The TB Portals program is an international consortium of physicians, radiologists, and microbiologists from countries with a heavy burden of drug-resistant tuberculosis working with data scientists and information technology professionals. Together, we have built the TB Portals, a repository of socioeconomic/geographic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberculosis backed by shareable, physical samples. Currently, there are 1,299 total cases from five country sites (Azerbaijan, Belarus, Moldova, Georgia, and Romania), 976 (75.1%) of which are multidrug or extensively drug resistant and 38.2%, 51.9%, and 36.3% of which contain X-ray, computed tomography (CT) scan, and genomic data, respectively. The top Mycobacterium tuberculosis lineages represented among collected samples are Beijing, T1, and H3, and single nucleotide polymorphisms (SNPs) that confer resistance to isoniazid, rifampin, ofloxacin, and moxifloxacin occur the most frequently. These data and samples have promoted drug discovery efforts and research into genomics and quantitative image analysis to improve diagnostics while also serving as a valuable resource for researchers and clinical providers. The TB Portals database and associated projects are continually growing, and we invite new partners and collaborations to our initiative. The TB Portals data and their associated analytical and statistical tools are freely available at https://tbportals.niaid.nih.gov/.KEYWORDS tuberculosis, digital health, interactive portals, MDR-TB, Mycobacterium tuberculosis, query, XDR-TB, drug-resistant TB T uberculosis (TB) continues to represent a major health problem worldwide. An estimated one-third of the world's population is living with latent TB (1). In 2015, there were an estimated 10.4 million new (incident) TB cases worldwide, of which 5.9 million (56%) were among men, 3.5 million (34%) were among women, and 1.0 million (10%) were among children. People living with HIV accounted for 1.2 million (11%) of

Background-Prognostic evaluation of patients with primary pulmonary hypertension (PPH) requires right heart catheterisation. The development of accurate non-invasive methods for monitoring these patients remains an important task. Cyclic guanosine monophosphate (cGMP) is an indicator of the action of natriuretic peptides and nitric oxide on target cells. Plasma and urinary cGMP concentrations are raised in patients with congestive heart failure in whom they correlate closely with haemodynamic parameters and disease severity. The aim of the present study was to determine whether the urinary concentration of cGMP could be used as a non-invasive marker of haemodynamic impairment in patients with severe PPH. Methods-Urinary

Background Noninvasive ventilation (NIV) reduces the rate of endotracheal intubation (ETI) and overall mortality in severe acute exacerbation of COPD (AECOPD) with acute respiratory failure and is increasingly applied in respiratory intermediate care units. However, inadequate patient selection and incorrect management of NIV increase mortality. We aimed to identify factors that predict the outcome of NIV in AECOPD. Also, we looked for factors that influence ventilator settings and duration. Methods A prospective cohort study was undertaken in a respiratory intermediate care unit in an academic medical center between 2016 and 2017. Age, BMI, lung function, arterial pH and pCO2 at admission (t0), at 1–2 h (t1) and 4–6 h (t2) after admission, creatinine clearance, echocardiographic data (that defined left heart dysfunction), mean inspiratory pressure during the first 72 h (mIPAP-72 h) and hours of NIV during the first 72 h (dNIV-72 h) were recorded. Main outcome was NIV failure (i.e., ETI or in-hospital death). Secondary outcomes were in-hospital mortality, length of stay (LOS), duration of NIV (days), mIPAP-72 h, and dNIV-72 h. Results We included 89 patients (45 male, mean age 67.6 years) with AECOPD that required NIV. NIV failure was 12.4%, and in-hospital mortality was 11.2%. NIV failure was correlated with days of NIV, LOS, in-hospital mortality ( p < 0.01), and kidney dysfunction ( p < 0.05). In-hospital mortality was strongly associated with days of NIV (OR 1.27, 95%CI: 1.07–1.5, p < 0.01) and with FEV1 ( p < 0.05). All other investigated parameters (including left heart dysfunction, dNIV-72 h, mIPAP-72 h, pH, etc.) did not influence NIV failure or mortality. dNIV-72 h and days of NIV were independent predictors of LOS ( p < 0.01). Regarding the secondary outcomes, left heart dysfunction and pH at 1-2 h independently predicted NIV duration (dNIV-72 h, p < 0.01), while BMI and baseline pCO2 predicted NIV settings (mIPAP-72 h, p < 0.01). Conclusion In-hospital mortality and NIV failure were not influenced by BMI, left heart dysfunction, age, nor by arterial blood gas values in the first 6 h of NIV. Patients with severe acidosis and left heart dysfunction required prolonged use of NIV. BMI and pCO2 levels influence the NIV settings in AECOPD regardless of lung function.

BackgroundMicrocalorimetric bacterial growth studies have illustrated that thermograms differ significantly with both culture media and strain. The present contribution examines the possibility of discriminating between certain bacterial strains by microcalorimetry and the qualitative and quantitative contribution of the sample volume to the observed thermograms. Growth patterns of samples of Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC 25922) were analyzed. Certain features of the thermograms that may serve to distinguish between these bacterial strains were identified.ResultsThe thermograms of the two bacterial strains with sample volumes ranging from 0.3 to 0.7 ml and same initial bacterial concentration were analyzed. Both strains exhibit a roughly 2-peak shape that differs by peak amplitude and position along the time scale. Seven parameters corresponding to the thermogram key points related to time and heat flow values were proposed and statistically analyzed. The most relevant parameters appear to be the time to reach a heat flow of 0.05 mW (1.67 ± 0.46 h in E. coli vs. 2.99 ± 0.53 h in S. aureus, p < 0.0001), the time to reach the first peak (3.84 ± 0.5 h vs. 5.17 ± 0.49 h, p < 0.0001) and the first peak value (0.19 ± 0.02 mW vs. 0.086 ± 0.012 mW, p < 0.0001). The statistical analysis on 4 parameters of volume-normalized heat flow thermograms showed that the time to reach a volume-normalized heat flow of 0.1 mW/ml (1.75 ± 0.37 h in E. coli vs. 2.87 ± 0.65 h in S. aureus, p < 0.005), the time to reach the first volume-normalized peak (3.78 ± 0.47 h vs. 5.12 ± 0.52 h, p < 0.0001) and the first volume-normalized peak value (0.35 ± 0.05 mW/ml vs. 0.181 ± 0.040 mW/ml, p < 0.0001) seem to be the most relevant. Peakfit® decomposition and analysis of the observed thermograms complements the statistical analysis via quantitative arguments, indicating that: (1) the first peak pertains to a faster, “dissolved oxygen” bacterial growth (where the dissolved oxygen in the initial suspension acts as a limiting factor); (2) the second peak indicates a slower “diffused oxygen” growth that involves transport of oxygen contained in the unfilled part of the microcalorimetric cell; (3) a strictly fermentative growth component may slightly contribute to the observed complex thermal signal.ConclusionThe investigated strains of Staphylococcus aureus and Escherichia coli display, under similar experimental conditions, distinct thermal growth patterns. The two strains can be easily differentiated using a selection of the proposed parameters. The presented Peakfit analysis of the complex thermal signal provides the necessary means for establishing the optimal growth conditions of various bacterial strains. These conditions are needed for the standardization of the isothermal microcalorimetry method in view of its further use in qualitative and quantitative estimation of bacterial growth.