Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

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.

Aqueous-phase formation of N-containing secondary organic compounds affected by the ionic strength.

The reaction of carbonyl-to-imine/hemiaminal conversion in the atmospheric aqueous phase is a critical pathway to produce the light-absorbing N-containing secondary organic compounds (SOC). The formation mechanism of these compounds has been wildly investigated in bulk solutions with a low ionic strength. However, the ionic strength in the aqueous phase of the polluted atmosphere may be higher. It is still unclear whether and to what extent the inorganic ions can affect the SOC formation. Here we prepared the bulk solution with certain ionic strength, in which glyoxal and ammonium were mixed to mimic the aqueous-phase reaction. Molecular characterization by High-resolution Mass Spectrometry was performed to identify the N-containing products, and the light absorption of the mixtures was measured by ultraviolet-visible spectroscopy. Thirty-nine N-containing compounds were identified and divided into four categories (N-heterocyclic chromophores, high-molecular-weight compounds with N-heterocycle, aliphatic imines/hemiaminals, and the unclassified). It was observed that the longer reaction time and higher ionic strength led to the formation of more N-heterocyclic chromophores and the increasing of the light-absorbance of the mixture. The added inorganic ions were proposed to make the aqueous phase somewhat viscous so that the molecules were prone to undergo consecutive and intramolecular reactions to form the heterocycles. In general, this study revealed that the enhanced ionic strength and prolonged reaction time had the promotion effect on the light-absorbing SOC formation. It implies that the aldehyde-derived aqueous-phase SOC would contribute more light-absorbing particulate matter in the industrial or populated area where inorganic ions are abundant.

ESCO2 promotes hypopharyngeal carcinoma progression in a STAT1-dependent manner.

BACKGROUND: The establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the development of multiple malignancies. However, its role in hypopharyngeal carcinoma (HPC) progression remains uncharacterized. METHODS: This study employed bioinformatics to determine the ESCO2 expression in head and neck squamous cell carcinoma (HNSC) and normal tissues. In vitro cell proliferation, migration, apoptosis, and/or cell cycle distribution assays were used to determine the function of ESCO2 and its relationship with STAT1. Xenograft models were established in nude mice to determine ESCO2 in HPC growth in vivo. Co-immunoprecipitation/mass spectrometry (Co-IP/MS) was conducted to identify the potential ESCO2 binding partners. RESULTS: We found that ESCO2 expression was elevated in HNSC tissues, and ESCO2 depletion suppressed tumor cell migration in vitro and inhibited tumor growth in vitro and in vivo. Co-IP/MS and immunoblotting assays revealed the interaction between ESCO2 and STAT1 in HPC cells. STAT1-overexpression compromised ESCO2-mediated suppressive effects on HPC cell proliferation, viability, and migration. CONCLUSIONS: These findings suggest that ESCO2 is crucial in promoting HPC malignant progression through the STAT1 pathway and provides novel therapeutic targets for HPC treatment.

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.

BACKGROUND: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION: Retrospectively registered.

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

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.

Use of symptom diary in primary students: association of nitrogen dioxide with prevalence of symptoms.

Air pollution is a global public health concern, and numerous studies have attempted to identify the health effects of air pollutants, including nitrogen dioxide (NO2). In China, there are few studies investigating the relationship between NO2 exposure and symptoms among children at an individual level. The aim of the study was to evaluate the acute effects of NO2 on prevalence of symptoms of primary students. An environmental and health questionnaire survey was administered to 4240 primary students in seven districts of Shanghai. Daily symptoms, as well as the daily air pollution and meteorological data from each community, were recorded during the corresponding period. A multivariable logistic regression model was utilized to analyze the relationship between the prevalence of symptoms and NO2 exposure in school-age children. A model with interaction items was adopted to estimate the interactive effects of NO2 and confounding factors on symptoms. The average NO2 level in central urban, industrial and rural areas were 62.07 ± 21.66, 54.86 ± 18.32 and 36.62 ± 21.23 µg m-3, respectively. Our findings demonstrate that the occurrence of symptoms was significantly affected by NO2 exposure in the short-term. The largest associations were observed for a 10 µg m-3 increase in 5-day moving average (lag04) NO2 concentration with prevalence of general symptoms (odds ratio [OR] = 1.15, 95% confidence interval [95% CI]: 1.07-1.22), throat symptoms (OR = 1.23, 95% CI: 1.13-1.35) and nasal symptoms (OR = 1.142, 95% CI: 1.02-1.27). Subgroup analysis showed that non-rural areas, boys, nearby environmental pollution source and history of present illness were all susceptible factors to the effects of NO2 exposure. Furthermore, there were interactive effects between NO2 exposure and area types on reported symptoms. NO2 can increase the risk of symptoms in primary students in the short-term, which could be significantly enhanced in central urban and industrial areas.

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

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-1ß. 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-1ß) 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-1ß pathway-related proteins decreased. CONCLUSION: The NLRP3 inflammasome could regulate the A1/A2 transformation of astrocytes in brain injury induced by CIH.

Serum neurofilament light reflects cognitive dysfunctions in children with obstructive sleep apnea.

BACKGROUND: In children, obstructive sleep apnea (OSA) can cause cognitive dysfunctions. Amyloid-beta and tau are elevated in OSA. Neurofilament light (NfL) is a marker of neuro-axonal damage, but there are no reports of NfL for OSA. The objective was to investigate the serum levels of NfL and tau in children with or without OSA and explore their relationship with cognitive dysfunctions caused by OSA. METHODS: This retrospective case-control study included children diagnosed with adenoid tonsil hypertrophy from July 2017 to September 2019 at the Second Affiliated Hospital of Xi'an Jiaotong University. Correlations between cognitive scores and tau and NfL were examined. RESULTS: Fifty-six OSA and 49 non-OSA children were included. The serum NfL levels were higher in the OSA group (31.68 (27.29-36.07) pg/ml) than in the non-OSA group (19.13 (17.32-20.95) pg/ml) (P < 0.001). Moreover, NfL was correlated with the course of the disease, apnea-hypopnea index (AHI), obstructive apnea index (OAI), obstructive apnea-hypopnea index (OAHI), average oxygen saturation (SaO2), respiratory arousal index (RAI), and cognitive dysfunctions evaluated by the Chinese Wechsler Intelligence Scale for Children (C-WISC) (all P < 0.05). The area under the receiver operating characteristics curve (AUC) of NfL was 0.816 (95%CI: 0.736-0.897). Multiple regression analysis revealed that NfL was significantly associated with verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ) and full-scale intelligence quotient (FIQ) (P < 0.001, respectively). CONCLUSIONS: Serum NfL levels are associated with the severity of cognitive dysfunctions in children diagnosed with adenoid tonsil hypertrophy and might be a candidate noninvasive, objective marker to identify cognitive dysfunctions in children with OSA.

The effects of Helicobacter pylori eradication therapy on salivary pepsin concentration in patients with laryngopharyngeal reflux.

OBJECTIVE: To investigate the effect of Helicobacter pylori (HP) eradication therapy on salivary pepsin concentration in laryngopharyngeal reflux (LPR) patients with HP infection. MATERIALS AND METHODS: A total of 477 patients with suspected LPR were enrolled from June 2020 to September 2021. Reflux symptom index, reflux finding score, the positive rates and disintegrations per minute values of HP infection detected by 14C urea breath test and salivary pepsin concentrations analyzed using enzyme-linked immunosorbent assay were compared in LPR patients and non-LPR patients with or without HP infection. HP-positive patients were treated with HP eradication therapy while HP-negative patients with PPI therapy. RESULTS: The scores of nagging cough (0.88 vs. 0.50, P = 0.035), erythema or hyperemia (1.93 vs. 1.78, P = 0.035) and vocal fold edema (1.04 vs. 0.85, P = 0.025) were higher in the LPR (+) Hp (+) subgroup than in LPR (+) Hp (-) subgroup. The concentrations of salivary pepsin in the Hp (+) subgroup were higher than in the Hp (-) subgroup either in LPR patients (75.24 ng/ml vs. 61.39 ng/ml, P = 0.005) or the non-LPR patients (78.42 ng/ml vs. 48.96 ng/ml, P = 0.024). Compared to baseline (before treatment), scores of nagging cough (0.35 vs. 0.84, P = 0.019) and erythema or hyperemia (1.50 vs. 1.83, P = 0.039) and the concentrations of salivary pepsin (44.35 ng/ml vs. 74.15 ng/ml, P = 0.017) in LPR patients with HP infection decreased after HP treatment; yet, this was not observed for the LPR patients without HP infection treated with PPI only (P > 0.05). CONCLUSION: HP infection may aggravate the symptoms and signs of LPR patients, partly by increasing their salivary pepsin concentration.

Global Trends and Hotspots in Research on the Health Risks of Organophosphate Flame Retardants: A Bibliometric and Visual Analysis.

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.

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

Background: People 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. 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 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. Results: The 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. Conclusion: In 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.

Association between obstructive sleep apnea and thyroid function: A 10-year retrospective study.

PURPOSE: To explore whether the obstructive sleep apnea (OSA) has an impact on thyroid function in patients. METHOD: The data of 853 patients were retrospectively collected from the Second Affiliated Hospital of Xi'an Jiaotong University in recent ten years. All the objects were divided into the control group, mild-moderate and severe OSA groups according to the result of polysomnography. RESULTS: In the non-elderly population (age <60), there were significant differences in serum free triiodothyronine (FT3) and total triiodothyronine (TT3) between the mild-moderate and severe OSA groups (all p < 0.05). And there were differences in serum total thyroxine, anti-thyroid peroxidase, and antithyroglobulin between the control and mild-moderate OSA groups (all p < 0.05). Moreover, FT3 was associated with age (OR = 0.98, p < 0.05) and apnea-hypopnea index (OR = 1.01, p < 0.05). The occurrence of thyroid nodules was associated with average transcutaneous oxygen saturation (Mean SaO2) (OR = 0.97, p < 0.05). In the elderly (age ≥60), there was no difference in FT3 and TT3 between the mild-moderate and severe OSA. While the occurrence of thyroid nodules was also associated with Mean SaO2 (OR = 0.90, p < 0.05). CONCLUSION: In the non-elderly population, the progress of OSA may promote the increase in thyroid hormone (especially FT3) levels, while in the elderly population not. In the whole age population, Mean SaO 2 is associated with the occurrence of thyroid nodules. Future research on the relationship between OSA and thyroid function, and age stratification is necessary.

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.

Relationship between serum brain-derived neurotrophic factor and cognitive impairment in children with sleep-disordered breathing.

Background: As an important neuroprotective factor, the brain-derived neurotrophic factor (BDNF) may have a key role in cognitive impairment in children with sleep-disordered breathing (SDB). The main aim of this study was to compare the levels of BDNF and tyrosine kinase receptor B (TrkB) in normal children and those with obstructive sleep apnea (OSA) and primary snoring (PS) and to explore a possible link between BDNF/TrkB, inflammation, and SDB with cognitive impairment in children. Methods: A total of 44 OSA children and 35 PS children who completed polysomnography between October 2017 and October 2019 were enrolled. At the same time, 40 healthy children during the same period were included as a control. Enzyme-linked immunosorbent assay was used to measure serum indices of BDNF, TrkB, interleukin-1beta (IL-1ß), and tumor necrosis factor-alpha (TNF-α). Correlation and pooled analyses were performed between the cognitive scores and four serological indicators. Logistic regression was used to analyze the risk factors for cognitive impairment. Results: Significant differences were found in serum BDNF, TrkB, IL-1ß, and TNF-α between the three groups (all P < 0.01). The serum BDNF and TrkB in the OSA and PS groups were lower than those in the control group, whereas the serum IL-1ß and TNF-α were higher than those in the control group (all P < 0.05). Moreover, among these four indices, the strongest correlation was found between BDNF and the Chinese Wechsler Intelligence Scale (all P < 0.05). Logistic regression analysis revealed a correlation between OSA status, TrkB, and course of mouth breathing and cognitive status. Conclusion: The levels of serum BDNF and TrkB were related to cognitive impairment in children with SDB. Also, BDNF and TrkB could be used as noninvasive and objective candidate markers and predictive indices of cognitive impairment in children with SDB.

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

Background: Obstructive 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 methods: We 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. Results: A 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. Conclusion: We 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.

Mutant p53 drives an immune cold tumor immune microenvironment in oral squamous cell carcinoma.

The critical role of the tumor immune microenvironment (TIME) in determining response to immune checkpoint inhibitor (ICI) therapy underscores the importance of understanding cancer cell-intrinsic mechanisms driving immune-excluded ("cold") TIMEs. One such cold tumor is oral cavity squamous cell carcinoma (OSCC), a tobacco-associated cancer with mutations in the TP53 gene which responds poorly to ICI therapy. Because altered TP53 function promotes tumor progression and plays a potential role in TIME modulation, here we developed a syngeneic OSCC models with defined Trp53 (p53) mutations and characterized their TIMEs and degree of ICI responsiveness. We observed that a carcinogen-induced p53 mutation promoted a cold TIME enriched with immunosuppressive M2 macrophages highly resistant to ICI therapy. p53-mutated cold tumors failed to respond to combination ICI treatment; however, the combination of a programmed cell death protein 1 (PD-1) inhibitor and stimulator of interferon genes (STING) agonist restored responsiveness. These syngeneic OSCC models can be used to gain insights into tumor cell-intrinsic drivers of immune resistance and to develop effective immunotherapeutic approaches for OSCC and other ICI-resistant solid tumors.

Targeting IL-1ß as an immunopreventive and therapeutic modality for K-ras-mutant lung cancer.

K-ras-mutant lung adenocarcinoma (KM-LUAD) is associated with abysmal prognosis and is tightly linked to tumor-promoting inflammation. A human mAb, canakinumab, targeting the proinflammatory cytokine IL-1ß, significantly decreased the risk of lung cancer in the Canakinumab Anti-inflammatory Thrombosis Outcomes Study. Interestingly, we found high levels of IL-1ß in the lungs of mice with K-rasG12D-mutant tumors (CC-LR mice). Here, we blocked IL-1ß using an anti-IL-1ß mAb in cohorts of 6- or 14-week-old CC-LR mice to explore its preventive and therapeutic effect, respectively. IL-1ß blockade significantly reduced lung tumor burden, which was associated with reprogramming of the lung microenvironment toward an antitumor phenotype characterized by increased infiltration of cytotoxic CD8+ T cells (with high IFN-γ and granzyme B expression but low programmed cell death 1 [PD-1] expression) while suppressing neutrophils and polymorphonuclear (PMN) myeloid-derived suppressor cells. When querying the Cancer Genome Atlas data set, we found positive correlations between IL1B expression and infiltration of immunosuppressive PMNs and expression of their chemoattractant, CXCL1, and PDCD1 expressions in patients with KM-LUAD. Our data provide evidence that IL-1ß blockade may be a preventive strategy for high-risk individuals and an alternative therapeutic approach in combination with currently available treatments for KM-LUAD.

Identification of core genes and clinical outcomes in tumors originated from endoderm (gastric cancer and lung carcinoma) via bioinformatics analysis.

ABSTRACT: During last decade, bioinformatics analysis has provided an effective way to study the relationship between various genes and biological processes. In this study, we aimed to identify potential core candidate genes and underlying mechanisms of progression of lung and gastric carcinomas which both originated from endoderm. The expression profiles, GSE54129 (gastric carcinoma) and GSE27262 (lung carcinoma), were collected from GEO database. One hundred eleven patients with gastric carcinoma and 21 health people were included in this research. Meanwhile, there were 25 lung carcinoma patients. Then, 75 differentially expressed genes were selected via GEO2R online tool and Venn software, including 31 up-regulated genes and 44 down-regulated genes. Next, we used Database for Annotation, Visualization, and Integrated Discovery and Metascpe software to analyze Kyoto Encyclopedia of Gene and Genome pathway and gene ontology. Furthermore, Cytoscape software and MCODE App were performed to construct complex of these differentially expressed genes . Twenty core genes were identified, which mainly enriched in extracellular matrix-receptor interaction, focal adhesion, and PI3K-Akt pathway (P < .01). Finally, the significant difference of gene expression between cancer tissues and normal tissues in both lung and gastric carcinomas was examined by Gene Expression Profiling Interactive Analysis database. Twelve candidate genes with positive statistical significance (P < .01), COMP CTHRC1 COL1A1 SPP1 COL11A1 COL10A1 CXCL13 CLDN3 CLDN1 matrix metalloproteinases 7 ADAM12 PLAU, were picked out to further analysis. The Kaplan-Meier plotter website was applied to examine relationship among these genes and clinical outcomes. We found 4 genes (ADAM12, SPP1, COL1A1, COL11A1) were significantly associated with poor prognosis in both lung and gastric carcinoma patients (P  < .05). In conclusion, these candidate genes may be potential therapeutic targets for cancer treatment.

Local Anti-PD-1 Delivery Prevents Progression of Premalignant Lesions in a 4NQO-Oral Carcinogenesis Mouse Model.

Although the principle of systemic treatment to prevent the progression of oral premalignant lesions (OPL) has been demonstrated, there remains a lack of consensus about an optimal approach that balances clinical efficacy with toxicity concerns. Recent advances in cancer therapy using approaches targeting the tumor immune microenvironment (TIME) including immune-checkpoint inhibitors indicate that these agents have significant clinically activity against different types of cancers, including oral cancer, and therefore they may provide an effective oral cancer prevention strategy for patients with OPLs. Our past work showed that systemic delivery of a monoclonal antibody to the programmed death receptor 1 (PD-1) immune checkpoint can inhibit the progression of OPLs to oral cancer in a syngeneic murine oral carcinogenesis model. Here we report a novel approach of local delivery of a PD-1 immune-checkpoint inhibitor loaded using a hydrogel, which significantly reduces the progression of OPLs to carcinomas. In addition, we detected a significant infiltration of regulatory T cells associated with oral lesions with p53 mutation, and a severe loss of expression of STING, which correlated with a decreased infiltration of dendritic cells in the oral lesions. However, a single local dose of PD-1 inhibitor was found to restore stimulator of interferon response cGAMP interactor 1 (STING) and CD11c expression and increase the infiltration of CD8+ T cells into the TIME irrespective of the p53 mutational status. Overall, we provide evidence for the potential clinical value of local delivery of biomaterials loaded with anti-PD-1 antibodies to prevent malignant progression of OPLs. PREVENTION RELEVANCE: Oral cancer is an aggressive disease, with an overall survival rate of 50%. Preinvasive histologic abnormalities such as tongue dysplasia represent an early stage of oral cancer; however, there are no treatments to prevent oral carcinoma progression. Here, we combined biomaterials loaded with an immunotherapeutic agent preventing oral cancer progression.

Assessment of event-related evoked potentials and China-Wechsler intelligence scale for cognitive dysfunction in children with obstructive sleep apnea.

OBJECTIVE: To explore the relationship between obstructive sleep apnea (OSA) and cognitive impairment by combining event-related evoked potentials (ERPs) and China-Wechsler Younger Children Scale (C-WISC) in children with sleep-disordered breathing (SDB) with vs. without OSA. METHODS: This was a retrospective case-control study of all consecutive children (n = 148) with adenoid tonsil hypertrophy between July 2017 and March 2019 at the Hospital. RESULTS: The children were divided into the OSA (n = 102) and non-OSA (n = 46) groups. The apnea-hypopnea index (AHI), obstructive apnea index (OAI), and obstructive apnea-hypopnea index (OAHI) in the OSA group were elevated compared with those of the non-OSA group (all P < 0.001). The mean oxygen saturation (SaO2) and SaO2 nadir were lower in the OSA group compared with the non-OSA group (both P < 0.001). The respiratory arousal index (RAI) values in the OSA group were larger than those of the non-OSA group (P < 0.001). The P300 and N100 latencies in the OSA group were longer than those of the non-OSA group (both P < 0.001). Pearson's correlation analysis revealed correlations of the P300 peak latency with full-scale intelligence quotient (FIQ) (P < 0.001 and r = -0.527), verbal intelligence quotient (VIQ) (P < 0.001 and r = -0.448), and performance intelligence quotient (PIQ) (P < 0.001 and r = -0.515). There was a correlation between the N100 peak latency and PIQ (P = 0.026 and r = -0.183). CONCLUSION: ERPs, as an objective measurement, might help assess cognitive impairment in children with OSA.