Link Between the G Protein-Coupled Receptor (GPCR), SARS-CoV-2 Spike Protein, ACE2, Vaccines, and Long COVID (preprint)

The knowledge of COVID-19 impact on the human body has increased rapidly. Although many people recover from COVID-19, some continue to experience persistent symptoms that have been identified as Long COVID. This condition can have a severe impact on quality of life, and it remains a significant concern for medical professionals and researchers. One of the key components of the SARS-CoV-2 virus that enables it to enter human cells is the spike (S) protein. Recent studies have revealed a complex network of interactions between G proteins, spike (S) protein, and the Renin-Angiotensin System (RAS) may be responsible, at least in part, for long COVID. SARS-CoV-2 can also affect the brain, leading to neurological symptoms such as confusion, memory loss, and fatigue. Increasing evidence suggests that COVID-19 is not just a respiratory illness since it is likely that the virus could influence signal transduction pathways such as G-protein-coupled receptor (GPCR), among others, in the brain, either directly or indirectly, affecting neural functions. These interactions with the spike (S) protein and RAS, alongside the brain, are complex and require deep research to understand their implications for Long COVID-19 manifestation fully. While recent research has shed light on the complex interactions between G proteins, spike (S) protein, the brain, and the angiotensin system, this article explores these interconnected pathways and their implications for long COVID-19 manifestations. The present review summarises current research on different molecular mechanisms in Long COVID pathophysiology and may help identify possible targets or new strategies for the diagnosis and treatment.

Assessment of the communication methods used by Local Dental Committees (LDCs) with their members, the importance of a website presence, and the impact of the Covid-19 pandemic. (preprint)

Aims: To gather the opinions of Local Dental Committee (LDC) members on their communication strategies, the importance of a website present and the impact of the Covid-19 pandemic. Design: Mixed method approach with descriptive cross-sectional survey and focus group. Setting: Online electronic survey and virtual focus group. Method & Materials: Survey circulated via the British Dental Association (BDA) to the LDCs within the UK via a link in the BDA LDC newsletter. Subsequent focus group held during a LDC Officials Day. Results: A total of 57 unique responses from different LDCs were received giving a response rate of 56.4%. Pre-pandemic the LDCs reported holding between 2 and 12 LDC meetings all of which were held Face to Face. As a result of the pandemic 19.3% (n=11) reduced the frequency of their meetings, 19.3% (n=11) held the same number, but 61.4% (n=35) had held more frequent meetings. During the pandemic, 98.2% (n=54) of LDCs used video conferencing platforms for their meetings, with 70.2% (n=40) expecting to continue to use this modality in the long term. The main methods of communication used by LDCs are email (87.7%, n=50), website (49.1%, n=28) and word of mouth (38.6%, n=22). 81.3% (n=39) of respondents had an LDC website with the majority (54.3%, n= 25) being open access. However, 73.6% (n=39,) who expressed a view, felt that there should be restricted access to an LDC website. Only 37.8% (17/45) felt that LDC websites were useful in their communication strategy. The LDC representatives felt that the top five core elements were: Purpose of the LDC/What is the LDC (90.6%, n=48); Membership of the LDC Committee (88.7%, n=47); Forthcoming LDC meeting details - Agenda/minutes/documents/location (77.4%, n=41); Links to external documents/guidance (75.5%, n=40); and Links to support services (75.5%, n=40). Although the relative importance of these elements varied. The thematic analysis of the focus group revealed the following main themes: 1) Investment – Financial and time 2) Levels of website utilization 3) Use of Social Media 4) Lack of awareness & engagement from local dentists with the LDC Conclusions: 1) The frequency of LDC meetings and the methods of holding them changed as a response to the pandemic. 2) It is expected that there will be a sustained shift towards holding more LDC meeting virtually in the long term. 3) There are significant difficulties in establishing a comprehensive and contemporaneous list of contact details for the dentists within an LDCs’ area. 4) There are contrasting opinions regarding the usefulness of an LDC website. 5) Establishment of regional LDCs websites offers an opportunity for improved efficiencies and engagement. 6) The majority of respondents felt that certain elements of an LDC website should only be accessible via a log in. 7) LDCs who felt to have the best engagement with the dentists in their area use a plethora of coordinated methods to optimise engagement, but acknowledge that this requires a significant time commitment.

Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in African continent (preprint)

Although local spatiotemporal analysis can improve understanding of the geographic variation of infectious diseases, its drivers, and the search for targeted interventions, it is limited in Africa; therefore, monitoring the spread and pattern is essential. Information on the spatial patterns will have substantial inferences for real awareness in future pandemics. Primarily, the linkage between infectious disease and socioeconomic indicators based on the perception of spatial connectivity should be understood as the spread nowadays between regions is progressively connected. This study applied spatial analysis, including the Moran-I index, the Local Indicator of Spatial Association (LISA) and spatial regression models to study the spatial variations in the effects of socioeconomic indicators on COVID-19 mortality in the African region. The preliminary test on the spatial correlation of COVID-19 showed a significant result; thus, a spatial regression approach was employed. The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analyzed in all five regions of Africa using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. In addition, among all the socioeconomic indicators included, the effects of Gross Domestic Product and age dependency ratio on COVID-19 mortality were the most critical indicators that described the pandemic evolution across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region.

Mental health outcomes and characteristics of autobiographical memory associated with odor cues of alcohol among individuals with problematic drinking behaviors (preprint)

The number of alcoholics worldwide has increased since the outbreak of the COVID-19 pandemic, and understanding and preventing alcohol addiction is of utmost importance. We investigated the relationships among odor cues of alcohol, autobiographical memory, and the mental health of individuals with and without problematic drinking behaviors. Overall, 711 and 332 Japanese participants with and without problematic drinking behaviors, respectively, were asked to recall autobiographical memory evoked by the odor of alcohol. Thereafter, the participants were asked to complete questionnaires to measure their autobiographical memory and mental health. The results revealed that participants without problematic drinking behaviors recalled more positive memories and exhibited better mental health than those with such behaviors. These results suggest that mental health may be enhanced by positive memory recall among problem drinkers. Clarifying how mental and physical health problems and well-being are specifically related to positive and negative memories among problem drinkers is important.

Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data (preprint)

COVID-19 has affected more than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and CNN Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) and Hidden-Unit BERT (HuBERT). We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86\% and the highest AUC of 0.93. The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art.

Calcifediol boosts the efficacy of ChAdOx1 nCoV-19 (COVISHIELD) vaccine via upregulation of key genes associated with memory T cell responses (preprint)

The ChAdOx1 nCoV-19 (COVISHIELD) vaccine has emerged as a pivotal tool in the global fight against the COVID-19 pandemic. In our previous study eligible subjects were supplemented with calcifediol, a direct precursor to the biologically active form of vitamin D, calcitriol with an objective to enhance the immunogenicity of the COVISHIELD vaccine. Herein we investigated the effects of calcifediol supplementation on gene expression profiles in individuals who received the COVISHIELD vaccine. Peripheral blood mononuclear cells were isolated from vaccinated individuals with and without calcifediol supplementation at baseline, 3 and 6 months, and the gene expression profiles were analyzed using high-throughput sequencing. The results revealed distinct patterns of gene expression associated with calcifediol supplementation, suggesting potential molecular mechanisms underlying the beneficial effects of calcifediol in improving the efficacy of COVISHIELD vaccine via augmentation of T cell memory responses, innate immune mechanisms such as NOD signaling pathway, JAK/STAT and TGF beta pathways. Calcifediol supplementation in vaccinated individuals also downregulated the pathways related to the Coronavirus disease. Taken together, our findings provide valuable insights into the interplay between vitamin D receptor (VDR) signaling and vaccine-induced immune responses and offer another approach in improving vaccination induced antiviral responses.

Spatial autoregressive model with measurement error in covariates (preprint)

The Spatial AutoRegressive model (SAR) is commonly used in studies involving spatial and network data to estimate the spatial or network peer influence and the effects of covariates on the response, taking into account the spatial or network dependence. While the model can be efficiently estimated with a Quasi maximum likelihood approach (QMLE), the detrimental effect of covariate measurement error on the QMLE and how to remedy it is currently unknown. If covariates are measured with error, then the QMLE may not have the $\sqrt{n}$ convergence and may even be inconsistent even when a node is influenced by only a limited number of other nodes or spatial units. We develop a measurement error-corrected ML estimator (ME-QMLE) for the parameters of the SAR model when covariates are measured with error. The ME-QMLE possesses statistical consistency and asymptotic normality properties. We consider two types of applications. The first is when the true covariate cannot be measured directly, and a proxy is observed instead. The second one involves including latent homophily factors estimated with error from the network for estimating peer influence. Our numerical results verify the bias correction property of the estimator and the accuracy of the standard error estimates in finite samples. We illustrate the method on a real dataset related to county-level death rates from the COVID-19 pandemic.

Spatial forecasting of online food delivery demand (preprint)

This work aims to predict demand for orders in the expanded center of S˜ao Paulo, Brazil, using the Spatial Autoregressive Model (SAR) and the Artificial Neural Network - Multilayer Perceptron Model (MLP), based on real data from a Brazilian online food delivery company. Accurately forecasting demand by geographical area is crucial for efficient logistics planning in the company. To address this, we proposed an approach that uses an augmented train matrix to incorporate order information from neighboring areas of the first order, allowing the neural network model to identify spatial autocorrelation in the data. The performance of both models was evaluated using Root Mean Squared Error (RMSE) and the coefficient of determination (R2). In the simulations, both of the models achieved satisfactory performance compared to the historical mean. The MLP model, when best tuned, outperformed the SAR model in both RMSE and R2 coefficient.

The double Burden of Stressful Life Events among professional nurses: public mass shootings during the COVID-19 Pandemic (preprint)

Background Stress is a significant consequence of frontline care. Nurses have recently been at the forefront of care for victims of both public mass shootings and the coronavirus disease 2019 (COVID-19) pandemic. They have been coping with stressful life events, fearing potential infection and the possibility of transmitting the virus to their patients and families, experiencing heavy workloads, high patient-nurse ratios, and extended shifts—all of which lead to exhaustion and stress. Nurses’ perceptions of mass shooting events during the COVID-19 pandemic should be explored to ensure that these professionals receive more efficient support to deliver high-quality care. This study explored nurses’ perceptions in the era of mass shootings and the COVID-19 pandemic.Methods This phenomenological-hermeneutic research was performed to examine the perspectives of 16 nurses caring for patients who were either injured during a public mass shooting or infected with COVID-19. Participants were selected through purposeful sampling. Data were collected using semi-structured questionnaires during online interviews using Microsoft Teams. Themes derived from structural understanding illuminated participant nurses' perceptions of patient care during the COVID-19 pandemic.Results Qualitative data analysis revealed five main themes: stressful life events, flashbulb memories, service-oriented mindset, team collaboration key, and professional needs. The theoretical interpretation points to the multidimensional perceptions of nurses and the need to confirm these perceptions and reconcile them with the psychological impact of stressful life events, making future adjustments and adaptations possible.Conclusions Interested stakeholders must consider service-oriented mindsets, team collaboration is key, and the professional needs of nurses to support and prepare novice to expert nurses for the delivery of optimal care during public health crises.

Neuropsychiatric sequelae in an experimental model of post-COVID syndrome in mice (preprint)

The global impact of the COVID-19 pandemic has been unprecedented, and presently, the world is facing a new challenge known as Post-COVID syndrome (PCS). Current estimates suggest that more than 65 million people are grappling with PCS, encompassing several manifestations, including pulmonary, musculoskeletal, metabolic, and neuropsychiatric sequelae (cognitive and behavioral). The mechanisms underlying PCS remain unclear. The present study aimed to: (i) comprehensively characterize the acute effects of pulmonary inoculation of the betacoronavirus MHV-A59 in immunocompetent mice at clinical, cellular, and molecular levels; (ii) examine potential acute and long-term pulmonary, musculoskeletal, and neuropsychiatric sequelae induced by the betacoronavirus MHV-A59; and to (iii) assess sex-specific differences. Male and female C57Bl/6 mice were initially inoculated with varying viral titers (3x103 to 3x105 PFU/30 L) of the betacoronavirus MHV-A59 via the intranasal route to define the highest inoculum capable of inducing disease without causing mortality. Further experiments were conducted with the 3x104 PFU inoculum. Mice exhibited an altered neutrophil/lymphocyte ratio in the blood in the 2nd and 5th day post-infection (dpi). Marked lung lesions were characterized by hyperplasia of the alveolar walls, infiltration of polymorphonuclear leukocytes (PMN) and mononuclear leukocytes, hemorrhage, increased concentrations of CCL2, CCL3, CCL5, and CXCL1 chemokines, as well as high viral titers until the 5th dpi. While these lung inflammatory signs resolved, other manifestations were observed up to the 60 dpi, including mild brain lesions with gliosis and hyperemic blood vessels, neuromuscular dysfunctions, anhedonic-like behavior, deficits in spatial working memory, and short-term aversive memory. These musculoskeletal and neuropsychiatric complications were exclusive to female mice and were prevented after ovariectomy. In summary, our study describes for the first time a novel sex-dependent model of PCS focused on neuropsychiatric and musculoskeletal disorders. This model provides a unique platform for future investigations regarding the effects of acute therapeutic interventions on the long-term sequelae unleashed by betacoronavirus infection.

Artificial Intelligence (AI) Based Prediction of Mortality, ICU Admission and Ventilation Support Requirement for COVID-19 Patients Using 122 Clinical and Demographic Parameters (preprint)

IntroductionCOVID-19 can rapidly lead to severe respiratory problems and can result in an overwhelming burden on healthcare systems worldwide, making it imperative to identify high-risk patients and predict survival and need for intensive care (ICU). Most of the proposed modes are not well reported making them less reproducible and prone to high risk of bias. MethodsIn this study, the performances of seven classical machine (Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), XGBoost, Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (NB)) and two deep leaning models (Deep Neural Network (DNN) and Long Short-Term Memory (LSTM)) in combination with two widely used feature selection methods (random forest and extra tree classifier) were investigated to predict "last status" representing mortality, "ICU requirement", and "ventilation days". Fivefold cross-validation was used for training and validation purposes. In each fold, 80% data were used for training the models and the rest 20% were preserved for validation. To minimize bias, the training and testing sets were split maintaining similar distributions. Before splitting, k-nearest neighbour (KNN) imputation algorithm was employed to resolve the issue of missing data. On the other hand, bootstrapping technique was used for both oversampling and undersampling to address the issue of data imbalance. Publicly available 122 demographic and clinical features of 1384 patients were used. The performances of the models were evaluated using accuracy, sensitivity, specificity, and AUC (Area Under the Curve) of Receiver operating characteristic curves (ROC). ResultsOnly 10 features out of 122 were found to be useful in prediction modelling with "Acute kidney injury during hospitalization" feature being the most important one. Blood pH presents a decent discrimination capability especially in predicting "ICU requirement", and "ventilated days", Whereas gender and age are found to be vital in predicting "last status". It was observed that selecting more than 10 features lower the prediction accuracy. The performances of different algorithms depend on number of features and data pre-processing techniques. LSTM with the with balanced data and 10 features performs the best in predicting "last status" as well as "ICU requirement" with an average of 90%, 92%, 86% and 95% accuracy, sensitivity, specificity, and AUC respectively. DNN performs the best in predicting "Ventilation days" with 88% accuracy. For "ICU requirement" which is a binary prediction task, data pre-processing technique does not have any influence in making prediction and performances of different methods are comparable (89%, 98%, 78% and 95% accuracy, sensitivity, specificity, and AUC respectively). However, the number of features selected vary with data pre-processing technique. ConclusionConsidering all the factors and limitations including absence of exact time point of clinical onset, LSTM with carefully selected features can accurately predict "last status" and "ICU requirement" with approximately 90% accuracy, sensitivity, and specificity. DNN performs the best in predicting "Ventilation days". Appropriate machine learning algorithm with carefully selected features and balance data can accurately predict mortality, ICU requirement and ventilation support. Such model can be very useful in emergency and pandemic where prompt and precise decision making is crucial.

Neurocognitive Changes in Patients with Post-COVID Depression (preprint)

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

Caveolin-1 mediates neuroinflammation and cognitive impairment in SARS-CoV-2 infection (preprint)

Leukocyte infiltration of the CNS can contribute to neuroinflammation and cognitive impairment. Brain endothelial cells regulate adhesion, activation, and diapedesis of T cells across the blood-brain barrier (BBB) in inflammatory diseases. The integral membrane protein Caveolin-1 (Cav-1) critically regulates BBB permeability, but its influence on T cell CNS infiltration in respiratory viral infections is unknown. In this study, we sought to determine the role of Cav-1 at the BBB in neuroinflammation in a COVID-19 mouse model. We used mice genetically deficient in Cav-1 to test the role of this protein in T cell infiltration and cognitive impairment. We found that SARS-CoV-2 infection upregulated brain endothelial Cav-1. Moreover, SARS-CoV-2 infection increased brain endothelial cell vascular cell adhesion molecule-1 (VCAM-1) and CD3+ T cell infiltration of the hippocampus, a region important for short term learning and memory. Concordantly, we observed learning and memory deficits. Importantly, genetic deficiency in Cav-1 attenuated brain endothelial VCAM-1 expression and T cell infiltration in the hippocampus of mice with SARS-CoV-2 infection. Moreover, Cav-1 KO mice were protected from the learning and memory deficits caused by SARS-CoV-2 infection. These results indicate the importance of BBB permeability in COVID-19 neuroinflammation and suggest potential therapeutic value of targeting Cav-1 to improve disease outcomes.

Association of Cognitive Deficits with Sociodemographic Characteristics among Adults with Post-COVID Conditions: Findings from the United States Household Pulse Survey (preprint)

BackgroundPeople infected with COVID-19 may continue to experience symptoms for several weeks or even months after acute infection, a condition known as long COVID. Cognitive problems such as memory loss are among the most commonly reported symptoms of long COVID. However, a comprehensive evaluation on the risks of cognitive decline following COVID infection among different sociodemographic groups has not been undertaken at the national level in the United States. MethodsWe conducted a secondary analysis on the datasets from U.S. Census Bureau Household Pulse Survey, encompassing the data collected from June 1, 2022 to December 19, 2022. Based on a cohort of 385,370 individuals aged 18 or older, we employed logistic regression analyses to examine the association between self-reported cognitive deficits and different sociodemographic factors among individuals with long COVID conditions. ResultsAmong individuals aged 18 or older, 44.7% percent of survey respondents report having been diagnosed with COVID in the past, and 29.0% of those with previous COVID infection experienced long COVID symptoms lasting for more than 3 months. We have demonstrated that individuals with long COVID had significantly higher risk of experiencing cognitive deficits compared to those with no history of COVID infection. Furthermore, females, young adults, people with multiple races, or low levels of education attainment are at high risk of cognitive deficits if they experience long COVID. At the state level, the prevalence of cognitive deficits among long COVID patients varied across different US states, with the highest prevalence in West Virginia and Kentucky, and the lowest prevalence in Connecticut and Maryland. The variation could be due to differences in racial composition and education level among long COVID patients in the four states. ConclusionsThe risks of cognitive deficits among adults with post-COVID conditions are substantial. Various sociodemographic groups can have different risks of developing cognitive deficits after experiencing long COVID. Findings of this large-scale study can help identify sociodemographic groups at higher risk of cognitive deficits, and facilitate medical interventions and guide resource allocation to target populations at risk and to prioritize areas with a high rate of cognitive decline.

Characterization of low copy number human angiotensin-convertase 2 (hACE2)-transgenic mice as an improved model of SARS-CoV-2 infection (preprint)

Coronaviridae are significant human pathogens, as evidenced by several outbreaks of severe respiratory infections in the past 20 years and culminating with the COVID-19 pandemic. Mouse models of COVID-19 have included transgenic expression of the main SARS coronavirus entry receptor on human cells, human angiotensin-convertase 2 (hACE2). However, the original hACE2-Tg mouse strain overexpresses many copies of the transgene, leading to neuropathology not representative of human infection. Aiming to improve physiological relevance, we generated two new lines of hACE2-Tg mice using the original transgene construct expressing hACE2 under the control of the keratin 18 promoter (K18-hACE2). We show that relative to the original strain, which expressed 8 copies of the transgene (8-hACE2-Tg), lines 1 and 2 expressed 1 and 2 copies of the transgene (1-hACE-2-Tg and 2-hACE-2-Tg, respectively). Upon intranasal (i.n.) infection with 103 plaque-forming units (pfu) SARS-CoV-2 WA-1/US, 8-hACE2-Tg mice succumbed to infection by d. 7. 2-hACE2-Tg mice exhibited 31% survival, with less viral replication in the lung and brain when compared to 8-hACE2-Tg mice. Furthermore, SARS-CoV-2 infection in 1-hACE2-Tg mice exhibited no mortality and had no detectable virus in the brain, although they did show clear virus replication in the lung. All three mouse strains analyzed showed SARS-CoV-2-related weight loss that tracked with the mortality rates. 1-hACE2-Tg mice mounted detectable primary and memory T effector cell and antibody responses. We conclude that these strains, particularly 1-hACE2-Tg mice, provide improved models to study hACE2-mediated viral infections.

Cognitive Profile of SARS-cov-2 Recovered Patients: A Comprehensive Analysis of Persistent Dysfunctions in Long COVID (preprint)

Long Covid is a term used for patients who have recovered from COVID-19 but exhibit persistent cognitive dysfunction, including mental confusion, difficulties in attention, impairment in executive functions and slow movements, among other common symptoms. A study was conducted with 65 patients who had a positive RT-PCR diagnosis and reported symptoms of cognitive impairment, such as memory loss and attention difficulties after recovery. The patients underwent neuropsychological evaluation and completed questionnaires on cognition, mood, and quality of life. During the cognitive screening, 71% of the patients showed alterations, with deficits in visual memory (69%), language (54%), visuospatial construction (49%), verbal episodic memory (37%), executive functions (36%), attentional abilities (34%), and premorbid intelligence (12%). It is important to highlight the need for treatments and further studies to understand the long-term side effects of this disease.

Long-term symptom profiles after COVID-19 vs other acute respiratory infections: a population-based observational study (COVIDENCE UK) (preprint)

Summary Background: Long COVID is a well recognised, if heterogeneous, entity. Acute respiratory infections (ARIs) due to other pathogens may cause long-term symptoms, but few studies compare post-acute sequelae between SARS-CoV-2 and other ARIs. We aimed to compare symptom profiles between people with previous SARS-CoV-2 infection, people with previous non-COVID-19 ARIs, and contemporaneous controls, and to identify clusters of long-term symptoms. Methods: COVIDENCE UK is a prospective, population-based UK study of ARIs in adults. We analysed data on 16 potential long COVID symptoms and health-related quality of life (HRQoL), reported in January, 2021, by participants unvaccinated against SARS-CoV-2. We classified participants as having previous SARS-CoV-2 infection or previous non-COVID-19 ARI ([≥]4 weeks prior) or no reported ARI. We compared symptoms by infection status using logistic and fractional regression, and identified symptom clusters using latent class analysis (LCA). Findings: We included 10,203 participants (1343 [13.2%] with SARS-CoV-2 infection, 472 [4.6%] with non-COVID-19 ARI). Both types of infection were associated with increased prevalence/severity of most symptoms and decreased HRQoL compared with no infection. Participants with SARS-CoV-2 infection had increased odds of taste/smell problems and hair loss compared with participants with non-COVID-19 ARIs. Separate LCA models identified three symptom severity groups for each infection type. In the most severe groups (including 23% of participants with SARS-CoV-2, and 21% with non-COVID-19 ARI), SARS-CoV-2 infection presented with a higher probability of memory problems, difficulty concentrating, hair loss, and taste/smell problems than non-COVID-19 ARI. Interpretation: Both SARS-CoV-2 and non-COVID-19 ARIs are associated with a wide range of long-term symptoms. Research on post-acute sequelae of ARIs should extend from SARS-CoV-2 to include other pathogens. Funding: Barts Charity.

Long-term outcomes of hospitalized SARS-CoV-2/COVID-19 patients with and without neurological involvement: 3-year follow-up assessment (preprint)

Background and ObjectivesAcute neurological manifestations are a common complication of acute COVID-19 disease. This study investigated the 3-year outcomes of patients with and without significant neurological manifestations during initial COVID-19 hospitalization. MethodsPatients infected by SARS-CoV-2 between March 1 and April 16, 2020 and hospitalized in the Montefiore Health System in the Bronx, an epicenter of the early pandemic, were included. Follow-up data was captured up to January 23, 2023 (3 years post COVID-19). This cohort consisted of 414 COVID-19 patients with significant neurological manifestations and 1199 propensity-matched COVID- 19 patients without neurological manifestations. Primary outcomes were mortality, stroke, heart attack, major adverse cardiovascular events (MACE), reinfection, and hospital readmission post-discharge. Secondary outcomes were clinical neuroimaging findings (hemorrhage, active stroke, prior stroke, mass effect, and microhemorrhage, white-matter changes, microvascular disease, and volume loss). Predictive models were used to identify risk factors of mortality post-discharge. ResultsMore patients in the neurological cohort were discharged to acute rehabilitation (10.54% vs 3.68%, p<0.0001), skilled nursing facilities (30.67% vs 20.78%, p=0.0002) and fewer to home (55.27% vs 70.21%, p<0.0001) compared to the matched controls. Incidence of readmission for any medical reason (65.70% vs 60.72%, p=0.036), stroke (6.28% vs 2.34%, p<0.0001), and MACE (20.53% vs 16.51%, p=0.032) was higher in the neurological cohort post-discharge. Neurological patients were more likely to die post-discharge (58 (14.01%) vs 94 (7.84%), p=0.0001) compared to controls (HR=2.346, 95% CI=(1.586, 3.470), p<0.0001). The major causes of death post-discharge were heart disease (14.47%), sepsis (13.82%), influenza and pneumonia (11.18%), COVID-19 (8.55%) and acute respiratory distress syndrome (7.89%). Factors associated with mortality after leaving the hospital were belonging to the neurological cohort (OR=1.802 (1.237, 2.608), p=0.002), discharge disposition (OR=1.508, 95% CI=(1.276, 1.775), p<0.0001), congestive heart failure (OR=2.281 (1.429, 3.593), p=0.0004), higher COVID-19 severity score (OR=1.177 (1.062, 1.304), p=0.002), and older age (OR=1.027 (1.010, 1.044), p=0.002). There were no group differences in gross radiological findings, except the neurological cohort showed significantly more age-adjusted brain volume loss (p<0.05) compared to controls. DiscussionCOVID-19 patients with neurological manifestations have worse long-term outcomes compared to matched controls. These findings raise awareness and the need for closer monitoring and timely interventions for COVID-19 patients with neurological manifestations.

The functional and structural changes in the hippocampus of COVID-19 patients.

Since the hippocampus is predominantly susceptible to injuries caused by COVID-19, there are increasing data indicating the likelihood of post-infection memory loss and quickening neurodegenerative disorders, such as Alzheimer's disease. This is due to the fact that the hippocampus has imperative functions in spatial and episodic memory as well as learning. COVID-19 activates microglia in the hippocampus and induces a CNS cytokine storm, leading to loss of hippocampal neurogenesis. The functional and structural changes in the hippocampus of COVID-19 patients can explain neuronal degeneration and reduced neurogenesis in the human hippocampus. This will open a window to explain memory and cognitive dysfunctions in "long COVID" through the resultant loss of hippocampal neurogenesis.

Poor immunogenicity upon SARS-CoV-2 mRNA vaccinations in autoimmune SLE patients is associated with pronounced EF-mediated responses and anti-BAFF/Belimumab treatment. (preprint)

Novel mRNA vaccines have resulted in a reduced number of SARS-CoV-2 infections and hospitalizations. Yet, there is a paucity of studies regarding their effectiveness on immunocompromised autoimmune subjects. In this study, we enrolled subjects naive to SARS- CoV-2 infections from two cohorts of healthy donors (HD, n=56) and systemic lupus erythematosus (SLE, n=69). Serological assessments of their circulating antibodies revealed a significant reduction of potency and breadth of neutralization in the SLE group, only partially rescued by a 3rd booster dose. Immunological memory responses in the SLE cohort were characterized by a reduced magnitude of spike-reactive B and T cell responses that were strongly associated with poor seroconversion. Vaccinated SLE subjects were defined by a distinct expansion and persistence of a DN2 spike-reactive memory B cell pool and a contraction of spike-specific memory cTfh cells, contrasting with the sustained germinal center (GC)-driven activity mediated by mRNA vaccination in the healthy population. Among the SLE-associated factors that dampened the vaccine responses, treatment with the monoclonal antibody anti-BAFF/Belimumab (a lupus FDA- approved B cell targeting agent) profoundly affected the vaccine responsiveness by restricting the de novo B cell responses and promoting stronger extra-follicular (EF)-mediated responses that were associated with poor immunogenicity and impaired immunological memory. In summary, this study interrogates antigen-specific responses and characterized the immune cell landscape associated with mRNA vaccination in SLE. The identification of factors associated with reduced vaccine efficacy illustrates the impact of SLE B cell biology on mRNA vaccine responses and provides guidance for the management of boosters and recall vaccinations in SLE patients according to their disease endotype and modality of treatment.