Citations

Abstract Background: The antibiotic resistance of pathogenic microbes due to the overuse of antibiotics is a growing threat to the public health and can eventually reduce the therapeutic effects of antibiotics. Hydrogel-based systems for local delivery of antibiotics can be a solution to this problem. Metal-organic frameworks (MOFs) have received considerable interest for use as antimicrobial materials due to their high porosity, large surface area to volume, diverse structures, and tunable topologies. Methods: In this study, we developed MOF-encapsulated and dual (photo & chemical)-crosslinkable alginate hydrogel as an antimicrobial scaffold. We synthesized antimicrobial Cu(AZPY)-MOF using solvothermal reaction and encapsulated them in an alginate-based hydrogel via visible light photo-crosslinking and calcium ion-induced chemical-crosslinking processes. Results: Powder X-ray diffraction, Fourier-transform infrared spectroscopy, thermogravimetric analysis, and scanning electron microscopy confirmed the successful fabrication of Cu(AZPY)-MOF encapsulated alginate hydrogel. The mechanical strength of the dual-crosslinked hydrogels was better than that of the photo-crosslinked hydrogel. Encapsulation of Cu(AZPY)-MOF in an alginate hydrogel resulted in a further increase in the mechanical strength of the hydrogel owing to the additional interaction of alginate with Cu-MOF. Moreover, the Cu(AZPY)-MOF encapsulated alginate hydrogel exhibited excellent antibacterial and antifungal properties against two bacterial strains (i.e., MRSA and S. mutans) and one fungal strain (C. albicans) as well as negligible cytotoxicity toward MEFs. Conclusions: The antimicrobial agents encapsulated dual-crosslinkable alginate hydrogel developed in this study is a promising candidate for use in tissue engineering and biomedical field.

Background: It is unclear whether peripheral arterial cannulation is superior to central arterial cannulation for postcardiotomy veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Methods: A systematic review was conducted using PubMed, Scopus, and Google Scholar to identify studies on postcardiotomy VA-ECMO for the present individual patient data (IPD) meta-analysis. Analysis was performed according to the intention-to-treat principle. Results: The investigators of 10 studies agreed to participate in the present IPD meta-analysis. Overall, 1269 patients were included in the analysis. Crude rates of in-hospital mortality after central versus peripheral arterial cannulation for VA-ECMO were 70.7% vs. 63.7%, respectively (adjusted OR 1.38, 95% CI 1.08–1.75). Propensity score matching yielded 538 pairs of patients with balanced baseline characteristics and operative variables. Among these matched cohorts, central arterial cannulation VA-ECMO was associated with significantly higher in-hospital mortality compared to peripheral arterial cannulation VA-ECMO (64.5% vs. 70.8%, p = 0.027). These findings were confirmed by aggregate data meta-analysis, which showed that central arterial cannulation was associated with an increased risk of in-hospital mortality compared to peripheral arterial cannulation (OR 1.35, 95% CI 1.04–1.76, I2 21%). Conclusions: Among patients requiring postcardiotomy VA-ECMO, central arterial cannulation was associated with an increased risk of in-hospital mortality compared to peripheral arterial cannulation. This increased risk is of limited magnitude, and further studies are needed to confirm the present findings and to identify the mechanisms underlying the potential beneficial effects of peripheral VA-ECMO.

Abstract Purpose: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. Experimental Design: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. Results: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02–1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34–1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA–positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). Conclusions: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Abstract Measuring prebiotic activities of foods on human microbiome and providing dietary guidelines on one’s health status are an emerging research topic. However, it is difficult to directly compare different foods using the prebiotic index (PI) proposed in 2003 to determine their prebiotic activities. This ambiguity can be resolved with the gut microbiome health index (GMHI), newly built in 2020, using a consortium of 50 microbial species associated with human health. As a translational application of this index, we used this formula to predict the PI of five commercial prebiotics, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2`-fucosyllactose (2FL), using in vitro human fecal fermentation. To calculate the GMHI-based PI values, microbial community changes were analyzed at the species level using shotgun sequencing during the fermentation of the prebiotics. As a result, the five compounds exerted a prebiotic effect, increasing the abundance of health-prevalent species, such as Bifidobacterium adolescentis and decreasing health-scarce species. In this context, the GMHI of prebiotics increased after 24 h, but that of the control group without carbon sources decreased. GMHI was highest in the order of IN (0.48 ± 0.06), FOS (0.47 ± 0.03), XOS (0.33 ± 0.02), GOS (0.26 ± 0.02), and 2FL (0.16 ± 0.06), which more accurately reflects the prebiotic effect than PI. Additionally, GMHI stratified the prebiotics more effectively than ecological metrics analyses, such as Shannon diversity, species richness, species evenness, and the inverse Simpson diversity index. This study demonstrates that GMHI-based PI can be used to measure prebiotic activities of several bifidogenic oligosaccharides and perhaps predict the health-beneficial effects of diets after collecting substantial amounts of population-scale meta-datasets.

Abstract This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into “rich club” and “non‐rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source.

AbstractBackgroundThe growing adoption of magnetic resonance imaging (MRI)‐guided radiation therapy (RT) platforms and a focus on MRI‐only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired‐data deep learning‐based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired‐ and unpaired‐data results can be limiting.PurposeWe present two distinct approaches aimed at improving unpaired‐data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired‐data setting.MethodsComparisons are made between the following models: (1) the paired‐data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired‐data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five‐fold cross‐validation scheme, with the patient‐specific IDOL models being trained for the five patients of fold 3, chosen at random.ResultsIn both the paired‐ and unpaired‐data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired‐ and unpaired‐data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal‐to‐noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired‐data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired‐data approach are statistically insignificant.ConclusionsWe have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired‐data setting offer significant improvements in image quality metrics for the unpaired‐data sCT reconstruction task. Closing the gap between paired‐ and unpaired‐data approaches is a step toward fully enabling these powerful and attractive unpaired‐data frameworks.

Mutations in homologous recombination (HR) genes, including BRCA1 , BRCA2 , and the RAD51 paralog RAD51C , predispose to tumorigenesis and sensitize cancers to DNA-damaging agents and poly(ADP ribose) polymerase inhibitors. However, ∼800 missense variants of unknown significance have been identified for RAD51C alone, impairing cancer risk assessment and therapeutic strategies. Here, we interrogated >50 RAD51C missense variants, finding that mutations in residues conserved with RAD51 strongly predicted HR deficiency and disrupted interactions with other RAD51 paralogs. A cluster of mutations was identified in and around the Walker A box that led to impairments in HR, interactions with three other RAD51 paralogs, binding to single-stranded DNA, and ATP hydrolysis. We generated structural models of the two RAD51 paralog complexes containing RAD51C, RAD51B-RAD51C-RAD51D-XRCC2 and RAD51C-XRCC3. Together with our functional and biochemical analyses, the structural models predict ATP binding at the interface of RAD51C interactions with other RAD51 paralogs, similar to interactions between monomers in RAD51 filaments, and explain the failure of RAD51C variants in binding multiple paralogs. Ovarian cancer patients with variants in this cluster showed exceptionally long survival, which may be relevant to the reversion potential of the variants. This comprehensive analysis provides a framework for RAD51C variant classification. Importantly, it also provides insight into the functioning of the RAD51 paralog complexes.