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Abstract Pemphigus represents a spectrum of autoimmune‐mediated blistering diseases associated with high morbidity, mortality and reduced quality of life (QoL). Despite an increase in pemphigus clinical trials, the varied instrument measurements of disease severity and QoL outcomes make comparisons between studies challenging. This study aimed to evaluate trends in the use of disease severity and QoL outcome measurements in pemphigus clinical trials. A review of pemphigus clinical trials was conducted using the PubMed, Embase, Cochrane Reviews and ClinicalTrials.gov databases up until September 2023. Only pemphigus randomized clinical trials that assessed at least one disease severity and/or QoL outcome were included. Overall, 53 clinical trials were eligible for this review. All clinical trials evaluated a disease severity outcome, with the Pemphigus Disease Area Index being the most used validated questionnaire (28.3% of trials) and more popular after 2015 (47.8% of trials since). The autoimmune bullous skin disorder intensity score (7.6%) and visual analogue measurements (7.6%) have fallen out of favour. Most studies now include lab parameters (56.5% of trials after 2015), with anti‐desmoglein 1 and 3 antibody levels (30.2%), immunoglobulins (IgG and/or IgM and IgA) (11.3%), and anti‐drug antibody levels (7.6%) being frequently evaluated. A small portion of trials evaluated QoL (26.5% of studies), with the autoimmune bullous quality of life being the most common (15.1%), however QoL utilization as an outcome measure has been increasing since 2015 (61.1% of trials since). Standardising the use of validated outcome measurements allows for better data interpretation, comparability and clinical application of results.

Abstract Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case–control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Objective Transthyretin amyloid cardiomyopathy (ATTR-CM) is an infiltrative cardiac disorder caused by deposition of wild type or mutated transthyretin. As ATTR-CM is associated with conduction disease, we sought to determine its prevalence in patients with idiopathic high-degree atrioventricular (AV) block requiring permanent pacemaker (PPM) implantation.Methods Consecutive patients aged 70–85 years undergoing PPM implantation for idiopathic high-degree AV block between November 2019 and November 2021 were offered a 3,3-diphosphono-1,2-propanodicarboxylic acid (DPD) scan. Demographics, comorbidities, electrocardiographic and imaging data from the time of device implantation were retrospectively collected.Results 39 patients (79.5% male, mean (SD) age at device implantation 76.2 (2.9) years) had a DPD scan. 3/39 (7.7%, all male) had a result consistent with ATTR-CM (Perugini grade 2 or 3). Mean (SD) maximum wall thickness of those with a positive DPD scan was 19.0 mm (3.6 mm) vs 11.4 mm (2.7 mm) in those with a negative scan (p=0.06). All patients diagnosed with ATTR-CM had spinal canal stenosis and two had carpal tunnel syndrome.Conclusions ATTR-CM should be considered in older patients requiring permanent pacing for high-degree AV block, particularly in the presence of left ventricular hypertrophy, carpal tunnel syndrome or spinal canal stenosis.

Abstract Background Rural, remote, and underserved communities have often struggled to provide adequate access to family physicians. To bridge this gap in Renfrew County, a large, rural region in Ontario, Canada, a community- based, hybrid care model was implemented, combining virtual care from family physicians and in-person care from community paramedics. Studies have demonstrated the clinical and cost effectiveness of this model but its acceptability to physicians has not been examined. This study investigates the experiences of participating family physicians. Methods A mixed-methods study, combining physician questionnaire response data and qualitative thematic analysis of focus group interview data. Results Data was collected from n = 17 survey respondents and n = 9 participants in two semi-structured focus groups (n = 4 and n = 5 respectively). Physicians reported high satisfaction, driven by skills development and patient gratitude, and felt empowered to reduce ED visits, care for unattached patients, and address simple medical needs. However, physicians found it difficult to provide continuous care and were sometimes unfamiliar with local healthcare resources. Conclusion This study found that a hybrid model of in-person and virtual care from family physicians and community paramedics was associated with positive physician experiences in two main areas: clinical impacts, especially avoiding unnecessary ED visits, and physician satisfaction with the service. Potential improvements for this hybrid model were identified, and include better support for patients with complex needs, and more information about local health-system services. Our findings should be of interest to policymakers and administrators seeking to improve access to care through a hybrid model of in-person and virtual care.

Abstract Background Cynomolgus macaque (Macaca fascicularis) is an attractive animal model for the study of human disease and is extensively used in biomedical research. Cynomolgus macaques share behavioral, physiological, and genomic traits with humans and recapitulate human disease manifestations not observed in other animal species. To improve the use of the cynomolgus macaque model to investigate immune responses, we defined and characterized the T cell receptor (TCR) repertoire. Result We identified and analyzed the alpha (TRA), beta (TRB), gamma (TRG), and delta (TRD) TCR loci of the cynomolgus macaque. The expressed repertoire was determined using 22 unique lung samples from Mycobacterium tuberculosis infected cynomolgus macaques by single cell RNA sequencing. Expressed TCR alpha (TRAV) and beta (TRBV) variable region genes were enriched and identified using gene specific primers, which allowed their functional status to be determined. Analysis of the primers used for cynomolgus macaque TCR variable region gene enrichment showed they could also be used to amplify rhesus macaque (M. mulatta) variable region genes. Conclusion The genomic organization of the cynomolgus macaque has great similarity with the rhesus macaque and they shared > 90% sequence similarity with the human TCR repertoire. The identification of the TCR repertoire facilitates analysis of T cell immunity in cynomolgus macaques.

Abstract Background Sepsis, the dysregulated host response to infection, triggers abnormal pro-coagulant and pro-inflammatory host responses. Limitations in early disease intervention highlight the need for effective diagnostic and prognostic biomarkers. Protein C’s role as an anticoagulant and anti-inflammatory molecule makes it an appealing target for sepsis biomarker studies. This meta-analysis aims to assess the diagnostic and prognostic value of protein C (PC) as a biomarker for adult sepsis. Methods We searched MEDLINE, PubMed, EMBASE, CINAHL and Cochrane Library from database inception to September 12, 2021. We included prospective observational studies of (1) adult patients (> 17) with sepsis or suspicion of sepsis that; (2) measured PC levels with 24 h of study admission with; and (3) the goal of examining PC as a diagnostic or prognostic biomarker. Two authors screened articles and conducted risk of bias (RoB) assessment, using the Quality in Prognosis Studies (QUIPS) and the Quality Assessment in Diagnostic Studies-2 (QUADAS-2) tools. If sufficient data were available, meta-analysis was conducted to estimate the standardized mean difference (SMD) between patient populations. Results Twelve studies were included, and 8 were synthesized for meta-analysis. Pooled analysis demonstrated moderate certainty of evidence that PC levels were less reduced in sepsis survivors compared to non-survivors (6 studies, 741 patients, SMD = 0.52, 95% CI 0.24–0.81, p = 0.0003, I 2 = 55%), and low certainty of evidence that PC levels were less reduced in septic patients without disseminated intravascular coagulation (DIC) compared to those with DIC (3 studies, 644 patients, SMD = 0.97, 95% CI 0.62–1.32, p

Summary: Determining the protection an individual has to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VoCs) is crucial for future immune surveillance, vaccine development, and understanding of the changing immune response. We devised an informative assay to current ELISA-based serology using multiplexed, baited, targeted proteomics for direct detection of multiple proteins in the SARS-CoV-2 anti-spike antibody immunocomplex. Serum from individuals collected after infection or first- and second-dose vaccination demonstrates this approach and shows concordance with existing serology and neutralization. Our assays show altered responses of both immunoglobulins and complement to the Alpha (B.1.1.7), Beta (B.1.351), and Delta (B.1.617.1) VoCs and a reduced response to Omicron (B1.1.1529). We were able to identify individuals who had prior infection, and observed that C1q is closely associated with IgG1 (r > 0.82) and may better reflect neutralization to VoCs. Analyzing additional immunoproteins beyond immunoglobulin (Ig) G, provides important information about our understanding of the response to infection and vaccination. Motivation: Assays for measuring serum antibody responses are typically limited to measurement of a total or single immunoglobulin isotype. The antibody response is far more complex, with multiple immunoglobulin classes, isotypes, and complement factors involved. This is a potential wealth of information that is typically understudied and missed by existing tests. The global COVID-19 pandemic has highlighted the need to understand better the immune response in respect to vaccine development and emerging new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants. Using the ability of tandem mass spectrometry to multiplex and directly and accurately measure the antibody complex, we devised an alternative assay to capture this valuable information.

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.