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Post-acute infection syndromes may develop after acute viral disease 1 . Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 2 – 4 . However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers. Individuals with long COVID show marked biological changes in cortisol and immune factors relative to convalescent populations.

Introduction: Post Coronavirus Disease 2019 (COVID-19) Syndrome also known as long COVID-19 would affect survivors of various patients. At present, the evidence for predicting a poor prognosis of COVID-19 remains insufficient. This study aims to explore potential predictors of post-COVID-19 syndrome. Methodology: A systematic review process and meta-analysis method are applied to identify the predictors. Systematic searches were conducted without language restrictions from December 1, 2019, to February 28, 2022, on PubMed, Embase, Google Scholar, Web of Science, and Cochrane Library using specific keywords relevant to our targets. The Newcastle Ottawa Scale observational research tool was used to assess study quality and the R (4.1.1) package meta was used for statistical analysis. Results: Our meta-analysis of 14 studies showed that females (OR = 1.42, 95% CI: 1.19-1.70), the severity of patients (OR = 2.43, 95% CI: 1.26-4.68), comorbidity (OR = 2.08, 95% CI: 1.29-3.35), dyspnea (OR = 2.02, 95% CI: 1.34-3.04) associated with a higher risk of post-COVID-19 syndrome. Conclusions: Our study showed that females, the severity of COVID-19, comorbidity, and dyspnea were associated with a higher risk of post-COVID-19 syndrome. More attention should be paid to these factors to prevent and treat post-COVID-19 syndrome.

Background Post-COVID- 19 syndrome (PCS) significantly impacts the quality of life of survivors. There is, however, a lack of a standardized approach to PCS diagnosis and management. Our bidirectional cohort study aimed to estimate PCS incidence, identify risk factors and biomarkers, and classify clinical phenotypes for enhanced management to improve patient outcomes. Methods A bidirectional prospective cohort study was conducted at five medical sites in Hatyai district in Songkhla Province, Thailand. Participants were randomly selected from among the survivors of COVID-19 aged≥18 years between May 15, 2022, and January 31, 2023. The selected participants underwent a scheduled outpatient visit for symptom and health assessments 12 to 16 weeks after the acute onset of infection, during which PCS was diagnosed and blood samples were collected for hematological, inflammatory, and serological tests. PCS was defined according to the World Health Organization criteria. Univariate and multiple logistic regression analyses were used to identify biomarkers associated with PCS. Moreover, three clustering methods (agglomerative hierarchical, divisive hierarchical, and K-means clustering) were applied, and internal validation metrics were used to determine clustering and similarities in phenotypes. Findings A total of 300 survivors were enrolled in the study, 47% of whom developed PCS according to the World Health Organization (WHO) definition. In the sampled cohort, 66.3% were females, and 79.4% of them developed PCS (as compared to 54.7% of males, p -value <0.001). Comorbidities were present in 19% (57/300) of all patients, with 11% (18/159) in the group without PCS and 27.7% (39/141) in the group with PCS. The incidence of PCS varied depending on the criteria used and reached 13% when a quality of life indicator was added to the WHO definition. Common PCS symptoms were hair loss (22%) and fatigue (21%), while mental health symptoms were less frequent (insomnia 3%, depression 3%, anxiety 2%). According to our univariate analysis, we found significantly lower hematocrit and IgG levels and greater ALP levels in PCS patients than in patients who did not develop PCS ( p -value < 0.05). According to our multivariable analysis, adjusted ALP levels remained a significant predictor of PCS (OR 1.02, p -value= 0.005). Clustering analysis revealed four groups characterized by severe clinical symptoms and mental health concerns (Cluster 1, 4%), moderate physical symptoms with predominant mental health issues (Cluster 2, 9%), moderate mental health issues with predominant physical symptoms (Cluster 3, 14%), and mild to no PCS (Cluster 4, 77%). The quality of life and ALP levels varied across the clusters. Interpretation This study challenges the prevailing diagnostic criteria for PCS, emphasizing the need for a holistic approach that considers quality of life. The identification of ALP as a biomarker associated with PCS suggests that its monitoring could be used for early detection of the onset of PCS. Cluster analysis revealed four distinct clinical phenotypes characterized by different clinical symptoms and mental health concerns that 'exhibited varying impacts on quality of life. This finding suggested that accounting for the reduced quality of life in the definition of PCS could enhance its diagnosis and management and that moving toward personalized interventions could both improve patient outcomes and help reduce medicalization and optimally target the available resources. Funding The research publication received funding support from Medical Council of Thailand (Police General Dr. Jongjate Aojanepong Foundation), Hatyai Hospital Charity and Wellcome Trust.

Introduction: The aim of this study was to evaluate the course of the coronavirus disease 2019 (COVID-19) symptoms and identify the prognostic factors in patients who continued to have symptoms for ≥ 3 months. The occurrence of symptoms was compared based on gender. Methodology: This was a prospective cohort study performed at a tertiary chest hospital in Turkey. The clinical features of patients with COVID-19, health anxiety scores, and the course of symptoms at admission and follow-up were compared based on gender. The primary outcome was the distribution and rate of persistent symptoms at the third month; and the secondary outcomes were the number and distribution of symptoms by gender, and the relationship between symptoms and health anxiety. Results: A total of 110 patients (mean age of 45 years) were followed. Of these, 53 (48%) patients were females. Forty-seven (43%) patients, including 17 (32%) females, were hospitalized. The number of highly symptomatic patients with mild disease severity (level 2) was significantly higher among females than males (p = 0.008). Eighty-one (74%) patients followed had at least 1 symptom persisting at the end of the third month. During the 3-month follow-up, the total number of symptoms and health anxiety scale scores were significantly higher in females (p = 0.04 and p = 0.004, respectively), especially in females aged < 50 years (p = 0.005). Conclusions: Thus, persistent symptoms remained at a high rate at 3 months post-COVID; and gender and neuro-psychiatric factors should be discussed in the etiology of long COVID.

Background After a COVID‐19 infection, some patients experience long‐term consequences known as Post‐Covid Syndrome, which often includes cognitive impairment. We investigated the congruence between subjectively experienced and objectively measured cognitive deficits after a COVID‐19 infection in an unselected, successively admitted cohort of 46 patients reporting subjective cognitive complaints (SCC). Methods We employed a comprehensive neuropsychological test battery to assess objective cognitive impairment across various cognitive domains. Three different cut‐off criteria were applied, commonly used in the literature to define objective neurocognitive disorder (NCD). Results We observed a notably low congruence between SCC and NCD in Post‐Covid Syndrome, regardless of the cut‐off criterion. Depending on the cognitive domain, only 4% to maximally 40% of the SCC could be objectified. Conclusions One possible explanation for this discrepancy could be the high rate of depressive symptoms observed in the group of patients studied, which may negatively influence the perception of one's cognitive abilities. These findings emphasize the need for careful evaluation of SCC in Post‐Covid Syndrome and suggest that treating depressive symptoms may also alleviate some of the perceived cognitive deficits.

Introduction Low physical activity and poor dietary quality can negatively influence Covid‐19 recovery and increase the risk and duration of post‐Covid‐19 condition (PCC). This proof‐of‐concept nested intervention study aimed to evaluate the feasibility of a digital personalised combined lifestyle intervention (CLI) in patients with PCC using a mixed‐methods design, assessing compliance, experiences and perceived effectiveness. Methods A nested intervention study, incorporating motivational interviewing aiming to enhance physical activity and dietary quality, was conducted within a multicentre prospective cohort study including 95 post‐Covid‐19 patients (aged 40–60) between May 2021 and September 2022. Patients in the intervention and control groups were followed at ±3–6 and ±12–15 months post Covid‐19. The intervention consisted of nine monthly individual counselling sessions (30 min), two interactive‐group sessions (60 min), and three educative webinars (45 min). Additionally, a nutritional supplement (NS; Remune, Smartfish, Oslo, Norway) high in omega‐3 fatty acids, vitamin D and protein was provided to facilitate recovery. After the intervention, a process evaluation was conducted, comprising an evaluation questionnaire and semi‐structured in‐depth interviews. Results The intervention‐to‐treat group consisted of 47 patients (age 54.7 ± 6.0 years; 40% males; BMI 30.6 ± 5.8 kg/m2) of whom 74% had ≥ 8 individual sessions via telephone (66%) or video call (34%). Over half of the group (55%) attended the educative webinars, while attendance was lower in the interactive‐group sessions, with 32% attending one session and 15% two sessions. The process evaluation indicated that patients were satisfied with the digital coaching and the frequency, duration and content of the sessions. Half of the patients reported perceived improvements in physical activity levels and dietary quality throughout the intervention, with the majority also reporting sustainment of these lifestyle changes post‐intervention. Conclusion A digital personalised CLI was well‐received among patients with PCC regarding compliance, experiences and perceived effectiveness. These findings will guide the development and implementation of tailored interventions to enhance overall well‐being among patients with PCC. Patient or Public Contribution Patients' experiences regarding the design and implementation of the study were retrieved. Although participants were not directly involved in the initial design of the study, their experiences were actively incorporated into the refinement and implementation of the study procedures, thereby ensuring meaningful patient involvement.

To investigate relevant biomarkers that might aid in the diagnosis and monitoring of long COVID (LC), an analysis of IFN-α, IFN-β, ISG15, and ISG56 transcripts was performed by Real-Time PCR among people of working age who had been infected with SARS-CoV-2 one year prior to the study [LC and non-long COVID (NLC)]. Despite no differences in the transcript levels of IFN-α, IFN-β, ISG15, and ISG56 between LC and NLC, higher IFN-β mRNA levels were observed among LC compared to NLC individuals who were hospitalized for more than 10 days during acute SARS-CoV-2 infection. Moreover, previously SARS-CoV-2 infected participants that did not require respiratory support and developed LC exhibited higher levels of IFN-α and IFN-β compared to NLC with the same clinical characteristics. These results highlight that SARS-CoV-2 infection leads to changes in peripheral innate immune pathways, which could have implications for the development of LC.

Patients with post‐acute sequelae of COVID‐19 (PASC) present with a decrease in physical fitness. The aim of this paper is to reveal the relations between the remaining symptoms, blood volume distribution, exercise tolerance, static and dynamic lung volumes, and overall functioning. Patients with PASC were retrospectively studied. Pulmonary function tests (PFT), 6‐minute walk test (6MWT), and cardiopulmonary exercise test were performed. Chest CT was taken and quantified. Patients were divided into two groups: minor functional limitations (MFL) and severe functional limitations (SFL) based on the completed Post‐COVID‐19 Functional Status scale (PCFS). Twenty one patients (3 M; 18 FM), mean age 44 (IQR 21) were studied. Eighteen completed the PCFS (8 MFL; 10 SFL). VO2max was suboptimal in both groups (not significant). 6MWT was significantly higher in MFL‐group (p = 0.043). Subjects with SFL, had significant lower TLC (p = 0.029). The MFL‐group had more air trapping (p = 0.036). Throughout the sample, air trapping correlated significantly with residual volume (RV) in L (p < 0.001). An increase in air trapping was related to an increase in BV5 (p < 0.001). Mean BV5 was 65% (IQR 5%). BV5% in patients with PASC was higher than in patients with acute COVID‐19 infection. This increase in BV5% in patients with PASC is thought to be driven by the air trapping in the lobes. This study reveals that symptoms are more driven by occlusion of the small airways. Patients with more physical complaints have significantly lower TLC. All subjects encounter physical limitations as indicated by suboptimal VO2max. Treatment should focus on opening or re‐opening of small airways by recruiting alveoli.

Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episodes of COVID-19 are significantly more likely to be hospitalized in the following months. The pathophysiological mechanisms for delayed complications are still poorly understood, with a dissociation seen between ongoing symptoms and objective measures of cardiopulmonary health. COVID-19 is anticipated to alter the long-term trajectory of many chronic cardiovascular and pulmonary diseases, which are common among those at risk of severe disease. This study aims to use a single, integrated device-MouthLab, which measures 10 vital health parameters in 60 seconds-and a cloud-based proprietary analytics engine to develop and validate the Aidar Decompensation Index (AIDI), to predict decompensation in health among patients who previously had severe COVID-19. Overall, 200 participants will be enrolled. Inclusion criteria are patients in the US Department of Veterans Affairs health care system; \"severe\" COVID-19 infection during the acute phase, defined as requiring hospitalization, within 3-6 months before enrollment; aged ≥18 years; and having 1 of 6 prespecified chronic conditions. All participants will be instructed to use the MouthLab device to capture daily physiological data and complete monthly symptom surveys. Structured data collection tables will be developed to extract the clinical characteristics of those who experience decompensation events (DEs). The performance of the AIDI will depend on the magnitude of difference in physiological signals between those experiencing DEs and those who do not, as well as the time until a DE (ie, the closer to the event, the easier the prediction). Information about demographics, symptoms (Medical Research Council Dyspnea Scale and Post-COVID-19 Functional Status Scale), comorbidities, and other clinical characteristics will be tagged and added to the biomarker data. The resultant predicted probability of decompensation will be translated into the AIDI, where there will be a linear relationship between the risk score and the AIDI. To improve prediction accuracy, data may be stratified based on biological sex, race, ethnicity, or underlying clinical characteristics into subgroups to determine if there are differences in performance and detection lead times. Using appropriate algorithmic techniques, the study expects the model to have a sensitivity of >80% and a positive predicted value of >70%. Recruitment began in January 2023, and at the time of manuscript submission, 204 patients have been enrolled. Publication of the complete results and data from the study is expected in 2025. The focus on identifying predictor variables using a combination of biosensor-derived physiological features should enable the capture of heterogeneous characteristics of complications related to post-COVID-19 condition across diverse populations. ClinicalTrials.gov NCT05220306; https://clinicaltrials.gov/study/NCT05220306.