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Summary Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep‐learning model that uses single‐lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time–frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single‐lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per‐segment and per‐recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per‐segment detection was 91.66%. A Bland–Altman plot revealed the consistency of true apnea–hypopnea index and predicted apnea–hypopnea index. We confirmed the feasibility of single‐lead electrocardiogram signals and deep‐learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

Background: Organophosphate flame retardants (OPFRs) are compounds with a wide range of industrial and commercial applications and are mainly used as flame retardants and plasticizers. The global consumption of OPFRs has risen rapidly in recent decades, and they have been widely detected in environmental media. Unfortunately, OPFRs have been associated with many adverse health outcomes. The issue of the health risks of OPFRs is attracting increasing attention. Therefore, there is a need to review the current state of research and trends in this field to help researchers and policymakers quickly understand the field, identify new research directions, and allocate appropriate resources for further development of the OPFR health risk research field. Methods: This study statistically analyzed 1162 relevant publications included in the Web of Science Core Collection from 2003–2023. The internal and external features of the literature, such as publication trends, countries, authors, journals, and keywords, were quantitatively analyzed and visually presented to identify the research hotspots, compositions, and paradigms of the field and to horizontally and vertically analyze the development trends and structural evolution of the field. Results: The development of the field can be divided into three stages, and the field entered a period of rapid development in 2016. China (649 papers) is the most prolific country, followed by the United States (188 papers). The authors STAPLETON HM and WANG Y have the highest combined impact. International collaboration between countries and researchers still needs to be strengthened. Science of The Total Environment is the most frequently published journal (162 papers), and Environmental Science and Technology is the most frequently cited journal (5285 citations). Endocrine disruption, developmental toxicity, and neurotoxicity are the health effects of greatest interest. Conclusions: Future research is expected to be multidisciplinary, and research hotspots may involve a comprehensive assessment of OPFR exposure in the population, exploration of the mechanisms of endocrine-disrupting effects and in vivo metabolic processes, and examination of the health effects of OPFR metabolites. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to perform an exposure assessment of PM2.5 (particulate matter less than 2.5μm in aerodynamic diameter) among children and to explore the potential sources of exposure from both indoor and outdoor environments. Methods: In terms of real-time exposure measurements of PM2.5, we collected data from 57 children aged 8–12 years (9.64 ± 0.93 years) in two schools in Shanghai, China. Simultaneously, questionnaire surveys and time-activity diaries were used to estimate the environment at home and daily time-activity patterns in order to estimate the exposure dose of PM2.5 in these children. Principle component regression analysis was used to explore the influence of potential sources of PM2.5 exposure. Results: All the median personal exposure and microenvironment PM2.5 concentrations greatly exceeded the daily 24-h PM2.5 Ambient Air Quality Standards of China, the USA, and the World Health Organization (WHO). The median Etotal (the sum of the PM2.5 exposure levels in different microenvironment and fractional time) of all students was 3014.13 (μg.h)/m3. The concentration of time-weighted average (TWA) exposure of all students was 137.01 μg/m3. The median TWA exposure level during the on-campus period (135.81 μg/m3) was significantly higher than the off-campus period (115.50 μg/m3, P = 0.013 < 0.05). Besides ambient air pollution and meteorological conditions, storey height of the classroom and mode of transportation to school were significantly correlated with children’s daily PM2.5 exposure. Conclusions: Children in the two selected schools were exposed to high concentrations of PM2.5 in winter of 2013 in Shanghai. Their personal PM2.5 exposure was mainly associated with ambient air conditions, storey height of the classroom, and children’s transportation mode to school. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. Methods: The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included. The least absolute shrinkage and selection operator（Lasso） logistic regression model was used to select potentially relevant features and establish a nomogram for OSA-related hypertension.The performance and clinical benefits of this nomogram were verified in terms of discrimination, calibration ability and clinical net benefit. Results: Multivariate logistic regression showed that body mass index（BMI）, family history of hypertension, lowest oxygen saturation（LSaO2）, age and cumulative percentage of total sleep time with oxygen saturation below 90% were independent risk factors for OSA-related hypertension. Lasso logistic regression identified BMI, family history of hypertension, LSaO2 and age as predictive factors for inclusion in the nomogram. The nomogram provided a favorable discrimination, with a C-indexes of 0.835（95% confidence interval[ CI ]0.806-0.863） 0.865（95% CI 0.829-0.900） for the training and validation cohort, respectively, and well calibrated. The clinical decision curve analysis displayed that the nomogram was clinically useful. Conclusion: Compared with cumulative percentage of total sleep time with blood oxygen saturation below 90%, LSaO2 may have a greater impact on the incidence of OSA-related hypertension, and the effects of different times and degrees of hypoxia on OSA-related hypertension should be further explored in the future. Apnea hypopnea index involvement is weak in predicting OSA-related hypertension, and the blood oxygen index may be a better predictor variable. Furthermore, we established a risk prediction model for OSA-related hypertension patients using nomogram, and demonstrated that this prediction model was helpful to identify high-risk OSA-related hypertension patients. This model can provide early and individualized diagnosis and treatment plans, protect patients from the serious., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.)

Background: People usually spend most of their time indoors, so indoor fine particulate matter (PM 2.5 ) concentrations are crucial for refining individual PM 2.5 exposure evaluation. The development of indoor PM 2.5 concentration prediction models is essential for the health risk assessment of PM 2.5 in epidemiological studies involving large populations., Methods: In 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 PM 2.5 concentration prediction models. Indoor PM 2.5 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., Results: The final predictor variables incorporated in the MLR model were outdoor PM 2.5 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 (R 2 ) 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, R 2  = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM 2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables., Conclusion: In this research, hourly average indoor PM 2.5 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shi, Du, Zhang, Han, Chen, Wang, Liu, Zhang, Dong and Sui.)

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., Competing Interests: Competing interests: No, there are no competing interests., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)