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.].

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.

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.

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.

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.

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.

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN. METHODS: Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks. RESULTS: In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients. CONCLUSION: This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes. CLINICAL RELEVANCE STATEMENT: In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent. KEY POINTS: • Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets.

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term "aggressive" or "bullying," can lead to the underdiagnosis of other "vulnerable" classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.

Reduced-visit antenatal care model combined with telemedicine for low-risk pregnant women: protocol for a randomised controlled trial.

INTRODUCTION: Antenatal care (ANC) is a critical measure to reduce maternal and perinatal morbidity and mortality. However, there are issues of too many visits and cumbersome procedures of ANC in many maternity hospitals of China. In the past 2 years, reduced-visit ANC models combined with remote monitoring have been recommended and implemented at most hospitals in China during the COVID-19 pandemic. Nevertheless, due to limited evaluations of the cost-effectiveness, policy-makers remain confused on how to appropriately integrate online delivery strategies with routine models to improve ANC quality and efficiency sustainably at scale. This trial aims to evaluate the effectiveness, acceptability and cost of a reduced-visit ANC model combined with telemedicine. METHODS AND ANALYSIS: A single-blind, randomised controlled trial will be conducted among low-risk pregnant women at Peking University Third Hospital in Beijing. 1476 patients (738 in each group) would be required, and they will be randomly assigned in a 1:1 ratio to receive the reduced-visit ANC combined with telemedicine services or the routine ANC. The primary outcome is the composite rate of adverse maternal and perinatal outcomes which will be extracted from the medical records. Secondary outcomes include acceptability of ANC models, which is assessed by satisfaction with ANC, pregnancy-related stress and ANC costs measured from the perspectives of both service providers and demanders. Both intention-to-treat and per-protocol analyses will be performed. Non-inferiority tests will be used to compare the two ANC models for the primary outcome. A cost-effectiveness analysis comparing the two ANC models will be conducted by estimating the incremental cost-effectiveness ratios. ETHICS AND DISSEMINATION: This study was approved by the ethical review committee of the Peking University Third Hospital (Beijing, China). The results of this study will be published in peer-reviewed scientific journals and presented at relevant academic conferences. TRIAL REGISTRATION NUMBER: NCT05290467.

Measuring Incompatibility and Clustering Quantum Observables with a Quantum Switch.

The existence of incompatible observables is a cornerstone of quantum mechanics and a valuable resource in quantum technologies. Here we introduce a measure of incompatibility, called the mutual eigenspace disturbance (MED), which quantifies the amount of disturbance induced by the measurement of a sharp observable on the eigenspaces of another. The MED provides a metric on the space of von Neumann measurements, and can be efficiently estimated by letting the measurement processes act in an indefinite order, using a setup known as the quantum switch, which also allows one to quantify the noncommutativity of arbitrary quantum processes. Thanks to these features, the MED can be used in quantum machine learning tasks. We demonstrate this application by providing an unsupervised algorithm that clusters unknown von Neumann measurements. Our algorithm is robust to noise and can be used to identify groups of observers that share approximately the same measurement context.

Identification of Distinct Clinical Phenotypes of Heterogeneous Mechanically Ventilated ICU Patients Using Cluster Analysis.

This retrospective study aimed to derive the clinical phenotypes of ventilated ICU patients to predict the outcomes on the first day of ventilation. Clinical phenotypes were derived from the eICU Collaborative Research Database (eICU) cohort via cluster analysis and were validated in the Medical Information Mart for Intensive Care (MIMIC-IV) cohort. Four clinical phenotypes were identified and compared in the eICU cohort (n = 15,256). Phenotype A (n = 3112) was associated with respiratory disease, had the lowest 28-day mortality (16%), and had a high extubation success rate (~80%). Phenotype B (n = 3335) was correlated with cardiovascular disease, had the second-highest 28-day mortality (28%), and had the lowest extubation success rate (69%). Phenotype C (n = 3868) was correlated with renal dysfunction, had the highest 28-day mortality (28%), and had the second-lowest extubation success rate (74%). Phenotype D (n = 4941) was associated with neurological and traumatic diseases, had the second-lowest 28-day mortality (22%), and had the highest extubation success rate (>80%). These findings were validated in the validation cohort (n = 10,813). Additionally, these phenotypes responded differently to ventilation strategies in terms of duration of treatment, but had no difference in mortality. The four clinical phenotypes unveiled the heterogeneity of ICU patients and helped to predict the 28-day mortality and the extubation success rate.

Ameliorative effect of silymarin on the quality of frozen-thawed boar spermatozoa.

Although Silymarin (SMN) has powerful antioxidant properties, little is known about its effects on the quality of frozen-thawed boar sperm. The present study aimed to evaluate the influences of SMN added to the thawing extender on boar sperm parameters essential for fertilization. The frozen-thawed semen was diluted in a Modena thawing extender supplemented with different concentrations of SMN (0, 5, 10, 20 and 50 µM respectively), and then the changes in quality parameters, antioxidant capacity, mitochondrial function and in vitro fertilization (IVF) capability of frozen-thawed sperm were assessed. Here we demonstrated that the motility, plasma membrane integrity and acrosomal integrity of frozen-thawed sperm improved efficiently by SMN (p < .05). In antioxidant parameters evaluation, the tROS level and MDA content of frozen-thawed spermatozoa were reduced in the 20 µM SMN group, while the T-AOC activity significantly increased (p < .05), indicating that the supplementation with SMN can promote the antioxidant capacity of frozen-thawed boar sperm. Besides, we also discovered that the addition of SMN significantly upregulated ATP content and enhanced the mitochondrial activity of sperm. More interestingly, SMN promoted the activities of mitochondrial respiratory chain complexes (MRCC) I, II, III and IV in frozen-thawed sperm significantly. Functionally, the higher penetration rate and increased total efficiency of fertilization were observed in the 20 µM SMN group. In summary, supplementation with SMN in the thawing medium ameliorates the quality of frozen-thawed boar sperm by enhancing mitochondrial respiratory capacity, producing large amounts of ATP and regulating ROS formation.

A disturbance suppression micro-Newton force sensor based on shadow method.

The precision of micro-force measurement depends on the force sensor sensitivity and the environmental disturbance magnitude. However, micro-force sensors generally have the poor anti-disturbance ability. Inspired by the shadow formed by water striders walking on water surface under sunlight, a viscoelastic-polymer micro-force (VPMF) sensor based on the shadow method was proposed, which could suppress disturbances effectively due to the high damping ratio of 0.22. The shadow diameter change and the applied force were proportional. The experimental results indicated that the sensitivity could reach 2.15 µN/pixel with a good linear performance. Furthermore, compared with the cantilever, it was capable of the reduction of the disturbance influence by approximately 96.35%. Therefore, the VPMF sensor can be applied to reliable micro-force measurement in complex environments such as industrial sites.

Development of a nanoscale displacement sensor based on the shadow method.

A novel, to the best of our knowledge, bionic coaxial micro-displacement sensor based on the shadow method is developed and experimentally demonstrated inspired by the water strider walking on the water. The water is used as the sensitive element to measure the micro- displacement. A meniscus is formed by the superhydrophobic circular plate subjected to a coaxial displacement excitation. Then a shadow is formed because of the refraction when the parallel light illuminates the meniscus. A maximum coaxial displacement sensitivity of 62 nm/pixel over the displacement range of 50 µm is achieved experimentally. The linearity error in the measurement range was 1.58%. Therefore, it is expected that this displacement sensor can be used in many important ultraprecision measurement fields because of the advantages of the easy structure and high resolution.

Boosting CO2 hydrogenation to methane over Ni-based ETS-10 zeolite catalyst.

The activation and conversion of the CO2 molecule have always been the most vexing challenge due to its chemical inertness. Developing highly active catalysts, which could overcome dynamic limitations, has emerged as a provable and effective method to promote CO2 activation-conversion. Herein, ETS-10 zeolite-based catalysts, with active nickel species introduced by in situ doping and impregnation, have been employed for CO2 methanation. Conspicuous CO2 conversion (39.7%) and perfect CH4 selectivity (100%) were achieved over the Ni-doped ETS-10 zeolite catalyst at 280°C. Comprehensive analysis, which include X-ray diffraction, N2 adsorption-desorption, SEM, TEM, H2 chemisorption, CO2 temperature programmed desorption, and X-ray photoelectron spectroscopy, was performed. Also, the results indicated that the resultant hierarchical structure, high metal dispersion, and excellent CO2 adsorption-activation capacity of the Ni-doped ETS-10 zeolite catalyst played a dominant role in promoting CO2 conversion and product selectivity.

Mannose ameliorates experimental colitis by protecting intestinal barrier integrity.

Metabolite alteration has been associated with the pathogenesis of inflammatory bowel disease (IBD), including colitis. Mannose, a natural bioactive monosaccharide that is involved in metabolism and synthesis of glycoproteins, exhibits anti-inflammatory and anti-oxidative activities. We show here that the circulating level of mannose is increased in patients with IBD and mice with experimental colitis. Mannose treatment attenuates intestinal barrier damage in two mouse colitis models, dextran sodium sulfate (DSS)-induced colitis and spontaneous colitis in IL-10-deficient mice. We demonstrate that mannose treatment enhanced lysosomal integrity and limited the release of cathepsin B, preventing mitochondrial dysfunction and myosin light chain kinase (MLCK)-induced tight junction disruption in the context of intestinal epithelial damage. Mannose exerts a synergistic therapeutic effect with mesalamine on mouse colitis. Cumulatively, the results indicate that mannose supplementation may be an optional approach to the treatment of colitis and other diseases associated with intestinal barrier dysfunction.

Effectiveness and Safety of Therapeutic Vaccines for Precancerous Cervical Lesions: A Systematic Review and Meta-Analysis.

Objective: This study systematically evaluated the effectiveness and safety of therapeutic vaccines for precancerous cervical lesions, providing evidence for future research. Methods: We systematically searched the literature in 10 databases from inception to February 18, 2021. Studies on the effectiveness and safety of therapeutic vaccines for precancerous cervical lesions were included. Then, we calculated the overall incidence rates of four outcomes, for which we used the risk ratio (RR) and 95% confidence interval (95% CI) to describe the effects of high-grade squamous intraepithelial lesions (HSILs) on recurrence. Results: A total of 39 studies were included, all reported in English, published from 1989 to 2021 in 16 countries. The studies covered 22,865 women aged 15-65 years, with a total of 5,794 vaccinated, and 21 vaccines were divided into six types. Meta-analysis showed that the overall incidence rate of HSIL regression in vaccine therapies was 62.48% [95% CI (42.80, 80.41)], with the highest rate being 72.32% for viral vector vaccines [95% CI (29.33, 99.51)]. Similarly, the overall incidence rates of HPV and HPV16/18 clearance by vaccines were 48.59% [95% CI (32.68, 64.64)] and 47.37% [95% CI (38.00, 56.81)], respectively, with the highest rates being 68.18% [95% CI (45.13, 86.14)] for bacterial vector vaccines and 55.14% [95% CI (42.31, 67.66)] for DNA-based vaccines. In addition, a comprehensive analysis indicated that virus-like particle vaccines after conization reduced the risk of HSIL recurrence with statistical significance compared to conization alone [RR = 0.46; 95% CI (0.29, 0.74)]. Regarding safety, only four studies reported a few severe adverse events, indicating that vaccines for precancerous cervical lesions are generally safe. Conclusion: Virus-like particle vaccines as an adjuvant immunotherapy for conization can significantly reduce the risk of HSIL recurrence. Most therapeutic vaccines have direct therapeutic effects on precancerous lesions, and the effectiveness in HSIL regression, clearance of HPV, and clearance of HPV16/18 is great with good safety. That is, therapeutic vaccines have good development potential and are worthy of further research. Systematic Review Registration: PROSPERO https://www.crd.york.ac.uk/PROSPERO/, CRD42021275452.

Ripening induced degradation of pectin and cellulose affects the far infrared drying kinetics of mangoes.

The quality parameters of mangoes change during ripening, which has a vital impact on processing characteristics. Effects of ripening stage (four stages from the lowest to highest degree-RS-1, RS-2, RS-3, RS-4) on cell wall polysaccharides and far infrared drying kinetics of mangoes were investigated. As ripening progressed, the water-soluble pectin contents increased by 213.5%; while the chelate-, sodium carbonate-soluble pectin and hemicellulose contents decreased by 44.0%, 59.5% and 65.8%, respectively. Moreover, the molecular weight reduction confirmed the degradation of pectin. These further caused the alteration of cell wall structure and changes in water distribution. Meanwhile, the drying time of mangos with different ripeness were in the order: RS-3 > RS-4 > RS-2 > RS-1. It correlated with the degradation of cell wall polysaccharides, the destruction of cell wall and the increases in free water during ripening. The ripeness classification could effectively improve the uniformity and efficiency of fruits drying processing.

A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty.

Artificial Intelligence (AI) has achieved state-of-the-art performance in medical imaging. However, most algorithms focused exclusively on improving the accuracy of classification while neglecting the major challenges in a real-world application. The opacity of algorithms prevents users from knowing when the algorithms might fail. And the natural gap between training datasets and the in-reality data may lead to unexpected AI system malfunction. Knowing the underlying uncertainty is essential for improving system reliability. Therefore, we developed a COVID-19 AI system, utilizing a Bayesian neural network to calculate uncertainties in classification and reliability intervals of datasets. Validated with four multi-region datasets simulating different scenarios, our approach was proved to be effective to suggest the system failing possibility and give the decision power to human experts in time. Leveraging on the complementary strengths of AI and health professionals, our present method has the potential to improve the practicability of AI systems in clinical application.

Metabolomic analyses reveal new stage-specific features of COVID-19.

The current pandemic of coronavirus disease 2019 (COVID-19) has affected >160 million individuals to date, and has caused millions of deaths worldwide, at least in part due to the unclarified pathophysiology of this disease. Identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual and can provide extensive insights into their pathophysiological significance at each stage of disease. We provide a comprehensive view of metabolic characterisation of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both the discovery cohort and the validation cohort, which suggests that metabolic reprogramming of glucose metabolism and the urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and the urea cycle may be a viable approach to fight COVID-19 at various stages along the disease course.