Association of Social Isolation and Loneliness With Incident Heart Failure in a Population-Based Cohort Study.

BACKGROUND: Social isolation and loneliness have emerged as important risk factors for cardiovascular diseases, particularly during the coronavirus disease pandemic. However, it is unclear whether social isolation and loneliness had independent and joint associations with incident heart failure (HF). OBJECTIVES: This study sought to examine the association of social isolation, loneliness, and their combination with incident HF. METHODS: The UK Biobank study is a population-based cohort study. Social isolation and loneliness were assessed using self-reported questionnaires. HF cases were identified by linking hospital records and death registries. The weighted polygenic risk score associated with HF was calculated. RESULTS: Among the 464,773 participants (mean age: 56.5 ± 8.1 years, 45.3% male), 12,898 incident HF cases were documented during a median follow-up of 12.3 years. Social isolation (most vs least: adjusted HR: 1.17; 95% CI:1.11-1.23) and loneliness (yes vs no: adjusted HR: 1.19; 95% CI: 1.11-1.27) were significantly associated with an increased risk of incident HF. The association between an elevated risk of HF and social isolation was modified by loneliness (Pinteraction = 0.034). A gradient of association between social isolation and the risk of incident HF was found only among individuals without loneliness (Ptrend < 0.001), but not among those with loneliness (Ptrend = 0.829). These associations were independent of the genetic risk of HF. CONCLUSIONS: Social isolation and loneliness were independently associated with a higher likelihood of incident HF regardless of genetic risk. The association between social isolation and incident HF was potentially modified by loneliness status.

BCG Vaccination: A potential tool against COVID-19 and COVID-19-like Black Swan incidents.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19), and its variants have brought unprecedented impacts to the global public health system, politics, economy, and other fields. Although more than ten COVID-19 specific vaccines have been approved for emergency use, COVID-19 prevention and control still face many challenges. Bacille Calmette-Guérin (BCG) is the only authorized vaccine used to fight against tuberculosis (TB), it has been hypothesized that BCG may prevent and control COVID-19 based on BCG-induced nonspecific immune responses. Herein, we summarized: 1) The nonspecific protection effects of BCG, such as prophylactic protection effects of BCG on nonmycobacterial infections, immunotherapy effects of BCG vaccine, and enhancement effect of BCG vaccine on unrelated vaccines; 2) Recent evidence of BCG's efficacy against SARS-COV-2 infection from ecological studies, analytical analyses, clinical trials, and animal studies; 3) Three possible mechanisms of BCG vaccine and their effects on COVID-19 control including heterologous immunity, trained immunity, and anti-inflammatory effect. We hope that this review will encourage more scientists to investigate further BCG induced non-specific immune responses and explore their mechanisms, which could be a potential tool for addressing the COVID-19 pandemic and COVID-19-like "Black Swan" events to reduce the impacts of infectious disease outbreaks on public health, politics, and economy.

Micro-PCR chip-based multifunctional ultrafast SARS-CoV-2 detection platform.

When dealing with infectious pathogens, the point-of-care screening and diagnosis strategy should be low-cost, simple, rapid and accurate. Here, we report a multifunctional rapid PCR platform allowing both simultaneous screening of suspected cases and accurate identification and quantification of the virus. Based on the platform, samples suspected of being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are screened first, after which subsequent precise quantification of the virus (SARS-CoV-2) can be performed if necessary. This fast screening technique offers a detection limit of 10 nucleic acid copies per test during the entire running time of 15 minutes, with a throughput of 9 samples at a time. Besides, depending on a droplet microfluidic chip, this platform could also provide assays of nucleic acids across four orders of magnitude of concentration within less than 15 minutes. Additionally, we successfully use the platform to quickly distinguish between positive and negative cases in clinical samples and rapidly quantify the viral load in each sample, which is consistent with standard RT-qPCR tests. As such, we demonstrate a promising and versatile rapid PCR platform for point-of-care diagnosis of infectious diseases.

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Credit evaluation is of high scientific significance and practical use, especially in today’s plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

BACS: blockchain and AutoML-based technology for efficient credit scoring classification.

Credit evaluation is of high scientific significance and practical use, especially in today's plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

Ultrafast multiplexed detection of SARS-CoV-2 RNA using a rapid droplet digital PCR system.

We report the first combination of droplet digital and rapid PCR techniques for efficient, accurate, and quantitative detection of SARS-CoV-2 RNA. The presented rapid digital PCR system simultaneously detects two specific targets (ORF1ab and N genes) and one reference gene (RNase P) with a single PCR thermal cycling period around 7 s and the total running time less than 5 min. A clear positive signal could be identified within 115 s via the rapid digital RT-PCR, suggesting its efficiency for the end-point detection. In addition, benchmark tests with serial diluted reference samples of SARS-CoV-2 RNA reveal the excellent accuracy of our system (R2>0.99). More importantly, the rapid digital PCR system gives consistent and accurate detection of low-concentration reference samples, whereas qPCR yields Ct values with significant variations that could lead to false-negative results. Finally, we apply the rapid digital PCR system to analyze clinical samples with both positive and control cases, where results are consistent with qPCR test outcomes. By providing similar accuracy with qPCR while minimizing the detection time-consuming and the false-negative tendency, the presented rapid digital PCR system represents a promising improvement on the rapid diagnosis of COVID-19.

Myoglobin and C-reactive protein are efficient and reliable early predictors of COVID-19 associated mortality.

Since the emergence of SARS-CoV-2, numerous studies have been attempting to determine biomarkers, which could rapidly and efficiently predict COVID-19 severity, however there is lack of consensus on a specific one. This retrospective cohort study is a comprehensive analysis of the initial symptoms, comorbidities and laboratory evaluation of patients, diagnosed with COVID-19 in Huoshenshan Hospital, Wuhan, from 4th February to 12th March, 2020. Based on the data collected from 63 severely ill patients from the onset of symptoms till the full recovery or demise, we found not only age (average 70) but also blood indicators as significant risk factors associated with multiple organ failure. The blood indices of all patients showed hepatic, renal, cardiac and hematopoietic dysfunction with imbalanced coagulatory biomarkers. We noticed that the levels of LDH (85%, P < .001), HBDH (76%, P < .001) and CRP (65%, P < .001) were significantly elevated in deceased patients, indicating hepatic impairment. Similarly, increased CK (15%, P = .002), Cre (37%, P = 0.102) and CysC (74%, P = 0.384) indicated renal damage. Cardiac injury was obvious from the significantly elevated level of Myoglobin (52%, P < .01), Troponin-I (65%, P = 0.273) and BNP (50%, P = .787). SARS-CoV-2 disturbs the hemolymphatic system as WBC# (73%, P = .002) and NEUT# (78%, P < .001) were significantly elevated in deceased patients. Likewise, the level of D-dimer (80%, P < .171), PT (87%, P = .031) and TT (57%, P = .053) was elevated, indicating coagulatory imbalances. We identified myoglobin and CRP as specific risk factors related to mortality and highly correlated to organ failure in COVID-19 disease.

Mortality burden from seasonal influenza in Chongqing, China, 2012-2018.

PURPOSE: To estimate influenza-associated excess mortality rates (EMRs) in Chongqing from 2012 to 2018. METHODS: We obtained weekly mortality data for all-cause and four underlying causes of death (circulatory and respiratory disease (CRD), pneumonia and influenza (P&I), chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IDH)), and influenza surveillance data, from 2012 to 2018. A negative-binomial regression model was used to estimate influenza-associated EMRs in two age groups (<65 years and ≥65 years). RESULTS: It was estimated that an annual average of 10025 influenza-associated deaths occurred in Chongqing, corresponding to 5.2% of all deaths. The average EMR for all-cause death associated with influenza was 33.5 (95% confidence interval (CI): 31.5-35.6) per 100 000 persons, and in separate cause-specific models we attributed 24.7 (95% CI: 23.3-26.0), 0.8 (95% CI: 0.7-0.8), 8.5 (95% CI: 8.1-9.0) and 5.0 (95% CI: 4.7-5.3) per 100 000 persons EMRs to CRD, P&I, COPD and IDH, respectively. The estimated EMR for influenza B virus was 20.6 (95% CI: 20.3-21.0), which was significantly higher than the rates of 5.3 (95% CI: 4.5-6.1) and 7.5 (95% CI: 6.7-8.3) for A(H3N2) and A(H1N1) pdm09 virus, respectively. The estimated EMR was 152.3 (95% CI: 136.1-168.4) for people aged ≥65 years, which was significantly higher than the rate for those aged <65 years (6.8, 95% CI: 6.3-7.2). CONCLUSIONS: Influenza was associated with substantial EMRs in Chongqing, especially among elderly people. Influenza B virus caused a relatively higher excess mortality impact compared with A(H1N1)pdm09 and A(H3N2). It is advisable to optimize future seasonal influenza vaccine reimbursement policy in Chongqing to curb disease burden.