EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction.

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology.

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and Clustering Methods.

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods. The source code has been publicly released and is now accessible on GitHub at https://github.com/xjnine/MGNR.

Association between blood ethylene oxide levels and the prevalence of periodontitis: evidence from NHANES 2013-2014.

BACKGROUND: The study aimed to establish a link between blood ethylene oxide (EO) levels and periodontitis, given the growing concern about EO's detrimental health effects. MATERIALS AND METHODS: The study included 1006 adults from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) dataset. We assessed periodontitis prevalence across groups, used weighted binary logistic regression and restricted cubic spline fitting for HbEO-periodontitis association, and employed Receiver Operating Characteristic (ROC) curves for prediction. RESULTS: In the periodontitis group, HbEO levels were significantly higher (40.57 vs. 28.87 pmol/g Hb, P < 0.001). The highest HbEO quartile showed increased periodontitis risk (OR = 2.88, 95% CI: 1.31, 6.31, P = 0.01). A "J"-shaped nonlinear HbEO-periodontitis relationship existed (NL-P value = 0.0116), with an inflection point at ln-HbEO = 2.96 (EO = 19.30 pmol/g Hb). Beyond this, ln-HbEO correlated with higher periodontitis risk. A predictive model incorporating sex, age, education, poverty income ratio, alcohol consumption, and HbEO had 69.9% sensitivity and 69.2% specificity. The model achieved an area under the ROC curve of 0.761. CONCLUSIONS: These findings suggest a correlation between HbEO levels and an increased susceptibility to periodontitis.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network.

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

Systemic Immune-Inflammation Index and Systemic Inflammation Response Index are Associated With Periodontitis: Evidence From NHANES 2009 to 2014.

INTRODUCTION AND AIMS: Periodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, is a substantial public health burrden whilst impacting the life quality of those affected. Elevated levels of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) have been implicated in various inflammatory conditions. This study aimed to investigate the relationship between SII and SIRI with periodontitis. METHODS: The study examined a total of 8666 participants in the 2009 to 2014 National Health and Nutrition Examination Survey (NHANES). The study compared the weighted prevalence of periodontitis among various groups. The association between SII, SIRI levels, and periodontitis was analyzed using binary logistic regression. Additionally, we explored nonlinear relationships between SII, SIRI, and the prevalence of periodontitis using restricted cubic spline (RCS) plots. RESULTS: Among participants in the fourth quartile (Q4) of SII and SIRI, the highest prevalence of periodontitis was observed, with rates of 44.87% and 48.41%, respectively. After adjusting for all covariates, the odds ratio (OR) for periodontitis associated with SII Q4 was 1.19 (95% CI 1.02, 1.39, P = .03), while for SIRI Q4, it was 1.18 (95% CI 1.01, 1.39, P = .04). In addition, the results of sensitivity analysis revealed consistent findings, indicating that after adjusting for all covariates, the OR for periodontitis associated with SII Q4 and SIRI Q4 remained statistically significant. Specifically, the OR for periodontitis associated with SII Q4 was 1.19 (95% CI 1.02, 1.39, P = .03), while for SIRI Q4, it was 1.19 (95% CI 1.01, 1.40, P = .04). CONCLUSIONS: These results indicate that elevated SII and SIRI levels are associated with an increased prevalence of periodontitis. CLINICAL RELEVANCE: These findings suggest a potential connection between systemic inflammation and periodontitis, highlighting the importance of periodontitis patients being aware of their systemic diseases that are inflammatory in nature such as chronic cardiovascular afflictions.

Corrigendum: Roles of ubiquitin-specific proteases in inflammatory diseases.

[This corrects the article DOI: 10.3389/fimmu.2024.1258740.].

Causal effects of gut microbiota on autism spectrum disorder: A two-sample mendelian randomization study.

There is increasing evidence that alterations in gut microbiota (GM) composition are associated with autism spectrum disorder (ASD), but no reliable causal relationship has been established. Therefore, a 2-sample Mendelian randomization (MR) study was conducted to reveal a potential causal relationship between GM and ASD. Instrumental variables for 211 GM taxa were obtained from genome-wide association studies (GWAS) and Mendelian randomization studies to estimate their impact on ASD risk in the iPSYCH-PGC GWAS dataset (18,382 ASD cases and 27,969 controls). Inverse variance weighted (IVW) is the primary method for causality analysis, and several sensitivity analyses validate MR results. Among 211 GM taxa, IVW results confirmed that Tenericutes (P value = .0369), Mollicutes (P value = .0369), Negativicutes (P value = .0374), Bifidobacteriales (P value = .0389), Selenomonadales (P value = .0374), Bifidobacteriaceae (P value = .0389), Family XIII (P value = .0149), Prevotella7 (P value = .0215), Ruminococcaceae NK4A214 group (P value = .0205) were potential protective factors for ASD. Eisenbergiella (P value = .0159) was a possible risk factor for ASD. No evidence of heterogeneous, pleiotropic, or outlier single-nucleotide polymorphism was detected. Additionally, further sensitivity analysis verified the robustness of the above results. We confirm a potential causal relationship between certain gut microbes and ASD, providing new insights into how gut microbes mediate ASD. The association between them needs to be further explored and will provide new ideas for the prevention and treatment of ASD.

Roles of ubiquitin-specific proteases in inflammatory diseases.

Ubiquitin-specific proteases (USPs), as one of the deubiquitinating enzymes (DUBs) families, regulate the fate of proteins and signaling pathway transduction by removing ubiquitin chains from the target proteins. USPs are essential for the modulation of a variety of physiological processes, such as DNA repair, cell metabolism and differentiation, epigenetic modulations as well as protein stability. Recently, extensive research has demonstrated that USPs exert a significant impact on innate and adaptive immune reactions, metabolic syndromes, inflammatory disorders, and infection via post-translational modification processes. This review summarizes the important roles of the USPs in the onset and progression of inflammatory diseases, including periodontitis, pneumonia, atherosclerosis, inflammatory bowel disease, sepsis, hepatitis, diabetes, and obesity. Moreover, we highlight a comprehensive overview of the pathogenesis of USPs in these inflammatory diseases as well as post-translational modifications in the inflammatory responses and pave the way for future prospect of targeted therapies in these inflammatory diseases.

Association between sleep apnea-specific hypoxic burden and severity of coronary artery disease.

PURPOSE: Sleep apnea-specific hypoxic burden (SASHB) is a polysomnographic metric that comprehensively measures the degree of nocturnal desaturation caused by obstructive sleep apnea. This research was conducted to elucidate the relationship between SASHB and coronary artery disease (CAD) severity. METHODS: We carried out a prospective study of hospitalized patients with CAD of unstable angina who were expected to undergo invasive coronary angiography at Beijing Anzhen Hospital from February to September 2023. SASHB values were calculated using a self-programmed C + + program. Multivariable logistic regression analysis was applied to identify the association between SASHB and the prevalence of severe CAD, documented by the Gensini Score, and the SYNTAX (Synergy between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) Score. RESULTS: This study enrolled 137 patients with a median age of 59 years, 96 (70.1%) of whom were male. A total of 125 (91.2%) patients had coronary stenosis of ≥ 50% in at least one location. Patients with a high SASHB of ≥ 18% min/h had a significantly higher Gensini Score (32.0 vs. 18.5, P = 0.002) and SYNTAX Score (14.0 vs. 7.0, P = 0.002) than those with a low SASHB. After adjusting for multiple covariates, a high SASHB was significantly associated with the prevalence of severe CAD, determined by a Gensini Score ≥ 21 (OR 2.67, P = 0.008) or a SYNTAX Score > 22 (OR 4.03, P = 0.016). CONCLUSION: Our findings revealed a robust and independent association between SASHB and CAD severity in patients with unstable angina, highlighting the potential value of SASHB as a predictor of risk and a target for interventions aimed at preventing cardiovascular diseases. TRIAL REGISTRATION: Chinese Clinical Trial Registry No. ChiCTR2300067991 on February 2, 2023.

Values of prognostic nutritional index for predicting Kawasaki disease: a systematic review and meta-analysis.

Objective: This systematic review and meta-analysis aimed to evaluate the relationship between the prognostic nutritional index (PNI) and intravenous immunoglobulin (IVIG) resistance and coronary artery lesion (CAL) in Kawasaki disease (KD). Methods: The relevant literature was searched on PubMed, Embase, Cochrane Library, Web of Science, and Google Scholar up to August 5, 2023. A pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under curve (AUC) were calculated to assess the predicted values of PNI in KD patients with IVIG resistance and CAL. Results: A total of 8 articles containing 10 studies involving 7,047 participants were included. The pooled results revealed a pooled sensitivity of 0.44 (0.25-0.65), a pooled specificity of 0.87 (0.73-0.94), a pooled PLR of 3.4 (2.0-5.9), a pooled NLR of 0.65 (0.48-0.87), a pooled DOR of 5.26 (2.76-10.02), and a pooled AUC of 0.75 (0.71-0.78) in the diagnosis of KD with CAL. The pooled results suggested that a pooled sensitivity was 0.69 (0.60-0.77), specificity was 0.76 (0.69-0.82), PLR was 2.9 (2.1-4.1), NLR was 0.40 (0.29-0.56), DOR was 7.27 (3.89-13.59), and AUC was 0.79 (0.75-0.82) in the diagnosis of KD with IVIG resistance. The combined results revealed the pooled sensitivity was 0.63 (0.58-0.67), specificity was 0.82 (0.80-0.83), PLR was 3.09 (1.06-8.98), NLR was 0.38 (0.07-2.02), DOR was 8.23 (0.81-83.16) in differentiating KD from febrile patients. These findings demonstrated low sensitivity and relatively high specificity of PNI for KD, KD-CAL, and IVIG-resistant KD. Conclusion: In conclusion, this study was the first systematic review and meta-analysis of the diagnostic value of PNI in KD with IVIG resistance and CAL. The results suggested that PNI could be used as biomarkers for distinguish KD, KD with CAL, and KD with IVIG resistance.

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model.

Osteosarcoma (OS) is the most common malignancy in children and adolescents and has a high probability of recurrence and metastasis. A growing number of studies have shown that neutrophil extracellular traps (NETs) are strongly associated with cancer metastasis, but in osteosarcoma, genes associated with NETs that promote osteosarcoma recurrence and metastasis remain to be explored. We systematically investigated the gene expression patterns of NETs in OS samples from the GEO database. NETs molecular typing was evaluated based on NETs expression profiles, and the association between NETs molecular subtypes and immune microenvironment and metastatic features were explored. Ultimately, we constructed a signature model and column line graph associated with metastasis prediction and screened possible potential drugs for metastatic osteosarcoma. We established two different molecular subtypes of NETs, which showed significant differences in metastatic status, metastasis time, tumor immune microenvironment, and biological effects. We also constructed a NETs-related gene metastasis signature(NRGMS) to assess the expression pattern of NETs in patients to predict metastatic recurrence in osteosarcoma patients. We screened for TOMM40 and FH associated with metastatic recurrence in osteosarcoma patients. Overall, this study constructs a predictive model for osteosarcoma metastasis of NETs-related genes, which is expected to provide new insights into the metastasis of osteosarcoma.

Penicimides A and B, two novel diels-alder [4 + 2] cycloaddition ergosteroids from Penicillium herquei.

Two novel naturally occurring [4 + 2] Diels-Alder cycloaddition ergosteroids (1 and 2), three undescribed oxidized ergosteroids (3-5), and eleven known analogs (6-16) were isolated from Penicillium herquei. Compounds 1 and 2 represent the first reported cycloadducts of a steroid with 1,4,6-trimethyl-1,6-dihydropyridine-2,5-dione or 4,6-dimethyl-1,6-dihydropyridine-2,5-dione to date. Compound 3 is the C-15 epimer of (22E,24R)-9α,11ß-dihydroxyergosta-4,6,8(14),22-tetraen-3-one (14). The chemical structures of these compounds were elucidated through widespread spectroscopic analyses, mainly including HRESIMS and 1D and 2D NMR data, calculated 13C NMR-DP4+ analysis, and electronic circular dichroism (ECD) data analyses. Biological evaluations of Compounds 1-16 revealed that 3, 9-11, and 15 inhibited the production of NO in LPS-induced RAW264.7 cells with an IC50 value from 7.37 ± 0.69 to 38.9 ± 2.25 µM (the positive control dexamethasone IC50: 9.54 ± 0.71 µM). In addition, Compound 3 exhibited a potent inhibitory effect on the secretion of the proinflammatory cytokines TNF-α and IL-6, the transcription level of the proinflammatory macrophage markers TNF-α, and the expression of the iNOS protein.

MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events.

The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration. (i) The global features of drug molecules should be paid attention to, rather than just their local characteristics. (ii) The fusion of multi-source features should also be studied to capture the comprehensive features of the drug. This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better than state-of-the-art models. Especially in cold start scenarios, the model can achieve the best performance. The effectiveness of the model is also supported by the case study on nervous system drugs. The source codes and data are available at https://github.com/BioCenter-SHU/MSFF-MA-DDI.

Association of noncoding RNAs with Kawasaki disease: A meta-analysis based on the current evidences.

BACKGROUND: In recent years, many studies have focused on the relationship between noncoding RNAs (ncRNAs) and Kawasaki disease (KD). Studies have indicated that ncRNAs are associated with the occurrence and development of KD. Thus, we performed a systematic review and meta-analysis to investigate the diagnostic value of ncRNAs in KD patients. METHODS: We searched the PubMed, Web of Science, Embase and Cochrane Library, China National Knowledge Infrastructure, VIP, China Biology Medicine disc databases, and Wanfang databases until August 25, 2023 and screened all eligible studies focusing on the diagnostic performance of ncRNAs in KD patients. RESULTS: In total, 535 articles were found, and 28 articles were included in this systematic review and meta-analysis. The calculated area under the curve value was 0.880 (95% confidence intervals, 0.840-0.900). The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.790, 0.830, 4.610, and 0.260, respectively. The pooled diagnostic odds ratio was 17.890 (95% confidence intervals, 13.110-24.420), indicating a relatively good diagnostic performance of the ncRNAs for detecting KD. In addition, the diagnostic value of micro RNAs in KD was better than that of long noncoding RNAs and circular noncoding RNAs. A subgroup analysis by specimen indicated a better diagnostic value of ncRNAs in plasma and platelet than serum. The diagnostic accuracy of ncRNAs was better in febrile controls than in healthy control groups, indicating a relatively good accuracy in distinguishing KD patients from febrile diseases. CONCLUSIONS: This systematic review and meta-analysis demonstrated that ncRNAs could be used as novel biomarkers for detecting KD. More studies should be conducted in the future to verify the diagnostic values of ncRNAs in KD.

Effect of SMOF lipid emulsion on physical growth and extrauterine growth retardation in very preterm infants: Insights from a multicenter retrospective cohort study.

OBJECTIVE: The aim of this study was to investigate the effects of soybean, medium-chain triacylglycerols (MCTs), olive oil, and fish oil (SMOF) on short-term clinical outcomes, physical growth, and extrauterine growth retardation (EUGR) in very preterm infants. METHODS: This was a multicenter retrospective cohort study of very preterm infants hospitalized in neonatal intensive care units at five tertiary hospitals in China between January 2021 and December 2021. According to the type of fat emulsion used in parenteral nutrition (PN), eligible very preterm infants were divided into the MCTs/long-chain triacylglycerol (MCT/LCT) group and SMOF group. Change in weight z-score (weight Δz) between measurements at birth and at 36 wk of postmenstrual age or at discharge, the incidence of EUGR, and short-term clinical outcomes between the two groups were compared and analyzed. RESULTS: We enrolled 409 very preterm infants, including 205 in the MCT/LCT group and 204 in the SMOF group. Univariate analysis showed that infants in the SMOF group had significantly longer duration of invasive mechanical ventilation and PN, longer days to reach total enteral nutrition, and a higher proportion of maximum weight loss than those in MCT/LCT group (all P < 0.05). After adjusting for the confounding variables, multifactorial logistic regression analysis of short-term clinical outcomes showed that SMOF had protective effects on PN-associated cholestasis (odds ratio [OR], 0.470; 95% confidence interval [CI], 0.266-0.831) and metabolic bone disease of prematurity (OR, 0.263; 95% CI, 0.078-0.880). Additionally, SMOF was an independent risk factor for lower weight growth velocity (ß = -0.733; 95% CI, -1.452 to -0.015) but had no effect on the incidence of EUGR (OR, 1.567; 95% CI, 0.912 to -2.693). CONCLUSION: Compared with MCT/LCT, SMOF can reduce the risk for PN-associated cholestasis and metabolic bone disease of prematurity in very preterm infants and has a negative effect on growth velocity but has no effect on the incidence of EUGR.

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

Persistent Hypoxia with Intermittent Aggravation Causes Imbalance in Smad3/Myocardin-Related Transcription Factor Signaling with Consequent Endothelial Senescence and Pulmonary Arterial Remodeling.

Loss of Smad3 and the consequent activation of myocardin-related transcription factor (MRTF) are associated with vascular pathologies. This study aimed to examine the impact of persistent hypoxia with intermittent aggravation (PI hypoxia) on cellular senescence and pulmonary arterial remodeling mediated by the Smad3/MRTF imbalance. We examined the effects of PI hypoxia on the Smad3/MRTF pathway and cellular senescence using human pulmonary artery endothelial cells (HPAECs) and in vivo studies in rats. The senescent degree was evaluated using ß-galactosidase staining, p16 quantitation and the measurement of senescence-associated secretory phenotype. Structural data in the pathological analysis of pulmonary artery remodeling were collected. Compared to the control, HPAECs and pulmonary tissue from rats exposed to PI hypoxia showed a significantly higher senescent degree, lower expression of Smad3, and higher MRTF levels. The overexpression of Smad3 significantly mitigated HPAECs senescence in vitro. Further, treatment with CCG-203971, which inhibits MRTF, increased Smad3 levels and reduced ß-galactosidase positive cells in rat lung tissue. This intervention also alleviated PI hypoxia-induced pathological changes, including remodeling indices of pulmonary arterial thickening, muscularization, and collagen formation. In conclusion, imbalanced Smad3/MRTF signaling is linked to PI hypoxia-induced senescence and pulmonary arterial remodeling, making it a potential therapeutic target for patients with sleep apnea and chronic obstructive pulmonary disease.

Association between probiotic consumption and periodontitis: Evidence from NHANES 2009-2014.

AIM: The study aimed to provide evidence of the relationship between probiotics consumption and periodontitis. MATERIALS AND METHODS: A total of 4577 adults who participated in the National Health and Nutrition Examination Survey (NHANES) during 2009-2014 were included in the study. The weighted prevalence of periodontitis was compared among different groups, and a weighted binary logistic regression analysis was conducted to explore the relationship between probiotic consumption and periodontitis. Receiver operating characteristic (ROC) curves were used to assess the role of probiotic consumption in the periodontitis prediction model. RESULTS: Participants who consumed probiotics had a significantly lower prevalence of periodontitis than those who did not (41.08% vs. 27.83%, p < .001). After fully adjusting for all factors, the odds ratio associated with periodontitis for consuming probiotics was 0.70 (95% confidence interval 0.54-0.92, p = .01) when compared with those who did not consume probiotics. A predictive model including age, sex, ethnicity, poverty income ratio, smoking status and probiotics had 77.0% sensitivity and 60.3% specificity in detecting periodontitis in US adults and achieved an area under the ROC curve of 0.749. CONCLUSIONS: These results indicate that consuming probiotics is associated with a reduced risk of periodontitis.

Sex-specific associations between daytime sleepiness, chronic diseases and mortality in obstructive sleep apnea.

Objective: Excessive daytime sleepiness (EDS) is common in obstructive sleep apnea (OSA) and has been linked to adverse outcomes, albeit inconsistently. Furthermore, whether the prognostic impact of EDS differs as a function of sex is unclear. We aimed to assess the associations between EDS and chronic diseases and mortality in men and women with OSA. Methods: Newly-diagnosed adult OSA patients who underwent sleep evaluation at Mayo Clinic between November 2009 and April 2017 and completed the Epworth Sleepiness Scale (ESS) for assessment of perceived sleepiness (N = 14,823) were included. Multivariable-adjusted regression models were used to investigate the relationships between sleepiness, with ESS modeled as a binary (ESS > 10) and as a continuous variable, and chronic diseases and all-cause mortality. Results: In cross-sectional analysis, ESS > 10 was independently associated with lower risk of hypertension in male OSA patients (odds ratio [OR], 95% confidence interval [CI]: 0.76, 0.69-0.83) and with higher risk of diabetes mellitus in both OSA men (OR, 1.17, 95% CI 1.05-1.31) and women (OR 1.26, 95% CI 1.10-1.45). Sex-specific curvilinear relations between ESS score and depression and cancer were noted. After a median 6.2 (4.5-8.1) years of follow-up, the hazard ratio for all-cause death in OSA women with ESS > 10 compared to those with ESS ≤ 10 was 1.24 (95% CI 1.05-1.47), after adjusting for demographics, sleep characteristics and comorbidities at baseline. In men, sleepiness was not associated with mortality. Conclusion: The implications of EDS for morbidity and mortality risk in OSA are sex-dependent, with hypersomnolence being independently associated with greater vulnerability to premature death only in female patients. Efforts to mitigate mortality risk and restore daytime vigilance in women with OSA should be prioritized.