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This is the first report showing two different sulfur reduction pathways coupled to different energy conservations could coexist in one sulfur-reducing microorganism, and demonstrates that most bacteria of Sulfurimonas could employ both periplasmic and cytoplasmic polysulfide reductases to perform cyclooctasulfur reduction. The capability of sulfur reduction coupling with hydrogen oxidation may partially explain the prevalenceof Sulfurimonas in deep-sea hydrothermal vent environments.Glasdegib is approved for treating acute myeloid leukemia in elderly patients at 100 mg once daily in combination with low-dose cytarabine. Exposure-efficacy analysis showed that the survival benefit of glasdegib was not glasdegib exposure-dependent. The relationship between glasdegib exposure and adverse event (AE) cluster terms of clinical concern was explored in this analysis. The incidence and severity of dysgeusia, muscle spasms, renal toxicity, and QT interval prolonged was modeled using ordinal logistic regression. AEs were graded using the National Cancer Institute Common Terminology Criteria for Adverse Events (version 4.03). Estimated pharmacokinetic parameters were used to derive glasdegib exposure metrics. Demographic characteristics, disease factors, and other variables of interest as potential moderators of safety signals were evaluated. Clinical trial data from patients who received single-agent glasdegib (N = 70; 5-640 mg once daily); or glasdegib (N = 202, 100-200 mg once daily) with low-dose cytarabine, decitabine, or daunorubicin and cytarabine were analyzed. Glasdegib exposure was statistically significantly associated with the cluster term safety end points dysgeusia, muscle spasms, renal toxicity, and QT interval prolonged. The impact of age on muscle spasms and baseline body weight and creatinine clearance on renal toxicity helped explain the AE grade distribution. At the 100 mg once daily clinical dose, the predicted probabilities of the highest AE grade were 11.3%, 6.7%, 7.7%, and 2.5% for dysgeusia, muscle spasms, renal toxicity, and QT interval prolonged, respectively. Overall, the predicted probability of developing an AE of any severity for these safety end points was low. Therefore, no starting dose adjustments are recommended for glasdegib based on the observed safety profile.Organometallic rhodium(III) complexes with curcuminoid ligands attracted considerable attention in biological-related fields and the variation of curcuminoid ligands may regulate the biological activity of these organometallic rhodium(III) complexes. To deeply evaluate the biological influences of these complexes, the binding interactions between three rhodium(III) complexes with curcuminoid ligands and human serum albumin (HSA) were comparably investigated by spectroscopic and electrochemical techniques. The results suggested that the intrinsic fluorescence of HSA was quenched by three complexes through static fluorescence quenching mode. Three complexes bonded with Sudlow’s site I of HSA to form ground-state compounds under the binding forces of van der Waals interactions, hydrogen bonds formation, and protonation. Finally, the native conformational structure and the thermal stability of HSA were all changed. Space steric hindrance of complexes took part in the differences of the fluorescence quenching processes, and the chemical polarity of the complexes played a vital role in the variations of the structure and biological activity of HSA. These results illustrated the molecular interactions between protein and organometallic rhodium(III) complexes with curcuminoid ligands, offering new insight about the prospective applications of analogical rhodium(III) complexes in biomedicine areas.Acquired hemophilia A (AHA) is a severe auto-immune bleeding disorder. Treatment of AHA is burdensome and optimal management is still unresolved. Therefore a retrospective nationwide multi-center cohort study (1992-2018) was performed to evaluate clinical presentation and treatment efficacy and safety of AHA in the Netherlands. Multivariate logistic and Cox regression analysis was used to study independent associations between patient characteristics and clinical outcomes. A total of 143 patients (median age 73 years; 52.4% male) were included with a median follow-up of 16.8 months (IQR 3.6-41.5 months). First-line immunosuppressive treatment was mostly steroid monotherapy (67.6%), steroids/cyclophosphamide (11.9%) and steroids/rituximab (11.9%), with success rates of 35.2%, 80.0% and 66.7% respectively, P less then  .05. Eventually 75% of patients achieved complete remission (CR). A high anti-FVIII antibody titer, severe bleeding and steroid monotherapy were associated with lower CR rates. Infections, the most important adverse event, occurred significantly more often with steroid combination therapy compared to steroids alone (38.7% vs 10.6%; P = .001). Overall mortality was 38.2%, mostly due to infections (19.2%) compared to 7.7% fatal bleeds. Advanced age, underlying malignancy and ICU admission were predictors for mortality. This study showed that AHA is characterized by significant disease-related and treatment-related morbidity and mortality. A high anti-FVIII titer, severe bleeding and steroid monotherapy were associated with a lower CR rate. The efficacy of steroid combination therapies however, was overshadowed by higher infection rates and infections represented the most important cause of death. The challenging and delicate balance between treatment effectivity and safety requires ongoing monitoring of AHA and further identification of prognostic markers.Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome-wide association studies (GWAS), together with well-characterized time-to-event phenotypes provide unprecedented opportunities for developing effective survival prediction models. Recent advances in deep learning have made extraordinary achievements in establishing powerful prediction models in the biomedical field. However, the applications of deep learning approaches in survival prediction are limited, especially with utilizing the wealthy GWAS data. read more Motivated by developing powerful prediction models for the progression of an eye disease, age-related macular degeneration (AMD), we develop and implement a multilayer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Various simulation studies are performed to compare the prediction performance of the DNN survival model with several other machine learning-based survival models.