Precise detection of awareness in disorders of consciousness using deep learning framework.

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Parental Migration Trajectories From Childhood Through Adolescence: Longitudinal Impact on Emotional and Behavioral Problems Among Chinese Adolescents.

It is estimated that there are about 23% of all children in China experiencing parental migration and being left behind at hometown. Existing research indicated a significant association between parental migration and children development but overlooked the dynamic changes in family structure caused by parental migration. In this study, data was derived from a nationally representative longitudinal survey-the China Family Panel Studies. The main analyses employed four waves of data (2012, 2014, 2016, and 2018) and included 1401 adolescents aged 10-15 years (Mean:12.35, SD:1.67; 54.2% female). Six typical trajectories of parental migration capturing both migration status at each timepoint and changes in the status across six years were created. Children's depression and internalizing problems and externalizing problems were concerned outcomes. The mediating roles of the caregiver-child interaction and caregiver's depression were examined. Adolescents in the trajectory group described as experiencing transitions between being left behind by both parents and non had a higher risk of depression and internalizing and externalizing problems. Caregivers' depression was a significant mediator between parental migration and adolescent depression.

Effectiveness of telemedicine for the prevention of mother-to-child transmission of HIV in low-income and middle-income countries: a systematic review and meta-analysis.

OBJECTIVES: We aimed to estimate the effectiveness of telemedicine for the prevention of mother-to-child transmission (PMTCT) program of HIV in low- and middle-income countries (LMICs). METHODS: We did a systematic literature search of 15 databases for articles published from database inception to October 26, 2022, and performed meta-analyses to estimate the pooled risk ratio of intervention effect (RR) and its 95% confidence interval (CI). We used subgroup analyses and meta-regressions to explore variation in the RRs. Funnel plots and Egger regression tests were also performed to assess publication bias. RESULTS: Seventeen studies were included in the systematic review, with a total sample size of 9118 participants. We found that telemedicine was beneficial for early infant diagnosis (EID) in the sixth week (RR 1.04 [95% CI 1.00-1.09]), exclusive breastfeeding (RR 1.12 [95% CI 1.01-1.24]) and PMTCT retention (RR 1.34 [95% CI 1.16-1.55]). However, we did not find a significant effect of telemedicine on infant prophylaxis, HIV transmission, and ART adherence. Besides, the heterogeneity of ART adherence was associated with enrollment time, while retention was related to ART initiation. CONCLUSIONS: Our meta-analysis demonstrated the benefits of telemedicine in improving PMTCT, especially for EID, exclusive breastfeeding, and PMTCT retention.

Wulingsan Alleviates MAFLD by Activating Autophagy via Regulating the AMPK/mTOR/ULK1 Signaling Pathway.

Here, we presented the study of the molecular mechanisms underlying the action of Wulingsan (WLS) in rats with metabolic-associated fatty liver disease (MAFLD) induced by a high-fat diet (HFD). High-performance liquid chromatography was employed to identify the chemical components of WLS. After 2 weeks of HFD induction, MAFLD rats were treated with WLS in three different doses for 6 weeks, a positive control treatment or with a vehicle. Lipid metabolism, liver function, oxidative stress, and inflammatory factors as well as pathomorphological changes in liver parenchyma were assessed in all groups. Finally, the expressions of autophagy-related markers, adenosine monophosphate-activated protein kinase (AMPK)/mechanistic target of rapamycin (mTOR)/unc-51-like kinase-1 (ULK1) signaling pathway-related genes, and proteins in liver were detected. The results revealed that WLS significantly ameliorated liver injury, the dysfunction of the lipid metabolism, the oxidative stress, and overall inflammatory status. Furthermore, WLS increased the expressions of LC3B-II, Beclin1, p-AMPK, and ULK1, along with decreased p62, p-mTOR, and sterol regulatory element-binding protein-1c levels. In conclusion, we showed that WLS is capable of alleviating HFD-induced MAFLD by improving lipid accumulation, suppressing oxidative stress and inflammation, and promoting autophagy.

Corrigendum: Mutation of Gemin5 causes defective hematopoietic stem/progenitor cells proliferation in zebrafish embryonic hematopoiesis.

[This corrects the article DOI: 10.3389/fcell.2021.670654.].

Biogenic manganese oxides promote metal(loid) remediation by shaping microbial communities in biological aqua crust.

Biological aqua crust (biogenic aqua crust-BAC) is a potentially sustainable solution for metal(loid) bioremediation in global water using solar energy. However, the key geochemical factors and underlying mechanisms shaping microbial communities in BAC remain poorly understood. The current study aimed at determining the in situ metal(loid) distribution and the key geochemical factors related to microbial community structure and metal(loid)-related genes in BAC of a representative Pb/Zn tailing pond. Here we showed that abundant metal(loid)s (e.g. Pb, As) were co-distributed with Mn/Fe-rich minerals (e.g. biogenic Mn oxide, FeOOH) in BAC. Biogenic Mn oxide (i.e. Mn) was the most dominant factor in shaping microbial community structure in BAC and source tailings. Along with the fact that keystone species (e.g. Burkholderiales, Haliscomenobacter) have the potential to promote Mn ion oxidization and particle agglomeration, as well as Mn is highly associated with metal(loid)-related genes, especially genes related to As redox (e.g. arsC, aoxA), and Cd transport (e.g. zipB), biogenic Mn oxides thus effectively enhance metal(loid) remediation by accelerating the formation of organo-mineral aggregates in biofilm-rich BAC system. Our study indicated that biogenic Mn oxides may play essential roles in facilitating in situ metal(loid) bioremediation in BAC of mine drainage.

A multimodal physiological and psychological dataset for human with mental stress induced myocardial ischemia.

Accurate differentiation between angina with no obstructive coronary arteries (ANOCA) and mental stress-induced myocardial ischemia (MSIMI) is crucial for tailored treatment strategies, yet public data scarcity hampers understanding. Given the higher incidence of both conditions in women, this study prospectively enrolled 80 female ANOCA and 39 age-matched female controls, subjecting them to three types of mental stress tasks. ECGs were continuously monitored across Rest, Stress, and Recover stages of the mental stress tasks, with PET/CT imaging during the Stress stage to evaluate myocardial perfusion. With PET/CT serving as the gold standard for MSIMI diagnosis, 35 of the 80 ANOCA patients were diagnosed as MSIMI. Using ECG variables from different stages of mental stress tasks, we developed five machine learning models to diagnose MSIMI. The results showed that ECG data from different stages provide valuable information for MSIMI classification. Additionally, the dataset encompassed demographic details, physiological, and blood sample test results of the patients. We anticipate this new dataset will significantly push further progress in ANOCA and MSIMI research.

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.

Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.

Shared whole environmental etiology between Alzheimer's disease and age-related macular degeneration.

The comorbidity of Alzheimer's disease (AD) and age-related macular degeneration (AMD) has been established in clinical and genetic studies. There is growing interest in determining the shared environmental factors associated with both conditions. Recent advancements in record linkage techniques enable us to identify the contributing factors to AD and AMD from a wide range of variables. As such, we first constructed a knowledge graph based on the literature, which included all statistically significant risk factors for AD and AMD. An environment-wide association study (EWAS) was conducted to assess the contribution of various environmental factors to the comorbidity of AD and AMD based on the UK biobank. Based on the conditional Q-Q plots and Bayesian algorithm, several shared environmental factors were identified, which could be categorized into the domains of health condition, biological sample parameters, body index, and attendance availability. Finally, we generated a shared etiology landscape for AD and AMD by combining existing knowledge with our novel findings.