Microbial biofilms: biosurfactants as antibiofilm agents.

Current microbial inhibition strategies based on planktonic bacterial physiology have been known to have limited efficacy on the growth of biofilm communities. This problem can be exacerbated by the emergence of increasingly resistant clinical strains. All aspects of biofilm measurement, monitoring, dispersal, control, and inhibition are becoming issues of increasing importance. Biosurfactants have merited renewed interest in both clinical and hygienic sectors due to their potential to disperse microbial biofilms in addition to many other advantages. The dispersal properties of biosurfactants have been shown to rival those of conventional inhibitory agents against bacterial and yeast biofilms. This makes them suitable candidates for use in new generations of microbial dispersal agents and for use as adjuvants for existing microbial suppression or eradication strategies. In this review, we explore aspects of biofilm characteristics and examine the contribution of biologically derived surface-active agents (biosurfactants) to the disruption or inhibition of microbial biofilms

Role of alternative polyadenylation in epigenetic silencing and antisilencing

Epigenetic marks such as DNA methylation and histone modifications are widely involved in regulating different aspects of developmental and environmental responses (1). Meanwhile, DNA methylation and histone modification are also used constitutively to silence transposable elements and repeat elements (TREs) (2). Such TRE-mediated silencing should necessarily be limited to the intended targets only and not spread to adjacent genes and their regulatory elements. Higher eukaryotic organisms have evolved antisilencing mechanisms to keep the balance between silencing and antisilencing that is required for precise gene expression regulation.Research in the authors' laboratory is supported by US National Science Foundation Grant IOS–0817829 (to Q.Q.L.) and US National Institutes of Health Grant 1R15GM94732-1 A1 (to Q.Q.L.)

Inverse temporal contributions of the dorsal hippocampus and medial prefrontal cortex to the expression of long-term fear memories

Retrograde amnesia following disruptions of hippocampal function is often temporally graded, with recent memories being more impaired. Evidence supports the existence of one or more neocortical long-term memory storage/retrieval site(s). Neurotoxic lesions of the medial prefrontal cortex (mPFC) or the dorsal hippocampus (DH) were made 1 day or 200 days following trace fear conditioning. Recently encoded trace fear memories were most disrupted by DH lesions, while remotely encoded trace and contextual memories were most disrupted by mPFC lesions. These data strongly support the consolidation theory of hippocampus function and implicate the mPFC as a site of long-term memory storage/retrieval

PFHydro: A New Watershed-Scale Model for Post-Fire Runoff Simulation

Runoff increases after wildfires that burn vegetation and create a condition of soil-water repellence (SWR). A new post-fire watershed hydrological model, PFHydro, was created to explicitly simulate vegetation interception and SWR effects for four burn severity categories: high, medium, low severity and unburned. The model was applied to simulate post-fire runoff from the Upper Cache Creek Watershed in California, USA. Nash–Sutcliffe modeling efficiency (NSE) was used to assess model performance. The NSE was 0.80 and 0.88 for pre-fire water years (WY) 2000 and 2015, respectively. NSE was 0.88 and 0.93 for WYs 2016 (first year post-fire) and 2017 respectively. The simulated percentage of surface runoff in total runoff of WY 2016 was about six times that of pre-fire WY 2000 and three times that of WY 2015. The modeling results suggest that SWR is an important factor for post-fire runoff generation. The model was successful at simulating SWR behavior

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. METHODS: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. RESULTS: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. CONCLUSIONS: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans

Chemical Evolution of CoCrMo Wear Particles: An in Situ Characterization Study

The unexpected high failure rates of CoCrMo hip implants are associated with the release of a large number of inflammatory wear particles. CoCrMo is nominally a stable material; however, previous chemical speciation studies on CoCrMo wear particles obtained from periprosthetic tissue revealed only trace amounts of Co remaining despite Co being the major component of the alloy. The unexpected high levels of Co dissolution in vivo raised significant clinical concerns particularly related to the Cr speciation in the dissolution process. At high electrochemical potentials, the alloy's Cr-rich passive film breaks down (transpassive polarization), facilitating alloy dissolution. The potential release of the carcinogenic Cr(VI) species in vivo has been a subject of debate. While the large-scale Co dissolution observed on in vivo produced particles could indicate a highly oxidizing in vivo environment, Cr(VI) species were not previously detected in periprosthetic tissue samples (except in the specific case of post-mortem tissue of diabetic patients). However, Cr(VI) is likely to be an unstable (transient) species in biological environments, and studies on periprosthetic tissue do not provide information about intermediate reaction products or the exposure history of the wear particles. Here, an in situ spectromicroscopy approach was developed, utilizing the high chemical resolution of synchrotron radiation, to study CoCrMo reactivity as a function of time and oxidizing conditions. The results reveal limited Co dissolution from CoCrMo particles, which increases dramatically at a critical electrochemical potential. Furthermore, in situ XAS detected only Cr(III) dissolution, even at potentials where Cr(VI) is known to be produced, suggesting that Cr(VI) species are extremely transient in simulated biological environments where the oxidation zone is small

Randomized phase III KEYNOTE-045 trial of pembrolizumab versus paclitaxel, docetaxel, or vinflunine in recurrent advanced urothelial cancer: results of >2 years of follow-up.

BackgroundNovel second-line treatments are needed for patients with advanced urothelial cancer (UC). Interim analysis of the phase III KEYNOTE-045 study showed a superior overall survival (OS) benefit of pembrolizumab, a programmed death 1 inhibitor, versus chemotherapy in patients with advanced UC that progressed on platinum-based chemotherapy. Here we report the long-term safety and efficacy outcomes of KEYNOTE-045.Patients and methodsAdult patients with histologically/cytologically confirmed UC whose disease progressed after first-line, platinum-containing chemotherapy were enrolled. Patients were randomly assigned 1 : 1 to receive pembrolizumab [200 mg every 3 weeks (Q3W)] or investigator's choice of paclitaxel (175 mg/m2 Q3W), docetaxel (75 mg/m2 Q3W), or vinflunine (320 mg/m2 Q3W). Primary end points were OS and progression-free survival (PFS) per Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1) by blinded independent central radiology review (BICR). A key secondary end point was objective response rate per RECIST v1.1 by BICR.ResultsA total of 542 patients were enrolled (pembrolizumab, n = 270; chemotherapy, n = 272). Median follow-up as of 26 October 2017 was 27.7 months. Median 1- and 2-year OS rates were higher with pembrolizumab (44.2% and 26.9%, respectively) than chemotherapy (29.8% and 14.3%, respectively). PFS rates did not differ between treatment arms; however, 1- and 2-year PFS rates were higher with pembrolizumab. The objective response rate was also higher with pembrolizumab (21.1% versus 11.0%). Median duration of response to pembrolizumab was not reached (range 1.6+ to 30.0+ months) versus chemotherapy (4.4 months; range 1.4+ to 29.9+ months). Pembrolizumab had lower rates of any grade (62.0% versus 90.6%) and grade ≥3 (16.5% versus 50.2%) treatment-related adverse events than chemotherapy.ConclusionsLong-term results (>2 years' follow-up) were consistent with those of previously reported analyses, demonstrating continued clinical benefit of pembrolizumab over chemotherapy for efficacy and safety for treatment of locally advanced/metastatic, platinum-refractory UC.Trial registrationClinicalTrials.gov: NCT02256436

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resource

On the evaluation of global sea-salt aerosol models at coastal/orographic sites

Sea-salt aerosol global models are typically evaluated against concentration observations at coastal stations that are unaffected by local surf conditions and thus considered representative of open ocean conditions. Despite recent improvements in sea-salt source functions, studies still show significant model errors in specific regions. Using a multiscale model, we investigated the effect of high model resolution (0.1 degrees x 0.1 degrees vs. 1 degrees x 1.4 degrees) upon sea-salt patterns in four stations from the University of Miami Network: Baring Head, Chatam Island, and Invercargill in New Zealand, and Marion Island in the sub-antarctic Indian Ocean. Normalized biases improved from +63.7% to +3.3% and correlation increased from 0.52 to 0.84. The representation of sea/land interfaces, mesoscale circulations, and precipitation with the higher resolution model played a major role in the simulation of annual concentration trends. Our results recommend caution when comparing or constraining global models using surface concentration observations from coastal stations. (C) 2014 The Authors. Published by Elsevier Ltd.Postprint (published version