Studies on the Catechin Constituents of Bark of Cinnamomum sieboldii.

Screening for bioactivity related to anti-infective, anti-methicillin-resistant Staphylococcus aureus (MRSA) and anti-viral activity, led us to identify active compounds from a methanol extract of Litsea japonica (Thub.) Juss. and the hot water extract of bark of Cinnamomum sieboldii Meisn (also known as Karaki or Okinawa cinnamon). The two main components in these extracts were identified as the catechin trimers (+)-cinnamtannin B1 and pavetannin B5. Moreover, these extracts exhibited anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activity. The structures of these catechin trimers were previously determined by chemical and spectroscopic methods. Pavetanin B5 has never been reported to be isolated as a pure form and has been obtained as a mixture with another component. Although other groups have reported the putative structure of pavetannin B5, preparation of the methylated derivative of pavetannin B5 in this study allowed us to obtain the pure form for the first time as the undecamethyl derivative and confirm its exact structure. Commercially available (+)-cinnamtannin B1 and aesculitannin B (C2'-epimer of cinnamtannin B1) both of which contained pavetannin B5 as a minor component, and C. sieboldii bark extract (approx. 5/2 mixture of (+)-cinnamtannin B1/pavetannin B5) were assessed for anti-SARS-CoV-2 activity. Both C. sieboldii bark extract and commercially available aesculitannin B showed viral growth inhibitory activity.

Investigation of Streptomyces sp. Strain EMB24 Secondary Metabolite Profile Has Unraveled Its Extraordinary Antibacterial Potency Against Drug-Resistant Bacteria.

With the overuse and misuse of antibiotics amid COVID-19 pandemic, the antimicrobial resistance, which is already a global challenge, has accelerated its pace significantly. Finding novel and potential antibiotics seems one of the probable solutions. In this work, a novel Streptomyces sp. strain EMB24 was isolated and found to be an excellent source of antimicrobials as confirmed by agar-plug assay. It showed antibacterial activity against infection-causing bacteria, namely Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. In addition, Streptomyces sp. strain EMB24 inhibited the growth methicillin-resistant Staphylococcus aureus (MRSA), tetracycline-resistant Neisseria gonorrhoeae, and ampicillin-resistant Neisseria gonorrhoeae. Furthermore, to get deep insights about the genome and biosynthetic gene clusters producing antibiotics, whole genome sequencing was done. The strain EMB24 is closely related to the Streptomyces longispororuber as revealed by phylogenetic analysis which is a potential source of antibiotics and pigments as undecylprodigiosin and metacycloprodigiosin belonging to the class prodigiosin. Naphthyridinomycin, alkylresorcinols, desferrioxamine B and E, venezuelin, aborycin, MS-271, and siamycin are potent therapeutics that shared 100% similarity with the reference strain as revealed by the online antiSMASH tool.

A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.

Background: The rising prevalence of multi-drug resistant organisms (MDROs), such as Methicillin-resistant Staphylococcus aureus (MRSA), Vancomycin-resistant Enterococci (VRE), and Carbapenem-resistant Enterobacteriaceae (CRE), is an increasing concern in healthcare settings. Materials and Methods: Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. We performed threshold optimization for converting predicted probabilities into binary predictions and identified the cut-off maximizing the sum of sensitivity and specificity. Results: Four thousand six hundred seventy ICU admissions (3,958 patients) were examined. MDRO colonization rate was 17.59% (13.03% VRE, 1.45% CRE, and 7.47% MRSA). Our study achieved the following sensitivity and specificity values with the best performing models, respectively: 80% and 66% for VRE with LR, 73% and 77% for CRE with XGBoost, 76% and 59% for MRSA with RF, and 82% and 83% for MDRO (i.e., VRE or CRE or MRSA) with RF. Further, we identified several predictors of MDRO colonization, including long-term care facility stay, current diagnosis of skin/subcutaneous tissue or infectious/parasitic disease, and recent isolation precaution procedures before ICU admission. Conclusion: Our data-driven modeling framework can be used as a clinical decision support tool for timely predictions, characterization and identification of high-risk patients, and selective and timely use of infection control measures in ICUs.