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To characterise subphenotypes of self-reported symptoms and outcomes (SRSOs) in postacute sequelae of COVID-19 (PASC).
Prospective, observational cohort study of subjects with PASC.
Academic tertiary centre from five clinical referral sources.
Adults with COVID-19 ≥20 days before enrolment and presence of any new self-reported symptoms following COVID-19.
We collected data on clinical variables and SRSOs via structured telephone interviews and performed standardised assessments with validated clinical numerical scales to capture psychological symptoms, neurocognitive functioning and cardiopulmonary function. We collected saliva and stool samples for quantification of SARS-CoV-2 RNA via quantitative PCR.
Description of PASC SRSOs burden and duration, derivation of distinct PASC subphenotypes via latent class analysis (LCA) and relationship with viral load.
We analysed baseline data for 214 individuals with a study visit at a median of 197.5 days after COVID-19 diagnosis. Participants reported ever having a median of 9/16 symptoms (IQR 6–11) after acute COVID-19, with muscle-aches, dyspnoea and headache being the most common. Fatigue, cognitive impairment and dyspnoea were experienced for a longer time. Participants had a lower burden of active symptoms (median 3 (1–6)) than those ever experienced (p
We identified three distinct PASC subphenotypes. We highlight that although most symptoms progressively resolve, specific PASC subpopulations are impacted by either high burden of constitutional symptoms or persistent olfactory/gustatory dysfunction, requiring prospective identification and targeted preventive or therapeutic interventions.
by Muntequa Imtiaz Siraji, Ahnaf Akif Rahman, Mirza Muntasir Nishat, Md Abdullah Al Mamun, Fahim Faisal, Lamim Ibtisam Khalid, Ashik Ahmed
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.by Mohamad Mobasher Amini, Soheila Mirzaei, Ahmad Heidari
Effective management of fungicide application programs requires monitoring the profile of resistant populations of Botrytis cinerea, given its high-risk nature. This research aimed to examine the sensitivity of 200 B. cinerea isolates collected from different plant species and regions across Iran towards thiophanate-methyl and carbendazim fungicides. To distinguish between susceptible and resistant isolates, the discriminatory dose assay was employed, followed by the selection of representative isolates from each group for EC50 analysis. To identify potential modifications in codon 198 of the β-tubulin gene in B. cinerea resistant isolates, the researchers employed the PCR-RFLP diagnostic method. More than two-thirds of the isolates exhibited a varying degree of resistance to MBC fungicides, even in farms where the application of these fungicides had not taken place in recent years. After treatment with the BsaI enzyme, the PCR product of sensitive isolates displayed two bands measuring 98 and 371 bp, while only one band of 469 bp was identified in resistant isolates. The study also evaluated whether resistance to fungicides could affect the pathogenicity and mycelial growth of the isolates. The findings showed no significant difference between the resistant and sensitive groups in terms of these factors, indicating that resistance does not come at a cost to the pathogen’s fitness. Considering the high incidence of resistance and the absence of negative consequences on fitness, it is recommended to exercise caution in the employment of benzimidazole fungicides as part of B. cinerea management strategies.
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