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The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of perceived learning outcomes whereas instructor–student dialogue was a determinant of student initiative. University support had no significant relationship with either perceived learning outcomes or student initiative. In terms of learning effectiveness, both perceived learning outcomes and student initiative determined student satisfaction. The results identified that student–student dialogue, course design, and instructor–student dialogue were the key predictive factors of CoOL learning effectiveness, which may determine the ultimate success of CoOL.

Background Long COVID, the persistent illness following COVID-19 infection, has emerged as a major public health concern since the outbreak of the pandemic. Effective disease surveillance is crucial for policymaking and resource allocation. Methods We investigated the potential of utilizing Google Trends data to enhance long COVID symptoms surveillance. Though Google Trends provides freely available search popularity data, limitations in data normalization and retrieval restrictions have hindered its predictive capabilities. In our study, we carefully selected 33 search terms and 20 related topics from the long COVID symptoms list provided by the Centers for Disease Control and Prevention and the database “scite”, and calculated their merged search volumes from Google Trends data using our developed statistical method for analysis. Results We identify four related topics (ageusia, anosmia, chest pain, and headaches) that consistently exhibit increased search popularity before that of “long COVID.” Additionally, nine related topics (aching muscle pain, anxiety, chest pain, clouding of consciousness, dizziness, fatigue, myalgia, shortness of breath, and hypochondriasis) show increased search popularity following that of “long COVID.” We demonstrate that the merged search volume (MSV), derived from the relative search volume data downloaded from Google, can be used to forecast the prevalence of long COVID in a prediction study, supporting the use of the methodology in risk management regarding the prevalence of long COVID. Conclusions By utilizing a comprehensive list of search terms and sophisticated statistical analytics, our study contributes to exploring the potential of Google Trends data for forecasting and monitoring long COVID prevalence. These findings and methodologies can be used as prior knowledge to inform future infodemiological and epidemiological investigations. Plain language summary Long COVID is a persistent illness that follows COVID-19 infection. It has emerged as a significant public health concern since the outbreak of the pandemic. Effective disease surveillance is crucial for policy making and resource allocation. We investigate the potential of using the number of searches of long COVID symptoms in Google to enhance surveillance and improve the predictability of long COVID prevalence. We found searches for several specific symptoms increased both before and after searches for long COVID, demonstrating that numbers of searches can predict long COVID prevalence. Google search results could therefore be used to monitor disease prevalence. Chu et al. use merged search volumes of long COVID symptoms, derived from Google Trends data, to investigate changing search patterns. Several specific symptoms increase in searches both before and after the rise in the searches for long COVID, suggesting that search volumes can predict long COVID prevalence.

The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients’ speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%. The findings indicate that digital health technology can be used to assist in the psychosocial health assessment. While the conventional method requires rigorous assessments by specialists with multiple rounds of questioning, the ASAP can provide a cost-effective and immediate initial assessment to identify high levels of stress among family caregivers so they can be referred to social workers and healthcare professionals for further assessments and treatments.

Objective To validate the Family Resilience Assessment Scale (FRAS) in Chinese family caregivers. Background Caregiver burden among family caregivers is a growing social issue. Family resilience is a crucial protective factor for easing caregiver burden. The FRAS is specifically designed to evaluate family resilience. However, the factor structure and validity of the FRAS have not yet been examined among Chinese family caregivers. Method Data were collected from 323 Chinese family caregivers. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were adopted to determine the factor structure and model fit. Concurrent validity and internal consistency were also studied. Results EFA analysis yielded a 42‐item five‐factor structure of the FRAS, which demonstrated satisfactory model fit by CFA analysis. The five‐factor model FRAS also showed adequate concurrent validity and internal consistency (α = 0.724–0.963). Conclusion The proposed five‐factor FRAS is likely a valid and reliable scale for evaluating the family resilience level of family caregivers in Chinese communities. Implications The present study recommends the use of the FRAS in family caregiving research to facilitate intervention development by social professionals for promoting family resilience and reducing caregiver burden in Chinese communities.

We analyze the COVID-19 pandemic development in Latin America by network analysis to demonstrate the effectiveness of air travel restriction in reducing pandemic risk and provide risk analysis for air travel reopening in Latin America. We reinforce the importance of restricting air travel before and during local transmission of COVID-19.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Adolescent idiopathic scoliosis (AIS) disrupts spinal alignment and increases the intrinsic demand for active stabilization to maintain postural stability. Understanding the paraspinal muscle fatigability and its effects on spinal alignment and kinematics informs the importance of paraspinal muscle endurance for postural stability. This study aims to investigate the effects of fatigue of thoracic erector spinae on the spinal muscle activity and spinal kinematics in individuals with scoliosis. Spinal muscle activity, posture and mobility measured by electromyography and surface tomography were compared between 15 participants with scoliosis and 15 age- and gender-matched healthy controls during unilateral shoulder flexion and abduction with and without holding a 2-kg weight and performed before and after a fatigue task (prone isometric chest raise). No between-groups difference was found for the spinal extensor endurance. Erector spinae activity at the convex side of AIS group was significantly higher than that at their concave side and than that of healthy controls during shoulder elevations, regardless of the fatigue status. Significant decreases in translational and rotational mobility were found at convex side of AIS group during weighted abduction tasks after fatigue. In contrast, a significant increase in rotational mobility was demonstrated at convex side of AIS participants during weighted flexion tasks after fatigue. Our results revealed a comparable level of spinal extensor endurance between individuals with or without AIS. The increase in muscle activation post-fatigue provides no additional active postural stability but may increase the risk of back pain over the convex side in individuals with scoliosis. Findings highlight imbalances in muscles and the potential implications in optimising neuromuscular activation and endurance capacity in the rehabilitation for AIS patients. Future research is needed to investigate if endurance training of the convex-sided back extensors could optimize the impaired neuromuscular control in the AIS patients.

Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.

Background Sarcopenia is defined as the loss of muscle mass, strength, and/or performance. It is strongly associated with all‐cause mortality. Fibroblast growth factor 23 (FGF23) is markedly elevated in patients with chronic kidney disease, especially those receiving maintenance dialysis. FGF23 has previously been shown to have a direct role in cardiac dysfunction mediated through left ventricular hypertrophy. However, its role in the development of (or protection from) sarcopenia is uncertain. This study is aimed at determining the relationship between FGF23 and muscle‐related parameters and to assess the effect of FGF23 on skeletal muscle myoblasts. Methods A single centre, cross‐sectional study examining maintenance haemodialysis patients was conducted. Sarcopenia was defined in accordance with the revised European Working Group on Sarcopenia in Older People and the Asian Working Group for Sarcopenia criteria. Clinical assessment methods included bioelectrical impedance analysis, anthropometric measurement, handgrip strength and physical performance appraisal. Both intact FGF23, which is biologically active, and the inactive C‐terminal FGF23 were measured using enzyme‐linked immunosorbent assays. The direct effects of FGF23 on skeletal muscle myoblast proliferation and myogenic differentiation were assessed using an in vitro culture system. Linear and logistic regression analyses were performed to examine the associations between FGF23 and muscle‐related parameters and sarcopenia, respectively. Results Eighty‐one patients were included with a median age of 75 years (interquartile range 67–80), and 63% were male. Log‐transformed serum FGF23 correlated positively with handgrip strength (r = 0.27, p = 0.01, 95% confidence interval (CI) 0.06–0.46) and calf circumference (r = 0.27, p = 0.01, 95% CI 0.06–0.46), and in multiple regression analyses, it was found to be a significant independent predictor of both handgrip strength (beta = 5.39, 95% CI 2.07–8.72) and sarcopenia (odds ratio = 0.14, 95% CI 0.02–0.75). FGF23 was found to promote myoblast proliferation but attenuate myogenic differentiation. At 48 h of differentiation, the expressions of MyoG and MyoD were significantly lower in cells treated with FGF23 than the control. The fusion index and myotube diameters were reduced on Day 7 of differentiation in FGF23‐treated cells compared to the control. Conclusions Higher serum FGF23 levels were associated with stronger handgrip strength and lower odds of having sarcopenia in maintenance haemodialysis patients. Our findings suggest that supraphysiological levels of FGF23 might play a role in muscle regeneration by promoting myoblast proliferation but repressing myogenic differentiation to support the expansion of the proliferative pool. FGF23 could potentially serve as a serum biomarker for muscle health in dialysis populations.

LC3-associated phagocytosis (LAP) is an emerging non-canonical autophagy process that bridges signaling from pattern-recognition receptors (PRRs) to autophagic machinery. LAP formation results in incorporation of lipidated LC3 into phagosomal membrane (termed LAPosome). Increasing evidence reveals that LAP functions as an innate defense mechanism against fungal pathogens. However, the molecular mechanism involved and the consequence of LAP in regulating anti-fungal immune response remain largely unexplored. Here we show that is taken into LAPosome upon phagocytosis by macrophages. Interaction of with Dectin-1 activates Syk and triggers subsequent NADPH oxidase-mediated reactive oxygen species (ROS) response that is involved in LAP induction. Inhibiting LAP induction by silencing LC3α/β or treatment with ROS inhibitor impairs the activation of MAPKs-AP-1 pathway, thereby reduces macrophage proinflammatory cytokine response to . Additionally, we unravel the importance of NLRX1 in fungus-induced LAP. NLRX1 facilitates LAP by interacting with TUFM which associates with autophagic proteins ATG5-ATG12 for LAPosome formation. Macrophages from mice or TUFM-silenced cells exhibit reduced LAP induction and LAP-mediated MAPKs-AP-1 activation for cytokine response to . Furthermore, inhibiting ROS production in macrophages almost completely abolishes -induced LC3 conversion, indicating that both Dectin-1/Syk/ROS-dependent pathway and NLRX1-TUFM complex-dependent pathway collaboratively contribute to LAP induction. Our findings reveal new pathways underlying LAP induction by for macrophage cytokine response.