Many patients who had coronavirus disease 2019 (COVID-19) had at least one symptom that persisted after recovery from the acute phase. Our purpose was to review the empirical evidence on symptom prevalence, complications, and management of patients with long COVID. We systematically reviewed the literature on the clinical manifestations of long COVID-19, defined by the persistence of symptoms beyond the acute phase of infection. Bibliographic searches in PubMed and Google Scholar were conducted to retrieve relevant studies on confirmed patients with long COVID that were published prior to August 30, 2021. The most common persistent symptoms were fatigue, cough, dyspnea, chest pains, chest tightness, joint pain, muscle pain, loss of taste or smell, hair loss, sleep difficulties, anxiety, and depression. Some of the less common persistent symptoms were skin rash, decreased appetite, sweating, inability to concentrate, and memory lapses. In addition to these general symptoms, some patients experienced dysfunctions of specific organs, mainly the lungs, heart, kidneys, and nervous system. A comprehensive understanding of the persistent clinical manifestations of COVID-19 can improve and facilitate patient management and referrals. Prompt rehabilitative care and targeted interventions of these patients may improve their recovery from physical, immune, and mental health symptoms.

Growing evidence shows that neuropsychiatric disorders, such as depression, are linked with gut microbiome through the gut–brain axis. Cistanches Herba is well known for the treatment of “kidney-yang” deficiency in traditional Chinese medicine (TCM), and has been used for treatment of neurodegenerative diseases in recent years. In this study, chronic unpredictable stress (CUS)-induced depression model was established to explore the impact of Cistanche tubulosa extract (CTE) on behavioral tests, monoamine neurotransmitters and neurotrophic factors in hippocampus and colon, gut microbiota composition, and short-chain fatty acids (SCFAs) production. Moreover, correlation analysis was used to evaluate the functional relationship between altered gut microbiota, changed neurotransmitters and neurotrophins in hippocampus and colon, and disturbed concentration of SCFAs. CTE significantly improved depression-like behaviors in rats under CUS. Brain level of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) expression in CUS rats were restored by CTE. The relative abundance of gut microbiota and the concentrations of acetate and hexanoic acid could also be modulated by CTE treatment. We further showed that the application of CTE in CUS rats led to strong correlation among disrupted gut microbiota composition, hippocampus neurotransmitter levels, and production of neuroactive metabolite SCFAs. Altogether, these results identify CTE as a potential treatment for depressive symptoms by restoring homeostasis of gut microbiota for microbiota–gut–brain axis disorders, opening new avenues in the field of neuropsychopharmacology.

To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attempted to directly approximate a pre-trained model by low-rank decomposition; however, small approximation errors in parameters can ripple over a large prediction loss. As a result, performance usually drops significantly and a sophisticated effort on fine-tuning is required to recover accuracy. Apparently, it is not optimal to separate low-rank approximation from training. Unlike previous works, this paper integrates low rank approximation and regularization into the training process. We propose Trained Rank Pruning (TRP), which alternates between low rank approximation and training. TRP maintains the capacity of the original network while imposing low-rank constraints during training. A nuclear regularization optimized by stochastic sub-gradient descent is utilized to further promote low rank in TRP. The TRP trained network inherently has a low-rank structure, and is approximated with negligible performance loss, thus eliminating the fine-tuning process after low rank decomposition. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet, outperforming previous compression methods using low rank approximation.

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Whole-genome sequencing (WGS) has shown tremendous potential in rapid diagnosis of drug-resistant tuberculosis (TB). In the current study, we performed WGS on drug-resistant M. tuberculosis isolates obtained from Shanghai (n=137) and Russia (n=78). We aimed to characterize the underlying and high-frequency novel drug resistance-conferring mutations, and also create valuable combinations of resistance mutations with high predictive sensitivity to predict multidrug-and extensively drug-resistant tuberculosis (MDR/XDR-TB) phenotype using a bootstrap method. Most strains belonged to L2.2, L4.2, L4.4, L4.5 and L4.8 lineages. We found that WGS could predict 82.07% of phenotypically drug-resistant domestic strains. The prediction sensitivity for rifampicin (RIF), isoniazid (INH), ethambutol (EMB), streptomycin (STR), ofloxacin (OFL), amikacin (AMK) and capreomycin (CAP) was 79.71%, 86.30%, 76.47%, 88.37%, 83.33%, 70.00% and 70.00%, respectively. The mutation combination with the highest sensitivity for MDR prediction was rpoB S450L+rpoB H445A/P + katG S315T+inhA I21T+inhA S94A, with a sensitivity of 92.17% [0.8615, 0.9646], and the mutation combination with highest sensitivity for XDR prediction was rpoB S450L + katG S315T + gyrA D94G + rrs A1401G, with a sensitivity of 92.86% [0.8158, 0.9796]. The molecular information presented here will be of particular value for the rapid clinical detection of MDR-and XDR-TB isolates through laboratory diagnosis.