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The flavoenzyme nicotine oxidoreductase (NicA2) is a promising injectable treatment to aid in the cessation of smoking, a behavior responsible for one in ten deaths worldwide. NicA2 acts by degrading nicotine in the bloodstream before it reaches the brain. Clinical use of NicA2 is limited by its poor catalytic activity in the absence of its natural electron acceptor CycN. Without CycN, NicA2 is instead oxidized slowly by dioxygen (O2), necessitating unfeasibly large doses in a therapeutic setting. Here, we report a genetic selection strategy that directly links CycN-independent activity of NicA2 to growth of Pseudomonas putida S16. This selection enabled us to evolve NicA2 variants with substantial improvement in their rate of oxidation by O2. The encoded mutations cluster around a putative O2 tunnel, increasing flexibility and accessibility to O2 in this region. These mutations further confer desirable clinical properties. A variant form of NicA2 is tenfold more effective than the wild type at degrading nicotine in the bloodstream of rats.

Asian American/Pacific Islanders (AAPIs) and Hispanics are growing minority United States populations, but are poorly represented in the cardiovascular literature. This study examines guideline adherence and outcomes in AAPIs and Hispanics compared with non-Hispanic Whites (NHWs) in a quaternary care center after inpatient percutaneous coronary intervention (PCI). The primary end points were inpatient post-PCI bleed, heart failure, cardiogenic shock, and all-cause mortality, whereas the secondary end point was the prescription rate of post-PCI guideline-directed medical therapy including aspirin, statins, P2Y12 receptor blockers, and cardiopulmonary rehabilitation. Intergroup differences were assessed through analysis of variance or two-way chi-square tests, and the association of race with binary outcomes was examined through logistic regression with NHW as the reference group. Compared with NHW, AAPIs, and Hispanics had higher odds of diabetes mellitus, and AAPIs had higher odds of hypertension and being on dialysis. Hispanics had higher odds of post-PCI mortality versus NHW, both in acute coronary syndrome (odds ratio [OR] 2.04, p = 0.03) and elective PCI (OR 2.51, p = 0.04). AAPI also trended toward higher mortality than NHW in both categories. AAPIs were found to have higher odds of statin prescription (OR 1.91, p = 0.04). Hispanics had lower odds of ticagrelor prescription versus NHW (OR 0.65, p = 0.04), and AAPIs trended toward such. No differences were found for cardiopulmonary rehabilitation prescriptions in groups. This study suggests that despite quality improvement efforts, disparities remain in postprocedural outcomes in minority groups in comparison with NHW.

Introduction: Asian-American/Pacific Islanders (AAPIs) and Hispanics are two of the most rapidly growing minorities, but both are poorly represented in the cardiovascular literature. In light of national quality improvement efforts to ensure adherence to cardiovascular therapy guidelines, this study examines guideline adherence and outcomes in AAPIs and Hispanics compared to non-Hispanic Whites (NHW) in a quaternary care center after percutaneous coronary intervention (PCI). Methods: 1,896 AAPI, Hispanics, and NHW adults from February 28, 2012 to December 30, 2020 who underwent emergent or elective PCI were included. The primary endpoint was the prescription of post-PCI guideline-directed medical therapy including aspirin, statins, P2Y12 receptor blockers, and cardiopulmonary rehabilitation. Secondary endpoints included comorbidity burden, post-PCI morbidity and mortality, and prior PCI. Analyses were adjusted for age, sex, and insurance type. Results: Hispanics had the lowest median age and the highest rates of government insurance. Hispanics had lower odds of either ACE inhibitor or ARB prescription versus NHW (OR = 0.75, p = 0.03). Odds of ticagrelor prescriptions were lower for both Hispanics (OR = 0.56, p = 0.01) and AAPIs (OR = 0.62, p = 0.02) versus NHW. However, odds of clopidogrel prescriptions were higher for Hispanics than NHW (OR = 1.57, p = 0.01), while AAPIs trended towards the same (OR = 1.42, p = 0.05). No differences were found for statin or cardiopulmonary rehabilitation prescriptions. AAPIs and Hispanics had significantly higher risks of diabetes (OR = 2.28 and 1.8 respectively, p < 0.01) and of being on dialysis (OR = 2.25 and 2.52 respectively, p < 0.01) than NHW. AAPIs also had higher odds of hypertension than NHW (OR = 1.51, p = 0.01). Hispanics had significantly higher risk of post-PCI mortality versus NHW (OR = 2.12, p < 0.01). Conclusions: AAPIs and Hispanics had lower odds of ticagrelor prescription than NHW, and Hispanics had higher odds of clopidogrel prescription. Hispanics also had higher odds of mortality post-PCI. Further, AAPIs and Hispanics had higher comorbidity burdens. This study suggests that despite quality improvement efforts, work remains to be done to narrow health disparities.

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Background: Metastatic breast cancers are frequently encountered in cytology and require immunocytochemistry (ICC). In this study, traditional and multiplex ICC (mICC) for GATA‐binding protein 3 (GATA3), gross cystic disease fluid protein 15 (GCDFP15), and mammaglobin (MMG) were performed with the aim of validating mICC in cell blocks, with further single‐cell expression pattern analysis to identify the single markers and combinations of markers most sensitive in subtypes of breast cancer. Methods: GATA3, GCDFP15, and MMG were paired with OptiView 3,3′‐diaminobenzidine and Ventana DISCOVERY Purple and Blue, respectively, with cyclical and serial staining. Bright‐field imaging was performed with the Mantra 2 system and analyzed with the inForm Tissue Finder (Akoya Biosciences). Cell detection and phenotyping were further confirmed by two pathologists. Results: In the 36 cases studied, traditional ICC and mICC demonstrated good concordance (kappa coefficient, >0.5; p <.01) at three cutoffs (1%, 5%, and 50%), except for GATA3 at the 1% cutoff. Single‐marker positivity outnumbered double‐marker positivity and the exceedingly rare triple‐marker positivity (<3%). GATA3 was the leading single marker–positive phenotype in all breast cancer subtypes, except for MMG in estrogen receptor–positive, progesterone receptor–positive, and human epidermal growth factor receptor 2–positive (ER+/PR+/HER2+) breast cancers. Limited to two markers, GATA3/MMG included the greatest number of tumor cells for luminal breast cancers (ER+/PR+/HER2+, 60.6%; ER+/PR+/HER2+, 31.4%), whereas HER2‐overexpressed breast cancers (27.4%) and triple‐negative breast cancers (26.4%) favored the combination of GATA3/GCDFP15. Conclusions: For a single marker, GATA3 displayed the highest sensitivity. The addition of MMG for hormone receptor‐positive breast cancers and GCDFP15 for hormone receptor‐negative breast cancers further increased sensitivity. The low proportion of multimarker‐positive cells suggested that the coexpression observed with traditional ICC is attributable to intratumoral heterogeneity, not genuine coexpression. Traditional and multiplex immunocytochemistry (mICC) for GATA‐binding protein 3 (GATA3), gross cystic disease fluid protein 15 (GCDFP15), and mammaglobin (MMG) were performed with the aim of validating mICC in cell block preparations of metastatic breast cancer, with further single‐cell expression pattern analysis to identify the single markers and combinations of markers most sensitive in subtypes of breast cancer. For a single marker, GATA3 displayed the highest sensitivity. The addition of MMG for hormone receptor‐positive breast cancers and GCDFP15 for hormone receptor‐negative breast cancers further increased sensitivity. [ABSTRACT FROM AUTHOR]

Urine cytology is a noninvasive, widely used diagnostic tool for screening and surveillance of genitourinary tract neoplasms. However, the absence of unified terminology and clear objective morphological criteria limits the clinical benefit of urine cytology. The Paris System for Reporting Urine Cytology (TPS) was developed with the goal of standardizing reporting and improving urine cytology performance in detecting high‐grade malignancy (HGM). We aimed to evaluate potential effects of TPS on improving urine cytology diagnostic performance and clinical utility by conducting a systematic review and meta‐analysis. We searched six electronic databases to identify cross‐sectional and cohort studies written in English assessing the accuracy of urine cytology in detecting genitourinary tract malignancies of patients under surveillance or with clinical suspicion of malignancy from January 2004 to December 2022. We extracted relevant data from eligible studies to calculate relative distribution of cytology diagnostic categories; ratio of atypical to HGM cytology diagnosis; and risk of HGM (ROHGM) and HGM likelihood ratio (HGM‐LR) associated with cytology diagnostic categories. We used a generalized linear mixed model with logit transformation to combine proportions and multilevel mixed‐effect logistic regression to pool diagnostic accuracy measurements. We performed meta‐regression to evaluate any significant difference between TPS and non‐TPS cohorts. We included 64 studies for 99,796 combined total cytology samples, across 31 TPS and 49 non‐TPS cohorts. Pooled relative distribution [95% confidence interval (CI)] of negative for high‐grade urothelial carcinoma (NHGUC)/negative for malignancy (NM); atypical urothelial cells (AUC); suspicious for high‐grade urothelial carcinoma (SHGUC)/suspicious for malignancy (SM); low‐grade urothelial neoplasm (LGUN); and HGM categories among satisfactory cytology cases were 83.8% (80.3%–86.9%), 8.0% (6.0%–10.6%), 2.2% (1.4%–3.3%), 0.01% (0.0%–0.1%), and 4.2% (3.2%–5.5%) in TPS versus 80.8% (76.8–2.7%), 11.3% (8.6%–14.7%), 1.8% (1.2%–2.7%), 0.01% (0.0%–0.1%), and 3.3% (2.5%–4.3%) in non‐TPS cohorts. Adopting TPS classification resulted in a significant increase in the frequency of NHGUC and a reduction in AUC cytology diagnoses, respectively. The AUC/HGM ratio in TPS cohort was 2.0, which showed a statistically significant difference from the atypical/HGM ratio of 4.1 in non‐TPS cohort (p‐value: 0.01). Moreover, the summary rate (95% CI) of LGUN called AUC on cytology significantly decreased to 20.8% (14.9%–28.3%) in the TPS compared with 34.1% (26.4%–42.8%) in non‐TPS cohorts. The pooled ROHGM (95% CI) was 20.4% (6.2%–50.0%) in nondiagnostic (NDX), 15.5% (9.6%–24.2%) in NHGUC, 40.2% (30.9%–50.2%) in AUC, 80.8% (72.9%–86.8%) in SHGUC, 15.1% (5.7%–34.3%) in LGUN, and 91.4% (87.3%–94.3%) in HGM categories in TPS studies. NHGUC, AUC, SHGUC, and HGM categories were associated with HGM‐LR (95% CI) of 0.2 (0.1–0.3), 0.9 (0.6–1.3), 6.9 (2.4–19.9), and 16.8 (8.3–33.8). Our results suggest that TPS 1.0 has reduced the relative frequency of AUC diagnosis, AUC/HGM ratio, and the frequency of LGUNs diagnosed as AUC on cytology. Adopting this classification has improved the clinical utility of SHGUC and HGM cytology diagnoses in ruling in high‐grade lesions. However, an NHGUC diagnosis does not reliably rule out the presence of a high‐grade lesion. [ABSTRACT FROM AUTHOR]

Monitoring pavement conditions using crowdsourced vehicular data can significantly contribute to real-time and cost-effective pavement maintenance decision-making. This paper presents the development of various machine learning models for predicting crucial pavement characteristics essential for ensuring roadway safety, including roadway longitudinal grade, cross slope, international roughness index, surface rutting, and pavement skid resistance. The study collected vehicular sensing data and paired it with field pavement characteristics obtained using innovative instruments on selected asphalt and concrete sections in Oklahoma. Seven machine learning models were trained using the AutoGluon platform, yielding highly accurate predictions for safety-related roadway characteristics. The weighted ensemble L2, random forest, and category boosting (CatBoost) models exhibited the highest accuracy, with R-squared values exceeding 0.9, while the k-nearest neighbor algorithm and LightGBM models showed lower competitiveness. The inference latency of the models varied, with CatBoost demonstrating the lowest latency and weighted ensemble L2 achieving the highest accuracy at the expense of slightly higher inference latency. The choice of model depends on the specific application, whether it be pavement network management or real-time roadway condition monitoring. The findings from this research empower transportation agencies to efficiently screen the pavement network for further inspection or maintenance, thus enhancing transportation safety by providing instant alerts to drivers about potential high-risk pavement sections, resulting in safer and more reliable transportation infrastructure. [ABSTRACT FROM AUTHOR]