System-wide mapping of peptide-GPCR interactions in C. elegans.

Neuropeptides and peptide hormones are ancient, widespread signaling molecules that underpin almost all brain functions. They constitute a broad ligand-receptor network, mainly by binding to G protein-coupled receptors (GPCRs). However, the organization of the peptidergic network and roles of many peptides remain elusive, as our insight into peptide-receptor interactions is limited and many peptide GPCRs are still orphan receptors. Here we report a genome-wide peptide-GPCR interaction map in Caenorhabditis elegans. By reverse pharmacology screening of over 55,384 possible interactions, we identify 461 cognate peptide-GPCR couples that uncover a broad signaling network with specific and complex combinatorial interactions encoded across and within single peptidergic genes. These interactions provide insights into peptide functions and evolution. Combining our dataset with phylogenetic analysis supports peptide-receptor co-evolution and conservation of at least 14 bilaterian peptidergic systems in C. elegans. This resource lays a foundation for system-wide analysis of the peptidergic network.

Automated scoring of nematode nictation on a textured background.

Entomopathogenic nematodes, including Steinernema spp., play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation-a behavior in which animals stand on their tails-as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans, but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors.

A retrospective analysis of the factors associated with surgical checklist compliance using data from a local health unit in Italy, 2018-2021.

RATIONALE: Since its publication, the World Health Organization Surgical Safety Checklist (SSC) has been progressively adopted by healthcare providers around the world to monitor and safeguard the delivery of surgeries. In one Italian region's health system, the SSC and other two surgery-specific checklists were supplemented by a document that records any non-conformity (NC) arising from the safety checks. AIMS AND OBJECTIVES: In this study, we investigated the factors associated with NCs using data from a local health unit (LHU). The secondary aim of this study was to explore the potential impact of the coronavirus crisis on surgical checklist compliance. METHODS: We used data on surgical activity from the Modena LHU between 2018 and 2021 and the accompanying NC documents. The primary goal was to estimate the relative risk (RR) of NCs according to several factors, including checklist incompleteness and surgery class (elective, urgent or emergency), using Poisson regression. A similar analysis was performed separately for 2018-2019 and 2020-2021 to assess the COVID-19 potential impact. RESULTS AND CONCLUSIONS: Checklist compliance in the LHU was 95%, with the presence of NCs in about 7% of surgeries. The factors that increased the RR were incompleteness of the checklist (adjusted RR = 3.12; 95% confidence interval [CI] = 2.86-3.40), urgent surgeries (adjusted RR [aRR] = 1.59; 95% CI = 1.47-1.72), emergencies (aRR = 2.09; 95% CI = 1.15-3.79), and surgeries with more than four procedures (aRR = 1.64; 95% CI = 1.41-1.92). Most notably, the RR for incomplete checklists showed a negative association with NCs before the COVID-19 outbreak but positive afterwards. Checklist compliance was overall satisfactory, though the observation of noncompliant checklists of about 1000 per year suggests there is still room for improvement. Moreover, attention to the checklist best practices and organization of outpatient workload may have been affected by the exceptional circumstances of the pandemic.

Automated scoring of nematode nictation on a textured background.

Entomopathogenic nematodes including Steinernema spp. play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation - a behavior in which animals stand on their tails - as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans , but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors.