Circulating calprotectin as a potential biomarker of persistent olfactory dysfunctions in Post-COVID-19 patients.

BACKGROUND: This longitudinal prospective study aims to investigate the potential of circulating calprotectin (cCLP) as a biomarker in persistent olfactory dysfunctions following COVID-19 infection. METHODS: Thirty-six patients with persistent hyposmia or anosmia post COVID-19 were enrolled (HT0) and re-evaluated after three months of olfactory training (HT1). Two control groups included 18 subjects without olfactory defects post COVID-19 (CG1) and 18 healthy individuals (CG2). Nasal brushing of the olfactory cleft and blood collection were performed to assess circulating calprotectin levels. RESULTS: Higher calprotectin levels were observed in serum and nasal supernatant of hyposmic patients (HT0) compared to control groups (CG1 and CG2). Post-olfactory training (HT1), olfactory function improved significantly, paralleled by decreased calprotectin levels in serum and nasal samples. Circulating calprotectin holds potential as a biomarker in persistent olfactory dysfunctions after COVID-19. The decrease in calprotectin levels post-olfactory training implies a role in monitoring and evaluating treatment responses. DISCUSSION AND CONCLUSIONS: These findings contribute to the growing literature on potential biomarkers in post-COVID-19 olfactory dysfunctions and underscore the importance of investigating novel biomarkers for personalized patient management. Nevertheless, further studies are needed to validate the application of calprotectin assay in nasal diseases and its correlation with nasal cytology.

Relationship between acute SARS-CoV-2 viral clearance with Long COVID Symptoms: a cohort study.

Introduction: The relationship between SARS-CoV-2 viral dynamics during acute infection and the development of long COVID is largely unknown. Methods: A total of 7361 asymptomatic community-dwelling people enrolled in the Test Us at Home parent study between October 2021 and February 2022. Participants self-collected anterior nasal swabs for SARS-CoV-2 RT-PCR testing every 24-48 hours for 10-14 days, regardless of symptom or infection status. Participants who had no history of COVID-19 at enrollment and who were subsequently found to have ≥1 positive SARS-CoV-2 RT-PCR test during the parent study were recontacted in August 2023 and asked whether they had experienced long COVID, defined as the development of new symptoms lasting 3 months or longer following SARS-CoV-2 infection. Participant's cycle threshold values were converted into viral loads, and slopes of viral clearance were modeled using post-nadir viral loads. Using a log binomial model with the modeled slopes as the exposure, we calculated the relative risk of subsequently developing long COVID with 1-2 symptoms, 3-4 symptoms, or 5+ symptoms, adjusting for age, number of symptoms, and SARS-CoV-2 variant. Adjusted relative risk (aRR) of individual long COVID symptoms based on viral clearance was also calculated. Results: 172 participants were eligible for analyses, and 59 (34.3%) reported experiencing long COVID. The risk of long COVID with 3-4 symptoms and 5+ symptoms increased by 2.44 times (aRR: 2.44; 95% CI: 0.88-6.82) and 4.97 times (aRR: 4.97; 95% CI: 1.90-13.0) per viral load slope-unit increase, respectively. Participants who developed long COVID had significantly longer times from peak viral load to viral clearance during acute disease than those who never developed long COVID (8.65 [95% CI: 8.28-9.01] vs. 10.0 [95% CI: 9.25-10.8]). The slope of viral clearance was significantly positively associated with long COVID symptoms of fatigue (aRR: 2.86; 95% CI: 1.22-6.69), brain fog (aRR: 4.94; 95% CI: 2.21-11.0), shortness of breath (aRR: 5.05; 95% CI: 1.24-20.6), and gastrointestinal symptoms (aRR: 5.46; 95% CI: 1.54-19.3). Discussion: We observed that longer time from peak viral load to viral RNA clearance during acute COVID-19 was associated with an increased risk of developing long COVID. Further, slower clearance rates were associated with greater number of symptoms of long COVID. These findings suggest that early viral-host dynamics are mechanistically important in the subsequent development of long COVID.

Post-COVID Conditions in US Primary Care: A PRIME Registry Comparison of Patients With COVID-19, Influenza-Like Illness, and Wellness Visits.

PURPOSE: COVID-19 is a condition that can lead to other chronic conditions. These conditions are frequently diagnosed in the primary care setting. We used a novel primary care registry to quantify the burden of post-COVID conditions among adult patients with a COVID-19 diagnosis across the United States. METHODS: We used the American Family Cohort, a national primary care registry, to identify study patients. After propensity score matching, we assessed the prevalence of 17 condition categories individually and cumulatively, comparing patients having COVID-19 in 2020-2021 with (1) historical control patients having influenza-like illness in 2018 and (2) contemporaneous control patients seen for wellness or preventive visits in 2020-2021. RESULTS: We identified 28,215 patients with a COVID-19 diagnosis and 235,953 historical control patients with influenza-like illness. The COVID-19 group had higher prevalences of breathing difficulties (4.2% vs 1.9%), type 2 diabetes (12.0% vs 10.2%), fatigue (3.9% vs 2.2%), and sleep disturbances (3.5% vs 2.4%). There were no differences, however, in the postdiagnosis monthly trend in cumulative morbidity between the COVID-19 patients (trend = 0.026; 95% CI, 0.025-0.027) and the patients with influenza-like illness (trend = 0.026; 95% CI, 0.023-0.027). Relative to contemporaneous wellness control patients, COVID-19 patients had higher prevalences of breathing difficulties and type 2 diabetes. CONCLUSIONS: Our findings show a moderate burden of post-COVID conditions in primary care, including breathing difficulties, fatigue, and sleep disturbances. Based on clinical registry data, the prevalence of post-COVID conditions in primary care practices is lower than that reported in subspecialty and hospital settings.

Hypocortisolemic ASIA: a vaccine- and chronic infection-induced syndrome behind the origin of long COVID and myalgic encephalomyelitis.

Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), long COVID (LC) and post-COVID-19 vaccine syndrome show similarities in their pathophysiology and clinical manifestations. These disorders are related to viral or adjuvant persistence, immunological alterations, autoimmune diseases and hormonal imbalances. A developmental model is postulated that involves the interaction between immune hyperactivation, autoimmune hypophysitis or pituitary hypophysitis, and immune depletion. This process might begin with a deficient CD4 T-cell response to viral infections in genetically predisposed individuals (HLA-DRB1), followed by an uncontrolled immune response with CD8 T-cell hyperactivation and elevated antibody production, some of which may be directed against autoantigens, which can trigger autoimmune hypophysitis or direct damage to the pituitary, resulting in decreased production of pituitary hormones, such as ACTH. As the disease progresses, prolonged exposure to viral antigens can lead to exhaustion of the immune system, exacerbating symptoms and pathology. It is suggested that these disorders could be included in the autoimmune/adjuvant-induced inflammatory syndrome (ASIA) because of their similar clinical manifestations and possible relationship to genetic factors, such as polymorphisms in the HLA-DRB1 gene. In addition, it is proposed that treatment with antivirals, corticosteroids/ginseng, antioxidants, and metabolic precursors could improve symptoms by modulating the immune response, pituitary function, inflammation and oxidative stress. Therefore, the purpose of this review is to suggest a possible autoimmune origin against the adenohypophysis and a possible improvement of symptoms after treatment with corticosteroid replacement therapy.

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

Background: Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post-COVID-19 condition), it is crucial to understand their perspectives and preferences regarding the use of AI chatbots. Objective: This study has two main objectives: (1) provide insights into AI chatbot acceptance among people with chronic health conditions, particularly adults older than 55 years and (2) explore the perceptions of using AI chatbots for health self-management and long COVID support. Methods: A web-based survey study was conducted between January and March 2023, specifically targeting individuals with diabetes and other chronic conditions. This particular population was chosen due to their potential awareness and ability to self-manage their condition. The survey aimed to capture data at multiple intervals, taking into consideration the public launch of ChatGPT, which could have potentially impacted public opinions during the project timeline. The survey received 1310 clicks and garnered 900 responses, resulting in a total of 888 usable data points. Results: Although past experience with chatbots (P<.001, 95% CI .110-.302) and online information seeking (P<.001, 95% CI .039-.084) are strong indicators of respondents' future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Less than one-third of the respondents (n=203, 30.1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot's capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots. Conclusions: AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces.

Persistent Vascular Complications in Long COVID: The Role of ACE2 Deactivation, Microclots, and Uniform Fibrosis.

Angiotensin-converting enzyme 2 (ACE2), a key regulator in vasoregulation and the renin-angiotensin system, is hypothesized to be downregulated in patients with COVID-19, leading to a cascade of cardiovascular complications. This deactivation potentially results in increased blood pressure and vessel injury, contributing to the formation and persistence of microclots in the circulation. Herein, we propose a hypothesis regarding the prolonged vascular complications observed in long COVID, focusing on the role of ACE2 deactivation and/or shedding, the persistence of microclots, and the unique pattern of fibrosis induced by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Furthermore, we propose that the distinctive, uniform fibrosis associated with COVID-19, which is challenging to detect through conventional X-ray imaging, exacerbates vascular injury and impairs oxygenation. The persistence of these microclots and the unique fibrosis pattern are suggested as key factors in the extended duration of vascular complications post-COVID-19 infection, regardless of the initial disease severity. Moreover, plasma ACE2 activity has the potential to serve as prognostic or diagnostic biomarkers for monitoring disease severity and managing long COVID symptoms. Elucidating the role of ACE2 deactivation and the consequent events is vital for understanding the long-term effects of COVID-19. The experimental verification of this hypothesis through in vitro studies, clinical longitudinal studies, and advanced imaging techniques could yield significant insights into the pathophysiological mechanisms underlying long COVID, thereby improving the management of patients, particularly those with cardiovascular complications.

Dyspnea and long COVID patients.

Patients with prior COVID-19 infections often develop chronic post-COVID symptoms, such as fatigue and dyspnea. Some patients have residual pulmonary disorders with abnormal pulmonary function tests and/or chest radiographs to explain their dyspnea. However, other patients appear to have dyspnea that is out of proportion to any measurable change in lung function. Some of these patients have abnormal cardiopulmonary exercise testing with definite cardiac or respiratory limits. However, others have normal cardiopulmonary exercise testing based on VO2 measurement but pronounced dyspnea during this testing. These patients often have abnormal respiratory patterns, referred to as dysfunctional breathing, with irregular and variable respiratory rates and/or tidal volumes. Consequently, their control of breathing is impaired, and this may represent residual effects from prior COVID-19 infection involving the central nervous system. Alternatively, patients may have acquired "a memory" of respiratory symptoms during their infection which persists post-infection. These patients should participate in pulmonary rehabilitation and breathing retraining.

Six-month follow-up of multidomain cognitive impairment in non-hospitalized individuals with post-COVID-19 syndrome.

Some people infected with SARS-CoV-2 report persisting symptoms following acute infection. If these persist for over three months, they are classified as post-COVID-19 syndrome (PCS). Although PCS is frequently reported, detailed longitudinal neuropsychological characterization remains scarce. We aimed to describe the trajectory of cognitive and neuropsychiatric PCS symptoms. 42 individuals with persisting cognitive deficits after asymptomatic to mild/moderate acute COVID-19 at study inclusion received neuropsychological assessment at baseline (BL) and follow-up (FU; six months after BL). Assessments included comprehensive testing of five neurocognitive domains, two cognitive screening tests, and questionnaires on depression, anxiety, sleep, fatigue, and health-related quality of life. Results showed high rates of subjective cognitive complaints at BL and FU (95.2% versus 88.1%) without significant change over time. However, objectively measured neurocognitive disorder (NCD) decreased (61.9% versus 42.9%). All cognitive domains were affected, yet most deficits were found in learning and memory, followed by executive functions, complex attention, language, and perceptual motor functions. In individuals with NCD, the first three domains mentioned improved significantly over time, while the last two domains remained unchanged. Cognitive screening tests did not prove valuable in detecting impairment. Neuropsychiatric symptoms remained constant except for quality of life, which improved. This study emphasizes the importance of comprehensive neuropsychological assessment in longitudinal research and provides valuable insights into the trajectory of long-term neuropsychological impairments in PCS. While cognitive performance significantly improved in many domains, neuropsychiatric symptoms remained unchanged.

SARS-CoV-2 S1 subunit produces a protracted priming of the neuroinflammatory, physiological, and behavioral responses to a remote immune challenge: A role for corticosteroids.

Long COVID is a major public health consequence of COVID-19 and is characterized by multiple neurological and neuropsychatric symptoms. SARS-CoV-2 antigens (e.g., spike S1 subunit) are found in the circulation of Long COVID patients, have been detected in post-mortem brain of COVID patients, and exhibit neuroinflammatory properties. Considering recent observations of chronic neuroinflammation in Long COVID patients, the present study explores the idea that antigens derived from SARS-CoV-2 might produce a long-term priming or sensitization of neuroinflammatory processes, thereby potentiating the magnitude and/or duration of the neuroinflammatory response to future inflammatory insults. Rats were administered S1 or vehicle intra-cisterna magna and 7d later challenged with vehicle or LPS. The neuroinflammatory, physiological, and behavioral responses to LPS were measured at various time points post-LPS. We found that prior S1 treatment potentiated many of these responses to LPS suggesting that S1 produces a protracted priming of these processes. Further, S1 produced a protracted reduction in basal brain corticosteroids. Considering the anti-inflammatory properties of corticosteroids, these findings suggest that S1 might disinhibit innate immune processes in brain by reducing anti-inflammatory drive, thereby priming neuroinflammatory processes. Given that hypocortisolism is observed in Long COVID, we propose that similar S1-induced innate immune priming processes might play role in the pathophysiology of Long COVID.

A Comparative Experimental and Computational Study on the Nature of the Pangolin-CoV and COVID-19 Omicron.

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.

Long COVID among Brazilian Adults and Elders 12 Months after Hospital Discharge: A Population-Based Cohort Study.

The persistence of symptoms for more than three months following infection with severe acute respiratory syndrome coronavirus 2 is referred to as "Long COVID". To gain a deeper understanding of the etiology and long-term progression of symptoms, this study aims to analyze the prevalence of Long COVID and its associated factors in a cohort of Brazilian adults and elders, twelve months after hospital discharge. An observational, prospective, and follow-up study was performed with a cohort of adults and older adults diagnosed with COVID-19 in 2020 in the State of Paraná, Brazil. Twelve months after hospital discharge, patients answered a phone questionnaire about the persistence of symptoms after three levels of exposure to COVID-19's acute phase (ambulatory, medical ward, and intensive care unit). According to the characteristics of participants, the prevalence of Long COVID-19 was calculated, and logistic regression analyses were conducted. We analyzed data from 1822 participants (980 adults [≥18-<60 years] and 842 older people [≥60 years]) across three exposure levels. The overall Long COVID prevalence was 64.2%. Long COVID was observed in 646 adults (55%; of which 326 were women) and 523 older people (45%; of which 284 were women). Females had a higher prevalence of long-term symptoms (52%) compared with men. The most common post-COVID-19 conditions in the 12-month follow-up were neurological (49.8%), followed by musculoskeletal (35.1%) and persistent respiratory symptoms (26.5%). Male individuals were less likely to develop Long COVID (aOR = 0.50). Other determinants were also considered risky, such as the presence of comorbidities (aOR = 1.41). Being an adult and having been hospitalized was associated with the development of Long COVID. The risk of developing Long COVID was twice as high for ward patients (aOR = 2.53) and three times as high for ICU patients (aOR = 3.56) when compared to non-hospitalized patients. Presenting clinical manifestations of digestive (aOR = 1.56), endocrine (aOR = 2.14), cutaneous (aOR = 2.51), musculoskeletal (aOR = 2.76) and psychological systems (aOR = 1.66) made adults more likely to develop Long COVID. Long COVID was present in a large proportion of people affected by the SARS-CoV-2 infection. Presence of Long COVID symptoms displayed a dose-response relationship with the level of disease exposure, with a greater prevalence of symptoms associated with the severe form in the acute period.

High throughput spatial immune mapping reveals an innate immune scar in post-COVID-19 brains.

The underlying pathogenesis of neurological sequelae in post-COVID-19 patients remains unclear. Here, we used multidimensional spatial immune phenotyping and machine learning methods on brains from initial COVID-19 survivors to identify the biological correlate associated with previous SARS-CoV-2 challenge. Compared to healthy controls, individuals with post-COVID-19 revealed a high percentage of TMEM119+P2RY12+CD68+Iba1+HLA-DR+CD11c+SCAMP2+ microglia assembled in prototypical cellular nodules. In contrast to acute SARS-CoV-2 cases, the frequency of CD8+ parenchymal T cells was reduced, suggesting an immune shift toward innate immune activation that may contribute to neurological alterations in post-COVID-19 patients.

Clusters of long COVID among patients hospitalized for COVID-19 in New York City.

BACKGROUND: Recent studies have demonstrated that individuals hospitalized due to COVID-19 can be affected by "long-COVID" symptoms for as long as one year after discharge. OBJECTIVES: Our study objective is to identify data-driven clusters of patients using a novel, unsupervised machine learning technique. METHODS: The study uses data from 437 patients hospitalized in New York City between March 3rd and May 15th of 2020. The data used was abstracted from medical records and collected from a follow-up survey for up to one-year post-hospitalization. Hospitalization data included demographics, comorbidities, and in-hospital complications. The survey collected long-COVID symptoms, and information on general health, social isolation, and loneliness. To perform the analysis, we created a graph by projecting the data onto eight principal components (PCs) and running the K-nearest neighbors algorithm. We then used Louvain's algorithm to partition this graph into non-overlapping clusters. RESULTS: The cluster analysis produced four clusters with distinct health and social connectivity patterns. The first cluster (n = 141) consisted of patients with both long-COVID neurological symptoms (74%) and social isolation/loneliness. The second cluster (n = 137) consisted of healthy patients who were also more socially connected and not lonely. The third cluster (n = 96) contained patients with neurological symptoms who were socially connected but lonely, and the fourth cluster (n = 63) consisted entirely of patients who had traumatic COVID hospitalization, were intubated, suffered symptoms, but were socially connected and experienced recovery. CONCLUSION: The cluster analysis identified social isolation and loneliness as important features associated with long-COVID symptoms and recovery after hospitalization. It also confirms that social isolation and loneliness, though connected, are not necessarily the same. Physicians need to be aware of how social characteristics relate to long-COVID and patient's ability to cope with the resulting symptoms.

Epidemiological and clinical characteristics of long COVID-19 among Iranians: A community-based study in southern Iran.

BACKGROUND: The study aimed to evaluate the prevalence and pattern of long COVID-19 (LC) symptoms among individuals who had contracted COVID-19, to calculate the incidence of LC, and to provide insights into risk factors associated with developing LC in this population. METHODS: This population-based cross-sectional survey was conducted in Fars province in 2023. Adult participants with a history of COVID-19 were recruited using a cluster random sampling method, alongside a control group with similar characteristics through the same methodology. Data were collected through in-person interviews using two researcher-developed data collection forms focused on demographic and clinical information. RESULTS: A total of 2010 participants, comprising 1561 (77.7%) and 449 (22.3%) individuals with and without a previous history of COVID-19 were included. Among those with COVID-19 history, the prevalence of experiencing any symptoms was 93.7% (95% CI of 92.3%-94.8%) during the disease acute phase and 36.4% (95% CI of 34.0%-38.8%) after recovery. The incidence of symptoms specifically related to COVID-19, calculated by comparing the symptom rates between participants with and without a history of COVID-19, was found to be 13%. Factors such as older age, previous hospitalization for COVID-19, presence of cardiovascular disease, and use of steroids/chemotherapy were associated with LC symptoms. CONCLUSIONS: Our investigation sheds light on long-term aspects of COVID-19, demonstrating a significant prevalence of LC with diverse manifestations. It also underscores the importance of establishing standardized criteria and control groups in research on LC to address challenges related to heterogeneity and potential overestimation of symptoms.

Respiratory SARS-CoV-2 Infection Causes Skeletal Muscle Atrophy and Long-Lasting Energy Metabolism Suppression.

Muscle fatigue represents the most prevalent symptom of long-term COVID, with elusive pathogenic mechanisms. We performed a longitudinal study to characterize histopathological and transcriptional changes in skeletal muscle in a hamster model of respiratory SARS-CoV-2 infection and compared them with influenza A virus (IAV) and mock infections. Histopathological and bulk RNA sequencing analyses of leg muscles derived from infected animals at days 3, 30, and 60 post-infection showed no direct viral invasion but myofiber atrophy in the SARS-CoV-2 group, which was accompanied by persistent downregulation of the genes related to myofibers, ribosomal proteins, fatty acid ß-oxidation, tricarboxylic acid cycle, and mitochondrial oxidative phosphorylation complexes. While both SARS-CoV-2 and IAV infections induced acute and transient type I and II interferon responses in muscle, only the SARS-CoV-2 infection upregulated TNF-α/NF-κB but not IL-6 signaling in muscle. Treatment of C2C12 myotubes, a skeletal muscle cell line, with combined IFN-γ and TNF-α but not with IFN-γ or TNF-α alone markedly impaired mitochondrial function. We conclude that a respiratory SARS-CoV-2 infection can cause myofiber atrophy and persistent energy metabolism suppression without direct viral invasion. The effects may be induced by the combined systemic interferon and TNF-α responses at the acute phase and may contribute to post-COVID-19 persistent muscle fatigue.

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Interplay between Comorbidities and Long COVID: Challenges and Multidisciplinary Approaches.

Long COVID, a name often given to the persistent symptoms following acute SARS-CoV-2 infection, poses a multifaceted challenge for health. This review explores the intrinsic relationship between comorbidities and autoimmune responses in shaping the trajectory of long COVID. Autoantibodies have emerged as significant players in COVID-19 pathophysiology, with implications for disease severity and progression. Studies show immune dysregulation persisting months after infection, marked by activated innate immune cells and high cytokine levels. The presence of autoantibodies against various autoantigens suggests their potential as comorbid factors in long COVID. Additionally, the formation of immune complexes may lead to severe disease progression, highlighting the urgency for early detection and intervention. Furthermore, long COVID is highly linked to cardiovascular complications and neurological symptoms, posing challenges in diagnosis and management. Multidisciplinary approaches, including vaccination, tailored rehabilitation, and pharmacological interventions, are used for mitigating long COVID's burden. However, numerous challenges persist, from evolving diagnostic criteria to addressing the psychosocial impact and predicting disease outcomes. Leveraging AI-based applications holds promise in enhancing patient management and improving our understanding of long COVID. As research continues to unfold, unravelling the complexities of long COVID remains paramount for effective intervention and patient care.

Comparison of the symptom networks of long-COVID and chronic fatigue syndrome: From modularity to connectionism.

The objective was to compare the symptom networks of long-COVID and chronic fatigue syndrome (CFS) in conjunction with other theoretically relevant diagnoses in order to provide insight into the etiology of medically unexplained symptoms (MUS). This was a cross-sectional comparison of questionnaire items between six groups identified by clinical diagnosis. All participants completed a 65-item psychological and somatic symptom questionnaire (GSQ065). Diagnostically labelled groups were long-COVID (N = 107), CFS (N = 254), irritable bowel syndrome (IBS, N = 369), fibromyalgia (N = 1,127), severe asthma (N = 100) and healthy group (N = 207). The 22 symptoms that best discriminated between the six groups were selected for network analysis. Connectivity, fragmentation and number of symptom clusters (statistically related symptoms) were assessed. Compared to long-COVID, the symptom networks of CFS, IBS and fibromyalgia had significantly lower connectivity, greater fragmentation and more symptom clusters. The number of clusters varied between 9 for CFS and 3 for severe asthma, and the content of clusters varied across all groups. Of the 33 symptom clusters identified over the six groups 30 clusters were unique. Although the symptom networks of long-COVID and CFS differ, the variation of cluster content across the six groups is inconsistent with a modular causal structure but consistent with a connectionist (network, parallel distributed processing) biological basis of MUS. A connectionist structure would explain why symptoms overlap and merge between different functional somatic syndromes, the failure to discover a biological diagnostic test and how psychological and behavioral interventions are therapeutic.

Quantifying the impact of post-acute sequelae of coronavirus on the cardiopulmonary endurance of athletes.

Post-acute sequelae of Coronavirus (PASC), or Long COVID, has emerged as a critical health concern. The clinical manifestations of PASC have been described, but studies have not quantified the cardiopulmonary effects. The goal of this study was to quantify PASC cardiopulmonary changes among endurance athletes. Endurance athletes were recruited via social media; 45 met inclusion criteria, 32 had PASC and 13 were asymptomatic at 3 months (control). Comprehensive interviews were conducted to assess: cardiopulmonary symptoms at 3 months; quantitative and qualitative changes in cardiovascular endurance; exercise hours per week at baseline and 3 months; and Modified Oslo, Dyspnea, and EQ-5D-5L scales. All collected data was based on self-reported symptoms. Wilcoxon rank sum compared PASC with control to distinguish the effects of PASC vs effects of COVID infection/lockdown. PASC subjects were more likely to be female (Table). The most common 3-month symptoms in PASC were fatigue and shortness of breath. Based on self-reported data, subjects endorsed a median decrease of 27% in cardiopulmonary endurance levels compared with 0% in controls (p = 0.0019). PASC subjects exercised less hours and had worse self-reported health as compared with controls. PASC subjects also had significantly worse Modified Oslo, Dyspnea, and EQ-5D-5L scores. Of the 32 PASC patients, 10 (31%) reported a complete inability to engage in any cardiovascular endurance exercise at 3 months. PASC leads to a significant, quantifiable decrease in cardiopulmonary health and endurance.