Altered Cortical Information Interaction During Respiratory Events in Children with Obstructive Sleep Apnea-Hypopnea Syndrome.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) significantly impairs children's growth and cognition. This study aims to elucidate the pathophysiological mechanisms underlying OSAHS in children, with a particular focus on the alterations in cortical information interaction during respiratory events. We analyzed sleep electroencephalography before, during, and after events, utilizing Symbolic Transfer Entropy (STE) for brain network construction and information flow assessment. The results showed a significant increase in STE after events in specific frequency bands during N2 and rapid eye movement (REM) stages, along with increased STE during N3 stage events. Moreover, a noteworthy rise in the information flow imbalance within and between hemispheres was found after events, displaying unique patterns in central sleep apnea and hypopnea. Importantly, some of these alterations were correlated with symptom severity. These findings highlight significant changes in brain region coordination and communication during respiratory events, offering novel insights into OSAHS pathophysiology in children.

A segmentation model to detect cevical lesions based on machine learning of colposcopic images.

Background: Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development. Methods: Between 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer. Results: Experiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively. Conclution: The DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.

Abnormal interaction between cortical regions of obstructive sleep apnea hypopnea syndrome children.

Obstructive sleep apnea hypopnea syndrome negatively affects the cognitive function of children. This study aims to find potential biomarkers for obstructive sleep apnea hypopnea syndrome in children by investigating the patterns of sleep electroencephalography networks. The participants included 16 mild obstructive sleep apnea hypopnea syndrome children, 12 severe obstructive sleep apnea hypopnea syndrome children, and 13 healthy controls. Effective brain networks were constructed using symbolic transfer entropy to assess cortical information interaction. The information flow pattern in the participants was evaluated using the parameters cross-within variation and the ratio of posterior-anterior information flow. Obstructive sleep apnea hypopnea syndrome children had a considerably higher symbolic transfer entropy in the full frequency band of N1, N2, and rapid eye movement (REM) stages (P < 0.05), and a significantly lower symbolic transfer entropy in full frequency band of N3 stage (P < 0.005), in comparison with the healthy controls. In addition, the cross-within variation of the ß frequency band across all sleep stages were significantly lower in the obstructive sleep apnea hypopnea syndrome group than in the healthy controls (P < 0.05). What is more, the posterior-anterior information flowin the ß frequency band of REM stage was significantly higher in mild obstructive sleep apnea hypopnea syndrome children than in the healthy controls (P < 0.05). These findings may serve as potential biomarkers for obstructive sleep apnea hypopnea syndrome in children and provide new insights into the pathophysiological mechanisms.

Cognitive reserve disorder in age-related hearing loss: cognitive cortical compensatory to auditory perceptual processing.

The aim of this study is to ascertain the mechanisms of cognitive reserve disorder in age-related hearing loss (ARHL), to study the correlation between ARHL and cognitive decline via EEG, and to reverse the adverse remodeling of auditory-cognitive connectivity with hearing aids (HAs). In this study, 32 participants were enrolled, including 12 with ARHLs, 9 with HAs, and 11 healthy controls (HCs), to undergo EEG, Pure Tone Average (PTA), Montreal Cognitive Assessment (MoCA), and other general cognitive tests. There were the lowest MoCA in the ARHL group (P = 0.001), especially in language and abstraction. In the ARHL group, power spectral density of the gamma in right middle temporal gyrus was significantly higher than HC and HA groups, while functional connectivity between superior frontal gyrus and cingulate gyrus was weaker than HC group (P = 0.036) and HA group (P = 0.021). In the HA group, superior temporal gyrus and cuneus had higher connectivity than in the HC group (P = 0.036). In the ARHL group, DeltaTM_DTA (P = 0.042) and CTB (P = 0.011) were more frequent than in the HC group, while there was less DeltaTM_CTA (P = 0.029). PTA was found to be associated with MoCA (r = -0.580) and language (r = -0.572), DeltaTM_CTB had a likewise correlation with MoCA (r = 0.483) and language (r = 0.493), while DeltaTM_DTA was related to abstraction (r = -0.458). Cognitive cortexes compensate for worse auditory perceptual processing in ARHL, which relates to cognitive decline. The impaired functional connectivity between auditory and cognitive cortexes can be remodeled by HAs. DeltaTM may serve as a biomarker for early cognitive decline and decreased auditory speech perception in ARHL.

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.

BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. CONCLUSIONS: DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.

DeepEBV: a deep learning model to predict Epstein-Barr virus (EBV) integration sites.

MOTIVATION: Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS: An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2-fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITYAND IMPLEMENTATION: DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Short-Term Impact of Sleep Apnea/Hypopnea on the Interaction Between Various Cortical Areas.

INTRODUCTION: Sleep apnea/hypopnea syndrome (SAHS) can change brain structure and function. These alterations are related to respiratory event-induced abnormal sleep, however, how brain activity changes during these events is less well understood. METHODS: To study information content and interaction among various cortical regions, we analyzed the variations of permutation entropy (PeEn) and symbolic transfer entropy (STE) of electroencephalography (EEG) activity during respiratory events. In this study, 57 patients with moderate SAHS were enrolled, including 2804 respiratory events. The events terminated with cortical arousal were independently researched. RESULTS: PeEn and STE were lower during apnea/hypopnea, and most of the brain interaction was higher after apnea/hypopnea termination than that before apnea in N2 stage. As indicated by STE, the respiratory events also affected the stability of information transmission mode. In N1, N2, and rapid eye movement (REM) stages, the information flow direction was posterior-to-anterior, but the anterior-to-posterior increased relatively during apnea/hypopnea. The above EEG activity trends maintained in events with cortical arousal. CONCLUSIONS: These results may be related to the intermittent hypoxia during apnea and the cortical response. Furthermore, increased frontal information outflow, which was related to the compensatory activation of frontal neurons, may associate with cognitive function.

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression.

Depression is a prevalent mental illness with high morbidity and is considered the main cause of disability worldwide. Brain activity while sleeping is reported to be affected by such mental illness. To explore the change of cortical information flow during sleep in depressed patients, a delay symbolic phase transfer entropy of scalp electroencephalography signals was used to measure effective connectivity between cortical regions in various frequency bands and sleep stages. The patient group and the control group shared similar patterns of information flow between channels during sleep. Obvious information flows to the left hemisphere and to the anterior cortex were found. Moreover, the occiput tended to be the information driver, whereas the frontal regions played the role of the receiver, and the right hemispheric regions showed a stronger information drive than the left ones. Compared with healthy controls, such directional tendencies in information flow and the definiteness of role division in cortical regions were both weakened in patients in most frequency bands and sleep stages, but the beta band during the N1 stage was an exception. The computable sleep-dependent cortical interaction may provide clues to characterize cortical abnormalities in depressed patients and should be helpful for the diagnosis of depression.

DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: huzheng1998@163.com, liangjiuxing@m.scnu.edu.cn, lizheyzy@163.com.

Changes of Altruistic Behavior and Kynurenine Pathway in Late-Life Depression.

BACKGROUND: Depressive patients show less altruistic behavior. While, older adults present higher tendencies for altruism than younger adults. Depression and age are two of the influencing factors of altruism, kynurenine (KYN), and its metabolites. However, the characteristics of altruism in late-life depression (LLD) and its possible underlying mechanism have not been studied. OBJECTIVE: We aimed to explore the characteristics of altruism in LLD patients and its neurobiological mechanism and structural brain network. We investigated whether the levels of metabolites in kynurenine pathway (KP) and white matter (WM) network topological features would influence the altruistic behavior in LLD patients. METHODS: Thirty-four LLD patients and 36 heathy controls (HCs) were included. Altruism was evaluated by the Dictator Game (DG) paradigm. Serum concentrations of KP metabolites were detected by the liquid chromatography-tandem mass spectrometry method. The topological features of the WM network were calculated from diffusion tensor imaging data in conjunction with graph-theoretical analysis. RESULTS: The LLD participants exhibited a higher level of altruism and WM global network properties than the HCs. Kynurenic acid to kynurenine (KYNA/KYN) ratio was associated with the DG performance in LLD group. KYNA/KYN ratio was associated with the WM network properties in HC group. CONCLUSIONS: KYN metabolism played an important role in altruistic behavior in LLD.

Variation of Electrocardiogram Features Across Sleep Stages in Healthy Controls and in Patients with Sleep Apnea Hypopnea Syndrome.

Sleep apnea hypopnea syndrome (SAHS) is an independent risk factor for various cardiovascular diseases. Electrocardiogram (ECG) features such as the RR, PR, QT, QTc, Tpe intervals and the Tpe/QT, Tpe/QTc ratios are used to predict and study cardiovascular diseases. It is not clear whether regular patterns of PR and Tpe-related features across sleep stages exist in SAHSs or healthy controls nor whether sleep stages affect the short- and long-range influences of respiratory events on ECG indices. We enrolled 36 healthy controls and 35 patients with SAHS in our study and analyzed the abovementioned ECG features. In the healthy controls, a significant regularity existed in these indices across sleep stages, which were weakened or disturbed in the patient group, especially the Tpe-related features. The differences between the patients and healthy controls were generally consistent across all sleep stages: patients had smaller RR, PR, QT and Tpe/QTc values, but larger QTc, Tpe and Tpe/QT values. After filtering the short-range influence of respiratory events, the differences in most features remained highly significant, except the QT interval. In the patient group, respiratory events decreased RR and PR intervals in most sleep stages and increased the Tpe-related features' values in deep sleep stages. These results may aid in the study of the relationships among SAHS, sleep disorders and cardiovascular diseases.

The correlations between electroencephalogram frequency components and restoration of stable breathing from respiratory events in sleep apnea hypopnea syndrome.

The purpose of this study was to explore the ways in which the Electroencephalogram (EEG) and oxygen saturation (SpO2) are involved in the progressive respiratory restoration process in patients with sleep apnea hypopnea syndrome (SAHS). Twenty-five SAHS patients were enrolled in the analysis. The respiratory events scored from polysomnography (PSG) recordings were divided into two groups as follows: the events followed by secondary events (SREs), which failed to recover stable breathing and those that spontaneously restored stable ventilation (N-SREs). The trends over the course from consecutive respiratory events (CRE) to stable breathing were also analyzed. Higher spectral powers of the δ, θ, and α bands and smaller sample entropy (SampEn) values in the EEG, along with a smaller SpO2 drop were observed in N-SREs, compared to those in SREs. It indicated there are correlations between these conditions and the restoration from respiratory events. The δ band power was the most relevant feature. In the CRE restoring process, the δ, θ, and α powers were significantly increased, while SampEn values exhibited the opposite tendency. Our results may reveal the relationship between EEG activity and respiratory rhythm control.

The independent and combined effects of respiratory events and cortical arousals on the autonomic nervous system across sleep stages.

PURPOSE: During sleep, respiratory events readily modulate the autonomic nervous system (ANS). Whether such modulation is caused by the respiratory event itself or the cortical arousal that follows and whether these influences differ across sleep stages are not clear. Thus, we aimed to study the independent and combined effects of respiratory events and cortical arousals on the ANS across sleep stages. METHODS: We recruited 22 male patients with sleep apnea-hypopnea syndrome (SAHS) and analyzed the differences in the indices of heart rate variability among normal respiration (NR), pathological respiratory events without cortical arousals (PR), cortical arousals without respiratory events (CA), and the coexistence of PR and CA (PR&CA), by sleep stage. RESULTS: Compared with NR, four indices of variation of the beat-to-beat interval demonstrated consistent results in all sleep stages generally: PR&CA showed the biggest difference, followed by PR and followed by CA, which exhibited the least difference. Thus, the respiratory event itself affects ANS modulation, but the cortical arousal that follows generally enhances this effect. For low-frequency power and low-frequency/high-frequency power ratio (LF/HF), PR&CA had the greatest impact. For mean beat-to-beat interval and high-frequency power (HFP), the influence of PR, CA, and PR&CA depended on sleep depth. However, PR&CA had a different influence on HFP in N2 stage vs. REM stage. CONCLUSIONS: Sleep stage also has an effect on this neuromodulatory mechanism. These findings may help clarify the relationship between SAHS and cardiovascular disease.

The Impact of Respiratory Events on the Autonomic Nervous System during Sleep.

Sleep apnea hypopnea syndrome (SAHS) is an independent risk factor for cardiovascular diseases. However, the pathophysiology between them is not yet clear. This paper seeks to understand how respiratory events impact the cardiovascular system by heart rate variability. We compared the differences between successional pathological respiratory events (PR) and pure normal respiration (NR) during sleep. The transitions between normal and pathological respiration (TR) were also analyzed. Thirteen patients who suffered moderate or severe SAHS were enrolled in this study. The results demonstrate that the beat-to-beat interval (RR interval) mean value and sample entropy are significantly lower during PR than during NR. RR interval standard deviation, the power of very low frequency (VLF) and low frequency (LF), total power, and the low frequency/high frequency (LF/HF) ratio were significantly larger during PR than during NR. However, the high frequency (HF) power was not significantly different between normal and pathological respiration. Additionally, the trends during TR also supported these significant differences. The results indicate that during pathological respiration, as the heart rate and its volatility increase, the complexity of its rhythm decreases. We conclude that the energy of the autonomic nervous system rapidly increases during pathological respiration, especially at the beginning. The HF power does not significantly change to modulate the heart rhythm, but the activity of the sympathetic nervous system will significantly increase, resulting in the imbalance of the LF/HF ratio. In addition to these findings, this paper discusses the influence of arousal on these indices during TR.

K-complex morphological features in male obstructive sleep apnea-hypopnea syndrome patients.

This study characterized the differences in K-complex (KC) morphology features between obstructive sleep apnea-hypopnea syndrome (OSAHS) patients and healthy controls and analyzed the effect of respiratory events on KC morphology. We enrolled 42 male subjects (21 OSAHS patients and 21 age-matched healthy controls) who underwent overnight polysomnography. KCs in stage N2 were manually identified. We found that KCs in healthy controls had larger negative and whole amplitudes, longer durations, and smoother positive waves than OSAHS patients but smaller positive amplitudes. Most features showed highly significant differences after Bonferroni correction (p<0.001/3). After separating out the KCs associated with respiratory events in OSAHS patients, the differences between the groups remained but were relatively smaller. In OSAHS patients, compared with the spontaneous KCs not evoked by obvious factors, the KCs occurring after respiratory events had larger amplitudes, steeper slopes, larger negative wave amplitude and duration ratios. KCs occurring during respiratory events showed lower amplitudes and shorter durations. These results may reveal the impact of respiratory events on sleep and brain function.

A tracer liquid image velocimetry for multi-layer radial flow in bioreactors.

BACKGROUND: This paper presents a Tracer Liquid Image Velocimetry (TLIV) for multi-layer radial flow in bioreactors used for cells cultivation of tissue engineering. The goal of this approach is to use simple devices to get good measuring precision, specialized for the case in which the uniform level of fluid shear stress was required while fluid velocity varied smoothly. METHODS: Compared to the widely used Particles Image Velocimetry (PIV), this method adopted a bit of liquid as tracer, without the need of laser source. Sub-pixel positioning algorithm was used to overcome the adverse effects of the tracer liquid deformation. In addition, a neighborhood smoothing algorithm was used to restrict the measurement perturbation caused by diffusion. Experiments were carried out in a parallel plates flow chamber. And mathematical models of the flow chamber and Computational Fluid Dynamics (CFD) simulation were separately employed to validate the measurement precision of TLIV. RESULTS: The mean relative error between the simulated and measured data can be less than 2%, while in similar validations using PIV, the error was around 8.8%. CONCLUSIONS: TLIV avoided the contradiction between the particles' visibility and following performance with tested fluid, which is difficult to overcome in PIV. And TLIV is easier to popularize for its simple experimental condition and low cost.