T-fiber, a highland barley fiber-rich powder, alleviates hyperglycemia and improves kidney pathology in Db/db mice

The role of dietary fiber in highland barley in lowering blood lipids has been continuously studied in recent years. However, its effects on diabetes and diabetic nephropathy are rarely studied. Considering that highland barley bran is rich in dietary fiber, the effective use of dietary fiber in highland barley bran can not only alleviate the symptoms of diabetes but also improve the local economy. This article aimed to study the effects of highland barley fiber-rich powder (T-fiber) with a high-quality natural dietary fiber ratio (insoluble fiber/soluble fiber = 3 : 1) on the symptoms of hyperglycemia in a diabetic mouse model. Compared with the model group's blood glucose level (30.80 mmol/L), glucose tolerance (28.57 mmol/L), and glycosylated serum protein (2.43 mmol/L), T-fiber presented significant reductions in blood glucose (23.69 mmol/L), better glucose tolerance (21.32 mmol/L), and glycosylated serum protein (1.78 mmol/L) in the diabetic mouse model. Meanwhile, T-fiber effectively alleviated hepatocellular lesions. In addition, T-fiber not only improved kidney function by reducing the 24-hour urine output (8.25 ml), urine protein levels (11.51 mg), and serum creatinine (13.80 mol/L) but also alleviated renal pathology, including glomerular hypertrophy, mesangial expansion, and fibrosis. The above results proved the ability of T-fiber to reduce blood glucose and alleviate liver and renal function in diabetic mice. Altogether, T-fiber is a capable formula for utilizing highland barley bran dietary fiber, which alleviates diabetes symptoms and endows highland barley with promising value.

Face masks are less effective than sunglasses in masking face identity.

The effect of covering faces on face identification is recently garnering interest amid the COVID-19 pandemic. Here, we investigated how face identification performance was affected by two types of face disguise: sunglasses and face masks. Observers studied a series of faces; then judged whether a series of test faces, comprising studied and novel faces, had been studied before or not. Face stimuli were presented either without coverings (full faces), wearing sunglasses covering the upper region (eyes, eyebrows), or wearing surgical masks covering the lower region (nose, mouth, chin). We found that sunglasses led to larger reductions in sensitivity (d') to face identity than face masks did, while both disguises increased the tendency to report faces as studied before, a bias that was absent for full faces. In addition, faces disguised during either study or test only (i.e. study disguised faces, test with full faces; and vice versa) led to further reductions in sensitivity from both studying and testing with disguised faces, suggesting that congruence between study and test is crucial for memory retrieval. These findings implied that the upper region of the face, including the eye-region features, is more diagnostic for holistic face-identity processing than the lower face region.

Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection (preprint)

Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solve the imbalance issue towards an unbiased model. In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics. Here the federated server averages the models trained on edge devices according to the predictive loss on the local data, rather than using only the number of samples as weights. As the predictive loss better quantifies the data distribution at a device, FedLoss alleviates the impact of data imbalance. Through a real-world dataset on respiratory sound and symptom-based COVID-$19$ detection task, we validate the superiority of FedLoss. It achieves competitive COVID-$19$ detection performance compared to a centralised model with an AUC-ROC of $79\%$. It also outperforms the state-of-the-art FL baselines in sensitivity and convergence speed. Our work not only demonstrates the promise of federated COVID-$19$ detection but also paves the way to a plethora of mobile health model development in a privacy-preserving fashion.

Unravelling Pathophysiology of Neurological and Psychiatric Complications of COVID-19 Using Brain Organoids.

Neuropsychiatric manifestations of coronavirus disease 2019 (COVID-19) have been increasingly recognized. However, the pathophysiology of COVID-19 in the central nervous system remains unclear. Brain organoid models derived from human pluripotent stem cells are potentially useful for the study of complex physiological and pathological processes associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as they recapitulate cellular heterogeneity and function of individual tissues. We identified brain organoid studies that provided insight into the neurotropic properties of SARS-CoV-2. While SARS-CoV-2 was able to infect neurons, the extent of neurotropism was relatively limited. Conversely, choroidal epithelial cells consistently showed a high susceptibility to SARS-CoV-2 infection. Brain organoid studies also elucidated potential mechanism for cellular entry, demonstrated viral replication, and highlighted downstream cellular effects of SARS-CoV-2 infection. Collectively, they suggest that the neuropsychiatric manifestations of COVID-19 may be contributed by both direct neuronal invasion and indirect consequences of neuroinflammation. The use of high throughput evaluation, patient-derived organoids, and advent of "assembloids" will provide a better understanding and functional characterization of the neuropsychiatric symptoms seen in post-acute COVID-19 syndrome. With advancement of organoid technology, brain organoids offer a promising tool for unravelling pathophysiologic clues and potential therapeutic options for neuropsychiatric complications of COVID-19.

Parental burnout and resilience intervention among Chinese parents during the COVID-19 pandemic.

Introduction: Parental burnout is a mental state that combines long-term stress and depression with an overwhelming feeling of parental pressure. Methods: In Study 1, we conducted a web-based survey of 390 Chinese parents (75.1% mothers) with children aged 1-18 years old (Mean age = 9.05 years, SD = 5.098) to examine the parental burnout during the COVID-19 global pandemic and to identify associated factors during the national lockdown. In Study 2, eight weeks of resilience intervention was administered to 20 parents to compare parental resilience and parental burnout before and after the intervention. Results: The correlational study showed that greater parental burnout occurred in parents with the lower educational levels and in parents of school-age children. The risk factors of parental burnout included household burden and children's interference with work; while protective factors included living materials, family atmosphere, and parent-child meeting frequency. The intervention study showed the effectiveness of meditation intervention in resilience and parental burnout, suggesting that meditation training can effectively increase parental resilience and reduce parental burnout. Discussion: These findings demonstrate the risk and protective factors associated with parental burnout during the COVID-19 lockdown and highlight the positive role of meditation in mitigating parental burnout.

Prevention and control strategies in medical institutions under the pandemic of omicron variant strain

OBJECTIVE: To discuss the infection prevention and control strategies of medical institutions under the epidemic situation of Omicron mutant so as to provide guidance for the emergency treatment work under special circumstances, such as the discovery of positive cases and positive mixed sampling tests, the discovery of close contacts in the hospital, the emergence of positive cases in emergency patients, and the emergence of positive cases in hospital staff. METHODS: Through reviewing the response behaviors and policies of local health administration departments and medical institutions since the current COVID-19 in Shandong Province, the paper summarized the emergency response process and exposed weaknesses, and explores corresponding strategies. RESULTS: The regional and systematic emergency response plan system for hospital infection control should be established and improved, the awareness of medical institutions' infection control should be enhanced, the organizational management ability, risk control ability and personal protection ability should be promoted, and emergency disposal guidelines for infection prevention and control in medical institutions should be formulated. CONCLUSION: The infection prevention and control strategies under the epidemic situation of Omicron mutant has exposed the weaknesses of medical institutions. The ability to integrate resources and medical service should be enhanced to promote the accurate prevention and control of hospital infection, and work should be done to allocate manpower shortage so as to ensure the service continuity of key departments and achieve collaborative linkage and governance.

Challenges and Innovation in Community-Based Organization HIV/AIDS Services During COVID-19 in China (preprint)

Introduction People living with HIV relied on community-based organizations (CBOs) in accessing HIV care and support during the COVID-19 pandemic in China. However, little is known on the impact of, and challenges faced by Chinese CBOs supporting PLHIV during lockdowns. Methods A mixed methods study was conducted among 29 CBOs serving PLHIV in China between November 10 and November 23, 2020. Participants were asked to complete a 20-minute online survey on their routine operations, organizational capacity building, service provided, and challenges during the pandemic. A focus group interview was conducted with CBOs after the survey to gain further insights on data interpretations. Quantitative data analysis was conducted using STATA 17.0 while qualitative data was examined using thematic analysis. Results HIV-focused CBOs in China serve diverse clients including PLHIV, high-risk groups, and the general public. The scope of services provided is broad, ranging from HIV testing to peer support. All CBOs maintained their services during the pandemic, many by switching to online or hybrid mode. Many CBOs reported adding new clients and services, such as mailing medications. Top challenges faced by CBOs included service reduction due to staff shortage, lack of PPE for staff, and lack of funding during COVID-19 lockdowns. In addition to the staff and funding needs, the ability to better network with other CBOs and other sectors (e.g., clinics, governments), a standard emergency response guideline, and ready strategies to help PLHIV build resilience are critical for future emergency preparation. Conclusions Chinese CBOs serving vulnerable populations affected by HIV/AIDS are instrumental in building resilience in their communities during the COVID-19 pandemic, and they can play significant roles in providing uninterrupted services during emergencies by mobilizing resources, creating new services and operation methods, and utilizing existing networks. Chinese CBOs’ experiences, challenges, and their policy recommendations can inform policy makers on how to support future CBO capacity building to bridge service gaps during crises and reduce health inequalities in China and globally.

A Summary of the ComParE COVID-19 Challenges (preprint)

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals' respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation (preprint)

Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics could lead to more timely treatment. The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed. We developed a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests. The strong performance for COVID-19 detection, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, displaying high consistency with test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort who reported recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery.

Sounds of COVID-19: exploring realistic performance of audio-based digital testing (preprint)

Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio based approaches, which collect respiratory audio data (cough, breathing and voice) can be used for testing, however there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside recent COVID-19 test result and symptoms intended as a ground truth. Within the collected dataset, we selected 5,240 samples from 2,478 participants and split them into different participant-independent sets for model development and validation. Among these, we controlled for potential confounding factors (such as demographics and language). The unbiased model takes features extracted from breathing, coughs, and voice signals as predictors and yields an AUC-ROC of 0.71 (95\% CI: 0.65$-$0.77). We further explore different unbalanced distributions to show how biases and participant splits affect performance. Finally, we discuss how the realistic model presented could be integrated in clinical practice to realize continuous, ubiquitous, sustainable and affordable testing at population scale.

Fitbeat: COVID-19 Estimation based on Wristband Heart Rate (preprint)

This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS research project that is investigating the feasibility of wearable devices and smart phones to monitor individuals with multiple sclerosis (MS), depression or epilepsy. Aspart of the project protocol, heart-rate data was collected from participants using a Fitbit wristband. The presence of COVID-19 in the cohort in this work was either confirmed through a positive swab test, or inferred through the self-reporting of a combination of symptoms including fever, respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal symptoms. Experimental results indicate that our proposed contrastive convolutional auto-encoder (contrastive CAE), i. e., a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted average recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100% and a specificity of 90.6% on a testset of 19 participants with MS who reported symptoms of COVID-19. Each of these participants was paired with a participant with MS with no COVID-19 symptoms.

Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data (preprint)

Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hindering the practice. First, collected datasets for model training are often imbalanced, with a considerably smaller proportion of users tested positive, making it harder to learn representative and robust features. Second, deep learning models are generally overconfident in their predictions. Clinically, false predictions aggravate healthcare costs. Estimation of the uncertainty of screening would aid this. To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data. As such, data are utilized more effectively compared to traditional up-sampling and down-sampling approaches: an AUC of 0.74 with a sensitivity of 0.68 and a specificity of 0.69 is achieved. Simultaneously, we estimate uncertainty from the disagreement across multiple models. It is shown that false predictions often yield higher uncertainty, enabling us to suggest the users with certainty higher than a threshold to repeat the audio test on their phones or to take clinical tests if digital diagnosis still fails. This study paves the way for a more robust sound-based COVID-19 automated screening system.

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates (preprint)

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data (preprint)

The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of $0.79$ has been attained, with a sensitivity of $0.68$ and a specificity of $0.82$. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.

Pilot Study On The Value of Echocardiography Combined With Lung Ultrasound In The Diagnosis And Treatment of COVID-19 Pneumonia (preprint)

Background: This study aimed to investigate the relationship between echocardiography results and lung ultrasound score (LUS) in coronavirus diseases 2019 (COVID-19) pneumonia patients and to evaluate the impact of their combined application in the diagnosis and treatment of COVID-19 pneumonia.Methods: Hospitalized COVID-19 pneumonia patients who underwent lung ultrasound and echocardiography daily were included in this study. Patients with tricuspid regurgitation within 3 days of admission were enrolled, and the correlation and differences between their pulmonary artery pressure (PAP) and LUS on days 3, 8, and 13 were compared. The inner diameter of the pulmonary artery root and the size of the atria and ventricles were also observed.Results: Pulmonary artery pressure within 3 days (on day 3, 8 and 13) of admission was positively correlated with LUS (r = 0.448, p = 0.003; r = 0.738, p = 0.000; r = 0.325, p = 0.036). On day 8 the values of both PAP and LUS were higher than their corresponding values on days 3 and 13 (p < 0.01). On day 8 the positive rate for increased PAP and LUS was 92.9% (39/42) and 90.5% (38/42), respectively, and the combined positive rate for these two was 97.6% (41/42). On day 8 the inner diameters of the right atrium, right ventricle, and pulmonary artery differed significantly from their corresponding values on days 3 and 13 (p < 0.05).Conclusions: PAP is positively correlated with LUS. The two should be combined for a more informative assessment of the status of recovery from COVID-19 pneumonia.

Prevention and Control Strategies Based on the Epidemiology of Imported COVID-19 in Mainland China (preprint)

Background: Imported COVID-19 cases are a serious public health problem worldwide. However, limited epidemiologic information of imported cases affects the choice of prevention and control strategies.Methods: In this study, we collected data from 22 January 2020 to 21 April 2020 related to imported COVID-19 cases in Mainland China from the daily public data of the National Health Commission and the provincial health commissions.Findings: A total of 1610 cases of COVID-19 were imported from 49 countries and regions to 27 provincial administrative regions. 79.81% cases were imported from European countries and the United States. Before 29 March, the imported cases were mainly from the United States (27.66%) and United Kingdom (42.55%). After 29 March 2020, the daily newly imported cases from Russia rapidly growed. After 12 April, the number of daily newly imported cases gradually decreased and remained at a low level (12±7 cases per day). Airport entry was encouraged and ground crossing entry was limited with the help of dynamic surveillance of the weekly proportion of confirmed cases at ports. 54.04% imported confirmed cases were in the asymptomatic incubation period on arrival in Mainland China. The compulsory centralized quarantine decreased the risk of onward transmission from imported cases compared to home quarantine ( P <0.05).Interpretation: Prevention and control strategies based on the epidemiological characteristics of imported cases effectively protected Mainland China against reintroduction of the virus and re-initiation of the epidemic when the epidemic was still in a surge worldwide. Such measures included: exit screening, entry screening, and compulsory centralized quarantine of all inbound travelers followed by strict contact tracing. The experiences from Mainland China provide an example of effective measures to reduce transmission of imported COVID-19 cases.Funding Statement: Shanghai Science and Technology Commission, Pudong Health Bureau of Shanghai and Krebsliga Schweiz, BIL KFS. Declaration of Interests: None to be declared.