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Inflammation triggers pulmonary vascular remodelling. Ferroptosis, a nonapoptotic form of cell death that is triggered by iron-dependent lipid peroxidation and contributes to the pathogenesis of several inflammation-related diseases, but its role in pulmonary hypertension (PH) has not been studied. We examined endothelial cell ferroptosis in PH and the potential mechanisms. Pulmonary artery endothelial cells (PAECs) and lung tissues from monocrotaline (MCT)-induced PH rats were analysed for ferroptosis markers, including lipid peroxidation, the labile iron pool (LIP) and the protein expression of glutathione peroxidase 4 (GPX4), ferritin heavy chain 1 (FTH1) and NADPH oxidase-4 (NOX4). The effects of the ferroptosis inhibitor ferrostatin-1 (Fer-1) on endothelial cell ferroptosis and pulmonary vascular remodelling in MCT-induced rats were studied in vitro and in vivo. Ferroptosis was observed in PAECs from MCT-induced PH rats in vitro and in vivo and was characterized by a decline in cell viability accompanied by increases in the LIP and lipid peroxidation, the downregulation of GPX4 and FTH1 expression and the upregulation of NOX4 expression. High-mobility group box 1 (HMGB1)/Toll-like receptor 4 (TLR4)/NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signalling was measured by western blotting. These changes were significantly blocked by Fer-1 administration in vitro and in vivo. These results suggest that Fer-1 plays a role in inhibiting ferroptosis-mediated PAEC loss during the progression of PH. The ferroptosis-induced inflammatory response depended on the activation of HMGB1/TLR4 signalling, which activated the NLRP3 inflammasome in vivo. We are the first to suggest that pulmonary artery endothelial ferroptosis triggers inflammatory responses via the HMGB1/TLR4/NLRP3 inflammasome signalling pathway in MCT-induced rats. Treating PH with a ferroptosis inhibitor and exploring new treatments based on ferroptosis regulation might be promising therapeutic strategies for PH.

Background Several recent observational studies have reported that gut microbiota composition is associated with preeclampsia. However, the causal effect of gut microbiota on preeclampsia-eclampsia is unknown. Methods A two-sample Mendelian randomization study was performed using the summary statistics of gut microbiota from the largest available genome-wide association study meta-analysis ( n =13,266) conducted by the MiBioGen consortium. The summary statistics of preeclampsia-eclampsia were obtained from the FinnGen consortium R7 release data (5731 cases and 160,670 controls). Inverse variance weighted, maximum likelihood, MR-Egger, weighted median, weighted model, MR-PRESSO, and cML-MA were used to examine the causal association between gut microbiota and preeclampsia-eclampsia. Reverse Mendelian randomization analysis was performed on the bacteria that were found to be causally associated with preeclampsia-eclampsia in forward Mendelian randomization analysis. Cochran’s Q statistics were used to quantify the heterogeneity of instrumental variables. Results Inverse variance weighted estimates suggested that Bifidobacterium had a protective effect on preeclampsia-eclampsia (odds ratio = 0.76, 95% confidence interval: 0.64–0.89, P = 8.03 × 10 −4 ). In addition, Collinsella (odds ratio = 0.77, 95% confidence interval: 0.60–0.98, P = 0.03), Enterorhabdus (odds ratio = 0.76, 95% confidence interval: 0.62–0.93, P = 8.76 × 10 −3 ), Eubacterium (ventriosum group) (odds ratio = 0.76, 95% confidence interval: 0.63–0.91, P = 2.43 × 10 −3 ), Lachnospiraceae (NK4A136 group) (odds ratio = 0.77, 95% confidence interval: 0.65–0.92, P = 3.77 × 10 −3 ), and Tyzzerella 3 (odds ratio = 0.85, 95% confidence interval: 0.74–0.97, P = 0.01) presented a suggestive association with preeclampsia-eclampsia. According to the results of reverse MR analysis, no significant causal effect of preeclampsia-eclampsia was found on gut microbiota. No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. Conclusions This two-sample Mendelian randomization study found that Bifidobacterium was causally associated with preeclampsia-eclampsia. Further randomized controlled trials are needed to clarify the protective effect of probiotics on preeclampsia-eclampsia and their specific protective mechanisms.

Currently, the SARS-CoV-2 promptly spread across China and around the world. However, there are controversies about whether preexisting chronic kidney disease (CKD) and acute kidney injury complication (AKI) are involved in the COVID-19 pandemic. Studies reported the kidney outcomes in different severity of COVID-19 were included in this study. Standardized mean differences or odds ratios were calculated by employing Review Manager meta-analysis software. Thirty-six trials were included in this systematic review with a total of 6395 COVID-19 patients. The overall effects indicated that preexisting CKD (OR = 3.28), complication of AKI (OR = 11.02), serum creatinine (SMD = 0.68), abnormal serum creatinine (OR = 4.86), blood urea nitrogen (SMD = 1.95), abnormal blood urea nitrogen (OR = 6.53), received continuous renal replacement therapy (CRRT) (OR = 23.63) were significantly increased in severe group than that in nonsevere group. Additionally, the complication of AKI (OR = 13.92) and blood urea nitrogen (SMD = 1.18) were remarkably elevated in the critical group than that in the severe group. CKD and AKI are susceptible to occur in patients with severe COVID-19. CRRT is applied frequently in severe COVID-19 patients than that in nonsevere COVID-19 patients. The risk of AKI is higher in the critical group than that in the severe group.

Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.

The surface electron density significantly affects the photocatalytic efficiency, especially the photocatalytic CO 2 reduction reaction, which involves multi-electron participation in the conversion process. Herein, we propose a conceptually different mechanism for surface electron density modulation based on the model of Au anchored CdS. We firstly manipulate the direction of electron transfer by regulating the vacancy types of CdS. When electrons accumulate on vacancies instead of single Au atoms, the adsorption types of CO 2 change from physical adsorption to chemical adsorption. More importantly, the surface electron density is manipulated by controlling the size of Au nanostructures. When Au nanoclusters downsize to single Au atoms, the strong hybridization of Au 5 d and S 2 p orbits accelerates the photo-electrons transfer onto the surface, resulting in more electrons available for CO 2 reduction. As a result, the product generation rate of Au SA /Cd 1−x S manifests a remarkable at least 113-fold enhancement compared with pristine Cd 1−x S. The electron density of reactive sites significantly affects catalytic performances. Here, authors demonstrate the electron density of different reactive sites can be modulated by regulating the type of vacancy and the size of Au, leading to effective CO 2 photoreduction.

Background The increasing prevalence of colorectal cancer (CRC) in China and the paucity of information about relevant expenditure highlight the necessity of better understanding the financial burden and effect of CRC diagnosis and treatment. We performed a survey to quantify the direct medical and non‐medical expenditure as well as the resulting financial burden of CRC patients in China. Methods We conducted a multicenter, cross‐sectional survey in 37 tertiary hospitals in 13 provinces across China between 2012 and 2014. Each enrolled patient was interviewed using a structured questionnaire. All expenditure data were inflated to the 2014 Chinese Yuan (CNY; 1 CNY = 0.163 USD). We quantified the overall expenditure and financial burden and by subgroup (hospital type, age at diagnosis, sex, education, occupation, insurance type, household income, clinical stage, pathologic type, and therapeutic regimen). We then performed generalized linear modeling to determine the factors associated with overall expenditure. Results A total of 2356 patients with a mean age of 57.4 years were included, 57.1% of whom were men; 13.9% of patients had stage I cancer; and the average previous‐year household income was 54,525 CNY. The overall average direct expenditure per patient was estimated to be 67,408 CNY, and the expenditures for stage I, II, III, and IV disease were 56,099 CNY, 59,952 CNY, 67,292 CNY, and 82,729 CNY, respectively. Non‐medical expenditure accounted for 8.3% of the overall expenditure. The 1‐year out‐of‐pocket expenditure of a newly diagnosed patient was 32,649 CNY, which accounted for 59.9% of their previous‐year household income and caused 75.0% of families to suffer an unmanageable financial burden. Univariate analysis showed that financial burden and overall expenditure differed in almost all subgroups (P < 0.05), except for sex. Multivariate analysis showed that patients who were treated in specialized hospitals and those who were diagnosed with adenocarcinoma or diagnosed at a later stage were likely to spend more, whereas those with a lower household income and those who underwent surgery spent less (all P < 0.05). Conclusions For patients in China, direct expenditure for the diagnosis and treatment of CRC seemed catastrophic, and non‐medical expenditure was non‐ignorable. The financial burden varied among subgroups, especially among patients with different clinical stages of disease, which suggests that, in China, CRC screening might be cost‐effective.

Multi-label learning studies the problem where one instance is associated with multiple labels. Weakly supervised multi-label learning has attracted considerable research attention because of the annotation difficulty. Majority of the studies on weakly supervised multi-label learning assume that one group of weak annotations is available for each instance; however, none of these studies considers multiple groups of weak annotations that can be easily acquired through crowdsourcing. Recent studies on crowdsourced multi-label learning observed that the current query strategies do not agree well with human habits and that data cannot be collected as expected. Therefore, this study aims to design a new query strategy in accordance with human behavior patterns to obtain multiple groups of weak annotations. Further, a learning algorithm is proposed based on neural networks for such type of data. In addition, this study qualitatively and empirically analyzes factors in the proposed query strategy that may impact further learning and provides insights to obtain better query strategy with respect to future crowdsourcing in case of multi-label data.

Open-set learning deals with the testing distribution where there exist samples from the classes that are unseen during training. They aim to classify the seen classes and recognize the unseen classes. Previous studies typically assume that the marginal distribution of the seen classes is fixed across the training and testing distributions. In many real-world applications, however, there may exist covariate shift between them, i.e., the marginal distribution of seen classes may shift. We call this kind of problem as open-set learning under covariate shift , aim to robustly classify the seen classes under covariate shift and be aware of the unseen classes.We present a new open-set learning framework with covariate generalization based on supervised contrastive learning, called SC–OSG, inspired by the latent connection between contrastive learning and representation invariance. Specifically, we theoretically justify supervised contrastive learning that could promote the conditional invariance of representations, a critical condition for covariate generalization. SC–OSG generates multi-source samples to promote the representation invariance and improve the covariate generalization. Based on this, we propose a detection score that is specific to the proposed training scheme. We evaluate the effectiveness of our method on several real-world datasets, on all of which we achieve competitive results with state-of-the-art methods.

During previous pandemic outbreaks, medical staff have reported high levels of psychological distress. The aim of the current study was to report a snapshot of the psychological impact of the coronavirus disease 2019 (COVID-19) pandemic and its correlated factors on medical staff in Guangdong, China. On the 2nd and 3rd February 2020, soon after the start of the COVID-19 pandemic, we surveyed medical staff at four hospitals in Guangdong, China, to collect demographic characteristics, Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS-14), and Insomnia Severity Index (ISI) scores. Complete responses were received from 1045 medical staff. Respondents were divided into high- and low-risk groups according to their working environment of contacting with potential or confirmed COVID-19 cases. The proportion of staff with anxiety (55.4% v. 43.0%, p < 0.001) or depression (43.6% v. 36.8%, p = 0.028) was significantly higher in the high-risk group than the low-risk group. The percentage of staff with severe anxiety was similar in the two groups. Doctors were more susceptible to moderate-to-severe depressive symptoms. The high-risk group had higher levels of clinical insomnia (13.5% v. 8.5%, p = 0.011) and were more likely to be in the upper quartile for stress symptoms (24.7% v. 19.3%, p = 0.037) than the low-risk group. Additionally, work experience negatively correlated with insomnia symptoms. It is important for hospitals and authorities to protect both the physical and psychological health of medical staff during times of pandemic, even those with a low exposure risk.

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.