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Abstract Background As an antioxidant, hydrogen (H2) can selectively react with the highly toxic hydroxyl radical (·OH) in tumor cells to break the balance of reactive oxygen species (ROS) and cause oxidative stress. However, due to the high diffusibility and storage difficulty of hydrogen, it is impossible to achieve long-term release at the tumor site, which highly limited their therapeutic effect. Results Photosynthetic bacteria (PSB) release a large amount of hydrogen to break the balance of oxidative stress. In addition, as a nontoxic bacterium, PSB could stimulate the immune response and increase the infiltration of CD4+ and CD8+ T cells. More interestingly, we found that hydrogen therapy induced by our live PSB did not lead to the up-regulation of PD-L1 after stimulating the immune response, which could avoid the tumor immune escape. Conclusion Hydrogen-immunotherapy significantly kills tumor cells. We believe that our live microbial hydrogen production system provides a new strategy for cancer hydrogen treatment combining with enhanced immunotherapy without up-regulating PD-L1. Graphical Abstract

Summary: Tumor immunotherapy has been convincingly demonstrated as a feasible approach for treating cancers. Although promising, the immunosuppressive tumor microenvironment (TME) has been recognized as a major obstacle in tumor immunotherapy. It is highly desirable to release an immunosuppressive “brake” for improving cancer immunotherapy. Among tumor-infiltrated immune cells, tumor-associated macrophages (TAMs) play an important role in the growth, invasion, and metastasis of tumors. The polarization of TAMs (M2) into the M1 type can alleviate the immunosuppression of the TME and enhance the effect of immunotherapy. Inspired by this, we constructed a therapeutic exosomal vaccine from antigen-stimulated M1-type macrophages (M1OVA-Exos). M1OVA-Exos are capable of polarizing TAMs into M1 type through downregulation of the Wnt signaling pathway. Mediating the TME further activates the immune response and inhibits tumor growth and metastasis via the exosomal vaccine. Our study provides a new strategy for the polarization of TAMs, which augments cancer vaccine therapy efficacy.

Accurate land cover mapping plays a critical role in enhancing our understanding of Earth’s energy balance, carbon cycle, and ecosystem dynamics. However, existing methods for producing multi-epoch land cover products still heavily depend on manual intervention, limiting their efficiency and scalability. This study introduces an automated approach for multi-epoch land cover mapping using remote sensing imagery and the model migration strategy. Landsat ETM+ and OLI images with a 30 m resolution were utilized as the primary data sources. An automatic training sample extraction method based on prior multi-source land cover products was first utilized. Then, based on the generated training dataset and a random forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then migrated to 2010 to test the effectiveness of automated land cover classification over multiple years. Our mapping results show high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. These results demonstrate the reliability of our proposed automated land cover mapping strategy. Additionally, the uncertainty analysis of the model migration strategy indicated that land cover types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, were the least suitable for model migration. Our results can offer valuable insights for medium-resolution, multi-epoch land cover mapping, which could facilitate more efficient and accurate environmental assessments.

Abstract The long-term stability of perovskite solar cells remains one of the most important challenges for the commercialization of this emerging photovoltaic technology. Here, we adopt a non-noble metal/metal oxide/polymer multiple-barrier to suppress the halide consumption and gaseous perovskite decomposition products release with the chemically inert bismuth electrode and Al2O3/parylene thin-film encapsulation, as well as the tightly closed system created by the multiple-barrier to jointly suppress the degradation of perovskite solar cells, allowing the corresponding decomposition reactions to reach benign equilibria. The resulting encapsulated formamidinium cesium-based perovskite solar cells with multiple-barrier maintain 90% of their initial efficiencies after continuous operation at 45 °C for 5200 h and 93% of their initial efficiency after continuous operation at 75 °C for 1000 h under 1 sun equivalent white-light LED illumination.

Background: Alzheimer's disease (AD) is a common form of dementia, and impaired mitophagy is a hallmark of AD. Mitophagy is mitochondrial-specific autophagy. Ginsenosides from Ginseng involve in autophagy in cancer. Ginsenoside Rg1 (Rg1 hereafter), a single compound of Ginseng, has neuroprotective effects on AD. However, few studies have reported whether Rg1 can ameliorate AD pathology by regulating mitophagy. Methods: Human SH-SY5Y cell and a 5XFAD mouse model were used to investigate the effects of Rg1. Rg1 (1μM) was added to β-amyloid oligomer (AβO)-induced or APPswe-overexpressed cell models for 24 hours. 5XFAD mouse models were intraperitoneally injected with Rg1 (10 mg/kg/d) for 30 days. Expression levels of mitophagy-related markers were analyzed by western blot and immunofluorescent staining. Cognitive function was assessed by Morris water maze. Mitophagic events were observed using transmission electron microscopy, western blot, and immunofluorescent staining from mouse hippocampus. The activation of the PINK1/Parkin pathway was examined using an immunoprecipitation assay. Results: Rg1 could restore mitophagy and ameliorate memory deficits in the AD cellular and/or mouse model through the PINK1-Parkin pathway. Moreover, Rg1 might induce microglial phagocytosis to reduce β-amyloid (Aβ) deposits in the hippocampus of AD mice. Conclusion: Our studies demonstrate the neuroprotective mechanism of ginsenoside Rg1 in AD models. Rg1 induces PINK-Parkin mediated mitophagy and ameliorates memory deficits in 5XFAD mouse models.

In recent years, increasing climate change has triggered the development of various techniques and strategies for predicting building energy consumption and thermal comfort. However, many features could affect prediction efficiency, and the coupling of appropriate learning algorithms with different sampling methods and input parameters requires further research. In this study, we evaluated 25 features of an office building model. Then, the comprehensive effects of five different sampling methods and cumulative contribution rates (CCR) of input parameters on the prediction performance of building carbon emission (BCE) and indoor discomfort hour (IDH) were explored in 10 machine learning algorithms. The results indicated that the Sobol sampling method could achieve the best prediction effect in the combinations of different contribution rates of features and machine learning algorithms. Meanwhile, an artificial neural network (ANN) was the best learning algorithm when the CCR was 100%. However, the optimal machine learning method corresponding to each CCR stage differed. When the CCR was reduced to 50%, only three influence factors were considered most important, and K-nearest neighbors (KNN) was the best prediction algorithm in this scenario, indicating that the input parameters were reduced by 88%, and the R2 value was only reduced by 6.1%. Therefore, we proposed a new strategy idea for the research of building performance prediction by determining the most important building impact factors and suitable machine learning algorithms, which could simplify the prediction process and improve prediction efficiency while reducing building parameters.

ObjectiveLimb paralysis, which is a sequela of stroke, limits patients' activities of daily living and lowers their quality of life. The purpose of this study was to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) combined with a motor relearning procedure (MRP) on motor function and limb spasticity in stroke patients.MethodsStroke patients were randomly divided into a combined treatment group (rTMS + MRP) and a control group (MRP) (n = 30 per group). The control group was given MRP in addition to conventional rehabilitation, and the combined treatment group was given 1 Hz rTMS combined with MRP. The treatment efficacy was assessed by the modified Ashworth scale (MAS), Fugl-Meyer motor function scale, and motor evoked potential (MEP) testing.ResultsAfter 4 weeks of treatment, the Brunnstrom score, Fugl-Meyer lower extremity motor function, and Fugl-Meyer balance function were significantly higher in the combination treatment group compared to the control group, while the MAS score was lower in the combination treatment group compared to the control group. The MEP extraction rate was higher in the combined treatment group compared to the control group, while the threshold and central motor conduction time (CMCT) were lower in the combined treatment group compared to the control group.ConclusionLow-frequency rTMS combined with MRP had better efficacy on spasticity and motor function in stroke patients with hemiparesis than MRP alone.

Introduction Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.Methods and analysis The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium–glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.Ethics and dissemination The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.

The importance of organizational resilience as an effective means to cope with crises and achieve sustainable growth in crisis situations is gradually gaining attention from both theoretical and practical circles. Based on the review of existing research on organizational resilience, this study uses the scientific knowledge mapping method from the macro perspective and the literature analysis method from the micro level to review the development of organizational resilience research during 1984-2021, and analyzes 108 representative papers on organizational resilience. Finally, this study proposes future research directions based on the existing studies, such as enriching the influence factors of organizational resilience, strengthening the study of the mechanism of organizational resilience and strengthening the study of the effect of organizational resilience. This paper not only provides a reference for domestic scholars to construct an overall framework for organizational resilience research, but also provides theoretical and practical guidelines for enterprises to achieve rebound survival and then sustainable growth in the face of adversity.

Objective To characterise the long-term outcomes of patients with COVID-19 admitted to a large New York City medical centre at 3 and 6 months after hospitalisation and describe their healthcare usage, symptoms, morbidity and mortality.Design Retrospective cohort through manual chart review of the electronic medical record.Setting NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical centre in New York City.Participants The first 1190 consecutive patients with symptoms of COVID-19 who presented to the hospital for care between 1 March and 8 April 2020 and tested positive for SARS-CoV-2 on reverse transcriptase PCR assay.Main outcome measures Type and frequency of follow-up encounters, self-reported symptoms, morbidity and mortality at 3 and 6 months after presentation, respectively; patient disposition information prior to admission, at discharge, and at 3 and 6 months after hospital presentation.Results Of the 1190 reviewed patients, 929 survived their initial hospitalisation and 261 died. Among survivors, 570 had follow-up encounters (488 at 3 months and 364 at 6 months). An additional 33 patients died in the follow-up period. In the first 3 months after admission, most encounters were telehealth visits (59%). Cardiopulmonary symptoms (35.7% and 28%), especially dyspnoea (22.1% and 15.9%), were the most common reported symptoms at 3-month and 6-month encounters, respectively. Additionally, a large number of patients reported generalised (26.4%) or neuropsychiatric (24.2%) symptoms 6 months after hospitalisation. Patients with severe COVID-19 were more likely to have reduced mobility, reduced independence or a new dialysis requirement in the 6 months after hospitalisation.Conclusions Patients hospitalised with SARS-CoV-2 infection reported persistent symptoms up to 6 months after diagnosis. These results highlight the long-term morbidity of COVID-19 and its burden on patients and healthcare resources.

This study proposed an improved air conditioning design strategy based on building performance simulation for a stepped hall. The air velocity and air change rate of the case were measured on-site, which were compared with the simulation data to verify the reliability of the building simulation model. Then, the fully mixing ventilation scheme and the design schemes proposed in this study were simulated. Finally, the building simulation results were summarized to confirm the applicability of the air conditioning design strategy. The building performance results showed that the air distribution performance index (ADPI) value was 76.95% in the original case. Nevertheless, the effective draft temperature (EDT) in the middle seat area exceeded the standard value, indicating that a local cold shock would occur. Moreover, its scale for ventilation efficiency (SVE6) in the residential area was 2.54. However, the SVE6s in the other schemes were between 0.89 and 0.92. It means that the proposed schemes only needed to take one-third of the time to exhaust air. These three indicators’ visualization results can analyze the advantages and disadvantages of each scheme. Therefore, the improved building performance simulation strategy could inspect the design effect and give suggestions quickly for air conditioning design.

Although the deep neural network has a strong fitting ability, it is difficult to be applied to safety-critical fields because of its poor interpretability. Based on the adaptive neuro-fuzzy inference system (ANFIS) and the concept of residual network, a width residual neuro-fuzzy system (WRNFS) is proposed to improve the interpretability performance in this paper. WRNFS is used to transform a regression problem of high-dimensional data into the sum of several low-dimensional neuro-fuzzy systems. The ANFIS model in the next layer is established based on the low dimensional data and the residual of the ANFIS model in the former layer. The performance of WRNFS is compared with traditional ANFIS on three data sets. The results showed that WRNFS has high interpretability (fewer layers, fewer fuzzy rules, and fewer adjustable parameters) on the premise of satisfying the fitting accuracy. The interpretability, complexity, time efficiency, and robustness of WRNFS are greatly improved when the input number of single low-dimensional systems decreases.

In this study, we investigated the impact of the lighting environment on psychological perception, physiology, and productivity and then designed lighting control strategies based on the experimental results. The research was conducted in a smart lighting laboratory, and 67 subjects were tested in different illuminances and correlated color temperatures (CCTs). During the experiment, the physiological data of subjects were continuously recorded, while the psychology and productivity results were evaluated by questionnaires and working tests, respectively. The experimental results found that both illuminance and CCT could significantly influence the feeling of comfort and relaxation of the subjects. Warm CCT and higher illuminance (3000 K–590 lux) made subjects feel more comfortable. Productivity reached its maximum value with illuminance above 500 lux and equivalent melanopic lux (EML) higher than 150. The brain-wave and heart-rate changes did not have a close relationship with either illuminance or CCT, but the heart rate slightly increased in the adjustable lighting mode. Regardless of the initial value setting, the subjects preferred intermediate CCT (4200 K) and bright illumination (500 lux) after self-adjustment. Finally, we proposed three comprehensive lighting control strategies based on psychology, productivity, circadian rhythm, and energy-saving.

BackgroundObesity has emerged as a risk factor for severe coronavirus disease 2019 (COVID-19) infection. To inform treatment considerations the relationship between obesity and COVID-19 complications and the influence of race, ethnicity, and socioeconomic factors deserves continued attention.ObjectiveTo determine if obesity is an independent risk factor for severe COVID-19 complications and mortality and examine the relationship between BMI, race, ethnicity, distressed community index and COVID-19 complications and mortality.MethodsA retrospective cohort study of 1,019 SARS-CoV-2 positive adult admitted to an academic medical center (n = 928) and its affiliated community hospital (n-91) in New York City from March 1 to April 18, 2020.ResultsMedian age was 64 years (IQR 52-75), 58.7% were men, 23.0% were Black, and 52.8% were Hispanic. The prevalence of overweight and obesity was 75.2%; median BMI was 28.5 kg/m2 (25.1-33.0). Over the study period 23.7% patients died, 27.3% required invasive mechanical ventilation, 22.7% developed septic shock, and 9.1% required renal replacement therapy (RRT). In the multivariable logistic regression model, BMI was associated with complications including intubation (Odds Ratio [OR]1.03, 95% Confidence Interval [CI]1.01-1.05), septic shock (OR 1.04, CI 1.01-1.06), and RRT (OR1.07, CI 1.04-1.10), and mortality (OR 1.04, CI 1.01-1.06). The odds of death were highest among those with BMI ≥ 40 kg/m2 (OR 2.05, CI 1.04-4.04). Mortality did not differ by race, ethnicity, or socioeconomic distress score, though Black and Asian patients were more likely to require RRT.Conclusions and relevanceSevere complications of COVID-19 and death are more likely in patients with obesity, independent of age and comorbidities. While race, ethnicity, and socioeconomic status did not impact COVID-19 related mortality, Black and Asian patients were more likely to require RRT. The presence of obesity, and in some instances race, should inform resource allocation and risk stratification in patients hospitalized with COVID-19.

Background and purposeHemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke.MethodsWe utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia.ResultsIn the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78.ConclusionsA HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.

Dermatophytes are the most common cause of onychomycosis, counting for 90% fungal nail infection. Although dermatophyte pathogens are normally susceptible to antifungal agents, onychomycosis often results in refractory chronic disease, and the formation of biofilms frequently underlines the inadequate responses and resistance to standard antifungal treatment. Numerous in vitro and in vivo antimicrobial photodynamic therapy (aPDT) studies have shown biofilm eradication or substantial reduction, however, such investigation has not yet been expanded to the biofilms of dermatophytes involved in onychomycosis. To shed a light on the potential application of aPDT in the clinic management of onychomycosis, in particular with the manifestation of dermatophytoma, we investigated photodynamic effects on the viabilities and the drug susceptibilities of the biofilm of dermatophytes in vitro. Here, methylene blue at the concentration of 8, 16, and 32 μg/ml applied as photosensitizing agent and LED (635 ± 10 nm, 60 J/cm2) as light source were employed against six strains of Trichophyton rubrum, ten strains of Trichophyton mentagrophytes and three strains of Microsporum gypseum isolated from clinical specimens. Our results indicated highly efficient photodynamic inhibition, exhibiting CFU (colony forming unit) reduction up to 4.6 log10, 4.3 log10, and 4.7 log10 against the biofilms formed by T. rubrum, T. mentagrophytes, and M. gypseum, respectively. Subjected biofilms displayed considerable decreases in SMICs (sessile minimum inhibitory concentrations) to multiple antifungal agents when compared with untreated groups, indicating the biofilms of dermatophytes became more susceptible to conventional antifungal drugs after aPDT. Additionally, the obliteration of biofilm after aPDT could be observed as shattered and ruptured structures being evident in SEM (Scanning Electron Microscopy) images. These findings suggest that aPDT is an attractive alternative treatment holding great promise for combating recalcitrant onychomycosis associated with the biofilm formation.

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value.

The oppression of women in rural China is more severe than in urban China, not only because the two areas differ in terms of social hierarchy, but also because urban women are more likely to fight against their subordination, which is endorsed by conventional social views on gender. To independently assess these relationships, we applied the Quadruple Process model to measure the processes underlying implicit gender attitudes in a sample of urban and rural females. The results indicated that the urban women had higher in-group favoritism than did the rural women. Application of the Quad model, however, showed that pro-women associations were similarly activated among urban and rural women, but that women in rural settings more effectively inhibited activated associations. Differences in inhibition, rather than in activated associations, appear to account for the less favorable attitudes among rural women. Thus, the differences in attitudinal responses among urban and rural women exaggerate the differences in underlying evaluative associations with respect to gender and conceal differences in self-regulating the expression of those associations.

BackgroundThe antiretroviral medication, tenofovir disoproxil fumarate (TDF), is used by most human immunodeficiency virus–infected persons in the United States despite higher risks of chronic kidney disease. Although chronic kidney disease is a strong risk factor for heart failure (HF), the association of TDF with incident HF is unclear. Methods and ResultsWe identified 21 435 human immunodeficiency virus–infected patients in the United States Veterans Health Administration actively using antiretrovirals between 2002 and 2011. We excluded patients with a prior diagnosis of HF. TDF was analyzed categorically (current, past, or never use) and continuously (per year of use). Proportional hazards regression and fully adjusted marginal structural models were used to determine the association of TDF exposure with risk of incident HF after adjustment for demographic, human immunodeficiency virus–related, and cardiovascular risk factors. During follow‐up, 438 incident HF events occurred. Unadjusted 5‐year event rates for current, past, and never users of TDF were 0.9 (95%CI 0.7‐1.1), 1.7 (1.4‐2.2), and 4.5 (3.9‐5.0), respectively. In fully adjusted analyses, HF risk was markedly lower in current TDF users (HR=0.68; 95%CI 0.53‐0.86) compared with never users. Among current TDF users, each additional year of TDF exposure was associated with a 21% lower risk of incident HF (95%CI: 0.68‐0.92). When limited to antiretroviral‐naive patients, HF risk remained lower in current TDF users (HR=0.53; 95%CI 0.36‐0.78) compared to never users. ConclusionsAmong a large national cohort of human immunodeficiency virus–infected patients, TDF use was strongly associated with lower risk of incident HF. These findings warrant confirmation in other populations, both with TDF and the recently approved tenofovir alafenamide fumarate.

Although some research has examined negative automatic aspects of attitudes toward mental illness via relatively indirect measures among Western samples, it is unclear whether negative attitudes can be automatically activated in individuals from non-Western countries. This study attempted to validate results from Western samples with Chinese college students. We first examined the three-component model of implicit stigma (negative cognition, negative affect, and discriminatory tendencies) toward mental illness with the Single Category Implicit Association Test (SC-IAT). We also explored the relationship between explicit and implicit stigma among 56 Chinese university college students. In the three separate SC-IATs and the combined SC-IAT, automatic associations between mental illness and negative descriptors were stronger relative to those with positive descriptors and the implicit effect of cognitive and affective SC-IATs were significant. Explicit and implicit measures of stigma toward mental illness were unrelated. In our sample, women's overall attitudes toward mental illness were more negative than men's were, but no gender differences were found for explicit measures. These findings suggested that implicit stigma toward mental illness exists in Chinese students, and provide some support for the three-component model of implicit stigma toward mental illness. Future studies that focus on automatic components of stigmatization and stigma-reduction in China are warranted.