Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID (preprint)

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Human mesenchymal stem cell therapy in severe COVID-19 patients: 2-year follow-up results of a randomized, double-blind, placebo-controlled trial.

BACKGROUND: Long-term effects of human mesenchymal stem cell (MSC) treatment on COVID-19 patients have not been fully characterized. The aim of this study was to evaluate the safety and efficacy of a MSC treatment administered to severe COVID-19 patients enrolled in our previous randomized, double-blind, placebo-controlled clinical trial (NCT04288102). METHODS: A total of 100 patients experiencing severe COVID-19 received either MSC treatment (n = 65, 4 × 107 cells per infusion) or a placebo (n = 35) combined with standard of care on days 0, 3, and 6. Patients were subsequently evaluated 18 and 24 months after treatment to evaluate the long-term safety and efficacy of the MSC treatment. Outcomes measured included: 6-min walking distance (6-MWD), lung imaging, quality of life according to the Short Form 36 questionnaire (SF-36), COVID-19-related symptoms, titers of SARS-CoV-2 neutralizing antibodies, tumor markers, and MSC-related adverse events (AEs). FINDINGS: Two years after treatment, a marginally smaller proportion of patients had a 6-MWD below the lower limit of the normal range in the MSC group than in the placebo group (OR = 0.19, 95% CI: 0.04-0.80, Fisher's exact test, p = 0.015). At month 18, the general health score from the SF-36 was higher in the MSC group than in the placebo group (50.00 vs. 35.00, 95% CI: 0.00-20.00, Wilcoxon rank sum test, p = 0.018). Total severity score of lung imaging and the titer of neutralizing antibodies were similar between the two groups at months 18 and 24. There was no difference in AEs or tumor markers at the 2-year follow-up between the two groups. INTERPRETATION: Long-term safety was observed for the COVID-19 patients who received MSC treatment. However, efficacy of MSC treatment was not significantly sustained through the end of the 2-year follow-up period. FUNDING: The National Key Research and Development Program of China (2022YFA1105604, 2020YFC0860900, 2022YFC2304401), the specific research fund of The Innovation Platform for Academicians of Hainan Province (YSPTZX202216) and the Fund of National Clinical Center for Infectious Diseases, PLA General Hospital (NCRC-ID202105,413FZT6).

The coverage of SARS-CoV-2 vaccination and the willingness to receive the SARS-CoV-2 variant vaccine among employees in China.

BACKGROUND: COVID-19, which is caused by SARS-CoV-2, is a major global health threat. The dominant variant of SARS-CoV-2 has changed over time due to continuous evolution. We aimed to evaluate the coverage of SARS-CoV-2 vaccination among employees in China, explore their willingness to receive the SARS-CoV-2 variant vaccine and examine the potential factors influencing vaccination coverage and willingness. METHODS: A cross-sectional epidemiological survey was conducted online from January 1, 2022, to January 30, 2022. The information collected in the survey included sociodemographic characteristics, lifestyle habits, vaccination coverage, willingness to be vaccinated against SARS-CoV-2 variants and the reasons for vaccination and willingness. Multivariable logistic regression models were used to assess the associations of potential factors with the rate of vaccination and the willingness to be vaccinated. RESULTS: Among 62,395 eligible participants, the coverage of SARS-CoV-2 vaccination was 98.9% for at least one dose and 70.1% for a booster. The great majority of vaccinated individuals (94.4%) voluntarily received the vaccine. A total of 60,694 respondents (97.7%) were willing to be vaccinated against SARS-CoV-2 variants, mainly due to confidence in the effectiveness of vaccines (92.8%). A total of 1431 respondents were unwilling to be vaccinated, mainly because of concerns about the adverse effects of vaccines (77.6%). Longer education duration was associated with a higher rate of SARS-CoV-2 vaccination and willingness to be vaccinated. General or poor health status and having no history of influenza vaccination were associated with a lower rate of SARS-CoV-2 vaccination and willingness to be vaccinated. Additionally, we observed a significant positive association of abuse experience with the willingness to be vaccinated. CONCLUSION: Although the rate of SARS-CoV-2 vaccination and the willingness to be vaccinated were relatively high in the study population, there were still some respondents with vaccine hesitancy. Relevant strategies based on significant related factors should be developed and implemented to encourage vaccination.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach.

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Scanning the medical phenome to identify new diagnoses after recovery from COVID-19 in a US cohort.

OBJECTIVE: COVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19. MATERIALS AND METHODS: We augmented an existing high-throughput phenotyping method (PheWAS) to identify new diagnoses occurring after an acute temporal event in the EHR. We then used the temporal-informed phenotypes to assess development of new medical problems among COVID-19 survivors enrolled in an EHR cohort of adults tested for COVID-19 at Vanderbilt University Medical Center. RESULTS: The study cohort included 186,105 adults tested for COVID-19 from March 5, 2020 to November 1, 2021; of which 30,088 (16.2%) tested positive. Median follow-up after testing was 412 days (IQR 274-528). Our temporal-informed phenotyping was able to distinguish phenotype chapters based on chronicity of their constituent diagnoses. PheWAS with temporal-informed phenotypes identified increased risk for 43 diagnoses among COVID-19 survivors during outpatient follow-up, including multiple new respiratory, cardiovascular, neurological, and pregnancy-related conditions. Findings were robust to sensitivity analyses, and several phenotypic associations were supported by changes in outpatient vital signs or laboratory tests from the pre-testing to post-recovery period. CONCLUSION: Temporal-informed PheWAS identified new diagnoses affecting multiple organ systems among COVID-19 survivors. These findings can inform future efforts to enable longitudinal health surveillance for survivors of COVID-19 and other acute medical conditions using the EHR.

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program (preprint)

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Establishment of a Big Data Monitoring Platform for Cinema Opening in the Postepidemic Era from the Perspective of Public Health

The emergence of COVID-19 has had a huge impact on people's lives around the world. With the vaccine and the effective policies of the government, the spread of the epidemic has been effectively contained. However, in the postepidemic era, public health and epidemic protection policies have forced the transformation of public places such as movie theaters. The cinema box office monitored by the traditional monitoring platform can no longer effectively reflect the opening of the transformed cinema. To make up for the shortcomings of the traditional monitoring platform, considering the large amount of data generated by the cinemas' online and offline platforms and public place codes, this study establishes an intelligent monitoring platform based on big data technology to monitor the opening of cinemas. The established intelligent monitoring platform can fully extract the feature information contained in numerous data collected from cinemas and output quantitative indicators that characterize the opening of cinemas based on the feature information. The performance of the established intelligent monitoring platform is analyzed through a case study. The research results show that the average relative error between the cinema opening indicators predicted by the intelligent monitoring platform and the real results is within 2%, which indicates that the intelligent monitoring platform has good prediction accuracy. In addition, the statistical analysis results show that the linear correlation coefficient between the predicted and real results is 0.9802 > 0.95, which further indicates the feasibility of the established intelligent monitoring platform to monitor the opening of cinemas in the postepidemic era.

Non-Pharmaceutical Interventions Implemented to Control the COVID-19 Were Associated With Reduction of Influenza Incidence.

BACKGROUND: Non-pharmaceutical interventions were implemented in most countries to reduce the transmission of COVID-19. We aimed to describe the incidence of influenza in four countries in the 2019-2020 season and examined the effect of these non-pharmaceutical interventions on the incidence of influenza. METHODS: We used the network surveillance data from 2015 to 2020 to estimate the percentage increase in influenza cases to explore the effect of non-pharmaceutical interventions implemented to control the COVID-19 on the incidence of influenza in China, the United States, Japan, and Singapore. RESULTS: We found that the incidence of influenza has been almost zero and reached a persistent near-zero level for a continuous period of six months since epidemiologic week 14 of 2020 in the four countries. Influenza incidence decreased by 77.71% and 60.50% in the early days of COVID-19 in the 2019-2020 season compared to the same period in preceding years in Japan and Singapore, respectively. Furthermore, influenza incidence decreased by 60.50-99.48% during the period of compulsory interventions in the 2019-2020 season compared to the same period in preceding years in the four countries. CONCLUSION: These findings suggest that the application of non-pharmaceutical interventions, even everyday preventive action, was associated with a reduction of influenza incidence, which highlights that more traditional public health interventions need to be reasserted and universalized to reduce influenza incidence.

The Usability and Impact of a Low-Cost Pet Robot for Older Adults and People With Dementia: Qualitative Content Analysis of User Experiences and Perceptions on Consumer Websites.

BACKGROUND: Worldwide, populations are aging exponentially. Older adults and people with dementia are especially at risk of social isolation and loneliness. Social robots, including robotic pets, have had positive impacts on older adults and people with dementia by providing companionship, improving mood, reducing agitation, and facilitating social interaction. Nevertheless, the issue of affordability can hinder technology access. The Joy for All (JfA) robotic pets have showed promise as examples of low-cost alternatives. However, there has been no research that investigated the usability and impact of such low-cost robotic pets based on perceptions and experiences of its use with older adults and people with dementia. OBJECTIVE: The aim of our study was to explore the usability and impact of the JfA robotic cat, as an example of a low-cost robot, based on perceptions and experiences of using the JfA cat for older adults and people with dementia. METHODS: We used a novel methodology of analyzing a large volume of information that was uploaded by reviewers of the JfA cat onto online consumer review sites. Data were collected from 15 consumer websites. This provided a total of 2445 reviews. Next, all reviews were screened. A total of 1327 reviews that contained information about use of the JfA cat for older adults or people with dementia were included for analysis. These were reviews that contained terms relating to "older adults," "dementia," and "institutional care" and were published in the English language. Descriptive statistics was used to characterize available demographic information, and textual data were qualitatively analyzed using inductive content analysis. RESULTS: Most reviews were derived from consumer sites in the United States, and most reviewers were family members of users (ie, older adults and people with dementia). Based on the qualitative content analysis, 5 key themes were generated: prior expectations, perceptions, meaningful activities, impacts, and practicalities. Reviewers had prior expectations of the JfA cat, which included circumstantial reasons that prompted them to purchase this technology. Their perceptions evolved after using the technology, where most reported positive perceptions about their appearance and interactivity. The use of the robot provided opportunities for users to care for it and incorporate it into their routine. Finally, reviewers also shared information about the impacts of device and practicalities related to its use. CONCLUSIONS: This study provides useful knowledge about the usability and impact of a low-cost pet robot, based on experiences and perceptions of its use. These findings can help researchers, robot developers, and clinicians understand the viability of using low-cost robotic pets to benefit older adults and people with dementia. Future research should consider evaluating design preferences for robotic pets, and compare the effects of low-cost robotic pets with other more technologically advanced robotic pets.

Impact of the COVID-19 pandemic on congenital diaphragmatic hernia patients: a single-center retrospective study (preprint)

Purpose To investigate the impact of COVID-19 on the treatment of children with congenital diaphragmatic hernia (CDH).Methods We retrospectively collected and compared the data of patients with CDH admitted between January 1, 2020 and December 31, 2021 with the CDH patients admitted before the pandemic between January 1, 2018 and December 31, 2019 (control group).Results During the pandemic, 41 patients with CDH diagnosed prenatally were transferred to our hospital, and 40 underwent surgical repair. The number of patients treated in our hospital increased by 24.2% compared with that before the pandemic. During the pandemic, the overall survival rate, postoperative survival rate and recurrence rate were 85.4%, 87.5% and 7.3%, respectively, and there were no significant differences compared with the control group. The average length of hospital stay in patients admitted during the pandemic was longer than that in the control group, and the incidence of nosocomial infection was higher than that in the control group.Conclusions CDH patients confirmed to be SARS-CoV-2 infection-free can receive routine treatment. Our data indicate that the implementation of protective measures during the COVID-19 pandemic, along with appropriate screening and case evaluation, do not have a negative impact on the prognosis of children.

Genetic Risk and Chronic Obstructive Pulmonary Disease Independently Predict the Risk of Incident Severe COVID-19.

Rationale: Both genetic variants and chronic obstructive pulmonary disease (COPD) contribute to the risk of incident severe coronavirus disease (COVID-19). Whether genetic risk of incident severe COVID-19 is the same regardless of preexisting COPD is unknown. Objectives: In this study, we aimed to investigate the potential interaction between genetic risk and COPD in relation to severe COVID-19. Methods: We constructed a polygenic risk score for severe COVID-19 by using 112 single-nucleotide polymorphisms in 430,582 participants from the UK Biobank study. We examined the associations of genetic risk and COPD with severe COVID-19 by using logistic regression models. Results: Of 430,582 participants, 712 developed severe COVID-19 as of February 22, 2021, of whom 19.8% had preexisting COPD. Compared with participants at low genetic risk, those at intermediate genetic risk (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.09-1.66) and high genetic risk (OR, 1.50; 95% CI, 1.18-1.92) had higher risk of severe COVID-19 (P for trend = 0.001), and the association was independent of COPD (P for interaction = 0.76). COPD was associated with a higher risk of incident severe COVID-19 (OR, 1.37; 95% CI, 1.12-1.67; P = 0.002). Participants at high genetic risk and with COPD had a higher risk of severe COVID-19 (OR, 2.05; 95% CI, 1.35-3.04; P < 0.001) than those at low genetic risk and without COPD. Conclusions: The polygenic risk score, which combines multiple risk alleles, can be effectively used in screening for high-risk populations of severe COVID-19. High genetic risk correlates with a higher risk of severe COVID-19, regardless of preexisting COPD.

Prognostic Value of Leucocyte to High-Density Lipoprotein-Cholesterol Ratios in COVID-19 Patients and the Diabetes Subgroup.

Background: Blood parameters, such as neutrophil-to-lymphocyte ratio, have been identified as reliable inflammatory markers with diagnostic and predictive value for the coronavirus disease 2019 (COVID-19). However, novel hematological parameters derived from high-density lipoprotein-cholesterol (HDL-C) have rarely been studied as indicators for the risk of poor outcomes in patients with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection. Here, we aimed to assess the prognostic value of these novel biomarkers in COVID-19 patients and the diabetes subgroup. Methods: We conducted a multicenter retrospective cohort study involving all hospitalized patients with COVID-19 from January to March 2020 in five hospitals in Wuhan, China. Demographics, clinical and laboratory findings, and outcomes were recorded. Neutrophil to HDL-C ratio (NHR), monocyte to HDL-C ratio (MHR), lymphocyte to HDL-C ratio (LHR), and platelet to HDL-C ratio (PHR) were investigated and compared in both the overall population and the subgroup with diabetes. The associations between blood parameters at admission with primary composite end-point events (including mechanical ventilation, admission to the intensive care unit, or death) were analyzed using Cox proportional hazards regression models. Receiver operating characteristic curves were used to compare the utility of different blood parameters. Results: Of 440 patients with COVID-19, 67 (15.2%) were critically ill. On admission, HDL-C concentration was decreased while NHR was high in patients with critical compared with non-critical COVID-19, and were independently associated with poor outcome as continuous variables in the overall population (HR: 0.213, 95% CI 0.090-0.507; HR: 1.066, 95% CI 1.030-1.103, respectively) after adjusting for confounding factors. Additionally, when HDL-C and NHR were examined as categorical variables, the HRs and 95% CIs for tertile 3 vs. tertile 1 were 0.280 (0.128-0.612) and 4.458 (1.817-10.938), respectively. Similar results were observed in the diabetes subgroup. ROC curves showed that the NHR had good performance in predicting worse outcomes. The cutoff point of the NHR was 5.50. However, the data in our present study could not confirm the possible predictive effect of LHR, MHR, and PHR on COVID-19 severity. Conclusion: Lower HDL-C concentrations and higher NHR at admission were observed in patients with critical COVID-19 than in those with noncritical COVID-19, and were significantly associated with a poor prognosis in COVID-19 patients as well as in the diabetes subgroup.

Inpatient use of metformin and acarbose is associated with reduced mortality of COVID-19 patients with type 2 diabetes mellitus.

AIMS: Type 2 diabetes mellitus (T2DM) is a strong risk factor for complications of coronavirus disease 2019 (COVID-19). The effect of T2DM medications on COVID-19 outcomes remains unclear. In a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 in Wuhan, we have previously found that metformin use prior to hospitalization is associated with reduced mortality. The current study aims to investigate the effects of inpatient use of T2DM medications, including metformin, acarbose, insulin and sulfonylureas, on the mortality of COVID-19 patients with T2DM during hospitalization. METHODS: We continue to carry out a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 and treated with different combinations of diabetes medications. RESULTS: We found that patients using metformin (p = .02) and acarbose (p = .04), alone or both together (p = .03), after admission were significantly more likely to survive than those who did not use either metformin or acarbose. 37 patients continued to take metformin after admission and 35 (94.6%) survived. Among the 57 patients who used acarbose after admission, 52 survived (91.2%). A total of 20 patients used both metformin and acarbose, while 57 used neither. Of the 20 dual-use patients, 19 (95.0%) survived. CONCLUSION: Our analyses suggest that inpatient use of metformin and acarbose together or alone during hospitalization should be studied in randomized trials.

Integrated Analysis of microRNAs and Metabolomics in Rat’s Serum Reveals Multi-action Modes of Qingfei Paidu Decoction for COVID-19 Treatment (preprint)

Background: During the fight against coronavirus disease 2019 (COVID-19) in China, Qingfei Paidu decoction (QFPDD) has been widely applied to treat COVID-19 patients. Retrospective studies showed that QFPDD could improve clinical outcomes of COVID-19. Thus, it is necessary and interesting to explore the action mode of QFPDD for further application and development. Methods: Sprague-Dawley (SD) rats were randomly divided into two groups, QFPDD (n=9) and control (n=10) groups. They were parallelly treated for 12 days with QFPDD and warm distilled water, respectively. At the endpoint, the microRNA (miRNA or miR) profiles in serum were detected to identify differently expressed miRNAs (DEMs). Then, the action mode of QFPDD were explored via review of potential roles of DEMs and functional enrichment analysis of their targets (e.g., GO enrichment and KEGG pathway analysis), especially focusing on the aspects of immunity, inflammation, virus infection and pulmonary fibrosis. Core genes were identified based on KEGG pathway analysis. Metabolomics were detected in serum and significantly changed metabolites (SCMs), especially the metabolic substrates and products of enzyme of core gene were identified as biomarkers to validate the regulation of DEMs to enzyme activity of core gene through metabolomic analysis and linear correlation analysis between SCMs and DEMs. Results: 23 DEMs were identified in the serum between QFPDD and control groups, with 1636 predicted genes. Reported evidence has showed that both the DEMs and their target genes involve regulation of immunity, inflammation, virus infection and pulmonary fibrosis. Phospholipase C, gamma 1 (Plcg1) was identified as a core gene and predicted to be upregulated attributed to downregulation of novel-89-mature. The levels of three SCMs, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)), PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) and PC(16:1(9Z)/16:1(9Z)), which were the metabolic substrates of phospholipase C, were significantly reduced in QFPDD group, in addition, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)) and PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) presented positively linear correlation with the expression level of novel-89-mature. The level of phosphorylcholine, a product of PCs metabolized by phospholipase C, was significantly elevated in QFPDD group. Conclusion: QFPDD can induce modification of miRNAs profile, and subsequently multi-regulate the immunity, inflammation, virus infection and pulmonary fibrosis in vivo, playing an important role for the positive outcomes of COVID-19 patients treated by QFPDD in China.

Human Mesenchymal Stem Cells Treatment for Severe COVID-19: 1-Year Follow-Up Results of a Randomized, Double-Blind, Placebo-Controlled Trial (preprint)

Background The long-term consequences of human umbilical cord-derived mesenchymal stem cell (UC-MSC) treatment for COVID-19 patients are yet to be reported. This study assessed the 1-year outcomes in patients with severe COVID-19, who were recruited in our previous UC-MSC clinical trial.Methods: In this prospective, longitudinal, cohort study, 100 patients enrolled in our phase 2 trial were prospectively followed up at 3-month intervals for 1 year to evaluate the long-term safety and effectiveness of UC-MSC treatment. The primary endpoint was an altered proportion of whole-lung lesion volumes measured by high-resolution CT. Other imaging outcomes, 6-minute walking distance (6-MWD), lung function, plasma biomarkers, and adverse events were also recorded and analyzed. This trial was registered with ClinicalTrials.gov (NCT04288102).Findings: Within 3 months, MSC administration exerted numerical improvement in whole-lung lesion volume compared with the placebo, leading to a significant difference of −10.82% (95% CI: −20.69%, −1.46%, P=0.030) on day 10. MSC also reduced the proportion of solid component lesion volume compared with the placebo at each follow-up point, with a significant difference of − 9.02% (95%CI: − 17.44%, − 0.10%, P=0.045) at month 9. More interestingly, 17.86% (10/56) of patients in the MSC group had normal CT images at month 12 ( P= 0.013), but none in the placebo group. The incidence of symptoms was lower in the MSC group than in the placebo group at each follow-up time, particularly sleep difficulties at month 3 (OR 0.19, 95% CI 0.07,0.50; P=0.001), and usual activity at month 12 (OR 0.15, 95% CI 0.03,0.79; P=0.018). Neutralizing antibodies were all positive, with a similar median inhibition rate (61.6% vs. 67.55%) in both groups at month 12. No difference in adverse events at the 1-year follow-up and tumor markers at month 12 were observed between the two groups.Interpretation: UC-MSC administration achieves a long-term benefit in the recovery of lung lesions and symptoms in COVID-19 patients.Trial Registration: This trial was registered with ClinicalTrials.gov (NCT04288102).Funding The National Key R&D Program of China, the Innovation Groups of the National Natural Science Foundation of China, and the National Science and Technology Major Project.Declaration of Interest: None to declare. Ethical Approval: This study was approved by the Ethics Committee of the Fifth Medical Center, Chinese PLA General Hospital (2020-013-D).

The Effects of Technological Interventions on Social Participation of Community-Dwelling Older Adults with and without Dementia: A Systematic Review.

Social isolation in community-dwelling older adults with dementia is a growing health issue that can negatively affect health and well-being. To date, little attention has been paid to the role of technology in improving their social participation. This systematic review aims to provide a systematic overview of the effects of technological interventions that target social participation in community-dwelling older adults with and without dementia. The scientific databases Medline (PubMed), PsycINFO, CINAHL, Web of Science, and the Cochrane Library were systematically searched and independently screened by two reviewers. Results were synthesized narratively. The methodological quality of included studies was independently assessed by two reviewers. In total, 36 studies of varying methodological quality were identified. Most studies evaluated social networking technology and ICT training programs. Three studies focused on people with dementia. Quantitative findings showed limited effects on loneliness, social isolation, and social support. Nevertheless, several benefits related to social participation were reported qualitatively. Social interaction, face-to-face contact, and intergenerational engagement were suggested to be successful elements of technological interventions in improving the social participation of community-dwelling older adults. Rigorous studies with larger sample sizes are highly needed to evaluate the long-term effects of technology on the multidimensional concept of social participation.

Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.

From the start of the coronavirus disease 2019 (COVID-19) pandemic, researchers have looked to electronic health record (EHR) data as a way to study possible risk factors and outcomes. To ensure the validity and accuracy of research using these data, investigators need to be confident that the phenotypes they construct are reliable and accurate, reflecting the healthcare settings from which they are ascertained. We developed a COVID-19 registry at a single academic medical center and used data from March 1 to June 5, 2020 to assess differences in population-level characteristics in pandemic and non-pandemic years respectively. Median EHR length, previously shown to impact phenotype performance in type 2 diabetes, was significantly shorter in the SARS-CoV-2 positive group relative to a 2019 influenza tested group (median 3.1 years vs 8.7; Wilcoxon rank sum P = 1.3e-52). Using three phenotyping methods of increasing complexity (billing codes alone and domain-specific algorithms provided by an EHR vendor and clinical experts), common medical comorbidities were abstracted from COVID-19 EHRs, defined by the presence of a positive laboratory test (positive predictive value 100%, recall 93%). After combining performance data across phenotyping methods, we observed significantly lower false negative rates for those records billed for a comprehensive care visit (p = 4e-11) and those with complete demographics data recorded (p = 7e-5). In an early COVID-19 cohort, we found that phenotyping performance of nine common comorbidities was influenced by median EHR length, consistent with previous studies, as well as by data density, which can be measured using portable metrics including CPT codes. Here we present those challenges and potential solutions to creating deeply phenotyped, acute COVID-19 cohorts.

ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.

OBJECTIVE: Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. METHODS: We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. RESULTS: We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). CONCLUSION: ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.