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Weight bias is pervasive in healthcare and leads to worse patient outcomes. A uniquely designed 4-h continuing medical education (CME) intervention was assessed for changing healthcare professionals’ (HCPs’) weight biases and clinical practice behaviors.

The intervention used a (1) pre/post design examining CME attendees’ self-reported weight bias at baseline, after, and 4- and 12-month follow-up, and (2) post/post design examining obesity practice behaviors 12 months after intervention in attendees and non-attendees.

Single medical center service area within Kaiser Permanente Southern California.

All HCPs (n = 472) from the target service area were eligible to attend. Analyses were done with 218 HCPs who attended and 89 who did not.

The intervention contained theory-based elements of changing attributions of responsibility of obesity, increasing empathy, creating self-awareness of weight bias, and creating a bias-free culture.For pre/post analyses, the primary outcome was self-reported weight bias. For comparative analyses of CME attendees and non-attendees, the outcomes were electronic medical record–confirmed rates of obesity diagnosis and referrals to evidence-based obesity treatments in the 12 months following the CME intervention.

Self-reported negative obesity stereotypes were significantly reduced compared to baseline while self-reported empathy and confidence in caring for patients with obesity were significantly increased immediately post intervention and were maintained at 4- and 12-month follow-up. After adjusting for years in practice, race/ethnicity, gender, profession type, practice type, and panel size, HCPs who attended the CME intervention had significantly increased odds (range 60–212%) of diagnosis and obesity-related referrals in the 12 months following the CME intervention when compared to HCPs who did not attend.

This intervention has promise to be a scalable program that goes beyond impacting HCP’s self-reported weight bias and also changes HCPs’ clinical practice behaviors related to obesity treatment.

 

Salmonella entericais a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins fromSalmonella entericapangenome. We classified 17,238 domains from 13,147 proteins from 79,758Salmonella entericastrains and studied in detail domains of 272 proteins from 14 characterizedSalmonellapathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters or novel potential pathogenicity islands (NPPIs) within theSalmonellagenome, suggesting their potential contribution to the bacterium’s virulence. One of the NPPIs revealed significant overrepresentation of potentially pathogenic proteins. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied.

 

This review discusses the intriguing yet controversial concept of metabolons, focusing on the malate dehydrogenase-citrate synthase (MDH-CISY) metabolon as a model. Metabolons are multienzyme complexes composed of enzymes that catalyze sequential reactions in metabolic pathways. Metabolons have been proposed to enhance metabolic pathway efficiency by facilitating substrate channeling. However, there is skepticism about the presence of metabolons and their functionality in physiological conditions in vivo. We address the skepticism by reviewing compelling evidence supporting the existence of the MDH-CISY metabolon and highlighting its potential functions in cellular metabolism. The electrostatic interaction between MDH and CISY and the intermediate oxaloacetate, channeled within the metabolon, has been demonstrated using various experimental techniques, including protein–protein interaction assays, isotope dilution studies, and enzyme coupling assays. Regardless of the wealth of in vitro evidence, further validation is required to elucidate the functionality of MDH-CISY metabolons in living systems using advanced structural and spatial analysis techniques. 

College science programs exhibit high rates of student attrition, especially among Students of Color, women, members of the LGBTQ+ community, and those with disabilities. Many of the reasons students choose to leave or feel pushed out of science can be mitigated through participation in faculty-mentored research. However, faculty resources are limited, and not every student has access to faculty mentoring due to systemic or structural barriers. By bringing authentic scientific research into the classroom context, course-based undergraduate research experiences (CUREs) expand the number of students who participate in research and provide benefits similar to faculty-mentored research. Instructors also benefit from teaching CUREs. Using a systematic review of 14 manuscripts concerning the Malate Dehydrogenase CUREs Community (MCC) and malate dehydrogenase (MDH) CUREs, we demonstrate that CUREs can be implemented flexibly, are authentic research experiences, generate new scientific discoveries, and improve student outcomes. Additionally, CURE communities offer substantial advantages to faculty wishing to implement CUREs.

 

The evolutionary classification of protein domains (ECOD) classifies protein domains using a combination of sequence and structural data (http://prodata.swmed.edu/ecod). Here we present the culmination of our previous efforts at classifying domains from predicted structures, principally from the AlphaFold Database (AFDB), by integrating these domains with our existing classification of PDB structures. This combined classification includes both domains from our previous, purely experimental, classification of domains as well as domains from our provisional classification of 48 proteomes in AFDB predicted from model organisms and organisms of concern to global health. ECOD classifies over 1.8 M domains from over 1000 000 proteins collectively deposited in the PDB and AFDB. Additionally, we have changed the F-group classification reference used for ECOD, deprecating our original ECODf library and instead relying on direct collaboration with the Pfam sequence family database to inform our classification. Pfam provides similar coverage of ECOD with family classification while being more accurate and less redundant. By eliminating duplication of effort, we can improve both classifications. Finally, we discuss the initial deployment of DrugDomain, a database of domain-ligand interactions, on ECOD and discuss future plans.

 

Bearing fault detection plays a crucial role in ensuring machinery reliability and safety. However, the existing bearing-fault-detection sensors are commonly too large to be embedded in narrow areas of bearings and too vulnerable to work in complex environment. Here, we demonstrate an approach to distinguish the presence of race faults in bearings and their types by using an optomechanical micro-resonator. The principle of the amplitude-frequency modulation model mixing fault frequency with mechanical frequency is raised to explain the asymmetrical sideband phenomena detected by the optical microtoroidal sensor. Kurtosis estimation used in this work can distinguish normal and faulty bearings in the time domain with the maximum accuracy rate of 91.72% exceeding the industry standard rate of 90%, while the amplitude-frequency modulation of the fault signal and mechanical mode is introduced to identify the types of the bearing faults, including, e.g., outer race fault and inner race fault. The fault-detection methods have been applied to the bearing on a mimic unmanned aerial vehicle (UAV), and correctly confirmed the presence of fault and the type of outer or inner race fault. Our study gives new perspectives for precise measurements on early fault warning of bearings, and may find applications in other fields such as vibration sensing.

 

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.