Abstract

Background:

Long COVID-19 causes neurophysiological, cardiopulmonary, and musculoskeletal issues. Increased neuropeptides and cytokines lead to neuroinflammation, resulting in neurocognitive impairments, fatigue, depression, anxiety, and severe cognitive deficits. The Neurokinin 1 receptor (NK1R) is a cellular receptor for the neuropeptide Substance P, and its dysregulation links to neuropsychological issues despite antipsychotic use.

Objectives:

In the present study, neuropsychological sequelae related to long COVID-19 were screened and the expression of related neuropeptides and cytokines was evaluated. Additionally, potential drugs have been evaluated computationally to reduce neuroinflammation in long COVID-19.

Methods:

After informed consent, subjects were screened by a medical physician for long COVID-19 in an outdoor patient clinic. Various biological scales were used to assess and categorize the severity of neuropsychological symptoms related to long COVID-19. After that, peripheral blood samples were collected from subjects using ELISA and RT-qPCR. Nine drugs were selected and subjected to virtual screening to identify potential drug antagonists for NK1R. The key drug-like properties, safety profile, pharmacokinetic analysis, and biological activity of the identified hits were assessed.

Results:

In this study the mean age of 90 patients (60% males and 40% females), was 33 ± 5 years in the symptomatic group and 31 ± 6 years in the asymptomatic long COVID-19 group for <40 years age-group. Whereas, the mean age of >40 years age-group was 58 ± 10 years in the symptomatic group and 54 ± 11 years in the asymptomatic long COVID-19 group. The minimum persistence of duration of long COVID-19 related symptoms in the <30 weeks group was observed to be 19 ± 6 weeks, while 44 ± 6 weeks in the >30 weeks group of symptomatic long COVID-19. A total of 48% patients had fatigue, 47% complained about headache, 28% had anxiety, 25% faced depression, 20% had psychosocial distress, 20% felt discomfort, and 13% had cognitive impairment. A total of 10% had reported dizziness sequelae among long COVID-19 survivors. Experimental data showed upregulation of IL-6, IL-10, and SP in both symptomatic and asymptomatic individuals compared with controls (p < 0.001). Drug screening analyses revealed aprepitant (−9.3 kcal/mol) and N- acetyl- L- tryptophan (−8.7 kcal/mol) stable interactions with NK1R and maintaining molecular dynamics stability (RMSD: 1.5–2.2 Å; RMSF 0.8–1.4 Å; Rg approximately 21.6 Å). These compounds also demonstrated favorable blood-brain barrier permeability and pharmacokinetic profiles, suggesting their potential as therapeutic antagonists for treating prolonged COVID-related neuroinflammation.

Conclusion:

IL-6, IL-10, and SP are found to be deregulated in long COVID-19 leading to neurophysiological sequelae. To overcome neuropsychological sequelae, binding of SP to NK1R can be hindered using aprepitant and N-Acetyl-L tryptophan which has been evaluated computationally and may require further in vivo and in vitro studies for validation.

1 Introduction

Long COVID-19 related neuropsychological sequelae have become a prominent concern globally, impacting a significant number of survivors months to years after initial infection (Huff et al., 2025). As of the current date, over 164 million confirmed cases of COVID-19 have been reported, and at least 3.4 million individuals have succumbed to the disease (WHO, 2024). COVID-19 infects all cells of the body, including brain cells and causes neuro-inflammatory symptoms even after a 12-week recovery from the COVID-19 onset (Al-Aly et al., 2024; Ali et al., 2023). Long COVID-19 syndrome refers to symptoms persisting beyond 12 weeks after SARS-CoV-2 infection and cannot be explained by any alternative diagnosis (WHO, 2024). Neuropsychological sequelae are characterized by cognitive impairments such as memory loss, depression, anxiety and emotional and behavioral dysfunction, as well as neuropsychiatric symptoms including anxiety, depression, and fatigue. These persistent symptoms are widely reported in both hospitalized and non-hospitalized individuals (Almeria et al., 2024; Efstathiou et al., 2022; Panagea et al., 2025).

Cognitive deficits of long COVID-19 manifest prominently in domains of executive function, attention, information processing speed and memory, often described under the umbrella of “brain fog” (Koch et al., 2026; Panagea et al., 2025). Longitudinal studies indicate that while some patients experience partial cognitive recovery, a substantial proportion continue to suffer persistent impairments that significantly affect daily functioning (Aretouli et al., 2025). Neuropsychological evaluations reveal heterogeneity in impairment profiles, suggesting multifactorial etiologies influenced by patient age, severity of acute illness and comorbidities (Pettemeridou et al., 2025). Significantly, subjective cognitive complaints sometimes diverge from objective test results, suggesting a contributory role for psychological distress and fatigue. Neuropsychological sequelae are associated with neuroinflammation that persists due to dysregulation of neuropeptide and cytokine expression (Davis et al., 2023). Mechanistically, neuroinflammation, microvascular injury and disruption of the blood-brain barrier are implicated in the cognitive and psychiatric symptoms of long COVID-19 (Efstathiou et al., 2022). Systemic hyperinflammation triggered by SARS-CoV-2 induces sustained neuroimmune activation, leading to neuronal dysfunction (Talkington et al., 2025). Neuroimaging studies confirmed structural and functional abnormalities in brain regions responsible for cognition and mood regulation in affected patients. Additionally, microvascular damage, which impairs cerebral perfusion, is recognized as a crucial factor in exacerbating cognitive decline (Talkington et al., 2025; Zhao et al., 2024).

COVID-19 infections have a tendency to invade the nervous system, leading to significant anoxic brain injury, disrupted metabolic processes and an imbalance between free radicals and antioxidants (Zhang et al., 2022). Studies have found that SARS-CoV-2 infection can cause oxidative stress and neuroinflammation by disrupting iron metabolism, leading to an excess production of reactive oxygen species (Sarkar et al., 2023). This oxidative stress damages neuronal cells, causes cerebral ischemia and disrupts metabolic function (Bowen et al., 2023). A noxious stimulus triggered the release of a neuropeptide at the nerve ending which might have triggered neuroinflammation in the brain and exacerbated neurophysiological sequelae (Henri et al., 2022). Substance P is a neuropeptide, and upon its release, it combines with its cellular receptor neurokinin 1 (NK1R) and is involved in systemic inflammation and causes systemic complications, especially cardio-respiratory, musculoskeletal, and respiratory issues among COVID-19 survivors (Mehboob et al., 2024). The NK1R belongs to the tachykinin receptor family, has a seven-transmembrane domain and is involved in pain transmission, the stress response and inflammation.

Sarkar et al. (2023) emphasized that viral invasion and the cytokine storm impair antioxidant defenses, resulting in substantial oxidative damage to the brain. Persistent elevation of plasma cytokines, especially interleukin-4 (IL-4) and IL-6, has been linked to inflammatory changes (Hunter and Jones, 2015; Mazza et al., 2020). These neuroimmune signaling alterations contribute to a wide range of mental health and neurological issues in long COVID-19 syndrome. Many recovered individuals report high rates of mental health problems, including post-traumatic stress disorder (28%), depression (31%), anxiety (42%), and insomnia (40%). Other neurological symptoms include headaches, fatigue, cognitive impairment ("brain fog"), dizziness, memory deficits, confusion, dysautonomia, and attention difficulties (Sun et al., 2021). Addressing these complex neurological and psychiatric aftereffects of COVID-19 can help healthcare providers improve quality of life and promote functional recovery, ultimately reducing the long-term burden on individuals and healthcare systems. Biomarkers have been critical in elucidating the neurobiological alterations underlying long COVID-19 sequelae. The upregulation of interleukin-10 (IL-10) and reductions in neurotrophic factors, such as nerve growth factor (NGF), are consistently observed in patients with persistent symptoms, indicating ongoing neuroinflammation and impaired neural repair (Moen et al., 2025). NICE guidelines recommend integrating biomarker evaluation with clinical and neuropsychological assessment to enhance diagnostic precision and to enable tailored treatment and rehabilitation plans (Venkatesan, 2021). Furthermore, blood-based biomarkers combined with advanced neuroimaging hold promise for early detection of individuals at risk (WHO, 2025; Guillen et al., 2024). Inflammatory (IL-06, IL-1β, TNFα) and anti-inflammatory cytokines are also produced and contribute to systemic inflammation (Schultheiß et al., 2022). WHO defines Long COVID-19 Condition (PCC) as symptoms lasting at least 2 months, starting within 3 months of acute COVID-19 illness, and requiring systematic monitoring and intervention strategies (WHO, 2025). The National Institute for Health and Care Excellence (NICE) guidelines emphasize a multidisciplinary approach that encompasses physical, cognitive and psychological assessments to manage these complex sequelae effectively (NICE, 2025). In clinical practice, comprehensive neuropsychological rehabilitation programs focused on cognitive training and compensatory strategies and have shown benefit in alleviating symptoms and improving functional outcomes (Lana et al., 2024). Emotional and psychological support addressing anxiety and depression is also paramount (García-Molina et al., 2024).

Neuropsychological findings help understanding structural and functional brain changes, guiding targeted neurorehabilitation to improve outcomes (Català et al., 2024). The goal of this study is to identify neuropsychological effects related to long COVID-19 and their biomarkers. Additionally, to explore and evaluate potential drugs by in silico analysis to reduce neuroinflammation in long COVID-19. The present study emphasized the importance of early detection and systematic assessment of neuropsychological sequelae of long COVID for timely intervention (WHO, 2025). Understanding the dysregulation of neuropeptides and cytokines, prevalence and spectrum of neurological symptoms related to long COVID-19 helps clinicians to tailor the care and systematic plans to mitigate neuropsychological effects. Incorporating biomarker analysis reveals neuroinflammatory mechanisms, such as SP and IL-10, offering insights into the pathophysiology of long COVID-19 and enabling personalized treatments. In silico drug analysis focusing on therapeutics targeting the NK explores interventions to reduce SP-mediated neuroinflammation, a key factor in cognitive dysfunction.

2 Materials and methods2.1 Study design and participants

After approval from the Ethical Committee of the University of Lahore (REC-UOL-90-I-04-2023), the present study was initiated in outpatient medical clinics of public primary care hospitals in Lahore. The primary aim was to identify and evaluate individuals in the convalescent phase of COVID-19 experiencing both physical and psychosocial issues over the past weeks. Inclusion criteria included a prior diagnosis of COVID-19 confirmed by a nasopharyngeal swab, a positive RT-PCR test for COVID-19, and after 2 weeks, absence of symptoms and negative nasopharyngeal tests (tested 24 h apart) and later developing symptoms after 12 weeks of recovery from acute COVID-19 that other diagnoses could not explain. Initially, patients who had received care during the acute phase and agreed to follow-up at medical outpatient clinics were included. Later, patients meeting the inclusion criteria were recruited from territory evaluations. The patients were divided into three groups: the first group included individuals with a confirmed history of positive RT-PCR for COVID-19 who developed neuropsychological sequelae after 12 weeks of recovery; the second group comprised individuals with a positive RT-PCR history for COVID-19 who did not develop neuropsychological sequelae after 12 weeks; and the third group consisted of healthy controls with no history of COVID-19 infection, no clinical manifestations and no positive RT-PCR test. Patients younger than 18 years, with prior neuropsychological issues, psychiatric disorders, or pregnant females were excluded from the study.

2.2 Sample size calculation and sampling technique

A sample size of 90 subjects was calculated to provide 80% power, 6% absolute precision, and an expected prevalence of IL-6 in the population of 72%. A purposive sampling technique was used and each group included 30 patients.

2.3 Data collection

With informed consent, medical history, demographic data, COVID vaccination type and dose, duration of recovery from an acute long COVID-19 infection and neurological and physical examinations for neuropsychological symptoms at 12 weeks after COVID-19 infection of included subjects were recorded and evaluated. The Yorkshire Rehabilitation Scale (YRS) was used to measure long COVID-19 symptoms (Sivan et al., 2022). The scale was translated from English to Urdu for better understanding (Google Translator). It was rated on a scale from 0 to 11, where 0 indicates no symptoms and 10 indicates severe symptoms such as breathlessness, fatigue, and cognitive impairment. Furthermore, neuropsychological evaluations were conducted by clinical psychologists and assessed using standardized scales.

2.4 Measures

Initially, the Fatigue Assessment Scale (FS) and the Brief Fatigue Inventory (BI) were used to assess fatigue presence and severity. In the structured screening process, fatigue was characterized 10 scale items with cut-off score ≥22 indicate the presence of fatigue (Zhang et al., 2015; Shahid et al., 2011). Fatigue severity was classified based on the mean score: < 1 indicating no fatigue; 1 to < 4 indicating mild fatigue; 4 to < 7 indicating moderate fatigue; and a cut-off score ≥1 indicating the presence of fatigue (Shahid et al., 2011). Secondly, the Migraine Disability Assessment Tool was used to evaluate headache impact, with scores ranging from 0 to ≥21 and a cut-off score ≥6 showed severe headaches (Zandifar et al., 2014; Lipton et al., 2001). Third, the coronavirus anxiety scale was utilized to assess anxiety dysfunction, employing a 5-items scale with each item rated 5 points (0–4). The maximum resulting score was 22 and cut-off score ≥9 indicated greater levels of anxiety (Lee, 2020). Fourth, the depression module of the Patient Health Questionnaire (PHQs-9) was employed to assess depressive symptoms with threshold score ≥10 (Kroenke et al., 2001; Ford et al., 2020). Based on the PHQs-9, patients' depressive symptoms were categorized as minimal (1–4), mild (5–9), moderate (10–14), moderately severe (15–19), and severe (20–27). Fifth, the Montreal Cognitive Assessment (MCA) was employed to assess cognitive dysfunction, characterized by a MCA cut-off score ≥26 (Freitas et al., 2013). The Kessler Psychological Distress Scale (K10) was used to assess psychological distress related to long COVID-19, consisting of 10 items. A cut-off score of ≤ 22 indicated significant psychological distress (Kessler et al., 2003; Andrews and Slade, 2001). The Pittsburgh Sleep Quality Index (PSI) was used to evaluate sleep quality and ranging 0–21 with score of 5 serving as the cuff-off threshold for identifying sleep disturbances (Backhaus et al., 2002; Dautzenberg et al., 2020).

2.5 Experimental analysis2.5.1 Cytokine assay by ELISA

A trained phlebotomist collected a peripheral blood sample from each participant to measure cytokines. IL-6, IL-1β, TNFα, IL-10, and SP were quantified using an ELISA kit (BT Lab, China) according to the manufacturer's instructions.

2.5.2 Real-time quantitative PCR

A peripheral blood sample was centrifuged to obtain serum and total RNA was extracted from the serum using Trizol reagents (Invitrogen, CA, USA). cDNA was synthesized with 500 ng of RNA in a 20 μl reaction mixture using the Thermo High Fidelity Kit (Thermo Scientific, Waltham, MA, USA) according to the manufacturer's instructions. For IL-6, IL-1β, TNFα, IL-10, and TAC1, quantitative real-time PCR (RT-qPCR) was performed using Universal SYBR Green Master Mix (Roche, Basel, Switzerland) on a Light-Cycler 96 system (Roche, Basel, Switzerland). For all remaining samples, the same procedure was carried out.

The primer sequences used in this study are provided in Supplementary File 1. A gradient PCR was performed prior RT-qPCR to validate amplified PCR products of target genes through Gel electrophoresis. A suitable band was cut and cleaned by QIAquick PCR Purification Kit (Qiagen, Germany) and sent to Macrogen Inc. (Seoul, South Korea) for Sanger sequencing. The sequence results of each product of related genes are provided in also Supplementary File 1. Obtained sequences were checked for their homology using nucleotide BLAST (http://www.ncbi.nlm.nih.gov/BLAST) to confirm desired gene identity and verify sequence accuracy.

Subsequently, RT-qPCR was performed using GAPDH as the housekeeping gene and target genes expression were normalized to GAPDH. Relative mRNA levels of target genes were calculated using the 2−ΔΔCt method. All assays included positive and negative controls and were performed in duplicates to ensure accuracy.

2.6 Data analysis and statistics

Independent variables (gender, age, duration of infection, biomarkers) were expressed as mean ± standard deviation (Aretouli et al., 2025). The distributions of dependent variables such as neuropsychological sequelae were analyzed and their percentages were displayed in graphs. Normality was assessed before statistical testing. Data that followed a normal distribution were analyzed using Student's t-test. Non-normally distributed data were evaluated with the Kruskal-Wallis rank test and the Mann-Whitney U-test. Two-way ANOVA was used for comparisons involving more than two groups. All analyses were conducted using GraphPad Prism and SPSS version 26 (SPSS Inc., Chicago, IL, United States). A p-value of less than 0.05 was considered statistically significant.

2.7 Drug target identification

Potential therapeutic drugs targeting NK1R; the cellular receptor of SP, were screened as mentioned in following steps.

2.7.1 Selection target

The 3D structure of Neurokinin 1 receptor (NK1R, PDB ID: 6HLO), was retrieved from the protein database (www.drugbank.ca). It was determined by X-ray diffraction at 2.96 Å. The selected structure of NK1R with its bound ligand was used and ligand was subsequently removed using AutoDock-Vina. The pH was set to 7.0, hydrogen ions added and the SDF file converted to PDBQT for docking.

2.7.2 Target preparation and active site prediction

AutoDock-Vina software was used to prepare the NK1R protein for computational analysis by first removing all water molecules and detaching ligands from the active sites (Trott and Olson, 2010). This was followed by energy minimization to achieve a more stable conformation of the NK1R structure. To account for electrostatic properties and accurate interactions, the NK1R protein was protonated by adjusting hydrogen atom positions based on the amino acid side chains. The MOE was adapted for active-site identification at NK1R (PDB ID: 6HLO) and the site-finder predicted a binding pocket and its interactions. During active site determination, dummy atoms were placed on the active site residue at the alpha center of the protein sphere (https://www.chemcomp.com/en/Products.html). Selected ligands were also prepared. The active site score helped identify potential therapeutic sites based on hydrogen-bond donors/acceptors and ligand-binding capacity.

2.7.3 Preparation of ligand molecules

To build a library of NK1R antagonists, nine compounds were chosen to test their potential to reverse long COVID-19-related neurological effects. Discovery Studio Visualizer BIOVIA (https://mybiosoftware.com/biovia-discovery-studio-visualizer-4-5-molecular-visualization.html) was used to generate 2D conformations of selected ligands (lanepitant, fosaprepitant, modafinil, indacaterol, alosetron, aprepitant, N-acetyl-L-tryptophan, netupitant, and selegiline) that were retrieved as SMILES (simplified molecular input line entry system) from PubChem databases and saved in MOL format. To achieve a stable configuration, the energy of each ligand was minimized and partial charges were added.

2.7.4 Grid generation for docking

The receptor grid was generated using PyRx with the NK1R binding sites (https://sourceforge.net/projects/pyrx/). After selecting the active site, the van der Waals radii of receptor atoms were scaled to 1, with a cutoff of 0.25. A site-specific grid was created for the selected residue of ligand length ≤ 20 Å.

2.7.5 Ligand-protein interactions

Ligands were subjected to molecular docking using Auto-Dock Vina to identify high-affinity binders (Eberhardt et al., 2021). The binding affinity (kcal/mol) was evaluated based on ligand-protein interactions. Each ligand was docked with NK1R and the interactions were assessed for affinity, stability and specificity utilizing PyMOL (Rosignoli and Paiardini, 2022). The five most promising compounds advanced to subsequent stages of analysis.

2.7.6 Visualization of molecular interactions

Ligand interactions were visualized using PyMOL to examine key interactions, including hydrogen bonds, hydrophobic interactions, and electrostatic forces. Each ligand-receptor complex was analyzed for essential interactions, including hydrogen bonds, hydrophobic contacts, and electrostatic forces.

2.7.7 Molecular dynamics simulations

Molecular dynamics simulations were conducted to assess the interactions of five ligands (Indacaterol, alosetron, netupitant, aprepitant, and N-Acetyl-L tryptophan) with the NK1R protein. Partial atomic charges for ligands were calculated using the Antechamber module in AMBER 20 (Arantes et al., 2021). Subsequently, the LEaP module was employed to add missing hydrogen atoms, neutralize the system, solvate the complexes and generate the required parameter and coordinate files for molecular dynamics simulations. The protein components were modeled with the ff14SB force field, while the ligands were parameterized with the generalized AMBER force field (GAFF). Protonated protein structures were neutralized with suitable counterions (Cl− or Na+) and each complex was solvated in an octahedral TIP3P water box with a 10.0 Å buffer. These solvated systems were saved as PDB files and all necessary topology and coordinate files were created. Before running the MD, the minimization involved three steps to fix steric clashes. First, ions and solvated water were optimized, then pocket residues, including backbone amino acids and finally, the whole system was relaxed to relax protein-ligand complexes. Each step used 2,500 steepest descent steps followed by 5,000 conjugate gradient steps. After minimization, the system was heated gradually from 0 to 300 K, then equilibrated at 300 K using Langevin dynamics with a collision frequency of 1 ps−1 and a force constant of 10 kcal mol−1 Å−2. The MD phase was conducted under the NPT ensemble at 300 K and 1 atm for 50 ns. The stability and interactions of docked complexes of prioritized inhibitors with their particular targets were assessed by simulating the actual condition for the protein ligand complex in the presence of solvent, membrane, and ions.

The AMBER software suite (https://www.ambermd.org/) was used to predict the optimal binding poses through molecular docking. Each selected docked complex (drug with NK1R) underwent energy minimization, equilibration and molecular dynamics simulations during the production phase. The resulting trajectories were analyzed for root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF) and radius of gyration (Rg). These analyses, spanning 50 ns, aimed to evaluate the complex's structural stability and flexibility (Yoo et al., 2025). All MD simulation analyses included a detailed examination of hydrogen bonding throughout the simulation and graphical plots were also generated using the AMBER suite. These analyses enhance understanding of the dynamic stability, conformational flexibility, and binding behavior of each study complex. Visualization was performed using Discovery BIOVIA software (Hollingsworth and Dror, 2018).

2.7.8 Pharmacokinetic profiling (ADMET)

After performing docking and interaction analysis of the top five ligands, the aim was to identify key features responsible for binding affinity and interaction (Yi et al., 2024). The drug-likeness properties of ligands were assessed using Swiss ADME and ProTox-III (Banerjee et al., 2024; Daina et al., 2017). This web platform evaluates compounds based on descriptors like molecular weight, logP, rotatable bonds, hydrogen bond acceptors and donors, rule violations, and TPSA. To determine drug-likeness, various filters were applied, including Lipinski's, Veber's, Egan's, and Muegge's rules. Lipinski's criteria: no more than 10 hydrogen bond acceptors, 5 donors and a molecular weight below 500 Da. Veber's rule: TPSA ≤ 140 Å2 and ≤ 10 rotatable bonds. Egan's filter: TPSA ≤ 140 Å2 and logP between −1 and 6. Muegge's rule: molecular weight 200–600 Da, logP −2 to 5, 1–15 rotatable bonds, TPSA < 150 Å2. ProTox-III was used to evaluate toxicity with reference drug. The top five with the highest binding affinity were selected and subjected to ADMET and molecular dynamics simulations for 50 ns. At last, drugs were selected that passed ADMET analysis and met appropriate RMSD, RMSF, Gry, and hydrogen-bonding criteria.

3 Results

A total of 90 patients were included in present study with 60% being male and 40% females. The mean age in the < 40 years group was 33 ± 5 years for the symptomatic group and 31 ± 6 years for the asymptomatic long COVID-19 group. The mean age in the symptomatic group was 58 ± 10 years and 54 ± 11 years in the asymptomatic long COVID-19 group. The minimum duration of symptoms related to long COVID-19 in the < 30 weeks group was 19 ± 6 weeks and 44 ± 6 weeks in the >30 weeks group of symptomatic long COVID-19—see the other details in Table 1.

S. NoDemographic dataDescription1.Total Subjects (males and females)n = 90(a)Symptomaticn = 30(b)Asymptomaticn = 30(c)Controlsn = 302.Gender: (males and females)(a)Symptomatic(18, 12)(b)Asymptomatic(18, 12)(c)Controls(18, 12)3.Age (Years)(a)Symptomatic48 ± 16(b)Asymptomatic37 ± 13(c)Controls33 ± 114.Age groups (years) (<40, ≥40)(a)Symptomatic33 ± 05, 58 ± 10(b)Asymptomatic31 ± 06, 54 ± 11(c)Controls25 ± 02, 46 ± 045.Duration (<30 & ≥30) weeks(a)Symptomatic19 ± 06 & 44 ± 06(b)Asymptomatic18 ± 08 & 42 ± 066.COVID-19 vaccination90(100%)7.Number of doses received 1Nil 267% 333%8.Type of vaccine received%Sinovac62Pfizer06Sinopharm329.Symptoms%Fatigue48Headache47Anxiety28Depression25Psychosocial distress20Discomfort20Cognitive impairment13Dizziness10

Demographic information analysis in long COVID-19 subjects.

Long COVID-19 sequelae. These symptoms lasted 44 ± 6 weeks. A total of 48% had fatigue, 47% headache, 28% had anxiety, 25% had depression, 20% had psychosocial distress, 20% had discomfort, and 13% had cognitive impairment. A total of 10% had reported dizziness sequelae among long COVID-19 survivors.

3.1 Expression analysis

The ELISA analysis measured biomarker levels (pg/mL) by gender, age and long COVID-19 duration, with no significant differences except for slightly higher levels in males, those over 40 and shorter duration cases as shown in Table 2. The analysis also quantified IL-6, TNFα, IL-1β, IL-10, and SP in long COVID-19 neuropsychological issues, which showed elevated levels in symptomatic individuals compared with asymptomatic individuals and controls (Figure 1). IL-6 and SP were notably elevated across symptoms like headache, anxiety, cognitive, and psychological symptoms (p < 0.01). These suggest that IL-6 and SP are linked to complication severity, with IL-6 higher in asymptomatic cases and SP higher in symptomatic cases. The biomarkers IL-6, TNFα, IL-10, and SP may relate to these symptoms while IL-10 was less elevated, indicating a limited anti-inflammatory response. These findings point to ongoing immune activation and sensory signaling pathways in long COVID-19 neuropsychological issues.

BiomarkersStudy GroupsGender Mean ±SDFpAge Mean ±SDFpDuration Mean ±SDFpMaleFemale<40≥40<30≥30IL-6 (pg/mL)Symptomatic3.76 ± 0.643.65 ± 0.620.070.793.56 ± 0.553.84 ± 0.672.940.093.72 ± 0.703.72 ± 0.560.370.55Asymptomatic2.45 ± 0.522.90 ± 0.072.36 ± 0.483.0 ± 0.172.47 ± 0.502.78 ± 0.58Control0.67 ± 0.210.49 ± 0.100.63 ± 0.150.65 ± 0.10--IL-1β (pg/mL)Symptomatic0.35 ± 0.050.42 ± 0.150.030.950.35 ± 0.050.39 ± 0.130.910.310.41 ± 0.130.34 ± 0.032.090.16Asymptomatic0.36 ± 0.030.36 ± 0.180.37 ± 0.030.35 ± 0.130.37 ± 0.070.33 ± 0.01Control0.16 ± 0.130.07 ± 0.040.11 ± 0.070.19 ± 0.18--TNFα (pg/mL)Symptomatic0.31 ± 0.10.37 ± 0.081.670.210.35 ± 0.090.32 ± 0.100.020.890.32 ± 0.100.35 ± 0.090.030.86Asymptomatic0.31 ± 0.10.41 ± 0.100.33 ± 0.110.34 ± 0.150.34 ± 0.110.29 ± 0.15Control0.05 ± 0.020.03 ± 0.010.04 ± 0.010.06 ± 0.03--IL-10 (pg/mL)Symptomatic0.89 ± 0.240.88 ± 0.190.030.850.89 ± 0.240.89 ± 0.210.690.410.87 ± 0.190.91 ± 0.262.470.13Asymptomatic0.74 ± 0.170.76 ± 0.100.71 ± 0.160.82 ± 0.140.70 ± 0.150.94 ± 0.04Control0.12 ± 0.050.07 ± 0.040.09 ± 0.050.13 ± 0.06--