Mycobacterium abscessus complex comprises a group of rapidly growing, multidrug-resistant, nontuberculous mycobacteria that are responsible for a wide spectrum of skin and soft tissue diseases, central nervous system infections, bacteremia, and ocular and other infections. M. abscessus complex is differentiated into 3 subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. massiliense, and M. abscessus subsp. bolletii. The 2 major subspecies, M. abscessus subsp. abscessus and M. abscessus subsp. massiliense, have different erm(41) gene patterns. This gene provides intrinsic resistance to macrolides, so the different patterns lead to different treatment outcomes. M. abscessus complex outbreaks associated with cosmetic procedures and nosocomial transmissions are not uncommon. Clarithromycin, amikacin, and cefoxitin are the current antimicrobial drugs of choice for treatment. However, new treatment regimens are urgently needed, as are rapid and inexpensive identification methods and measures to contain nosocomial transmission and outbreaks.

Despite the well-documented association between diabetes and active tuberculosis, evidence of the association between diabetes and latent tuberculosis infection (LTBI) remains limited and inconsistent. We included observational studies that applied either the tuberculin skin test or the interferon gamma release assay for diagnosis of LTBI and that provided adjusted effect estimate for the association between diabetes and LTBI. We searched PubMed and EMBASE through 31 January 2016. The risk of bias of included studies was assessed using a quality assessment tool modified from the Newcastle-Ottawa scale. Thirteen studies (1 cohort study and 12 cross-sectional studies) were included, involving 38263 participants. The cohort study revealed an increased but nonsignificant risk of LTBI among diabetics (risk ratio, 4.40; 95% confidence interval [CI], 0.50–38.55). For the cross-sectional studies, the pooled odds ratio from the random-effects model was 1.18 (95% CI, 1.06–1.30), with a small statistical heterogeneity across studies (I², 3.5%). The risk of bias assessment revealed several methodological issues, but the overall direction of biases would reduce the positive causal association between diabetes and LTBI. Diabetes was associated with a small but statistically significant risk for LTBI. Findings from this review could be used to inform future cost-effectiveness analysis on the impact of LTBI screening programs among diabetics.

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Sepsis-related mortality has been found increased in RAG-1 knockout mice. However, in patients admitted to medical intensive care units, it is unknown whether severe lymphocyte depletion at admission is associated with increased interleukin (IL)-7 and IL-15 levels in circulation, and increased mortality. We prospectively enrolled 92 patients who were admitted to medical intensive care units for severe sepsis or septic shock. At admission, 24 patients (26.1%) had severe lymphopenia, defined as lymphocyte counts of less than 0.5 × 10³/μL. Severe lymphopenia was associated with significantly higher plasma levels of tumor necrosis factor α, IL-6, IL-8, and IL-10 and was also independently associated with 28-day mortality (adjusted hazard ratio, 3.532; 95% confidence interval, 1.482−8.416; P = 0.004). The levels of plasma IL-15, but not IL-7, were increased modestly in patients with severe lymphopenia compared with those without (median, 12.2 vs. 6.4 pg/mL; P = 0.005). The elevated plasma IL-15 levels were contrarily associated with significantly decreased B-cell lymphoma 2 mRNA expression in peripheral blood mononuclear cells. In conclusion, severe lymphopenia was associated with increased mortality in patients with severe sepsis. We found that patients with sepsis with severe lymphopenia had down-regulated B-cell lymphoma 2 mRNA expression in peripheral blood mononuclear cells, despite increased plasma IL-15 concentrations. Whether IL-7 and IL-15 are insufficient in patients with severe lymphopenia during severe sepsis warrants further investigations.

We describe six patients with hospital-acquired bacteremia caused byMethylobacteriumspecies, includingM. radiotolerans(n= 2),M. thiocyanatum(n= 2),M. aminovorans(n= 1), andM. lusitanum(n= 1), which were confirmed to species level by 16S rRNA gene sequence analysis. Among these patients, five had catheter-related bacteremia and all had favorable outcomes.