined in this study. The results showed that Hg in coal mainly exists mainly in the form of inorganic Hg, and Hg has experienced Hg2+ photoreduction prior to incorporating into coal. In addition, the composition of Hg isotopes differed significantly among different topsoil types, and the δ202Hg value of the farmland soil exhibited large negative excursions compared to the coal mining soil. The ternary mixed model further revealed the presence of substantial differences in potential Hg sources among the two regions, with the coal mining soil being greatly disturbed by anthropogenic activity, and the relative contributions of Hg from raw coal, coal gangue, and background soil to coal mining soil being 33.42%, 34.4%, and 32.19%, respectively. However, Hg from raw coal, coal gangue and background soil contributed 17.04%, 21.46%, and 61.51% of the Hg in the farmland soil, indicating that the accumulation of Hg in farmland soil was derived primarily from the background soil. Our study demonstrated that secondary pollution in soil caused by immense accumulation of solid waste (gangue) by mining activities offers a significant challenge to ecological security. These findings provide new insights into controlling soil Hg in mining areas and further highlight the urgency of strict protective measures for contaminated sites.

The Synergistic Mechanisms of AIE, ESIPT and ICT in the α-cyanostilbene-based Derivative: A Red-fluorescence Probe With a Large Stokes' Shift for Copper (II) Ion Determination and Reversible Response to Amine/acid Vapor.

Herein, α-cyanostilbene-based luminogen with an electron donor-π-electron acceptor (D-π-A) architecture was formylated into the salicylaldehyde-analogue luminogen, followed by the Schiff base reaction with phenylamine, a red-emitting luminogen was elaborately designed and successfully synthesized in a high yield of 89%. Its well-defined structure was confirmed by FT-IR, MALDI-TOF-MS, HR-MS and 1H/13C NMR technologies. Based on the synergistic mechanisms of aggregation-induced emission (AIE), excited-state intramolecular proton transfer (ESIPT) and intramolecular charge transfer (ICT), it enjoyed a red-fluorescence emission at 627 nm in THF/water mixtures (fw = 95%) and was used as a probe. Moreover, the TLC-based test strips loaded with the probe not only exhibited the reversible fluorescence response to amine/acid vapor but also showed sensitive and selective fluorescence response towards Cu2+. Furthermore, the fluorescence titration experiment between the probe and Cu2+ in THF/water mixtures (fw = 95%, pH = 7.4) revealed that the detection limit was 1.18 × 10-7 M and the binding constant was 1.59 × 105. Job's plot experiment and HR-MS analysis revealed the 2:1 binding stoichiometry of the probe with Cu2+. The method enabled real-time assessment for Cu2+ in real water samples. This study could offer insightful opinions on the development of long-wavelength emissive luminogens based on α-cyanostilbene.

Restoration effect of sulfhydryl-modified sepiolite on cadmium in contaminated soil and its effect on the growth of spinach (Spinacia oleracea L).

Coal mining has produced a large amount of coal gangue. It makes the soil around the mining area seriously polluted by heavy metals, affects the growth of crops, and endangers human health. Therefore, there is an urgent need to develop new materials for remediation of Cd in soil. In this study, mercaptosilane-modified sepiolite (Q-Sep) was used as a basic passivator, and it was pretreated with acid (H-Q-Sep) and high temperature (R-Q-Sep) respectively. By analyzing the forms of Cd and pH values in soil after adding modified sepiolite, we compared the remediation effects of two modified methods on Cd in soil. The enrichment of spinach (Spinacia oleracea L) to Cd and changes in physiological and biochemical indexes of spinach were determined, and the effect of modified sepiolite on the growth of spinach was judged. The experimental results showed that the addition of modified sepiolite could significantly increase the soil pH values (p < 0.05); the content of exchangeable Cd in soil decreased by 60.4%; and the maximum increase of residual state was 32.9%. The absorption of Cd in soil by spinach decreased, and root length, plant height, and biomass of spinach all increased. It was proved that the addition of modified sepiolite can improve the productivity of soil, reduce toxicity of heavy metals in soil, and promote growth of plants. As a result, the addition of H-Q-Sep and R-Q-Sep can effectively repair Cd in gangue filled soil, which provides a certain theoretical basis for the passivation remediation of Cd in soil.

Prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and quantitative analysis of driving factors.

The prediction of the spatiotemporal dynamic evolution of vegetation cover in the Huainan mining area and the quantitative evaluation of its driving factors are of great significance for protecting and restoring the environment in this area. This study uses the Landsat 5 TM and Landsat 8 OLI time-series data to estimate the vegetation cover and uses the transition matrix to analyze the spatiotemporal transfer of vegetation cover from 1989 to 2004, 2004 to 2021, and 2021 to 2030. In addition, a structural equation model (SEM) was established in this study to assess the driving factors of vegetation cover. The quantitative analysis and the cellular automata (CA)-Markov model were performed to predict the future vegetation cover in the Huainan mining area. The results are as follows: (1) In different periods, the vegetation cover types were mainly high cover types transferred to other vegetation cover types; (2) human activities are the key factors affecting the vegetation growth, while topographical factor is the most influential factor promoting the vegetation growth; (3) highly consistent CA-Markov and multi-criteria evaluation (MCE) predicted results of vegetation cover in 2030 compared to that in 2021. The proportion of bare soil and low cover types had increased significantly, mainly concentrated in the internal area of the mines. The prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and the quantitative change in driving factors are of significant importance for the restoration of the environment in mining areas.

The mechanism of chronic unpredictable mild stress induced high blood pressure in rats: a proteomic and targeted metabolomic analysis.

Chronic stress, a leading factor for high blood pressure (BP) and even hypertension, affects health quality seriously. However, the management is rather difficult in our rapidly developing modern society, and the underlying mechanism that caused hypertension remains incompletely understood. In this study, we established a rat model of high BP induced by chronic unpredictable mild stress (CUMS). The results showed that CUMS increased the BP and heart rate, as well as the concentrations of CORT, NA, and ACTH. Based on tandem mass tag (TMT)-labeled proteomics, 13 proteins changed in RVLM. Then, targeted metabolomics together with real-time qPCR were applied to validate the levels of the biomolecules quantitatively. The related molecules were confirmed to reveal that CUMS has a great role in the upregulation of muscle contraction, synthesis of cAMP and transport of metals, while down-regulating ralaxin signaling. This finding facilitates a better understanding of the mechanism of hypertension induced by chronic stress and could provide an insight into the prevention and treatment of hypertension.

Simulation of land use trends and assessment of scale effects on ecosystem service values in the Huaihe River basin, China.

Land use demand change in the Huaihe River basin (HRB) and ecosystem service values (ESVs) in watersheds are important for the sustainable development and use of land resources. This paper takes the HRB as the research object, and using remote sensing images of land use as the data source adopts the comprehensive evaluation analysis method of ESVs based on equivalent factors and sensitivity analysis of the performance characteristics of ESV changes of different land use types. The PLUS model is used to predict spatiotemporal land use change characteristics to 2030 combining inertial development, ecological development, and cultivated land development. The spatial distribution and aggregation of ESVs at each scale were also explored by analyzing ESVs at municipal, county, and grid scales. Considering also hotspots, the contribution of land use conversion to ESVs was quantified. The results showed that (1) from 2000 to 2020, cultivated land decreased sharply to 28,344.6875 km2, while construction land increased sharply to 26,914.563 km2, and the change of other land types was small. (2) The ESVs in the HRB were 222,019 × 1012 CNY in 2000, 235,015 × 1012 CNY in 2005, 234,419 × 1012 CNY in 2010, 229,885 × 1012 CNY in 2015, and 224,759 × 1012 CNY in 2020, with an overall fluctuation, first increasing and then decreasing. (3) The ESVs were 219,977 × 1012 CNY, 218,098 × 1012 CNY, 219,757 × 1012 CNY, and 213,985 × 1012 CNY under the four simulation scenarios of inertial development, ecological development, cultivated land development, and urban development, respectively. At different scales, the high-value areas decreased, and the low-value areas increased. (4) The hot and cold spots of ESV values were relatively clustered, with the former mainly clustered in the southeast region and the latter mainly clustered in the northwest region. The sensitivity of ecological value was lower than 1, while the ESV was inelastic to the ecological coefficient, and the results were plausible. The mutual conversion of cultivated land to water contributed the most to ESVs. Based on the multi-scenario simulation of land use in the HRB by the PLUS model, we identified the spatial distribution characteristics of ESVs at different scales, which can provide a scientific basis and multiple perspectives for the optimization of land use structure and socio-economic development decisions.

Geochemical Characteristics of Rare-Metal, Rare-Dispersed, and Rare-Earth Elements and Depositional Environments in the Shanxi Formation Coal, Huainan Coalfield, Anhui, China.

Coal, being one of the major energy sources for power generation, contains several critical trace elements. There is a growing scarcity and expense of these critical elements as a result of the increased demand and limitation of mining sources. To explore the geochemical characteristics of the rare-metal, rare-dispersed (scattered), and rare-earth elements (TREs) in coal, 25 coal seam samples of the Shanxi Formation in the Huainan coalfield were collected. The major element oxides, minerals, and TREs were analyzed by X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), and inductively coupled plasma-mass spectrometry (ICP-MS). The results revealed that the coal of the Shanxi Formation had ultra-low moisture and low ash yield and was medium-high-volatility with low sulfur content and high calorific value. Concerning minerals, the coal was mainly composed of kaolinite, illite, quartz, calcite, dolomite, and pyrite. Compared with Chinese coal and world hard coal, rare-metal element Li and rare-dispersed element Se were enriched, whereas Ga and Ta were only slightly enriched. The average content of REYs was 51.34 µg/g, which is lower than the average content of REYs in Chinese coal. It has the enrichment characteristics of light REYs. In the vertical direction, the content of most TREs was higher in the roof and floor of the coal seam and the parting, indicating that the sedimentary microenvironment plays an important role in controlling the migration and enrichment of elements. The experimental results of sequential chemical extraction and correlation analysis showed that the TREs in the Shanxi Formation coal mainly exist in a residual and carbonate bound state, and occur in clay minerals and carbonate minerals. The enrichment of Se may be due to its high organic form ratio. The C-value, B content, w(Sr)/w(Ba), and REY geochemical parameters indicated that the Shanxi Formation Coal seam was developed in a transitional, semi-saline, deltaic sedimentary environment. With their development affected by seawater, REYs in coal are greatly supplied by terrigenous clastics. The complex sedimentary environment is an important reason for the varying occurrence states of TREs in the Shanxi Formation coals.

Seasonal succession of microbial community co-occurrence patterns and community assembly mechanism in coal mining subsidence lakes.

Coal mining subsidence lakes are classic hydrologic characteristics created by underground coal mining and represent severe anthropogenic disturbances and environmental challenges. However, the assembly mechanisms and diversity of microbial communities shaped by such environments are poorly understood yet. In this study, we explored aquatic bacterial community diversity and ecological assembly processes in subsidence lakes during winter and summer using 16S rRNA gene sequencing. We observed that clear bacterial community structure was driven by seasonality more than by habitat, and the α-diversity and functional diversity of the bacterial community in summer were significantly higher than in winter (p < 0.001). Canonical correspondence analysis indicated that temperature and chlorophyll-a were the most crucial contributing factors influencing the community season variations in subsidence lakes. Specifically, temperature and chlorophyll-a explained 18.26 and 14.69% of the community season variation, respectively. The bacterial community variation was driven by deterministic processes in winter but dominated by stochastic processes in summer. Compared to winter, the network of bacterial communities in summer exhibited a higher average degree, modularity, and keystone taxa (hubs and connectors in a network), thereby forming a highly complex and stable community structure. These results illustrate the clear season heterogeneity of bacterial communities in subsidence lakes and provide new insights into revealing the effects of seasonal succession on microbial assembly processes in coal mining subsidence lake ecosystems.

Quantitative identification of nitrate and sulfate sources of a multiple land-use area impacted by mine drainage.

The rapid increase in urbanization and intensive coal mining activities have accelerated the deterioration of surface water quality. Environmental problems caused by the accumulation of nitrate and sulfate from natural, urban, and agricultural sources have attracted extensive attention. Information on nitrate and sulfate sources and their transformations is crucial for understanding the nitrogen and sulfur cycles in surface water. In this study, we monitored nitrate and sulfate in three representative rivers in mining cities in northern China. The main pollution sources and biogeochemical processes were identified by using stable isotopes (δD, δ18OH2O, δ15N, δ18ONO3, δ34S and δ18OSO4) and hydrochemistry. The contribution of natural and anthropogenic sources was quantitatively estimated based on a Bayesian mixed model. The results indicated a large variation in sulfate and nitrate sources between the different rivers. Nitrate in the Tuohe River mainly derived from manure/sewage (57.9%) and soil N (26.9%), while sulfate mainly derived from manure/sewage (41.7%) and evaporite dissolution (26.8%). For the Suihe River, nitrate was primarily sourced from chemical fertilizer (37.9%) and soil nitrogen (34.8%), while sulfate was mainly sourced from manure/sewage (33.1%) and chemical fertilizer (21.4%). For the Huihe River, nitrate mainly derived from mine drainage (56.6%) and manure/sewage (30.6%), while sulfate predominantly originated from mine drainage (58.3%) and evaporite dissolution (12.9%). Microbial nitrification was the major pathway for the migration and transformation of nitrate in the surface water. However, denitrification and bacterial sulfate reduction (BSR) did not play a significant role as aerobic conditions prevailed. In this study, we elucidated the sources and transformation mechanisms of nitrate and sulfate. Additionally, we provided a reference for formulating a comprehensive strategy for effective management and remediation of surface water contaminated with nitrate and sulfate in mining cities.

The effect of nutritional status on clinical outcome in lung transplantation.

BACKGROUND AND OBJECTIVES: Nutritional status greatly impacts the clinical outcome of the patients receiving lung transplantation. The objective of this study was to evaluate the effect of nutritional status on the clinical outcome in lung transplant recipients. METHODS AND STUDY DESIGN: A single-center retrospective study was conducted including 73 patients received lung transplantation from December 2015 to April 2022 in the Affiliated Henan Provincial People's Hospital of Zhengzhou University. Data were collected from the hospital information system. The records of BMI, malnutrition defined by the Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, hemoglobin and plasma albumin before operation were accessed. The primary outcome assessed was survival or mortality represented by Kaplan-Meier survival curves; the log-rank test and multivariate Cox proportional hazards regression were used to evaluate the influence of each factor on survival. RESULTS: Kaplan-Meier survival analysis showed that malnutrition, hemoglobin and plasma albumin were predictors of survival in lung transplantation (Log Rank p<0.05). Multivariate Cox regression showed that pre-operative hemoglobin <130 g/L (HR 2.532, p=0.036) and plasma albumin <35 g/L (HR 2.723, p=0.016) were associated with the decreased survival rate. CONCLUSIONS: Preoperative anemia and hypoalbuminemia increase the mortality risk of the lung transplantation patients. Pre-operative nutrition support, therefore, is likely to be critical for improving clinical outcome in patients undergoing lung transplantation.

Preparation, AIE and ESIPT behaviour, controllable solid-state fluorescence and application of Co2+probe based onα-cyanostilbene.

A simpleα-cyanostilbene-functioned salicylaldehyde-based Schiff-base probe, which exhibited outstanding 'aggregation-induced emission and excited state intramolecular proton transfer (AIE + ESIPT)' emission in solution, aggregation and solid states, was synthesized in high yield of 87%. Its solid-states with different morphologies emitted different fluorescence after crystallization in EtOH/H2O (1/2, v/v) mixtures or pure EtOH solvent. Besides, it exhibited an obvious spectro-photometrical fluorescence quenching for highly selective sensing of Co2+in THF/water system (ƒw= 60%, pH = 7.4), accompanied by an intense green fluorescence turn-off behavior under UV365nmillumination. The binding stochiometry between the ligand and Co2+was found to be 2:1, and the detection limit (DL) was calculated to be 0.41 × 10-8M. In addition, it could be applied to detect Co2+in real water samples and on silica gel testing strip.

A WeChat-based smoking cessation intervention for Chinese smokers: a feasibility study.

WeChat is the largest social media platform in China, yet few WeChat-based smoking cessation interventions have been investigated to date. The objective of this study was to develop and test the feasibility of a WeChat-based smoking cessation intervention for smokers in China. Participants were recruited using WeChat and were then randomized into one of three groups: the Standard Group, the Enhanced Group, and the wait-list Control Group. Feasibility indicators including program reach, recruitment rate, recruitment efficiency, cost per person, attrition rate, intervention message exposure, group discussion utilization rate, intervention message engagement, satisfaction, and the likelihood of recommending to others were measured with assessed. Analyses included Chi-square and Fisher exact test, as well as analysis of variance test. A responsive participant is defined as a participant that responded to a certain assessment. A total of 1,132 individuals connected with ("friended") our project on WeChat between July 1 and August 5, 2019. Of these, 403 were eligible to participate, consented, and completed the baseline assessment. As a result, 136, 135, and 132 smokers were randomly assigned to the Standard Group, the Enhanced Group, and the Control Group, respectively. The total program recruitment rate was 35.6% and the attrition rate was 46.4%. The program cost was $0.85 per person. All responsive participants read at least one message during the intervention and engaged with intervention messages 56.8% of the time. Most responsive participants reported being very or somewhat satisfied, highly or somewhat engaged, and were willing to recommend our program to others. This study demonstrated the feasibility of a smoking cessation interventions using WeChat. The program could be expanded to deliver smoking cessation interventions to a large population of smokers.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling.

Objective: Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features. Methods: From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression. Results: A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms. Conclusion: Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.