ObjectivesThe aim of this study was to evaluate the clinical validity of early Sjögren’s syndrome (SS) autoantibodies (eSjA), which were originally marketed for early diagnosis of SS, for juvenile SS (JSS) in a recently identified pediatric cohort.MethodsA total of 105 symptomatic subjects with eSjA results available were evaluated at the Center for Orphaned Autoimmune Disorders at the University of Florida and enrolled for this study. JSS diagnosis was based on the 2016 ACR/EULAR SS criteria. Demographic/clinical/laboratory parameters were compared between JSS (n = 27) and non-JSS (n = 78) for % positivity, sensitivity, and specificity of eSjA (SP1, anti-salivary protein; CA6, anti-carbonic anhydrase VI; PSP, anti-parotid secretory protein) and classic SS-autoantibodies (cSjA; ANA, SSA/SSB, RF, and others) either alone or in combination. Associations between eSjA and diagnostic/glandular parameters were also determined by Fisher’s exact test.ResultsCompared to non-JSS, JSS patients exhibited sicca symptoms demonstrating reduced unstimulated salivary flow rate (USFR) and abnormal glandular features revealed by salivary gland ultrasound (SGUS). Among cSjA, ANA demonstrated the highest sensitivity of 69.2%, while SSA, SSB, and RF showed around 95% specificities for JSS diagnosis. The % positive-SSA was notably higher in JSS than non-JSS (56% vs. 5%). Of eSjA, anti-CA6 IgG was the most prevalent without differentiating JSS (37%) from non-JSS (32%). Sensitivity and specificity of eSjA were 55.6 and 26.9%, respectively. Autoantibodies with potentially applicable specificity/sensitivity for JSS were seen only in cSjA without a single eSjA included. There were no associations detected between eSjA and focus score (FS), USFR, SSA, SGUS, and parotitis/glandular swelling analyzed in the entire cohort, JSS, and non-JSS. However, a negative association between anti-PSP and parotitis/glandular swelling was found in a small group of positive-SSA (n = 19, p = 0.02) whereas no such association was found between anti-PSP-positive compared to anti-PSP-negative. JSS and non-JSS groups differed in FS, USFR, and EULAR SS Patient Reported Index Dryness/Mean in CA6/PSP/ANA, SP1, and SSA-positive groups, respectively. Additionally, a higher FS was found in RF-positive than RF-negative individuals.ConclusionseSjA underperformed cSjS in differentiating JSS from non-JSS. The discovery of clinical impact of eSjA on early diagnosis of JSS necessitates a longitudinal study.

US over time among laypeople and organizational entities? and RQ3) What are the sentiments associated with GMO-related tweets among laypeople? CCS CONCEPTS• Networks → Network types; • Information systems → Information retrieval; Information retrieval.

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.

We screened 65 longitudinally-collected nasal swab samples from 31 children aged 0-16 years who were positive for SARS-CoV-2 omicron BA.1. By day 7 after onset of symptoms 48% of children remained positive by rapid antigen test. In a sample subset we found 100% correlation between antigen test results and virus culture.

Introduction Urinary tract infections (UTIs) are common infections for which initial antibiotic treatment decisions are empirically based, often without antibiotic susceptibility testing to evaluate resistance, increasing the risk of inappropriate therapy. We hypothesized that models based on electronic health records (EHR) could assist in the identification of patients at higher risk for antibiotic-resistant UTIs and help guide the selection of antimicrobials in hospital and clinic settings. Methods EHR from multiple centers in North-Central Florida, including patient demographics, previous diagnoses, prescriptions, and antibiotic susceptibility tests, were obtained for 9990 patients diagnosed with a UTI during 2011–2019. Decision trees, boosted logistic regression (BLR), and random forest models were developed to predict resistance to common antibiotics used for UTI management [sulfamethoxazole-trimethoprim (SXT), nitrofurantoin (NIT), ciprofloxacin (CIP)] and multidrug resistance (MDR). Results There were 6307 (63.1%) individuals with a UTI caused by a resistant microorganism. Overall, the population was majority female, white, non-Hispanic, and older aged (mean = 60.7 years). The BLR models yielded the highest discriminative ability, as measured by the out-of-bag area under the receiver-operating curve (AUROC), for the resistance outcomes [AUROC = 0.58 (SXT), 0.62 (NIT), 0.64 (CIP), and 0.66 (MDR)]. Variables in the best performing model were sex, history of UTIs, catheterization, renal disease, dementia, hemiplegia/paraplegia, and hypertension. Conclusions The discriminative ability of the prediction models was moderate. Nonetheless, these models based solely on EHR demonstrate utility for the identification of patients at higher risk for resistant infections. These models, in turn, may help guide clinical decision-making on the ordering of urine cultures and decisions regarding empiric therapy for these patients. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-022-00677-x.

No abstract

BACKGROUND Modest weight loss is recommended for individuals with type 2 diabetes (T2D) to help improve glycemic control and reduce cardiovascular disease risk. Continuous glucose monitoring (CGM) is recommended as a standard of care for individuals with T2D who use insulin, but more research is needed to inform recommendations for CGM use in all T2D populations. Digital diabetes self-management programs can help individuals with T2D lose weight and improve glycemic control. Less is known about the impact of CGM on program engagement within digital programs for diabetes self-management and the relationship between CGM use and weight loss for those with T2D. OBJECTIVE The primary objective of this study was to examine the difference in percent weight loss from baseline to 6 months between CGM users and non-users among commercially insured members with T2D in a digital program for diabetes self-management (Omada for Diabetes). METHODS A non-randomized retrospective observational cohort study was performed using data from 2,612 Omada for Diabetes program members (mean age=52.3 years, 56.4% female, 63.5% white) who started the program between January 1, 2021 and September 30, 2021. Data analyses examined the difference in percent weight loss over 6 months between CGM users and non-users overall as well as stratified by body mass index, program engagement, and CGM-adherence, and associations between program engagement and CGM group. RESULTS A higher percentage of CGM users (57.6%) than non-users (48.4%) were classified as ‘highly engaged’ with the Omada for Diabetes program (p<.001). Both groups showed significant within-group mean percent weight loss from baseline to 6 months (-2.0% CGM users, -1.8% non-users, p<.001), but no differences were detected between groups. When stratified by program engagement, highly engaged CGM users and non-users had significantly greater percent weight loss compared to those with normal/low engagement (CGM users: -2.50% vs. -1.33%, p=.004; non-users: -2.43% vs. -1.30%, p<.001). In fully adjusted models, CGM users and non-users had significant reductions in percent weight loss (β=-2.0%, 95% CI (-2.42, -1.57), β=-1.87%, 95% CI (-2.11, -1.63), respectively). CONCLUSIONS Members participating in the Omada for Diabetes program had a significant change in mean percent body weight from program start to 6 months regardless of CGM status. A higher percentage of CGM users were more engaged with the program than non-users, and higher engagement across both groups was associated with greater percent weight loss. Further understanding the impact that CGM has in digital diabetes self-management programs for T2D could enhance program effectiveness, encourage sustained engagement, and inform standard of care recommendations. CLINICALTRIAL N/A

Synthetic pesticides are important agricultural tools that increase crop yield and help feed the world's growing population. These products are also highly regulated to balance benefits and potential environmental and human risks. Public perception of pesticide use, safety, and regulation is an important topic necessitating discussion across a variety of stakeholders from lay consumers to regulatory agencies since attitudes toward this subject could differ markedly. Individuals and organizations can perceive the same message(s) about pesticides differently due to prior differences in technical knowledge, perceptions, attitudes, and individual or group circumstances. Social media platforms, like Twitter, include both individuals and organizations and function as a townhall where each group promotes their topics of interest, shares their perspectives, and engages in both well‐informed and misinformed discussions. We analyzed public Twitter posts about pesticides by user group, time, and location to understand their communication behaviors, including their sentiments and discussion topics, using machine learning‐based text analysis methods. We extracted tweets related to pesticides between 2013 and 2021 based on relevant keywords developed through a “snowball” sampling process. Each tweet was grouped into individual versus organizational groups, then further categorized into media, government, industry, academia, and three types of nongovernmental organizations. We compared topic distributions within and between those groups using topic modeling and then applied sentiment analysis to understand the public's attitudes toward pesticide safety and regulation. Individual accounts expressed concerns about health and environmental risks, while industry and government accounts focused on agricultural usage and regulations. Public perceptions are heavily skewed toward negative sentiments, although this varies geographically. Our findings can help managers and decision‐makers understand public sentiments, priorities, and perceptions and provide insights into public discourse on pesticides. Integr Environ Assess Manag 2023;00:1–19. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).