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Respiratory inflammation and hyperresponsiveness caused by various pathological stimulants and microbial infections are associated with excessive production of nitric oxide (NO) in airway epithelial and phagocytic cells. For example, in viral pneumonia caused by influenza and Sendai virus, NO contributes critically to inflammatory processes and pathological exacerbation in the airway. NO-mediated respiratory complications may be dependent on the complex reactions of NO, with simultaneously produced superoxide forming several reactive nitrogen species, most notably ONOO−. These reactive nitrogen oxides can react with biologically important proteins, enzymes, lipids, and nucleic acids via oxidation, nitration, or nitrosylation reactions, producing nitrative stress. Recent studies indicate that nitrative stress is involved in inflammation by a number of mechanisms including modulation of host immune response, destruction of tissue architecture, activation or inactivation of enzymes and transcription factors, and so on. There is accumulating evidence for the involvement of NO and nitrative stress in respiratory diseases. In this review, we focus on the molecular mechanisms of NO-induced and nitrative stress-mediated respiratory inflammation and pathogenesis during respiratory viral infections. [ABSTRACT FROM AUTHOR]

This study presents a quantitative description of tempromandibular joint (TMJ) sounds provided by a rule-based classification system based on sound classification by three dentists, who listened to and classified the sound recordings as no sound, click, coarse crepitus and fine crepitus. The sounds were recorded with microphones in the ear canal from 126 subjects during vertical opening, digitized at 15 000 Hz, and replayed using a computer sound card and speakers. The dentists’ classification of a test set resulted in intra- and inter-tester j values ranging from 0·71 to 0·81 and 0·61–0·73, respectively. Pooled j values for the dentists and the dentists plus the rules were 0·67 and 0·58, respectively, which were not significantly different in terms of the sound features on which the rules were based (P = 0·13). Linear discriminant analysis showed the four TMJ sound types were significantly different (P < 0·001). The performance of the rules was equivalent to the dentists and marginally better than the linear discriminant functions (P = 0·08), establishing the validity of the quantitative descriptions they provide. The recording and rebroadcast methodology produced sounds very similar to those observed in the clinic and could be used to train clinicians in classifying TMJ sounds. [ABSTRACT FROM AUTHOR]

The prevalence of antibiotic-resistant Escherichia coli isolates from faecal samples from 117 poultry industry workers, 100 patients and119 healthy chicken were compared. Resistance of E. coli chicken isolates to ampicillin, chloramphenicol, gentamicin, spectinomycin, tetracycline and trimethoprim + sulphamethoxazole (TMP + SMX) (range 57% - 99.1%) were significantly higher than those isolated from patients (range 21.9% - 71.4%) and workers (range 35% - 71.8%). However, for drugs not used in poultry, such as amoxicillin + cluvalanate (AMX + CLV), ceftazidime and nitrofurantoin, resistance rates of chicken isolates (range 0% - 2.6%) were significantly lower than those of patient isolates (range 8.7% - 30%). Resistance to spectinomycin reached 96% in E. coli chicken isolates and 71% in organisms isolated from humans. Use of this drug in Saudi Arabia is mostly limited to veterinary purposes. Multidrug resistance is alarmingly high in all groups but was highest in chicken isolates (77.4%). Serotyping of E. coli isolates showed that 27% of the organisms isolated from patients were overlapping with 10.9% of the chicken isolates, indicating the possibility of chicken being a source of the resistance pool for humans. We therefore call for the banning of antibiotics in the poultry industry as growth promoters and recommend that their use be restricted to treating infections. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate and compare the ability of large language models (LLMs) to diagnose various ailments in otolaryngology., Methods: We collected all 100 clinical vignettes from the second edition of Otolaryngology Cases-The University of Cincinnati Clinical Portfolio by Pensak et al. With the addition of the prompt "Provide a diagnosis given the following history," we prompted ChatGPT-3.5, Google Bard, and Bing-GPT4 to provide a diagnosis for each vignette. These diagnoses were compared to the portfolio for accuracy and recorded. All queries were run in June 2023., Results: ChatGPT-3.5 was the most accurate model (89% success rate), followed by Google Bard (82%) and Bing GPT (74%). A chi-squared test revealed a significant difference between the three LLMs in providing correct diagnoses (p = 0.023). Of the 100 vignettes, seven require additional testing results (i.e., biopsy, non-contrast CT) for accurate clinical diagnosis. When omitting these vignettes, the revised success rates were 95.7% for ChatGPT-3.5, 88.17% for Google Bard, and 78.72% for Bing-GPT4 (p = 0.002)., Conclusions: ChatGPT-3.5 offers the most accurate diagnoses when given established clinical vignettes as compared to Google Bard and Bing-GPT4. LLMs may accurately offer assessments for common otolaryngology conditions but currently require detailed prompt information and critical supervision from clinicians. There is vast potential in the clinical applicability of LLMs; however, practitioners should be wary of possible "hallucinations" and misinformation in responses., Level of Evidence: 3 Laryngoscope, 134:3997-4002, 2024., (© 2024 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.)

In the title compound, C10H11FN3O3S, the 2-fluoro-benzoyl and proponic acid groups maintain a trans-cis conformation with respect to the thiono C=S bond across their C-N bonds. The propionic acid group adopts an anti conformation about the C-C bond, with an N-C-C-C torsion angle of 173.8 (2)°. The amino groups are involved in the formation of intra-molecular N-H⋯O and N-H⋯F hydrogen bonds. In the crystal, pairs of O-H⋯O hydrogen bonds link mol-ecules into inversion dimers.