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Toan Nguyen Duy,1,2 Quyen Dao Bui Quy,3 Loc Nguyen Duc,4 Diem Ho Viet Le,5 Khoa Le Ha,6 Tuyen Do Gia,6 Kien Nguyen Trung,1,2 Tam Nguyen Van,1,2 Oanh Nguyen Oanh,1,2 Thang Le Viet1,2 1Military Hospital 103, Hanoi, Vietnam; 2Vietnam Military Medical University, Hanoi, Vietnam; 3Cho Ray Hospital, Ho Chi Minh, Vietnam; 4An Sinh Hospital, Ho Chi Minh, Vietnam; 5Link-02 Clinic, Pham Ngoc Thach, Da Lat, Lam Dong, Vietnam; 6Hanoi Medical University, Hanoi, VietnamCorrespondence: Thang Le Viet, Military Hospital 103, Vietnam Military Medical University, Department of Nephrology and Hemodialysis, 261 Phung Hung, Ha Dong, Hanoi, Vietnam, Tel +84982249968, Email lethangviet@yahoo.co.ukObjective: To evaluate the value of contrast volume/glomerular filtration ratio (Vc/eGFR ratio) and urine Neutrophil Gelatinase-Associated Lipocalin (uNGAL) in predicting the progression contract associated-acute kidney injury (CA-AKI) to chronic kidney disease (CKD) in planned percutaneous coronary intervention (PCI) patients.Patients and Methods: We examined 387 adult patients who had undergone planned percutaneous coronary intervention (PCI). We determined acute kidney injury (AKI) and chronic kidney disease (CKD) using the criteria set by the Kidney Disease: Improving Global Outcomes (KDIGO). We calculated the estimated glomerular filtration rate (eGFR) using the CKD-EPI formula based on serum creatinine levels. To determine the Vc/eGFR ratio, we considered the contrast medium volume and eGFR for each patient. Additionally, we measured urine NGAL levels using the ELISA method.Results: The percentage of CA-AKI patients who developed CKD after planned PCI was 36.36%. Within the CA-AKI to CKD group, the Vc/eGFR ratio was 2.82, and uNGAL levels were significantly higher at 72.74 ng/mL compared to 1.93 ng/mL for Vc/eGFR ratio and 46.57 ng/mL for uNGAL in the recovery CA-AKI group. This difference was statistically significant (p< 0.001). Diabetic mellitus, urine NGAL concentration, and Vc/eGFR ratio were found to be independent factors in the progression of CA-AKI to CKD. The Vc/eGFR ratio and uNGAL showed predictive capabilities for progressing CA-AKI to CKD with an AUC of 0.884 and 0.878, respectively. The sensitivity was 81.3% for both, while the specificity was 89.3% for Vc/eGFR ratio and 85.7% for uNGAL.Conclusion: The Vc/eGFR ratio and uNGAL were good predictors for CA-AKI to CKD in planned PCI patients.Keywords: planned percutaneous coronary intervention, contract associated-acute kidney injury, CA-AKI progression to CKD, Vc/eGFR ratio, uNGAL

Viet Tran Quoc,1 Dung Nguyen Thi Ngoc,2,3 Trung Nguyen Hoang,4 Hoa Vu Thi,4 Minh Tong Duc,4 Thanh Do Pham Nguyet,2 Thanh Nguyen Van,5 Diep Ho Ngoc,2 Giang Vu Son,6 Thanh Bui Duc7 1Intensive Care Unit, Military Hospital 175, Ho Chi Minh City, Vietnam; 2Department of Military Science and Training, Military Hospital 175, Ho Chi Minh City, Vietnam; 3Hanoi University of Public Health, Hanoi, Vietnam; 4Department of Military Hygiene, Vietnam Military Medical University, Hanoi, Vietnam; 5Department of General Planning, Military Hospital 175, Ho Chi Minh City, Vietnam; 6Department of Personnel, Military Hospital 175, Ho Chi Minh City, Vietnam; 7Institute of Trauma and Orthopedics, Military hospital 175, Ho Chi Minh City, VietnamCorrespondence: Thanh Bui Duc, Military Hospital 175, 786 Nguyen Kiem, 3 Ward, Go Vap District, Ho Chi Minh City, Vietnam, Tel +84 975 430 258, Email buiducthanh175@yahoo.comIntroduction: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is designed to predict outcome variables using an algorithm to enable “machines” to learn the “rules” from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals.Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses.Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1-score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2.Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients’ EMRs.Keywords: antibiotic resistance, machine learning, XGBoost, LightGBM, random forest

BackgroundBreastfeeding is associated with reduced risk of becoming overweight or obese later in life. Breastfed babies grow more slowly during infancy than formula-fed babies. Among offspring exposed in utero to maternal glucose intolerance, prospective data on growth during infancy have been unavailable. Thus, scientific evidence is insufficient to conclude that breastfeeding reduces the risk of obesity among the offspring of diabetic mothers (ODM). To address this gap, we devised the Study of Women, Infant Feeding and Type 2 Diabetes after GDM Pregnancy and Growth of their Offspring, also known as the SWIFT Offspring Study. This prospective, longitudinal study recruited mother-infant pairs from the SWIFT Study, a prospective study of women with recent gestational diabetes mellitus (GDM). The goal of the SWIFT Offspring Study is to determine whether breastfeeding intensity and duration, compared with formula feeding, are related to slower growth of GDM offspring during the first year life. This article details the study design, participant eligibility, data collection, and methodologies. We also describe the baseline characteristics of the GDM mother-infant pairs.MethodsThe study enrolled 466 mother-infant pairs among GDM deliveries in northern California from 2009-2011. Participants attended three in-person study exams at 6-9 weeks, 6 months and 12 months after delivery for infant anthropometry (head circumference, body weight, length, abdominal circumference and skinfold thicknesses), as well as maternal anthropometry (body weight, waist circumference and percent body fat). Mothers also completed questionnaires on health and lifestyle behaviors, including infant diet, sleep and temperament. Breastfeeding intensity and duration were assessed via several sources (diaries, telephone interviews, monthly mailings and in-person exams) from birth through the first year of life. Pregnancy course, clinical perinatal and newborn outcomes were obtained from health plan electronic medical records. Infant saliva samples were collected and stored for genetics studies.DiscussionThis large, racially and ethnically diverse cohort of GDM offspring will enable evaluation of the relationship of infant feeding to growth during infancy independent of perinatal characteristics, sociodemographics and other risk factors. The longitudinal design provides the first quantitative measures of breastfeeding intensity and duration among GDM offspring during early life.

The 11 lytic transglycosylases of Pseudomonas aeruginosa have overlapping activities in the turnover of the cell‐wall peptidoglycan. Rare lipoprotein A (RlpA) is distinct among the 11 by its use of only peptidoglycan lacking peptide stems. The spatial localization of RlpA and its interactome within P. aeruginosa are unknown. We employed suppression of introduced amber codons at sites in the rlpA gene for the introduction of the unnatural‐amino‐acids Νζ‐[(2‐azidoethoxy)carbonyl]‐l‐lysine (compound 1) and Nζ‐[[[3‐(3‐methyl‐3H‐diazirin‐3‐yl)propyl]amino]carbonyl]‐l‐lysine (compound 2). In live P. aeruginosa, full‐length RlpA incorporating compound 1 into its sequence was fluorescently tagged using strained‐promoted alkyne‐azide cycloaddition and examined by fluorescence microscopy. RlpA is present at low levels along the sidewall length of the bacterium, and at higher levels at the nascent septa of replicating bacteria. In intact P. aeruginosa, UV photolysis of full‐length RlpA having compound 2 within its sequence generated a transient reactive carbene, which engaged in photoaffinity capture of neighboring proteins. Thirteen proteins were identified. Three of these proteins—PBP1a, PBP5, and MreB—are members of the bacterial divisome. The use of the complementary methodologies of non‐canonical amino‐acid incorporation, photoaffinity proximity analysis, and fluorescent microscopy confirm a dominant septal location for the RlpA enzyme of P. aeruginosa, as a divisome‐associated activity. This accomplishment adds to the emerging recognition of the value of these methodologies for identification of the intracellular localization of bacterial proteins. [ABSTRACT FROM AUTHOR]