Effects of heat waves on heat stroke in Shanghai, 2013—2023

BackgroundThe substantial health damage attributed to heat waves, along with their increasing intensity and frequency in the context of global warming, highlights the importance of exploring the health effects of heat waves. ObjectiveTo calculate the excess heat stroke cases during heat waves in the summer of 2013—2023 in Shanghai, analyze the association between heat waves and heat stroke, and to further explore the modifying effects of heat wave characteristics on heat stroke. MethodsUsing a retrospective ecological study design, data on heat stroke cases were collected from the heat stroke case reporting system of the Chinese Center for Disease Control and Prevention, and concurrent meteorological data from Xujiahui Meteorological Station. A heat wave was defined as at least 3 consecutive days with daily maximum temperature meeting or exceeding 35 ℃ in this study, excess heat stroke cases related to heat waves were assessed as the difference between the numbers of heat stroke cases observed on a given day and the corresponding 31 d (15 d before and after that day) moving average, and statistical analyses using generalized linear model based on time series study were performed to assess the impact of heat waves on heat stroke. ResultsOverall 25 heat waves during the study period were observed, leading to a total of estimated 792.6 extra heat stroke cases. The risk of heat stroke significantly increased during heat waves (RR=2.60, 95%CI: 2.08, 3.26), but no statistically significant differences in heat wave effects were observed among different genders, ages, or regions. In terms of the timing of heat waves, the risk of heat stroke was highest during the first heat wave (RR=3.58, 95%CI: 2.82, 4.55), which was significantly higher than that during the second heat wave (RR=2.19, 95%CI: 1.66, 2.90), and no significant effect was observed during the third or subsequent heat waves. The impact of heat waves on heat stroke persisted for more than 4 d, with the risk higher on the fourth day and beyond (RR=2.95, 95%CI: 2.28, 3.83), significantly higher than on the first day of heat wave (RR=1.74, 95%CI: 1.18, 2.56). ConclusionHeat waves had a substantial effect on heat stroke in Shanghai from 2013 to 2023, and special attention need to be paid to heat waves with early onset and long duration

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

BackgroundPeople usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.MethodsIn this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.ResultsThe final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.ConclusionIn this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction

TP53 Gain-of-Function Mutation Modulates the Immunosuppressive Microenvironment in Non-HPV-Associated Oral Squamous Cell Carcinoma

BACKGROUND: TP53, the most mutated gene in solid cancers, has a profound impact on most hallmarks of cancer. Somatic TP53 mutations occur in high frequencies in head and neck cancers, including oral squamous cell carcinoma (OSCC). Our study aims to understand the role of TP53 gain-of-function mutation in modulating the tumor immune microenvironment (TIME) in OSCC. METHODS: Short hairpin RNA knockdown of mutant p53R172H in syngeneic oral tumors demonstrated changes in tumor growth between immunocompetent and immunodeficient mice. HTG EdgeSeq targeted messenger RNA sequencing was used to analyze cytokine and immune cell markers in tumors with inactivated mutant p53R172H. Flow cytometry and multiplex immunofluorescence (mIF) confirmed the role of mutant p53R172H in the TIME. The gene expression of patients with OSCC was analyzed by CIBERSORT and mIF was used to validate the immune landscape at the protein level. RESULTS: Mutant p53R172H contributes to a cytokine transcriptome network that inhibits the infiltration of cytotoxic CD8+ T cells and promotes intratumoral recruitment of regulatory T cells and M2 macrophages. Moreover, p53R172H also regulates the spatial distribution of immunocyte populations, and their distribution between central and peripheral intratumoral locations. Interestingly, p53R172H-mutated tumors are infiltrated with CD8+ and CD4+ T cells expressing programmed cell death protein 1, and these tumors responded to immune checkpoint inhibitor and stimulator of interferon gene 1 agonist therapy. CIBERSORT analysis of human OSCC samples revealed associations between immune cell populations and the TP53R175H mutation, which paralleled the findings from our syngeneic mouse tumor model. CONCLUSIONS: These findings demonstrate that syngeneic tumors bearing the TP53R172H gain-of-function mutation modulate the TIME to evade tumor immunity, leading to tumor progression and decreased survival

Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society

Quantitative Weighted Visual Cryptographic (k, m, n) Method

The weighted visual cryptographic scheme (WVCS) is a secret sharing technology, where weights are assigned to each shadow (participant) according to its importance. Among WVCS, the random grid-based WVCS (RGWVCS) is a frequently visited subject. It considers the premise of equality of all participants, without taking into account the existence of privileged people in reality. To address this problem of RGWVCS, this paper designs a new model, named as (k, m, n)-RGWVCS (where m<k<n), in which the secret is encrypted into n shares and sent to k participants. In the recovery end, the secret could be reconstructed by minimum m shares when the privileged join in; otherwise, k shares are needed. The experimental results show that our method has the advantage of no pixel expansion and no codebook design by means of random grid. Moreover, the contrast of our model increased by 32.85% on average compared with that of other WVCS

Trends in gut-heart axis and heart failure research (1993–2023): A bibliometric and visual analysis

Background: The incidence of heart failure, the terminal stage of several cardiovascular diseases, is increasing owing to population growth and aging. Bidirectional crosstalk between the gut and heart plays a significant role in heart failure. This study aimed to analyze the gut-heart axis and heart failure from a bibliometric perspective. Methods: We extracted literature regarding the gut-heart axis and heart failure from the Web of Science Core Collection database (January 1, 1993, to June 30, 2023) and conducted bibliometric and visualization analyses using Microsoft Excel, CiteSpace, VOSviewer, and the R package “bibliometrix.” Results: The final analysis included 1646 articles with an average of 35.38 citations per article. Despite some fluctuations, the number of articles published per year has steadily increased over the past 31 years, particularly since 2018. A total of 9412 authors from 2287 institutions in 86 countries have contributed to this field. The USA and China have been the most productive countries, with the Cleveland Clinic in the USA and Charité-Universitätsmedizin Berlin in Germany being the most active institutions. The cooperation between countries/regions and institutions was relatively close. Professor Tang WHW was the most productive author in the field and the journal Shocks published the highest number of articles. ''Heart failure,'' ''gut microbiota,'' ''trimethylamine N-oxide,'' and ''inflammation'' were the most common keywords, representing the current research hotspots. The keyword burst analysis indicated that ''gut microbiota'' and ''short-chain fatty acids'' are the current frontier research topics in this field. Conclusion: Research on the gut-heart axis and heart failure is increasing. This bibliometric analysis indicated that the mechanisms associated with the gut-heart axis and heart failure, particularly the gut microbiota, trimethylamine N-oxide, inflammation, and short-chain fatty acids, will become hotspots and emerging trends in research in this field. These findings provide valuable insights into current research and future directions

NLRP3 inflammasome regulates astrocyte transformation in brain injury induced by chronic intermittent hypoxia

Abstract Background Obstructive sleep apnea (OSA) is mainly characterized by sleep fragmentation and chronic intermittent hypoxia (CIH), the latter one being associated with multiple organ injury. Recently, OSA-induced cognition dysfunction has received extensive attention from scholars. Astrocytes are essential in neurocognitive deficits via A1/A2 phenotypic changes. Nucleotide oligomerization domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is considered the most important factor inducing and maintaining neuroinflammation. However, whether the NLRP3 regulates the A1/A2 transformation of astrocytes in CIH-related brain injury remains unclear. Methods We constructed an OSA-related CIH animal model and assessed the rats' learning ability in the Morris water maze; the histopathological assessment was performed by HE and Nissl staining. The expression of GFAP (astrocyte marker), C3d (A1-type astrocyte marker), and S100a10 (A2-type astrocyte marker) were detected by immunohistochemistry and immunofluorescence. Western blotting and RT-qPCR were used to evaluate the changes of A1/A2 astrocyte-related protein and NLRP3/Caspase-1/ASC/IL-1β. Results The learning ability of rats decreased under CIH. Further pathological examination revealed that the neurocyte in the hippocampus were damaged. The cell nuclei were fragmented and dissolved, and Nissl bodies were reduced. Immunohistochemistry showed that astrocytes were activated, and morphology and number of astrocytes changed. Immunofluorescence, Western blotting and RT-qPCR showed that the expression of C3d was increased while S100a10 was decreased. Also, the expression of the inflammasome (NLRP3/Caspase-1/ASC/IL-1β) was increased. After treatment of MCC950 (a small molecule inhibitor of NLRP3), the damage of nerve cells was alleviated, the Nissl bodies increased, the activation of astrocytes was reduced, and the expression of A2-type astrocytes was increased. In contrast, A1-type astrocytes decreased, and the expression of inflammasome NLRP3/Caspase-1/ASC/IL-1β pathway-related proteins decreased. Conclusion The NLRP3 inflammasome could regulate the A1/A2 transformation of astrocytes in brain injury induced by CI

The relationship of tongue fat content and efficacy of uvulopalatopharyngoplasty in Chinese patients with obstructive sleep apnea

Abstract Background To investigate the relationship between tongue fat content and severity of obstructive sleep apnea (OSA) and its effects on the efficacy of uvulopalatopharyngoplasty (UPPP) in the Chinese group. Method Fifty-two participants concluded to this study were diagnosed as OSA by performing polysomnography (PSG) then they were divided into moderate group and severe group according to apnea hypopnea index (AHI). All of them were also collected a series of data including age, BMI, height, weight, neck circumference, abdominal circumference, magnetic resonance imaging (MRI) of upper airway and the score of Epworth Sleepiness Scale (ESS) on the morning after they completed PSG. The relationship between tongue fat content and severity of OSA as well as the association between tongue fat content in pre-operation and surgical efficacy were analyzed.Participants underwent UPPP and followed up at 3rd month after surgery, and they were divided into two groups according to the surgical efficacy. Results There were 7 patients in the moderate OSA group and 45 patients in the severe OSA group. The tongue volume was significantly larger in the severe OSA group than that in the moderate OSA group. There was no difference in tongue fat volume and tongue fat rate between the two groups. There was no association among tongue fat content, AHI, obstructive apnea hypopnea index, obstructive apnea index and Epworth sleepiness scale (all P > 0.05), but tongue fat content was related to the lowest oxygen saturation (r=-0.335, P < 0.05). There was no significantly difference in pre-operative tongue fat content in two different surgical efficacy groups. Conclusions This study didn’t show an association between tongue fat content and the severity of OSA in the Chinese group, but it suggested a negative correlation between tongue fat content and the lowest oxygen saturation (LSaO2). Tongue fat content didn’t influence surgical efficacy of UPPP in Chinese OSA patients. Trial registration This study didn’t report on a clinical trial, it was retrospectively registered