Tracheostomy in the COVID-19 era: global and multidisciplinary guidance

Global health care is experiencing an unprecedented surge in the number of critically ill patients who require mechanical ventilation due to the COVID-19 pandemic. The requirement for relatively long periods of ventilation in those who survive means that many are considered for tracheostomy to free patients from ventilatory support and maximise scarce resources. COVID-19 provides unique challenges for tracheostomy care: health-care workers need to safely undertake tracheostomy procedures and manage patients afterwards, minimising risks of nosocomial transmission and compromises in the quality of care. Conflicting recommendations exist about case selection, the timing and performance of tracheostomy, and the subsequent management of patients. In response, we convened an international working group of individuals with relevant expertise in tracheostomy. We did a literature and internet search for reports of research pertaining to tracheostomy during the COVID-19 pandemic, supplemented by sources comprising statements and guidance on tracheostomy care. By synthesising early experiences from countries that have managed a surge in patient numbers, emerging virological data, and international, multidisciplinary expert opinion, we aim to provide consensus guidelines and recommendations on the conduct and management of tracheostomy during the COVID-19 pandemic

Characterization of the Phytochelatin Synthase of Schistosoma mansoni

Treatment for schistosomiasis, which is responsible for more than 280,000 deaths annually, depends exclusively on the use of praziquantel. Millions of people are treated annually with praziquantel and drug resistant parasites are likely to evolve. In order to identify novel drug targets the Schistosoma mansoni sequence databases were queried for proteins involved in glutathione metabolism. One potential target identified was phytochelatin synthase (PCS). Phytochelatins are oligopeptides synthesized enzymatically from glutathione by PCS that sequester toxic heavy metals in many organisms. However, humans do not have a PCS gene and do not synthesize phytochelatins. In this study we have characterized the PCS of S. mansoni (SmPCS). The conserved catalytic triad of cysteine-histidine-aspartate found in PCS proteins and cysteine proteases is also found in SmPCS, as are several cysteine residues thought to be involved in heavy metal binding and enzyme activation. The SmPCS open reading frame is considerably extended at both the N- and C-termini compared to PCS from other organisms. Multiple PCS transcripts are produced from the single encoded gene by alternative splicing, resulting in both mitochondrial and cytoplasmic protein variants. Expression of SmPCS in yeast increased cadmium tolerance from less than 50 µM to more than 1,000 µM. We confirmed the function of SmPCS by identifying PCs in yeast cell extracts using HPLC-mass spectrometry. SmPCS was found to be expressed in all mammalian stages of worm development investigated. Increases in SmPCS expression were seen in ex vivo worms cultured in the presence of iron, copper, cadmium, or zinc. Collectively, these results indicate that SmPCS plays an important role in schistosome response to heavy metals and that PCS is a potential drug target for schistosomiasis treatment. This is the first characterization of a PCS from a parasitic organism

O-Antigen Delays Lipopolysaccharide Recognition and Impairs Antibacterial Host Defense in Murine Intestinal Epithelial Cells

Although Toll-like receptor (TLR) 4 signals from the cell surface of myeloid cells, it is restricted to an intracellular compartment and requires ligand internalization in intestinal epithelial cells (IECs). Yet, the functional consequence of cell-type specific receptor localization and uptake-dependent lipopolysaccharide (LPS) recognition is unknown. Here, we demonstrate a strikingly delayed activation of IECs but not macrophages by wildtype Salmonella enterica subsp. enterica sv. (S.) Typhimurium as compared to isogenic O-antigen deficient mutants. Delayed epithelial activation is associated with impaired LPS internalization and retarded TLR4-mediated immune recognition. The O-antigen-mediated evasion from early epithelial innate immune activation significantly enhances intraepithelial bacterial survival in vitro and in vivo following oral challenge. These data identify O-antigen expression as an innate immune evasion mechanism during apical intestinal epithelial invasion and illustrate the importance of early innate immune recognition for efficient host defense against invading Salmonella

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

ISSN exercise & sport nutrition review: research & recommendations

Sports nutrition is a constantly evolving field with hundreds of research papers published annually. For this reason, keeping up to date with the literature is often difficult. This paper is a five year update of the sports nutrition review article published as the lead paper to launch the JISSN in 2004 and presents a well-referenced overview of the current state of the science related to how to optimize training and athletic performance through nutrition. More specifically, this paper provides an overview of: 1.) The definitional category of ergogenic aids and dietary supplements; 2.) How dietary supplements are legally regulated; 3.) How to evaluate the scientific merit of nutritional supplements; 4.) General nutritional strategies to optimize performance and enhance recovery; and, 5.) An overview of our current understanding of the ergogenic value of nutrition and dietary supplementation in regards to weight gain, weight loss, and performance enhancement. Our hope is that ISSN members and individuals interested in sports nutrition find this review useful in their daily practice and consultation with their clients

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work