Major Bleeding Events in Hospitalized COVID-19 Patients: A Retrospective Observational Study.

Thromboprophylaxis/anticoagulation treatment is often required in hospitalized COVID-19 patients. We aimed to estimate the prevalence of major bleeding events in hospitalized COVID-19 patients. This was a retrospective observational study including all COVID-19 hospitalized patients ≥18 years of age at one reference center in northern Italy. The crude prevalence (between February 2020-2022) of major bleeding events was estimated as the number of major bleeding episodes divided by patients at risk. Uni- and multivariable Cox models were built to assess factors potentially associated with major bleeding events. Twenty-nine (0.98%) out of 2,945 COVID-19 patients experienced a major bleeding event [prevalence of 0.55% (95%CI 0.37-0.79)], of which five were fatal. Patients who experienced a major bleeding event were older [78 years (72-84 IQR) vs. 67 years (55-78 IQR), p-value < 0.001] and more frequently exposed to anti-aggregating therapy (44.8% vs. 20.0%, p-value 0.002) when compared to those who did not. In the multivariable Cox model, age [per 1 year more AHR 1.05 (CI95% 1.02-1.09)] was independently associated with an increased risk of major bleeding events. A strict monitoring of older hospitalized COVID-19 patients is warranted due to the risk of major bleeding events.

DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets.

Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds ∼ 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 72% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.

Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering.

Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository.

Mismanagement of SARS-CoV-2 Infection Pre Hospitalisation during the Omicron Era: Antibiotics and Steroids Instead of Early Antivirals.

The aim of this study was to assess the prevalence of inappropriate treatment among hospitalised patients affected by SARS-CoV-2 infection before hospital admission during the Omicron era. This single-centre, retrospective observational study included all the patients hospitalised because of SARS-CoV-2 infection during three periods characterised by the Italian prevalence of an Omicron variant of concern: (1) January-May 2022 (BA.1-BA.2), (2) June-October 2022 (BA.5), and (3) November 2022-March 2023 (BQ.1-XBB). Inappropriate treatment was defined as pre-hospitalisation exposure to antibiotics and/or steroids in the absence of a documented bacterial infection or the need for steroid treatment of an underlying medical condition. A total of 931 subjects were hospitalised: 394 in period 1, 334 in period 2, and 203 in period 3. Of the 157 patients undergoing inappropriate treatment (16.9%), 142 (15.3%) received antibiotics and 52 (5.6%) steroids. The proportion of inappropriately treated patients significantly decreased over time, from 23.1% in period 1 to 11.7% in period 2 and 13.3% in period 3 (p < 0.001), and there was a parallel decrease in antibiotic (p < 0.001) and steroid treatment (p < 0.013). Only 13 subjects (1.4%) received early pre-hospitalisation treatment for SARS-CoV-2. A significant proportion of hospitalised COVID-19 patients were exposed to inappropriate treatment before hospital admission.

Design and evaluation of an incoherent feed-forward loop for an arsenic biosensor based on standard iGEM parts.

The diversity and flexibility of life offers a wide variety of molecules and systems useful for biosensing. A biosensor device should be robust, specific and reliable. Inorganic arsenic is a highly toxic water contaminant with worldwide distribution that poses a threat to public health. With the goal of developing an arsenic biosensor, we designed an incoherent feed-forward loop (I-FFL) genetic circuit to correlate its output pulse with the input signal in a relatively time-independent manner. The system was conceived exclusively based on the available BioBricks in the iGEM Registry of Standard Biological Parts. The expected behavior in silico was achieved; upon arsenic addition, the system generates a short-delayed reporter protein pulse that is dose dependent to the contaminant levels. This work is an example of the power and variety of the iGEM Registry of Standard Biological Parts, which can be reused in different sophisticated system designs like I-FFLs. Besides the scientific results, one of the main impacts of this synthetic biology project is the influence it had on team's members training and career choices which are summarized at the end of this article.