Associations between serum amino acids and incident type 2 diabetes in Chinese rural adults

Background and aimsSome amino acids (AAs) may be associated with type 2 diabetes (T2DM). This study aimed to determine the associations of individual AAs with the development of T2DM in rural Chinese adults.Methods and resultsA cohort study of 1199 individuals aged 18 years or older was conducted from 2006 to 2008 in a rural community of Deqing, China, a repeated survey was done in 2015 and data linkage with the electronic health records system was performed each year for identifying new T2DM cases. A high-performance liquid chromatography approach was used to measure the baseline serum concentrations of 15 AAs. Cox proportional hazards models were used to examine the associations between AAs and the risk of incident T2DM. A total of 98 new T2DM cases were identified during the follow-up of 12 years on average. Among 15 AAs, proline was associated with an increased risk of incident T2DM after adjusted for age, sex, body mass index, fasting plasma glucose, family history of T2DM, smoking status, alcohol use, and history of hypertension, the adjusted hazard ratio for 1-standard deviation increment was 1.20 (95% confidence interval: 1.00, 1.43). The association tended to be more marked in subjects younger than 60 years and overweight/obese subjects. Among participants without hypertension, proline and phenylalanine were associated with an increased risk of incident T2DM, while aspartic acid was associated with a decreased risk.ConclusionSerum proline was associated with the risk of incident T2DM in rural Chinese adults and might be a potential predictor.     

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review

BackgroundThe matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination.ObjectivesThe aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra.Data sourcesUsing PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification.Study eligibility criteriaOriginal research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded.MethodsA systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted.ResultsFrom the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets.ConclusionsA growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.     

Statins use and its impact in EGFR‐TKIs resistance to prolong the survival of lung cancer patients: A Cancer registry cohort study in Taiwan

Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal growth factor receptor‐tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population‐based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non‐statins and 41 statins users who had undergone EGFR‐TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan‐Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non‐statins group (4‐y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%‐81.4% vs. 85.5%; 95% CI, 78.5%‐98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio [HR], 0.51, 95% CI, 0.29‐0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41‐0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR‐TKIs and played a synergistic anticancer role.     

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii

ObjectivesIncreased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.MethodsA. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters.ResultsMonotherapies resulted in regrowth to ~10¹⁰ cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC₅₀ (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 10⁸ cfu/mL starting inoculum to a point of 10⁰ cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be ‘loaded’ with 80.5% and 42.2% of the daily dose, respectively).ConclusionThis study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the ‘traditional’ indices of antibiotic action.     

Analysis of AcrB in Klebsiella pneumoniae reveals natural variants promoting enhanced multidrug resistance

Infections caused by Klebsiella pneumoniae are often difficult to manage due to the high frequency of multidrug resistance, often conferred by efflux pumps. In this study, we analyzed sequence variations of the major RND family multidrug efflux pump AcrB from 387 assembled K. pneumoniae genomes. We confirm that AcrB is a highly-conserved efflux pump in K. pneumoniae, and identified several variants that were prevalent in clinical isolates. Molecular dynamics analyses on two of these variants (L118M and S966A) suggested conformational changes that may correlate with increased drug efflux capabilities. The L118M change resulted in enhanced protein rigidity while the flexibility of drug binding pockets was stable or increased, and the interactions between the proximal pockets and water molecules were stronger. For S966A, the significantly enlarged proximal pocket suggested higher drug accommodation ability. Consistent with these predictions, the L118M and S966A variants conferred a slightly increased ability to grow in the presence of tetracycline and to survive cefoxitin exposure when overexpressed. In summary, our results suggest that the emergence of enhanced-function AcrB variants may be a potential risk for increased antibiotic resistance in clinical K. pneumoniae isolates.     

Inflammatory and fibrotic mechanisms in NAFLD—Implications for new treatment strategies

Non-alcoholic fatty liver disease is comprised of either simple steatosis (non-alcoholic fatty liver) or a more advanced inflammatory and fibrogenic stage (non-alcoholic steatohepatitis [NASH]). NASH affects a growing proportion of the global adult and pediatric population, leading to rising rates of liver fibrosis and hepatocellular carcinoma. NASH is a multifactorial disease that is part of a systemic metabolic disorder. Here, we provide an overview of the metabolic underpinnings of NASH pathogenesis and established drivers of inflammation and fibrosis. Clarification of underlying fibrogenic and inflammatory mechanisms will advance the development of novel treatment strategies as there are no approved therapies at present. We discuss emerging experimental approaches and potential novel investigational strategies derived from animal models including the inflammasome, epigenetic reprogramming, Hippo signaling, Notch signaling, engineered T cells to remove fibrogenic HSCs, and HSC-specific targeting therapies. Recently completed and ongoing clinical trials and antifibrotics are discussed, illuminating the growing expectation that one or more therapies will yield clinical benefit in NASH in the coming years.