Surficial grafting of organoimido moieties enhances the capacity performance of oxometallic clusters.

Molybdenum trioxide (MoO3) with a theoretical specific capacity of 1117 mA h g-1 is widely considered a promising anode material for lithium-ion batteries. However, the irreversible conversion reactions, low electrical conductivity, and detrimental volume expansion upon Li intercalation between the one-dimensional layered structures of MoO3 hinder its practical implementation. Herein, we report a facile synthetic protocol that allows surficial modification by replacing the terminal and bridging oxo groups of molybdenum oxide clusters. Successful organoimido functionalization resulted in a large cathodic shift in Mo(VI/V) reduction by 0.6 V, pronounced electronic communication between the organic moiety and the metal-oxide unit, and significant increase in electrical conductivity (80-100 Ω interfacial charge-transfer resistance). Combined with the enlarged active surface area due to the structural hindrance induced by the organic functionality, the steady specific capacity of the organoimido-modified molybdenum oxide clusters was greater than 1200 mA h g-1 at 900 mA g-1 at the end of 360 cycles, where the best value of 1653 mA h g-1 was achieved for the nitroaniline-substituted species. The steady capacity of 480 mA h g-1 was achieved in the fast charge-discharge process (3000 mA g-1) over 1400 cycles. The results indicate that the surficial modification of metal oxides with organo moieties using our facile synthetic method has broad application potential for metal oxides to be used as high-capacity electrode materials in the future.

Demethylation of an artificial hydrogenase agent for prolonged CO release and enhanced anti-tau aggregation activity.

Carbon monoxide (CO) plays an important role in signaling in cells, making its use as a therapeutic tool highly intriguing. Reduced burst emissions are important to avoid the cytotoxicity and tissue damage caused by CO. Here, we developed a stable diiron carbonyl [FeFe] hydrogenase agent that enables prolonged CO release activity (half-life of over 9 h) in cells. The integrated analysis allowed the identification of the key intermediate sites and CO accumulations with subcellular resolution. We observed that the [FeFe]A complex was enriched in neurons with S-methyl bond rupture. Furthermore, the [FeFe]A complex efficiently reduced the aggregation of tau proteins (49.3% reduction) and showed superior biocompatibility in nerve cells (∼ 95% survival).

Statistical learning in preclinical drug proarrhythmic assessment.

Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.

Comparison of Methicillin-Resistant Staphylococcus aureus Isolates from Cellulitis and from Osteomyelitis in a Taiwan Hospital, 2016-2018.

Methicillin-resistant Staphylococcus aureus (MRSA) causes superficial infections such as cellulitis or invasive infections such as osteomyelitis; however, differences in MRSA isolates from cellulitis (CL-MRSA) and from osteomyelitis (OM-MRSA) at the same local area remain largely unknown. A total of 221 MRSA isolates including 106 CL-MRSA strains and 115 OM-MRSA strains were collected at Chang-Gung Memorial Hospital in Taiwan between 2016 and 2018, and their genotypic and phenotypic characteristics were compared. We found that OM-MRSA isolates significantly exhibited higher rates of resistance to multiple antibiotics than CL-MRSA isolates. Genotypically, OM-MRSA isolates had higher proportions of the SCCmec type III, the sequence type ST239, and the spa type t037 than CL-MRSA isolates. Besides the multidrug-resistant lineage ST239-t037-SCCmecIII more prevalent in OM-MRSA, higher antibiotic resistance rates were also observed in several other prevalent lineages in OM-MRSA as compared to the same lineages in CL-MRSA. Furthermore, when prosthetic joint infection (PJI) associated and non-PJI-associated MRSA strains in osteomyelitis were compared, no significant differences were observed in antibiotic resistance rates between the two groups, albeit more diverse genotypes were found in non-PJI-associated MRSA. Our findings therefore suggest that deep infections may allow MRSA to evade antibiotic attack and facilitate the convergent evolution and selection of multidrug-resistant lineages.

Hierarchical Bayes approach for subgroup analysis.

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

Environmental Factors Can Influence Mitochondrial Inheritance in the Saccharomyces Yeast Hybrids.

Mitochondria play a critical role in the generation of metabolic energy and are crucial for eukaryotic cell survival and proliferation. In most sexual eukaryotes, mitochondrial DNA (mtDNA) is inherited from only one parent in non-Mendelian inheritance in contrast to the inheritance of nuclear DNA. The model organism Saccharomyces cerevisiae is commonly used to study mitochondrial biology. It has two mating types: MATa and MATα. Previous studies have suggested that the mtDNA inheritance patterns in hybrid diploid cells depend on the genetic background of parental strains. However, the underlying mechanisms remain unclear. To elucidate the mechanisms, we examined the effects of environmental factors on the mtDNA inheritance patterns in hybrids obtained by crossing S. cerevisiae with its close relative S. paradoxus. The results demonstrated that environmental factors can influence mtDNA transmission in hybrid diploids, and that the inheritance patterns are strain dependent. The fitness competition assay results showed that the fitness differences can explain the mtDNA inheritance patterns under specific conditions. However, in this study, we found that fitness differences cannot fully be explained by mitochondrial activity in hybrids under stress conditions.

Use of prior information for Bayesian evaluation of bridging studies.

The ICH E5 guideline defines a bridging study as a supplementary study conducted in the new region to provide pharmacodynamic or clinical data on efficacy, safety, dosage, and dose regimen to allow extrapolation of the foreign clinical data to the population of the new region. Therefore, a bridging study is usually conducted in the new region only after the test product has been approved for commercial marketing in the original region based on its proven efficacy and safety. In this paper we address the issue of analysis of clinical data generated by the bridging study conducted in the new region to evaluate the similarity for extrapolation of the foreign clinical data to the population of the new region. Information on efficacy, safety, dosage, and dose regimen of the original region cannot be concurrently obtained from the local bridging studies but available in the trials conducted in the original region. Liu et al. (2002) have proposed a Bayesian approach to synthesize the data generated by the bridging study and foreign clinical data generated in the original region for assessment of similarity based on superior efficacy of the test product over a placebo control. However, the results of the bridging studies using their approach will be overwhelmingly dominated by the results of the original region due to an imbalance of sample sizes between the regions. Therefore, in this paper we propose a Bayesian approach with the use of a mixture prior for assessment of similarity between the new and original region based on the concept of positive treatment effect. Methods for sample size determination for the bridging study are also proposed. Numerical examples illustrate applications of the proposed procedures in different scenarios.