To Lick One's Wounds: A Case of Pasteurella canis oralis Osteomyelitis and Neisseria animaloris Infection from Canine Saliva.

The complications of wound infections caused by animal related trauma are well known and explored. Of the numerous polymicrobial etiologies, Neisseria animaloris and Pasteurella canis oralis have been reported only in a limited number of cases. This manuscript explores the rare finding of these species in the case of an 83-year-old male with a diabetic foot wound complicated by infection from the saliva of his pet dog. The case highlights the first instance of P. canis oralis without the setting of a penetrating animal bite, emphasizing the vulnerability of open lesions in patients whose comorbidities impair proper wound healing. These bacteria are susceptible to beta-lactams with beta-lactamase inhibitors and can be treated once identified. It is crucial to recognize rare pathogens and initiate appropriate treatment early, and to emphasize proper wound care, especially in the context of pet interactions.

Risk Factors for Pediatric Sepsis in the Emergency Department: A Machine Learning Pilot Study.

OBJECTIVE: To identify underappreciated sepsis risk factors among children presenting to a pediatric emergency department (ED). METHODS: A retrospective observational study (2017-2019) of children aged 18 years and younger presenting to a pediatric ED at a tertiary care children's hospital with fever, hypotension, or an infectious disease International Classification of Diseases (ICD)-10 diagnosis. Structured patient data including demographics, problem list, and vital signs were extracted for 35,074 qualifying ED encounters. According to the Improving Pediatric Sepsis Outcomes Classification, confirmed by expert review, 191 patients met clinical sepsis criteria. Five machine learning models were trained to predict sepsis/nonsepsis outcomes. Top features enabling model performance (N = 20) were then extracted to identify patient risk factors. RESULTS: Machine learning methods reached a performance of up to 93% sensitivity and 84% specificity in identifying patients who received a hospital diagnosis of sepsis. A random forest classifier performed the best, followed by a classification and regression tree. Maximum documented heart rate was the top feature in these models, with importance coefficients (ICs) of 0.09 and 0.21, which represent how much an individual feature contributes to the model. Maximum mean arterial pressure was the second most important feature (IC 0.05, 0.13). Immunization status (IC 0.02), age (IC 0.03), and patient zip code (IC 0.02) were also among the top features enabling models to predict sepsis from ED visit data. Stratified analysis revealed changes in the predictive importance of risk factors by race, ethnicity, oncologic history, and insurance status. CONCLUSIONS: Machine learning models trained to identify pediatric sepsis using ED clinical and sociodemographic variables confirmed well-established predictors, including heart rate and mean arterial pressure, and identified underappreciated relationships between sepsis and patient age, immunization status, and demographics.

Diphenylether-Modified 1,2-Diamines with Improved Drug Properties for Development against Mycobacterium tuberculosis.

New treatments for tuberculosis infection are critical to combat the emergence of multidrug- and extensively drug-resistant Mycobacterium tuberculosis (Mtb). We report the characterization of a diphenylether-modified adamantyl 1,2-diamine that we refer to as TBL-140, which has a minimal inhibitory concentration (MIC99) of 1.2 µg/mL. TBL-140 is effective against drug-resistant Mtb and nonreplicating bacteria. In addition, TBL-140 eliminates expansion of Mtb in cell culture infection assays at its MIC. To define the mechanism of action of this compound, we performed a spontaneous mutant screen and biochemical assays. We determined that TBL-140 treatment affects the proton motive force (PMF) by perturbing the transmembrane potential (ΔΨ), consistent with a target in the electron transport chain (ETC). As a result, treated bacteria have reduced intracellular ATP levels. We show that TBL-140 exhibits greater metabolic stability than SQ109, a structurally similar compound in clinical trials for treatment of MDR-TB infections. Combined, these results suggest that TBL-140 should be investigated further to assess its potential as an improved therapeutic lead against Mtb.