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.

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.

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.