Epigenomic characterization of Clostridioides difficile finds a conserved DNA methyltransferase that mediates sporulation and pathogenesis

Clostridioides (formerly Clostridium) difficile is a leading cause of healthcare-associated infections. Although considerable progress has been made in the understanding of its genome, the epigenome of C. difficile and its functional impact has not been systematically explored. Here, we perform a comprehensive DNA methylome analysis of C. difficile using 36 human isolates and observe a high level of epigenomic diversity. We discovered an orphan DNA methyltransferase with a well-defined specificity, the corresponding gene of which is highly conserved across our dataset and in all of the approximately 300 global C. difficile genomes examined. Inactivation of the methyltransferase gene negatively impacts sporulation, a key step in C. difficile disease transmission, and these results are consistently supported by multiomics data, genetic experiments and a mouse colonization model. Further experimental and transcriptomic analyses suggest that epigenetic regulation is associated with cell length, biofilm formation and host colonization. These findings provide a unique epigenetic dimension to characterize medically relevant biological processes in this important pathogen. This study also provides a set of methods for comparative epigenomics and integrative analysis, which we expect to be broadly applicable to bacterial epigenomic studies

Substance P and experimental joint inflammation

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Machine learning for decision making in healthcare

This chapter presents a very important use‐case from Rizwan et al. to highlight the role of machine learning in making autonomous decisions for the provision of healthcare services. The scenario presented in this chapter involves use of the data collected for an important bio‐marker, Galvanic Skin Response measured with electrodermal activity sensors, and use of machine learning for auto diagnosis of hydration levels in the human body. The main steps of the bio‐electrical impedance analysis methodology followed in the development of the hydration level detection model are illustrated briefly. Like many real data‐based healthcare studies the main objectives of this chapter are the identification of the appropriate body posture and optimal interval of time for the data collection of bio‐markers and selection of the right combination of features and reliable algorithm for the model development for the auto diagnosis. In the light of the analytical study, the impact of these factors is discussed

The penicillin allergy delabeling program: A Multicenter Whole-of-Hospital health services intervention and comparative effectiveness study

Background Penicillin allergies are associated with inferior patient and antimicrobial stewardship outcomes. We implemented a whole-of-hospital program to assess the efficacy of inpatient delabeling for low-risk penicillin allergies in hospitalized inpatients. Methods Patients ≥ 18 years of age with a low-risk penicillin allergy were offered a single-dose oral penicillin challenge or direct label removal based on history (direct delabeling). The primary endpoint was the proportion of patients delabeled. Key secondary endpoints were antibiotic utilization pre- (index admission) and post-delabeling (index admission and 90 days). Results Between 21 January 2019 and 31 August 2019, we assessed 1791 patients reporting 2315 antibiotic allergies, 1225 with a penicillin allergy. Three hundred fifty-five patients were delabeled: 161 by direct delabeling and 194 via oral penicillin challenge. Ninety-seven percent (194/200) of patients were negative upon oral penicillin challenge. In the delabeled patients, we observed an increase in narrow-spectrum penicillin usage (adjusted odds ratio [OR], 10.51 [95% confidence interval {CI}, 5.39–20.48]), improved appropriate antibiotic prescribing (adjusted OR, 2.13 [95% CI, 1.45–3.13]), and a reduction in restricted antibiotic usage (adjusted OR, 0.38 [95% CI, .27–.54]). In the propensity score analysis, there was an increase in narrow-spectrum penicillins (OR, 10.89 [95% CI, 5.09–23.31]) and β-lactam/β-lactamase inhibitors (OR, 6.68 [95% CI, 3.94–11.35]) and a reduction in restricted antibiotic use (OR, 0.52 [95% CI, .36–.74]) and inappropriate prescriptions (relative risk ratio, 0.43 [95% CI, .26–.72]) in the delabeled group compared with the group who retained their allergy label. Conclusions This health services program using a combination of direct delabeling and oral penicillin challenge resulted in significant impacts on the use of preferred antibiotics and appropriate prescribing