A systematic review on aquaculture wastewater: Pollutants, impacts, and treatment technology.

Aquaculture is the major way to solve the global food sacrcity. As the global population increases, the demand for aquaculture increases. Fish feed, drugs and chemicals, and metabolic waste or mortalities of aquatic organisms also increase, eventually resulting in the production of a large amount of aquaculture wastewater. These aquaculture discharges contain a variety of pollutants, such as conventional pollutants, organic compounds, heavy metals, and biological contaminants, inducing occupational hazards and risks, food security, the environment pollution. Proper wastewater treatment technologies are required to remove hazardous pollutants for minimizing their impacts on environmental and human health. Recirculating aquaculture systems, some biological and physicochemical methods have been applied to remove some pollutants from the aquaculture wastewater, but their efficiency in removing pollutants still requires to be further improved for achieving zero-waste discharge and ensuring sustainable aquaculture development. Meanwhile, sound regulation and legislation needs to be established for ensuring the normal operation of aquaculture industries and the standard discharge of wastewater. This review aims to provide comprehensive information of aquaculture wastewater for the researchers and promote the healthy development of aquaculture.

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.

Structural and functional disruption of subcortical limbic structures related with executive function in pediatric bipolar disorder.

BACKGROUND: Impaired cognition has been demonstrated in pediatric bipolar disorder (PBD). The subcortical limbic structures play a key role in PBD. However, alternations of anatomical and functional characteristics of subcortical limbic structures and their relationship with neurocognition of PBD remain unclear. METHODS: Thirty-six PBD type I (PBD-I) (15.36 ± 0.32 years old), twenty PBD type II (PBD-II) (14.80 ± 0.32 years old) and nineteen age-gender matched healthy controls (HCs) (14.16 ± 0.36 years old) were enlisted. Primarily, the volumes of the subcortical limbic structures were obtained and differences in the volumes were evaluated. Then, these structures served as seeds of regions of interest to calculate the voxel-wised functional connectivity (FC). After that, correlation analysis was completed between volumes and FC of brain regions showing significant differences and neuropsychological tests. RESULTS: Compared to HCs, both PBD-I and PBD-II patients showed a decrease in the Stroop color word test (SCWT) and digit span backward test scores. Compared with HCs, PBD-II patients exhibited a significantly increased volume of right septal nuclei, and PBD-I patients presented increased FC of right nucleus accumbens and bilateral pallidum, of right basal forebrain with right putamen and left pallidum. Both the significantly altered volumes and FC were negatively correlated with SCWT scores. SIGNIFICANCE: The study revealed the role of subcortical limbic structural and functional abnormalities on cognitive impairments in PBD patients. These may have far-reaching significance for the etiology of PBD and provide neuroimaging clues for the differential diagnosis of PBD subtypes. CONCLUSIONS: Distinctive features of neural structure and function in PBD subtypes may contribute to better comprehending the potential mechanisms of PBD.

Jiedu Xiaozheng Yin Inhibits the Progression of Colitis Associated Colorectal Cancer by Stimulating Macrophage Polarization Towards an M1 Phenotype via the TLR4 Pathway.

To investigate the effect of Jiedu Xiaozheng Yin (JXY) on the polarization of macrophages in colitis-associated colon cancer (CAC). An orthotopic model of CAC was established to monitor changes in the pathological state of mice. Colon length, number of colon tumors were recorded, and indices for liver, spleen, and thymus were calculated. Hematoxylin and eosin (H&E) staining was employed to observe intestinal mucosal injury and tumor formation. Immunohistochemistry (IHC) staining was utilized to investigate the effect of JXY on M1 and M2 polarization of macrophages in the colonic mucosa of CAC mice. For in vitro experiments, RT-qPCR (Reverse Transcription-quantitative PCR) and flow cytometry were used to observe the effect of JXY on various M1-related molecules such as IL-1ß, TNF-α, iNOS, CD80, CD86, and its phagocytic function as well as M2-related molecules including Arg-1, CD206, and IL-10. Subsequently, after antagonizing the TLR4 pathway with antagonists (TAK242, PDTC, KG501, SR11302, LY294002), the expression of IL-6, TNF-α, iNOS, and IL-1ß mRNA were detected by RT-qPCR. In vivo experiments, the results showed that JXY improved the pathological condition of mice in general. And JXY treatment decreased the shortening of colon length and number of tumors as compared to non-treated CAC mice. Additionally, JXY treatment improved the lesions in the colonic tissue and induced a polarization of intestinal mucosal macrophages towards the M1 phenotype, while inhibiting polarization towards the M2 phenotype. In vitro experiments further confirmed that JXY treatment promoted the activation of macrophages towards the M1 phenotype, leading to increased expression of IL-1ß, TNF-α, iNOS, CD80, CD86, as well as enhanced phagocytic function. JXY treatment concomitantly inhibited the expression of M2-phenotype related molecules Arginase-1 (Arg-1), CD206, and IL-10. Furthermore, JXY inhibited M1-related molecules such as IL-6, TNF-α, iNOS, and IL-1ß after antagonizing the TLR4 pathway. Obviously, JXY could exhibit inhibitory effects on the development of colon tumors in mice with CAC by promoting M1 polarization through TLR4-mediated signaling and impeding M2 polarization of macrophages.

Preparation of strong, UV-blocking and sustainable glucose-cross-linked cellulose plastics via hydroxyl-yne click reaction.

The construction of functional cellulose plastics possessing strong UV-blocking, hydrophilicity, and biodegradability is challenging. Therefore, we provide a novel strategy to successfully prepare sustainable and hydrophilic glucose-cross-linked cellulose (GC) plastics showing effective UV-blocking and excellent mechanical properties via hydroxyl-yne click reaction at room temperature. The results demonstrated that hydroxyl-yne click chemistry enabled efficient crosslinking of cellulose with glucose using 4-dimethylamino pyridine (DMAP) as a catalyst. Moreover, the DMAP residue imparted good UV-shielding properties to GC films exhibiting nearly 100 % UVC (200-275 nm) and 100 % UVB (320-275 nm) shielding ratios. The introduction of glucose imparted superior hydrophilicity (water contact angle of 40.3-43.2°) and improved water adsorption. Additionally, the mechanical properties of the GC films increased with the increasing crosslinking density, and the highest tensile stress was 94 MPa. The water-induced breaking and hydrogen bond reforming strategy led to a stress of 127 MPa and a strain of 25.6 % for the final GC2 film, which were excellent compared to those of the most reported cellulose films. Additionally, GC films were biosafe, exhibited improved oxygen barrier, and good biodegradability. Hence, this study provides a promising and efficient approach for preparing high-performance cellulose plastics.

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis.

Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.

The co-activation pattern between the DMN and other brain networks affects the cognition of older adults: evidence from naturalistic stimulation fMRI data.

Brain function changes affect cognitive functions in older adults, yet the relationship between cognition and the dynamic changes of brain networks during naturalistic stimulation is not clear. Here, we recruited the young, middle-aged and older groups from the Cambridge Center for Aging and Neuroscience to investigate the relationship between dynamic metrics of brain networks and cognition using functional magnetic resonance imaging data during movie-watching. We found six reliable co-activation pattern (CAP) states of brain networks grouped into three pairs with opposite activation patterns in three age groups. Compared with young and middle-aged adults, older adults dwelled shorter time in CAP state 4 with deactivated default mode network (DMN) and activated salience, frontoparietal and dorsal-attention networks (DAN), and longer time in state 6 with deactivated DMN and activated DAN and visual network, suggesting altered dynamic interaction between DMN and other brain networks might contribute to cognitive decline in older adults. Meanwhile, older adults showed easier transfer from state 6 to state 3 (activated DMN and deactivated sensorimotor network), suggesting that the fragile antagonism between DMN and other cognitive networks might contribute to cognitive decline in older adults. Our findings provided novel insights into aberrant brain network dynamics associated with cognitive decline.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

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.

Immediate visual reproduction negatively correlates with brain entropy of parahippocampal gyrus and inferior occipital gyrus in bipolar II disorder adolescents.

BACKGROUND: Brain entropy reveals complexity and irregularity of brain, and it has been proven to reflect brain complexity alteration in disease states. Previous studies found that bipolar disorder adolescents showed cognitive impairment. The relationship between complexity of brain neural activity and cognition of bipolar II disorder (BD-II) adolescents remains unclear. METHODS: Nineteen BD-II patients (14.63 ±1.57 years old) and seventeen age-gender matched healthy controls (HCs) (14.18 ± 1.51 years old) were enlisted. Entropy values of all voxels of the brain in resting-state functional MRI data were calculated and differences of them between BD-II and HC groups were evaluated. After that, correlation analyses were performed between entropy values of brain regions showing significant entropy differences and clinical indices in BD-II adolescents. RESULTS: Significant differences were found in scores of immediate visual reproduction subtest (VR-I, p = 0.003) and Stroop color-word test (SCWT-1, p = 0.015; SCWT-2, p = 0.004; SCWT-3, p = 0.003) between the two groups. Compared with HCs, BD-II adolescents showed significant increased brain entropy in right parahippocampal gyrus and right inferior occipital gyrus. Besides, significant negative correlations between brain entropy values of right parahippocampal gyrus, right inferior occipital gyrus and immediate visual reproduction subtest scores were observed in BD-II adolescents. CONCLUSIONS: The findings of the present study suggested that the disrupted function of corticolimbic system is related with cognitive abnormality of BD-II adolescents. And from the perspective temporal dynamics of brain system, the current study, brain entropy may provide available evidences for understanding the underlying neural mechanism in BD-II adolescents.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.

BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition. METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label. RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods. CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

Sex differences in structural covariance network based on MRI cortical morphometry: effects on episodic memory.

Sex differences in episodic memory (EM), remembering past events based on when and where they occurred, have been reported, but the neural mechanisms are unclear. T1-weighted images of 111 females and 61 males were acquired from the Dallas Lifespan Brain Study. Using surface-based morphometry and structural covariance (SC) analysis, we constructed structural covariance networks (SCN) based on cortical volume, and the global efficiency (Eglob) was computed to characterize network integration. The relationship between SCN and EM was examined by SC analysis among the top-n brain regions that were most relevant to EM performance. The number of SC connections (females: 3306; males: 437, P = 0.0212) and Eglob (females: 0.1845; males: 0.0417, P = 0.0408) of SCN in females were higher than those in males. The top-n brain regions with the strongest SC in females were located in auditory network, cingulo-opercular network (CON), and default mode network (DMN), and in males, they were located in frontoparietal network, CON, and DMN. These results confirmed that the Eglob of SCN in females was higher than males, sex differences in EM performance might be related to the differences in network-level integration. Our study highlights the importance of sex as a research variable in brain science.

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.

The effect of OSA on the negative pressure and acoustic compliance of middle ear cavity in children.

OBJECTIVE: To explore the effect of obstructive sleep apnea (OSA) on the negative pressure and acoustic compliance of middle ear cavity in children. METHODS: The clinical data of 258 children with suspected OSA, who complained of mouth breathing or snoring at night in the Department of Otolaryngology Head and neck surgery of the Second Affiliated Hospital of Xi'an Jiao Tong University from August 2020 to March 2022, were enrolled and analyzed retrospectively. The OSA and otitis media with effusion (OME) were determined by polysomnography (PSG) and acoustic immittance examination, respectively. Then, the parameters of tympanometry were compared between OSA and non-OSA children or among the children with various severity of OSA. RESULTS: There was no significant difference in the incidence of OME between children with OSA and those with non-OSA (15.80% vs 11.80%, P = 0.422). Compared with non-OSA children, OSA children had lower negative pressure (-56.42 vs -12.38, P < 0.001) and higher acoustic compliance (0.45 vs 0.38, P = 0.030) in middle ear cavity. There were also significant differences in negative pressure and acoustic compliance among children with mild, moderate and severe OSA (P < 0.001; P = 0.001). However, only the absolute value of negative pressure was markedly decreased after surgical therapy accompanied with transformation from OSA to non-OSA (-156.67 vs -45.67, P < 0.05), while this was not observed for acoustic compliance (0.48 vs 0.40, P > 0.05). CONCLUSION: OSA may have an adverse influence on the negative pressure and acoustic compliance of middle ear cavity in children.

Association between omentin and obstructive sleep apnea: A meta-analysis.

BACKGROUND: Obstructive sleep apnea (OSA) can lead to multisystem and multiorgan damage, which has attracted widespread attention from scholars. The pathogenesis of OSA is complex, and obesity plays an important role. Adipokine is secreted by adipose tissue, and its abnormal expression may be closely related to OSA. The relationship between omentin (a novel adipokine) and OSA is controversial. This study focuses on the important role of omentin in OSA and explores whether it can be regarded as a new target for the diagnosis and treatment of OSA. METHOD: PubMed, Embase, Web of Science, the Cochrane library, WANFANG, VIP, and Chinese National Knowledge Infrastructure were systematically searched for retrieving eligible studies until May 2022. Documents were screened according to strict inclusion and exclusion criteria, and data were extracted using Excel spreadsheets. The quality of the literature was assessed using the Newcastle-Ottawa Scale. RevMan 5.3 and Stata 12.0 software were used in this meta-analysis for data synthesis. RESULT: A total of eight eligible studies with 23 databases involving 914 participants were included in this meta-analysis. Combined data indicated that omentin levels in OSA patients were lower than that in controls (standardized mean difference = -1.54, 95% confidence interval = -2.07 to -1.00, p < 0.001). According to the subgroup analysis results of different races, sample source, gender, and the severity of the disease, compared with that in the control group, the level of omentin in OSA patients was significantly lower. When conducting sensitivity analysis, the results of the study were less stable. Meta-analysis indicated that there was no publication bias in this study. The omentin levels were significantly lower in OSA patients. The findings suggest that omentin may be a potential marker for the diagnosis and treatment of OSA. However, the heterogeneity of this study is high, and more high-quality large-sample studies will be needed in the future.

Submucosal Clustered Brownish Microvessels Based on NBI Endoscopy: A Characteristic of LPR.

OBJECTIVE: This study aimed to analyze the characteristics of laryngopharyngeal reflux (LPR) by using narrow band imaging (NBI) endoscopy. STUDY DESIGN: A prospective study. SETTING: A large-volume practice with tertiary care providers. METHODS: A total of 67 patients with suspected LPR who underwent 24-hour multichannel intraluminal impedance-pH monitoring were included from June 2020 to March 2022. Manifestations of NBI endoscopy included submucosal clustered brownish microvessels (CBMs), spotted brownish microvessels, and no special microvessels; the latter 2 formed the non-CBM group. The manifestations of all patients and their changes were observed after 8 weeks of proton pump inhibitor and symptomatic treatment for patients with LPR, and symptomatic treatment for patients without LPR. RESULTS: According to the results of 24-hour multichannel intraluminal impedance-pH monitoring, the incidence of submucosal CBMs was significantly higher in patients with LPR (30 cases) than in those without LPR (37 cases, P < .001), particularly in the posterior cricoid area (P < .001). Besides Reflux Finding Score, the incidence of signs such as subglottic edema and vocal fold edema was significantly higher in the CBM group than the non-CBM group (P < .05). Finally, 22 patients with LPR (91.7%) and only 2 patients without LPR (28.6%) underwent a transformation from CBMs to spotted brownish microvessels after continuous medication for 8 weeks in the CBM group (χ2 = 15.916, P < .001), while no significant change was observed in patients with or without LPR in the non-CBM group (P > .05). CONCLUSION: Submucosal CBMs in the posterior cricoid area under NBI endoscopy may be a characteristic of LPR.

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.