Interventional therapy combined with tyrosine kinase inhibitors with or without immune checkpoint inhibitors as initial treatment for hepatocellular carcinoma with portal vein tumor thrombosis: a systematic review and meta-analysis.

BACKGROUND: Interventional therapy, in conjunction with tyrosine kinase inhibitors (TKIs), has shown promising outcomes for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT). With the advent of immunotherapy, the combined use of immune checkpoint inhibitors (ICIs) has attracted great attention due to their potential effectiveness in advanced HCC. This study aims to compare the efficacy and safety of a triple therapy regimen (Interventional therapy, TKIs and ICIs, IT-TKI-ICI) with a dual therapy regimen (Interventional therapy and TKIs, IT-TKI) in the treatment of HCC and PVTT (HCC-PVTT). METHODS: A comprehensive search was carried out in PubMed, Web of Science, Embase, Scopus, and the Cochrane Library databases. Primary outcome measures were overall survival (OS) and progression-free survival (PFS), while secondary outcomes included tumor response rate, adverse event incidence as well as downstaging surgery rate. Statistical analysis was conducted using Revman 5.4 software. RESULTS: The meta-analysis finally included 6 cohort studies. The triple therapy group demonstrated significantly prolonged OS and PFS compared to the dual therapy group. Meanwhile, the former exhibited significantly higher rates of objective response rate (ORR), disease control rate (DCR) and better downstaging effects with a higher salvage surgery rate without significantly increasing adverse events. CONCLUSION: In comparison to dual therapy, the triple therapy with interventional therapy, TKIs, and ICIs demonstrates superior efficacy and equivalent safety for HCC-PVTT.

Feasibility and Diagnostic Performance of Functional SYNTAX Score Derived From Dynamic CT Myocardial Perfusion Imaging.

BACKGROUND: Computed tomography (CT) fractional flow reserve (FFR)-derived functional SYNTAX score (FSSCT-FFR) is a valuable method for guiding treatment strategy in patients with multivessel coronary artery disease. Dynamic CT myocardial perfusion imaging (CT-MPI) demonstrates higher diagnostic accuracy than CT-FFR in identifying hemodynamically significant coronary artery disease. We aimed to evaluate the feasibility of CT-MPI-derived FSS (FSSCT-MPI) with reference to invasive FSS. METHODS: In this retrospective study, patients with multivessel coronary artery disease who underwent dynamic CT-MPI+ coronary CT angiography and invasive coronary angiography or FFR within 4 weeks were consecutively included. Invasive (FSSinvasive) and noninvasive FSS (FSSCT-MPI and FSSCT-FFR) were calculated by an online calculator, which assigned points to lesions with hemodynamic significance (defined as FFRinvasive ≤0.80, invasive coronary angiography diameter stenosis ≥90%, CT-FFR ≤0.80, and myocardial ischemia on CT-MPI). Weighted κ value and net reclassification index were calculated to determine the consistency and incremental discriminatory power of FSSCT-MPI. Receiver operating characteristic curve analysis was used for the comparison of FSSCT-MPI and FSSCT-FFR in detecting intermediate- to high-risk patients. RESULTS: A total of 119 patients (96 men; 64.6±10.6 years) with 305 obstructive lesions were included. The average FSSCT-MPI, FSSCT-FFR, and FSSinvasive were 15.58±13.03, 16.18±13.30, and 13.11±12.22, respectively. The agreement on risk classification based on the FSSCT-MPI tertiles was good (weighted κ, 0.808). With reference to FSSinvasive, FSSCT-MPI correctly reclassified 27 (22.7%) patients from the intermediate- to high SYNTAX score group to the low-score group (net reclassification index, 0.30; P<0.001). In patients with severe calcification, FSSCT-MPI had better diagnostic value than FSSCT-FFR in detecting intermediate- to high-risk patients when compared with FSSinvasive (area under the curve, 0.976 versus 0.884; P<0.001). CONCLUSIONS: Noninvasive FSS derived from CT-MPI is feasible and has strong concordance with FSSinvasive. It allows accurate categorization of FSS in patients with multivessel coronary artery disease, in particular with severe calcification.

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve [Formula: see text] AUC, [Formula: see text] accuracy, [Formula: see text] sensitivity, [Formula: see text] specificity, and [Formula: see text] F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

Heatwave characteristics complicate the association between PM2.5 components and schizophrenia hospitalizations in a changing climate: Leveraging of the individual residential environment.

BACKGROUND: In the era characterized by global environmental and climatic changes, understanding the effects of PM2.5 components and heatwaves on schizophrenia (SCZ) is essential for implementing environmental interventions at the population level. However, research in this area remains limited, which highlights the need for further research and effort. We aim to assess the association between exposure to PM2.5 components and hospitalizations for SCZ under different heatwave characteristics. METHODS: We conducted a 16 municipalities-wide, individual exposure-based, time-stratified, case-crossover study from January 1, 2017, to December 31, 2020, encompassing 160736 hospitalizations in Anhui Province, China. Daily concentrations of PM2.5 components were obtained from the Tracking Air Pollution in China dataset. Conditional logistic regression models were used to investigate the association between PM2.5 components and hospitalizations. Additionally, restricted cubic spline models were used to identify protective thresholds of residential environment in response to environmental and climate change. RESULTS: Our findings indicate a positive correlation between PM2.5 and its components and hospitalizations. Significantly, a 1 µg/m3 increase in black carbon (BC) was associated with the highest risk, at 1.58% (95%CI: 0.95-2.25). Exposure to heatwaves synergistically enhanced the impact of PM2.5 components on hospitalization risks, and the interaction varied with the intensity and duration of heatwaves. Under the 99th percentile heatwave events, the impact of PM2.5 and its components on hospitalizations was most pronounced, which were PM2.5 (2-4d: 4.59%, 5.09%, and 5.09%), sulfate (2-4d: 21.73%, 23.23%, and 25.25%), nitrate (2-4d: 17.51%, 16.93%, and 20.31%), ammonium (2-4d: 27.49%, 31.03%, and 32.41%), organic matter (2-4d: 32.07%, 25.42%, and 24.48%), and BC (2-4d: 259.36%, 288.21%, and 152.52%), respectively. Encouragingly, a protective effect was observed when green and blue spaces comprised more than 17.6% of the residential environment. DISCUSSION: PM2.5 components and heatwave exposure were positively associated with an increased risk of hospitalizations, although green and blue spaces provided a mitigating effect.

Green space and its types can attenuate the associations of PM2.5 and its components with prediabetes and diabetes-- a multicenter cross-sectional study from eastern China.

BACKGROUND: The effect of fine particulate matter (PM2.5) components on prediabetes and diabetes is of concern, but the evidence is limited and the specific role of different green space types remains unclear. This study aims to investigate the relationship of PM2.5 and its components with prediabetes and diabetes as well as the potential health benefits of different types and combinations of green spaces. METHODS: A multicenter cross-sectional study was conducted in eastern China by using a multi-stage random sampling method. Health screening and questionnaires for 98,091 participants were performed during 2017-2020. PM2.5 and its five components were estimated by the inverse distance weighted method, and green space was reflected by the Normalized Difference Vegetation Index (NDVI), percentages of tree or grass cover. Multivariate logistic regression and quantile g-computing were used to explore the associations of PM2.5 and five components with prediabetes and diabetes and to elucidate the potential moderating role of green space and corresponding type combinations in these associations. RESULTS: Each interquartile range (IQR) increment of PM2.5 was associated with both prediabetes (odds ratio [OR]: 1.15, 95%CI [confidence interval]: 1.10-1.20) and diabetes (OR: 1.18, 95% CI: 1.11-1.25), respectively. All five components of PM2.5 were related to prediabetes and diabetes. The ORs of PM2.5 on diabetes were 1.49 (1.35-1.63) in the low tree group and 0.90 (0.82-0.98) in the high tree group, respectively. In the high tree-high grass group, the harmful impacts of PM2.5 and five components were significantly lower than in the other groups. CONCLUSION: Our study suggested that PM2.5 and its components were associated with the increased risk of prediabetes and diabetes, which could be diminished by green space. Furthermore, the coexistence of high levels of tree and grass cover provided greater benefits. These findings had critical implications for diabetes prevention and green space-based planning for healthy city.

Cross-subject EMG hand gesture recognition based on dynamic domain generalization.

Electromyography (EMG) signal based cross-subject gesture recognition methods reduce the influence of individual differences using transfer learning technology. These methods generally require calibration data collected from new subjects to adapt the pre-trained model to existing subjects. However, collecting calibration data is usually trivial and inconvenient for new subjects. This is currently a major obstacle to the daily use of hand gesture recognition based on EMG signals. To tackle the problem, we propose a novel dynamic domain generalization (DDG) method which is able to achieve accurate recognition on the hand gesture of new subjects without any calibration data. In order to extract more robust and adaptable features, a meta-adjuster is leveraged to generate a series of template coefficients to dynamically adjust dynamic network parameters. Specifically, two different kinds of templates are designed, in which the first one is different kinds of features, such as temporal features, spatial features, and spatial-temporal features, and the second one is different normalization layers. Meanwhile, a mix-style data augmentation method is introduced to make the meta-adjuster's training data more diversified. Experimental results on a public dataset verify that the proposed DDG outperforms the counterpart methods.

Association between gut microbiota and NAFLD/NASH: a bidirectional two-sample Mendelian randomization study.

Background: Recent studies have suggested a relationship between gut microbiota and non-alcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH). However, the nature and direction of this potential causal relationship are still unclear. This study used two-sample Mendelian randomization (MR) to clarify the potential causal links. Methods: Summary-level Genome-Wide Association Studies (GWAS) statistical data for gut microbiota and NAFLD/NASH were obtained from MiBioGen and FinnGen respectively. The MR analyses were performed mainly using the inverse-variance weighted (IVW) method, with sensitivity analyses conducted to verify the robustness. Additionally, reverse MR analyses were performed to examine any potential reverse causal associations. Results: Our analysis, primarily based on the IVW method, strongly supports the existence of causal relationships between four microbial taxa and NAFLD, and four taxa with NASH. Specifically, associations were observed between Enterobacteriales (P =0.04), Enterobacteriaceae (P =0.04), Lachnospiraceae UCG-004 (P =0.02), and Prevotella9 (P =0.04) and increased risk of NAFLD. Dorea (P =0.03) and Veillonella (P =0.04) could increase the risks of NASH while Oscillospira (P =0.04) and Ruminococcaceae UCG-013 (P=0.005) could decrease them. We also identified that NAFLD was found to potentially cause an increased abundance in Holdemania (P =0.007) and Ruminococcus2 (P =0.002). However, we found no evidence of reverse causation in the microbial taxa associations with NASH. Conclusion: This study identified several specific gut microbiota that are causally related to NAFLD and NASH. Observations herein may provide promising theoretical groundwork for potential prevention and treatment strategies for NAFLD and its progression to NASH in future.

Association of outdoor artificial light at night with metabolic syndrome and the modifying effect of tree and grass cover.

BACKGROUND: Epidemiological studies show that outdoor artificial light at night (ALAN) is linked to metabolic hazards, but its association with metabolic syndrome (MetS) remains unclear. We aimed to investigate the association of outdoor ALAN with MetS in middle-aged and elderly Chinese. METHODS: From 2017-2020, we conducted a cross-sectional study in a total of 109,452 participants living in ten cities of eastern China. MetS was defined by fasting blood glucose (FG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), blood pressure (BP), and waist circumference (WC). In 2021, we followed up 4395 participants without MetS at the baseline. Each participant's five-year average exposure to outdoor ALAN, as well as their exposure to green space type, were measured through matching to their address. Generalized linear models were used to assess the associations of outdoor ALAN with MetS. Stratified analyses were performed by sex, age, region, physical activity, and exposure to green space. RESULTS: In the cross-sectional study, compared to the first quantile (Q1) of outdoor ALAN exposure, the odds ratios (ORs) of MetS were 1.156 [95 % confidence interval (CI): 1.111-1.203] and 1.073 (95 %CI: 1.021-1.128) respectively in the third and fourth quantiles (Q3, Q4) of outdoor ALAN exposure. The follow-up study found that, compared to the first quantile (Q1) of outdoor ALAN exposure, the OR of MetS in Q4 of ALAN exposure was 1.204 (95 %CI: 1.019-1.422). Adverse associations of ALAN with MetS components, including high FG, high TG, and obesity, were also found. Greater associations of ALAN with MetS were found in males, the elderly, urban residents, those with low frequency of physical activity, and those living in areas with low levels of grass cover and tree cover. CONCLUSIONS: Outdoor ALAN exposure is associated with an increased MetS risk, especially in males, the elderly, urban residents, those lacking physical activity, and those living in lower levels of grass cover and tree cover.

The association between greenery type and gut microbiome in schizophrenia: did all greenspaces play the equivalent role?

In recent years, attention has been focused on the benefit of greenspace on mental health, and it is suggested this link may vary with the type of greenspace. More and more studies have emphasized the influence of the gut microbiome on schizophrenia (SCZ). However, the effects of greenspaces on the gut microbiota in SCZ and the effect of different types of greenspaces on the gut microbiota remain unclear. We aim to examine if there were variations in the effects of various greenspace types on the gut microbiome in SCZ. Besides, we sink to explore important taxonomic compositions associated with different greenspace types. We recruited 243 objects with schizophrenia from Anhui Mental Health Center and collected fecal samples for 16Sr RNA gene sequencing. Three types of greenery coverage were calculated with different circular buffers (800, 1500, and 3000 m) corresponding to individual addresses. The association between greenspace and microbiome composition was analyzed with permutational analysis of variance (PERMANOVA). We conducted the linear regression to capture specific gut microbiome taxa associated with greenery coverage. Tree coverage was consistently associated with microbial composition in both 1500 m (R2 = 0.007, P = 0.030) and 3000 m (R2 = 0.007, P = 0.039). In contrast, there was no association with grass cover in any of the buffer zones. In the regression analysis, higher tree coverage was significantly correlated with the relative abundance of several taxa. Among them, tree coverage was positively associated with increased Bifidobacterium longum (ß = 1.069, P = 0.004), which was the dominant composition in the gut microbiota. The relationship between greenspace and gut microbiome in SCZ differed by the type of greenspace. Besides, "tree coverage" may present a dominant effect on the important taxonomic composition. Our findings might provide instructive evidence for the design of urban greenspace to optimize health and well-being in SCZ as well as the whole people.

Association of residential greenness with chronotype among children.

BACKGROUND: The association between residential greenness and chronotype remains unclear, especially among children. The current study aimed to explore the associations between residential greenness and chronotype parameters in children and examine potential pathways for these associations. METHODS: In this cross-sectional study, 16,421 children ages 3-12 were included. Two satellite-derived vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. The mid-sleep point on a workday (MSW) and the mid-sleep point on free days (MSF) were considered. And mid-sleep time on free days adjusted for sleep debt (MSFsc) was used as an indicator of chronotype. In addition to multivariable linear regression models, subgroup analyses were conducted to explore effect modifiers, and mediation analyses were used to explore possible mediating mechanisms of air pollutants underlying the associations. RESULTS: An interquartile range (IQR) increase in both NDVI500-m and EVI500-m was significantly associated with an earlier MSFsc of -0.061 (95 % confidence interval (CI): -0.072, -0.049) and -0.054 (95 % CI: -0.066, -0.042), respectively. Non-linear dose response relationships were discovered between greenness indices and MSFsc and MSF. The results of stratified analyses showed the effect of residential greenness on MSW was stronger among primary school children and individuals with higher household income than among kindergarten children and those with lower household income. The joint mediation effects of PM2.5, PM1, PM10, NO2, and SO2 on the associations of NDVI500-m and EVI500-m with MSFsc were 89.6 % and 76.0 %, respectively. CONCLUSIONS: Higher levels of residential greenness may have beneficial effects on an earlier chronotype in the child population, by reducing the effects of air pollutants, especially PM2.5. Our research hopes to promote the deployment of green infrastructure and healthy urban design strategies.

Young people exposure to antibiotics: Implication for health risk and the impact from eating habits of takeaway food.

Exposure to antibiotics, mainly from animal food ingestion, may have adverse effects on human health. Takeaway food is the preferred choice for the dietary of most Chinese young people nowadays, but the relationship between takeaway eating and antibiotic exposure is not yet adequately understood. In the present study, 297 young people were recruited to collect urine samples and questionnaires with an emphasis on their takeaway eating habits. The internal exposure to 16 antibiotics and three metabolites was measured in urine samples by high-performance liquid chromatography-tandem mass spectrometry, as well as a DNA oxidative damage marker, 8-hydroxydeoxyguanosine (8-OHdG). At least one kind of antibiotic was found in over 90 % of urine samples, with total concentrations from 0.667 to 3.02 × 104 ng/mL. High exposure levels of antibiotics were more likely to be found in individuals with a larger body mass index. The concentrations of six antibiotics were significantly different among people with different overall weekly eating frequencies, usually an upward trend. The estimated daily intakes of antibiotics were on the levels of 0.001-1.0 µg/kg/day, mainly contributed by clarithromycin, ciprofloxacin and oxytetracycline, indicating a potential health risk based on the microbiological effect. A significantly positive correlation was found between DNA oxidative damage and exposure for four categories of antibiotics, conformed by both Spearman correlation and multiple linear regression analysis. The levels of 8-OHdG were 355 %, 239 %, 234 %, and 334 % higher with elevated levels of phenicols, macrolides, tetracyclines and sulfonamides from quartiles 2 to 4. Our results suggest that high-frequency consumption of takeaways may exacerbate oxidative stress trends through human exposure to antibiotics.

Clinical outcomes of robotic-assisted and manual total hip arthroplasty in the same patient: A case report.

BACKGROUND: Total hip arthroplasty (THA) is an effective treatment for advanced osteonecrosis of the femoral head, which can significantly relieve pain and improve patients' quality of life. Robotic-assisted THA enhances the accuracy and stability of THA surgery and achieves better clinical outcomes than manual THA. CASE SUMMARY: We report the clinical outcomes of robotic-assisted THA and manual THA in the same patient with osteonecrosis of the femoral head. A 49-year-old male patient attended our hospital due to more than 3 years of pain in both hip joints. The left hip was treated with robotic-assisted THA. The patient underwent manual THA of the right hip 3 mo after robotic-assisted THA. We obtained postoperative radiograph parameters, Harris hip score and forgotten joint score of the patient 1 year after surgery. CONCLUSION: Compared with manual THA, the patient's left hip felt better 1 year after robotic-assisted THA. Robotic-assisted THA resulted in a better Harris hip score and forgotten joint score than manual THA in the same patient with osteonecrosis of the femoral head.

The global burden of schizophrenia and the impact of urbanization during 1990-2019: An analysis of the global burden of disease study 2019.

BACKGROUND AND HYPOTHESIS: The burden of schizophrenia is increasing. Assessing the global distribution of schizophrenia and understanding the association between urbanization factors and schizophrenia are crucial. STUDY DESIGN: We conducted a two-stage analysis utilizing public data from GBD (global burden of disease) 2019 and the World Bank. First, the distribution of schizophrenia burden at the global, regional, and national levels as well as temporal trends was analyzed. Then, four composite indicators of urbanization (including demographic, spatial, economic, and eco-environment urbanization) were constructed from ten basic indicators. Panel data models were used to explore the relationship between urbanization indicators and the burden of schizophrenia. RESULTS: In 2019, there were 23.6 million people with schizophrenia, an increase of 65.85% from 1990, and the country with the largest ASDR (age-standardized disability adjusted life years rate) was the United States of America, followed by Australia, and New Zealand. Globally, the ASDR of schizophrenia rose with the sociodemographic index (SDI). In addition, six basic urbanization indicators including urban population proportion, employment in industry/services proportion, urban population density, the population proportion in the largest city, GDP, and PM2.5 concentration were positively associated with ASDR of schizophrenia, with the largest coefficients being urban population density. Overall, demographic, spatial, economic, and eco-environment urbanization all had positive effects on schizophrenia, and the estimated coefficients indicated that demographic urbanization was the most significant influence. CONCLUSIONS: This study provided a comprehensive description of the global burden of schizophrenia and explored urbanization as a factor contributing to the variation in the burden of schizophrenia, and highlighted policy priorities for schizophrenia prevention in the context of urbanization.

Individual and joint association of phenols, parabens, and phthalates with childhood lung function: Exploring the mediating role of peripheral immune responses.

The functioning of the respiratory system can be interfered with by exposure to mixtures of environmental chemicals, however, the evidence is still ambiguous. We evaluated the association of exposure to mixtures of 14 chemicals, including 2 phenols, 2 parabens, and 10 phthalates, with four major lung function metrics. Based on data from the National Health and Nutrition Examination Survey 2007-2012, this analysis was conducted among 1462 children aged 6-19 years. Linear regression, Bayesian kernel machine regression, quantile-based g-computation regression, and a generalized additive model were performed to estimate the associations. Mediation analyses were performed to investigate plausible biological pathways mediated by immune cells. Our results indicated that the phenols, parabens, and phthalates mixture was negatively related to lung function parameters. And BPA and PP were identified as important contributors to negative associations with FEV1, FVC, and PEF, with non-linear relationships observed between BPA and those outcomes. The most influential factor for a probable FEF25-75 % decline was MCNP. BPA, and MCNP had an interaction effect on FEF25-75 %. The association of PP with FVC and FEV1 has been postulated to be mediated by neutrophils and monocytes. The findings offer insights into the associations of chemical mixtures with respiratory health and the possible driving mechanism, which would be of significance in adding novel evidence of the role of peripheral immune responses, as well as calling for remediation actions to be prioritized during childhood.

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients.

BACKGROUND: To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever. METHODS: We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic. We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations. The patients were divided into the rejection and acceptance groups. Then we analyzed the rejected examination items in order to obtain the corresponding reasons. RESULTS: We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics. The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified. The average F1 score reached 0.94. A total of 58.4% of the patients accepted Xiaoyi's suggestions (acceptance group), and 41.6% refused (rejection group). Imaging examinations were rejected by most patients (46.7%). The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%). The difficulty of sampling was the main reason for rejecting routine tests (41.9%). CONCLUSION: Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.

Sunshine duration and risks of schizophrenia hospitalizations in main urban area: Do built environments modify the association?

BACKGROUND: Although studies have explored the relationship between sunshine duration and schizophrenia, the evidence was ambiguous. Different built environments may alter the effect of sunlight on schizophrenia, thus the purpose of this study was to investigate the effects of built environments on the sunshine duration-schizophrenia association. MATERIALS AND METHODS: Daily schizophrenia hospitalizations data during 2017-2020 in Hefei's main urban area, China, and corresponding meteorological factors as well as ambient pollutants were collected. The impact of sunshine duration on schizophrenia admissions in urban areas was investigated using a generalized additive model combined with a distributed lagged nonlinear model. Additionally, the various modifying effects of different Building Density, Building Height, Normalized Vegetation Index, and Nighttime Light were also explored between sunshine duration and schizophrenia. RESULTS: We observed that inadequate sunshine duration (<5.3 h) was associated with an increase in schizophrenia hospital admissions, with a maximum relative risk of 1.382 (95 % confidence interval (CI): 1.069-1.786) at 2.9 h. In turn, adequate sunshine duration reduced the risk of schizophrenia hospitalizations. Subgroup analyses indicated females and old patients were particularly vulnerable. In the case of insufficient sunshine duration, significant positive effects were noticed on schizophrenia risk at High-Building Density and High-Nighttime Light. Higher NDVI as well as Building Height were found to be associated with lower risks of schizophrenia. CONCLUSIONS: Given that sunshine duration in various built environments might lead to distinct effects on schizophrenia hospitalizations. Our findings assist in identifying vulnerable populations that reside in particular areas, thus suggesting policymakers provide advice to mitigate the onset of schizophrenia by allocating healthcare resources rationally and avoiding adverse exposures to vulnerable populations timely.

The impact of hazes on schizophrenia admissions and the synergistic effect with the combined atmospheric oxidation capacity in Hefei, China.

OBJECTIVES: Currently, most epidemiological studies on haze focus on respiratory diseases, cardiovascular diseases, etc. However, the relationship between haze and mental health has not been adequately explored. The purpose of this study was to investigate the influence of hazes on schizophrenia admissions and to further explore the potential interaction effect with the combined atmospheric oxidative indices (Ox and Oxwt). METHODS: We collected 5328 cases during the cold season from 2013 to 2015 in Hefei, China. By integrating the Poisson Generalized Linear Models with the Distributed Lag Non-linear Models, the association between haze and schizophrenia admissions was evaluated. The interaction between hazes and two combined oxidation indexes was tested by stratifying hazes and Ox, and Oxwt. RESULTS: Haze was found to be significantly linked to an increased risk of hospitalization for schizophrenia, and a 9-day lag effect on schizophrenia (lag 3-lag 11), with the largest effect on lag 6 (RR = 1.080, 95% confidence interval (CI): 1.046-1.116). Males, females, and <40 y (people under 40 years old) were sensitive to hazes. Furthermore, in the stratified analysis, we found synergies between two combined oxidation indexes and hazes. The interaction relative risk (IRR) and relative excess risk due to interaction (RERI) between Ox and hazes were 1.170 (95% CI: 1.071-1.277) and 0.149 (95% CI: 0.045-0.253), respectively. For Oxwt, the IRR and RERI were 1.179 (95% CI: 1.087-1.281) and 0.159 (95% CI: 0.056-0.263), respectively. It is noteworthy that this synergistic effect was significant in males and <40 y when examining the various subgroups in the interaction analysis. CONCLUSIONS: Our findings suggest that exposure to haze significantly increases the risk of hospitalization for schizophrenia. More significant public health benefits can be obtained by prioritizing haze periods with high combined atmospheric oxidation capacity.

Short-term exposure to air pollution is an emerging but neglected risk factor for schizophrenia: A systematic review and meta-analysis.

OBJECTIVE: This meta-analysis aimed to explore the association between short-term exposure to air pollution and schizophrenia (SCZ)1, and investigate the susceptible population and the lag characteristics of different pollutants. METHODS: A systematic review and meta-analysis was conducted by searching PubMed, Cochrane, Web of Sciences, and CNKI for relevant literature published up to 28 Feb 2022. Meta-analysis was performed separately to investigate the association of ambient particulates (diameter ≤ 2.5 µm (PM2.5)2, 2.5 µm < diameter < 10 µm (PMC)3, ≤10µm (PM10)4) and gaseous pollutants (nitrogen dioxide (NO2)5, sulfur dioxide (SO2)6, carbon monoxide (CO)7) with SCZ. Relative risk (RR)8 per 10 µg/m3 increase in air pollutants concentration was used as the effect estimate. Subgroup analyses were conducted by age, gender, country, median pollutant concentration, and median temperature. RESULTS: We identified 17 articles mainly conducted in Asia, of which 13 were included in the meta-analysis. Increased risk of SCZ was associated with short-term exposure to PM2.5 (RR: 1.0050, 95 % confidence interval (CI)9: 1.0017, 1.0083), PMC (1.0117, 1.0023, 1.0211), PM10 (1.0047, 1.0025, 1.0070), NO2 (1.0275, 1.0132, 1.0420), and SO2 (1.0288, 1.0146, 1.0432) exposure. Subgroup analyses showed that females may be more susceptible to SO2 and NO2, and the young seem to be more sensitive to PM2.5 and PM10. Gaseous pollutants presented the immediate risk, and particulates showed the delayed risk. CONCLUSIONS: The present meta-analysis suggests that short-term exposure to PM2.5, PMC, PM10, SO2, and NO2 exposure may be associated with an elevated risk of SCZ.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database.

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

Association between sequential extreme precipitation-heatwaves events and hospitalizations for schizophrenia: The damage amplification effects of sequential extremes.

OBJECTIVES: In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss. METHODS: A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events. RESULTS: EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)). CONCLUSIONS: Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.