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OBJECTIVES/GOALS: We report an automated manufacturing system, and a series of cylindrical multi-layer microfluidic artificial lungs manufactured with the system and tested for fluidic fidelity and function. METHODS/STUDY POPULATION: A Roll-to-Roll (R2R) system to engrave multiple-layer devices was assembled. A 100 um-thick silicone sheet passes through an embedded CO2 laser engraver, which creates patterns of any geometry on the surface. The sheet is plasma-activated to create an irreversible bond, and rerolled into a processed device. Unlike typical applications of R2R, this process is synchronized to achieve consistent radial positioning. This allows the fluidics in the device to be accessed without being unwrapped. The result is a cylindrical core surrounded by many layers of microfluidic channels that can be accessed through the side of the device or through fluidic vias. This core is cut to expose the microfluidic layers, and then installed into a housing which routes the fluids into their respective microfluidic flow paths. RESULTS/ANTICIPATED RESULTS: To demonstrate the capabilities of the R2R manufacturing system, this method was used to manufacture multi-layer microfluidic artificial lungs (µALs). Gas and blood flow channels are engraved in alternating layers and routed orthogonally. The close proximity of gas and blood separated by gas-permeable PDMS permits CO2 and O2 exchange. Three µALs were successfully manufactured. Their flow paths were visualized using dyed water and checked for leaks. Then they were evaluated using water for pressure drop and CO2 gas-exchange. The top performing device had 15 alternating blood and gas layers. Test with whole blood demonstrated oxygenation from venous (70%) saturation levels to arterial (95%) saturation levels at a flow rate of 3 ml/min. DISCUSSION/SIGNIFICANCE: The ability to cost-effectively produce high surface area microfluidic devices would bring many small-scale technologies from the realm of research to clinical and commercial applications. In particular, most microfluidic artificial lungs only have small rated flows due to a lack of manufacturing processes able to create high surface area devices.

Abstract Background Many factors can contribute to the exact makeup of the salivary microbiome. Differences in the oral microbiome occur with old age, which may be due to oral conditions and diseases associated with old age, such as edentulism, as well as other unknown causes. Methods The salivary microbiome was sampled in patients from a large urban clinic. For all subjects age, gender, periodontal status, caries status, presence of edentulism, medications, and tobacco usage were recorded. Multifactor analysis was used to study variation in salivary microbiome profiles linked to these factors. Results In the population sampled, there were significantly higher numbers of edentulous subjects, and increased levels of polypharmacy found with aging. Large differences in alpha diversity and beta diversity of the salivary microbiome in the old age group were largely linked to edentulism. However, multivariable analysis revealed, even after adjusting for differences in edentulism, polypharmacy, tobacco usage, periodontal disease, caries level, and gender, that old age itself was associated with lower levels of taxa Porphyromonas endodontalis , Alloprevotella tannerae, Filifactor alocis, Treponema, Lautropia Mirabilis and Pseudopropionibacterium sp._HMT_194. Surprisingly, of these taxa, most were ones known to reside on or near tooth surfaces. Conclusions Another factor or factors beyond edentulism, polypharmacy and periodontal disease play a role in the differences seen in oral microbiome with old age. The nature of this factor(s) is not known.

BackgroundCOVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications. ObjectiveThe aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit. MethodsWe extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values. ResultsThe analysis identified significant predictors (P1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment. ConclusionsThe analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases.

AbstractHere, we report the discovery of a novel Sediminibacterium

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

Artificial lung (AL) systems provide respiratory support to patients with severe lung disease, but none can adapt to the changing respiratory needs of the patients. Precisely, none can automatically adjust carbon dioxide (CO2) removal from the blood in response to changes in patient activity or disease status. Because of this, all current systems limit patient comfort, activity level, and rehabilitation. A portable servoregulation controller that automatically modulates CO2 removal in ALs to meet the real-time metabolic demands of the patient is described. The controller is based on a proportional-integral-derivative (PID) based closed-loop feedback control system that modulates sweep gas (air) flow through the AL to maintain a target exhaust gas CO2 partial pressure (target EGCO2 or tEGCO2). The presented work advances previous research by (1) using gas-side sensing that avoids complications and clotting associated with blood-based sensors, (2) incorporating all components into a portable, battery-powered package, and (3) integrating smart moisture removal from the AL to enable long term operation. The performance of the controller was tested in vitro for ∼12 h with anti-coagulated bovine blood and 5 days with distilled water. In tests with blood, the sweep gas flow was automatically adjusted by the controller rapidly (CO2 level when confronted with changes in inlet blood partial pressure of CO2 (pCO2) levels at various AL blood flows. Overall, the CO2 removal from the AL showed a strong correlation with blood flow rate and blood pCO2 levels. The controller successfully operated continuously for 5 days when tested with water. This study demonstrates an important step toward ambulatory AL systems that automatically modulate CO2 removal as required by lung disease patients, thereby allowing for physiotherapy, comfort, and activity.

Early detection is critical to reduce cancer deaths as treating early stage cancers is more likely to be successful. However, patients with early stage diseases are often asymptomatic and thus less likely to be diagnosed. Here, we utilized four microarray datasets with a standardized platform to investigate comprehensive microRNA expression profiles from 7536 serum samples. A 4-miRNA diagnostic model was developed from the lung cancer training set (n = 416, 208 lung cancer patients and 208 non-cancer participants). The model showed 99% sensitivity and specificity in the lung cancer validation set (n = 3328, 1358 cancer patients and 1970 non-cancer participants); and the sensitivity remained to be >99% for patients with stage 1 disease. When applied to the additional combined dataset of 3792 participants including 2038 cancer patients across 12 different cancer types and 1754 independent non-cancer controls, the model demonstrated high sensitivities ranging from 83.2 to 100% for biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancers, and showed reasonable sensitivities of 68.2 and 72.0% for ovarian cancer and sarcoma, respectively, while maintaining 99.3% specificity. Our study provided a proof-of-concept data in demonstrating that the 4-miRNA model has the potential to be developed into a simple, inexpensive and noninvasive blood test for early detection of multiple cancers with high accuracy.

Hepatocellular carcinoma (HCC) is the second most common cause of cancer death worldwide, with a majority of HCC patients not suitable for curative therapies. Approximately 70% of initially diagnosed patients cannot undergo surgical resection or transplantation due to locally advanced disease, poor liver function/underlying cirrhosis, or additional comorbidities. Local therapeutic options for patients with unresectable HCC, who are not suitable for thermal ablation, include transarterial embolization (bland, chemoembolization, radioembolization) and/or external beam radiation therapy (EBRT). Regarding EBRT specifically, technological advancements provide a means for safe and effective radiotherapy delivery in a wide spectrum of HCC patients. In multiple prospective studies, EBRT delivery in a variety of different fractionation schemes or in combination with transcatheter arterial chemoembolization (TACE) demonstrate improved outcomes, particularly with combination therapy. The Barcelona Clinic Liver Cancer classification provides a framework for treatment selection; however, given the growing complexity of treatment strategies, this classification system tends to simplify decision-making. In this review, we discuss the current literature regarding unresectable HCC and propose a modified treatment algorithm that emphasizes the role of radiation therapy for Child-Pugh score A or B patients with ≤3 nodules measuring >3 cm, multinodular disease or portal venous thrombosis.

Ribonucleotide reductase (RR) is a crucial enzyme in de novo DNA synthesis, where it catalyses the rate determining step of dNTP synthesis. RRs consist of a large subunit called RR1 (α), that contains two allosteric sites and one catalytic site, and a small subunit called RR2 (β), which houses a tyrosyl free radical essential for initiating catalysis. The active form of mammalian RR is an anbm hetero oligomer. RR inhibitors are cytotoxic to proliferating cancer cells. In this brief review we will discuss the three classes of RR, the catalytic mechanism of RR, the regulation of the dNTP pool, the substrate selection, the allosteric activation, inactivation by ATP and dATP, and the nucleoside drugs that target RR. We will also discuss possible strategies for developing a new class of drugs that disrupts the RR assembly.