Ethyl 4′-ethenyl-2′-oxo-4-phenyl-2-(3,4,5-trimethoxy­phen­yl)spiro­[pyrrolidine-3,3′-indoline]-5-carboxyl­ate monohydrate

In the title compound, C31H32N2O6·H2O, the pyrrolidine ring adopts an envelope conformation. The ethyl C atoms of the ethoxy­cabonyl group are disordered over two positions with occupancies of ca 0.80 and 0.20. Intra­molecular N—H⋯O hydrogen bonds form S(5) and S(6) ring motifs. Mol­ecules are linked into a three-dimensional framework by O—H⋯O, N—H⋯O and C—H⋯O hydrogen bonds, and by C—H⋯π inter­actions

Observing superluminous supernovae and long gamma ray bursts as potential birthplaces of repeating fast radio bursts

Superluminous supernovae (SLSNe) and long gamma ray bursts (LGRBs) have been proposed as progenitors of repeating Fast Radio Bursts (FRBs). In this scenario, bursts originate from the interaction between a young magnetar and its surrounding supernova remnant (SNR). Such a model could explain the repeating, apparently non-Poissonian nature of FRB121102, which appears to display quiescent and active phases. This bursting behaviour is better explained with a Weibull distribution, which includes parametrisation for clustering. We observed 10 SLSNe/LGRBs for 63 hours, looking for repeating FRBs with the Effelsberg-100 m radio telescope, but have not detected any bursts. We scale the burst rate of FRB121102 to an FRB121102-like source inhabiting each of our observed targets, and compare this rate to our upper burst rate limit on a source by source basis. By adopting a fiducial beaming fraction of 0.6, we obtain 99.99\% and 83.4\% probabilities that at least one, and at least half of our observed sources are beamed towards us respectively. One of our SLSN targets, PTF10hgi, is coincident with a persistent radio source, making it a possible analogue to FRB121102. We performed further observations on this source using the Effelsberg-100~m and Parkes-64~m radio telescopes. Assuming that PTF10hgi contains an FRB121102-like source, the probabilities of not detecting any bursts from a Weibull distribution during our observations are 14\% and 16\% for Effelsberg and Parkes respectively. We conclude by showing that a survey of many short observations increases burst detection probability for a source with Weibull distributed bursting activity.Comment: 11 pages, 5 figure

Long-term follow-up of persons diagnosed with multidrug-resistant TB in Chennai, India, 2013-2020

India has the largest number of multidrug-resistant TB (MDR-TB) cases, defined as Mycobacterium tuberculosis resistant to at least isoniazid (INH) and rifampicin (RIF).1 However, less than half of all persons with MDR-TB in India successfully complete treatment.1 Although initial end-of-treatment outcomes offer a standardised time point to assess the effect of treatment, these tend to underestimate the overall burden of unfavourable long-term outcomes among persons treated for TB.2,3 The long-term outcomes of persons diagnosed with MDR-TB in India, including the proportion with recurrent TB disease or mortality, are unknown. This analysis was conducted under programmatic conditions in a high-burden setting, with no regular check-ups after treatment. The results can be used to show the burden of recurrent illness and death following treatment, and can be used as a benchmark to measure improvement

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Polymorphous adenocarcinoma of the salivary glands : reappraisal and update

Although relatively rare, polymorphous adenocarcinoma (PAC) is likely the second most common malignancy of the minor salivary glands (MiSG). The diagnosis is mainly based on an incisional biopsy. The optimal treatment comprises wide surgical excision, often with adjuvant radiotherapy. In general, PAC has a good prognosis. Previously, PAC was referred to as polymorphous low-grade adenocarcinoma (PLGA), but the new WHO classification of salivary gland tumours has also included under the PAC subheading, the so-called cribriform adenocarcinoma of minor salivary glands (CAMSG). This approach raised controversy, predominantly because of possible differences in clinical behaviour. For example, PLGA (PAC, classical variant) only rarely metastasizes, whereas CAMSG often shows metastases to the neck lymph nodes. Given the controversy, this review reappraises the definition, epidemiology, clinical presentation, diagnostic work-up, genetics, treatment modalities, and prognosis of PAC of the salivary glands with a particular focus on contrasting differences with CAMSG.Peer reviewe

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine