Long Covid Syndrome and Role of Autonomic Nervous System
Bibliographic record
Abstract
Long Covid is a complex syndrome characterised by symptoms affecting several systems that persist for weeks and months after Covid-19 infection. A better understanding of this complex syndrome is important for the development of diagnostic and therapeutic strategies. In a meta-analysis of 257,348 COVID-19 patients, some of the most common long COVID symptoms at three to six months included fatigue (32%), dyspnoea (25%), and concentration difficulty (22%), reflecting the multisystemic nature of long COVID. In addition to these symptoms, there is a specific group of neurological symptoms and sequelae of long COVID. Some of the most common neurological symptoms persist for longer than a year after the initial diagnosis [1, 2]. Among the most common neurological symptoms these include fatigue (37%), brain fog (32%), memory problems (28%), attention disorders (22%), myalgia (28%), anosmia (12%), dysgeusia (10%) and headaches (15%) [3]. Overall, these symptoms can lead to significant dysfunction and disability. Recent evidence shows a functional deficit in the autonomic nervous system and responsible for symptomatic manifestations of long-term COVID-19. The dysfunction of the autonomic nervous system can partly explain the symptoms of long-COVID in different organs and systems of the body. Considering the different functions of the autonomic nervous system, and the distribution of peripheral autonomic nerves, it is logical to think that a dysfunctionality of the system can cause a variety of symptoms [4]. Among 106 post-COVID patients with high COMPASS-31 and modified Toronto Neuropathy (mTORONTO) symptom scores, 38 (36%) had neuropathic or autonomic complaints or both, associated with fatigue and headache [5]. An interesting study showed that among individuals with long COVID symptoms, orthostatic hypotension was the most frequently reported symptom [6]. Some evidence also suggests that the enteric nervous system may be a pathway of SARS-CoV-2 access into the body [7]. The underlying autonomic nervous system dysfunction may be an autoimmune/inflammatory explanation. An interesting study investigated the levels of several regulatory autoantibodies targeting G-protein coupled receptors (GPCRs) in 80 patients with long-term syndrome, with those of 38 healthy participants from anti-IgG seronegative controlSARS-CoV-2 and 40 post-symptomatic COVID112 individuals [8]. In general, patients with long-COVID syndrome had reduced levels of regulatory autoantibodies, and the strongest associations between levels of regulatory autoantibodies and disease outcomes concerned autoantibodies against β2 adrenoceptors, stabilin-1 and α2A adrenoceptors, and levels of regulatory antibodies were correlated with symptom severity in persons with long-COVID syndrome, particularly with vasomotor symptoms and chronic fatigue. In particular, increased severity of fatigue and vasomotor symptoms was positively associated with β2 adrenoceptor autoantibody levels in patients with long COVID syndrome [9]. On the basis of these considerations, it is logical to think that immunomodulation, and the administration of pharmacological agents acting on the sympathetic/parasympathetic system, could be functional and therapeutic in long COVID-19 syndrome, however, evidence in this direction is still scarce. In conclusion, the long-term COVID syndrome has yet to be fully understood and described, the pathophysiological and molecular mechanisms have not yet been completely described. Some recent evidence suggests that the dysfunction of the autonomic nervous system may be one of the concomitant causes, but in this direction there is still too little evidence for a complete demonstration. It would also be interesting to understand when the autonomic nervous system dysfunction occurs, so that it acts therapeutically effectively. Promising treatments already being studied clinically include drugs and biological agents targeting autoimmunity, drugs acting on adrenoreceptors, sympathetic ganglion block and transcranial electrical stimulation [10]. The author has nothing to report. The author has nothing to report. The author has nothing to report. The author has nothing to report. The author declares no conflicts of interest. Data sharing not applicable to this article as no datasets were generated or analysed during the current study. The author declare that the opinions expressed are of a personal nature and do not in any way commit the responsibility of the Administrations to which they belong.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".