Background Previous studies assessing the prevalence of COVID-19 sequelae in adults and children were performed in the absence of an agreed definition. We investigated prevalence of post-COVID-19 condition (PCC) (WHO definition), at 6- and 12-months follow-up, amongst previously hospitalised adults and children and assessed risk factors. Methods Prospective cohort study of children and adults with confirmed COVID-19 in Moscow, hospitalised between April and August, 2020. Two follow-up telephone interviews, using the International Severe Acute Respiratory and Emerging Infection Consortium survey, were performed at 6 and 12 months after discharge. Results One thousand thirteen of 2509 (40%) of adults and 360 of 849 (42%) of children discharged participated in both the 6- and 12-month follow-ups. PCC prevalence was 50% (95% CI 47–53) in adults and 20% (95% CI 16–24) in children at 6 months, with decline to 34% (95% CI 31–37) and 11% (95% CI 8–14), respectively, at 12 months. In adults, female sex was associated with PCC at 6- and 12-month follow-up (OR 2.04, 95% CI 1.57 to 2.65) and (OR 2.04, 1.54 to 2.69), respectively. Pre-existing hypertension (OR 1.42, 1.04 to 1.94) was associated with post-COVID-19 condition at 12 months. In children, neurological comorbidities were associated with PCC both at 6 months (OR 4.38, 1.36 to 15.67) and 12 months (OR 8.96, 2.55 to 34.82) while allergic respiratory diseases were associated at 12 months (OR 2.66, 1.04 to 6.47). Conclusions Although prevalence of PCC declined one year after discharge, one in three adults and one in ten children experienced ongoing sequelae. In adults, females and persons with pre-existing hypertension, and in children, persons with neurological comorbidities or allergic respiratory diseases are at higher risk of PCC.

Activity of β-galactosidase at pH 6 is a classic maker of senescence in cellular biology. Cellular senescence, a state of highly stable cell cycle arrest, is often compared to apoptosis as an intrinsic tumor suppression mechanism. It is also thought that SA-β-gal is crucial in malignant cell transformation. High levels of senescence-associated β-galactosidase (SA-β-gal) can be found in cancer and benign lesions of various localizations making the enzyme a highly promising diagnostic marker for visualization of tumor margins and metastases. These findings facilitate the research of therapy induced senescence as a promising therapeutic strategy. In this review, we address the need to collect and analyze the bulk of clinical and biological data on SA-β-gal mechanisms of action to support wider implementation of this enzyme in medical diagnostics. The review will be of interest to pathologists, biologists, and biotechnologists investigating cellular senescence for purposes of regenerative medicine and oncology.

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Control over endogenous reparative mechanisms is the future of regenerative medicine. The rabbit ear defect is a rare model which allows the observation of the epimorphic regeneration of elastic cartilage. However, the mechanisms of phenotypical restoration of this highly differentiated tissue have not been studied. We modelled circular ear defects of different sizes (4, 6, and 8 mm in diameter) in 12 laboratory rabbits, and observed them during 30, 60, 90, and 120 day periods. Excised tissues were processed and analyzed by standard histological methods and special histochemical reactions for senescence associated-β-galactosidase and lectin markers. We demonstrated that larger defects caused significant elevation of senescence associated-β-galactosidase in chondrocytes. The fullness of epimorphic regeneration of elastic cartilage depended on the activation of cellular senescence and synthesis of elastic fibers. Further investigation into the role of cells with senescence-associated secretory phenotype in damaged tissues can present new targets for controlled tissue regeneration.

In this paper, we present a new Python library called mPyPl, which is intended to simplify complex data processing tasks using functional approach. This library defines operations on lazy data streams of named dictionaries represented as generators (so-called multi-field datastreams), and allows enriching those data streams with more 'fields' in the process of data preparation and feature extraction. Thus, most data preparation tasks can be expressed in the form of neat linear 'pipeline', similar in syntax to UNIX pipes, or |> functional composition operator in F#.We define basic operations on multi-field data streams, which resemble classical monadic operations, and show similarity of the proposed approach to monads in functional programming. We also show how the library was used in complex deep learning tasks of event detection in video, and discuss different evaluation strategies that allow for different compromises in terms of memory and performance.mPyPl library with some documentation and intro videos are available at http://github.com/shwars/ mPyPl and http://shwars.github.io/mPyPl.