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Summary: Local cryoablation can engender systemic immune activation/anticancer responses in tumors otherwise resistant to immune checkpoint blockade (ICB). We evaluated the safety/tolerability of preoperative cryoablation plus ipilimumab and nivolumab in 5 early-stage/resectable breast cancers. The primary endpoint was met when all 5 patients underwent standard-of-care primary breast surgery undelayedly. Three patients developed transient hyperthyroidism; one developed grade 4 liver toxicity (resolved with supportive management). We compared this strategy with cryoablation and/or ipilimumab. Dual ICB plus cryoablation induced higher expression of T cell activation markers and serum Th1 cytokines and reduced immunosuppressive serum CD4+PD-1hi T cells, improving effector-to-suppressor T cell ratio. After dual ICB and before cryoablation, T cell receptor sequencing of 4 patients showed increased T cell clonality. In this small subset of patients, we provide preliminary evidence that preoperative cryoablation plus ipilimumab and nivolumab is feasible, inducing systemic adaptive immune activation potentially more robust than cryoablation with/without ipilimumab.

Acute myeloid leukemia (AML) patients undergoing induction chemotherapy receive transfusion support to manage severe cytopenias and associated sequelae. Jehovah's Witness (JW) patients typically decline transfusion of most or all blood products. This can lead to exclusion of JW patients from otherwise life-saving treatments due to safety concerns. We present two cases demonstrating the successful induction of JW patients without the need for red cell or platelet transfusion support; the first, an older AML patient induced with azacitidine & venetoclax; the second, a patient with acute promyelocytic leukemia induced using arsenic trioxide and all-trans retinoic acid. Both patients required modifications to the induction regimens to accommodate their wishes. These cases support growing evidence that selected JW patients with AML can be successfully treated using appropriate accommodations.

We developed a set of two precision, small-scale, water balance lysimeters to provide accurate measurements of bare soil evaporation. Each lysimeter comprises a soil tank, a balance assembly with load cell, a wicking drainage system, and a stilling well to measure drained water. Fiberglass wicks installed at the bottom of the soil tanks provide −60 cm of tension to the base of the soil column, and soil water drainage is quantified to close the water balance within the lysimeter. The calibrated lysimeters return mass changes with uncertainties ranging from 3 to 8 g, corresponding to uncertainties of 0.02–0.05 mm of water. Installed at a semi-arid site in northern Nevada, the two lysimeters are filled with uniform construction sand and silt loam. Over a six-month pilot observation period, bare soil evaporation rates of 0.19 and 0.40 mm/day were measured for the construction sand and silt loam, respectively, which is consistent with meteorological data and models of potential evapotranspiration at the site. The design of the lysimeter can be adapted to specific research goals or site restrictions, and these instruments can contribute significantly to our ability to close the soil water balance.

Abstract Background Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis. Methods The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988–2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions (“Rule of 2”) were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification). Results 52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798. Conclusions This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients’ health outcomes.

Objective: To determine the consumption pattern of selected households consuming traditional green leafy vegetables (TGLVs) during three seasons and potential contribution to recommended nutrient intakes. Design: Repeated household survey using food frequency questionnaire to obtain consumption pattern of selected TGLVs. Setting and Subjects: Selected households in a small rural farming community: pre-rainy (n = 43), rainy (n = 36) and post-rainy seasons (n = 40). Results: Corchorus olitorius and A. hybridus were the most consumed by > 70% of households across three seasons. A. esculentus and A. viridis were the least consumed TGLVs through all the seasons. Uncultivated and cultivated TGLVs could contribute between 26–> 100% and 18–96%, respectively, of recommended intake for vitamin A for children and women. For young children, both the cultivated and uncultivated TGLVs could contribute more than 50% of their RDA for iron. Conclusion: seasonal availability, and preference for certain species, determine the consumption pattern of the TGLVs studied. Uncultivated and less utilised vegetables have the potential to make significant contributions to the micronutrient requirements of women and children, especially vitamin A and iron if frequently used.

Objective: To determine the micronutrient composition of fresh and boiled traditional green leafy vegetables (TGLVs). Design: Sixteen TGLVs categorised into cultivated and uncultivated vegetables were analysed for vitamin C (ascorbic acid [AA] and dehydroascorbate [DHAA]), β-carotene, lutein and minerals. Results: Basella alba had the highest AA (72 mg/100 g) content; Vernonia amygdalina (unwashed), had the highest β-carotene and lutein concentrations (14.1 and 29.0 mg/100 g, respectively); Amaranthus hybridus had the highest AA (43 mg/100 g) and β-carotene (9.3 mg/100 g) content, for cultivated sp. Celosia argentea had the highest Fe content; Zn content of all the vegetables was low, 0.4–2.6 mg/100 g. Cooking resulted in significant losses in AA content in all the samples, 19% in B. alba to 100% in Crassocephalum crepidioides. Carotenoid losses were observed in 10 samples and six samples had increased values of β-carotene (12% to 183%) and lutein (64% to double). Conclusion: Traditional green leafy vegetables studied were found to be rich in the micronutrients of interest, especially in carotenoids. Boiling of leafy vegetables, as traditionally done, led to considerable losses of the micronutrients. The micronutrient content of uncultivated leafy vegetables compared well with commonly cultivated species.

BackgroundThe Patient-Reported Outcomes, Burdens, and Experiences (PROBE) questionnaire is a tool for assessing the quality of life and disease burden in people living with hemophilia. ObjectiveThe objectives of our study were (1) to assess the needs of relevant stakeholders involved in the use of PROBE, (2) to develop the software infrastructure needed to meet these needs, and (3) to test the usability of the final product. MethodsWe conducted a series of semistructured interviews of relevant stakeholders, including PROBE investigators, people with hemophilia, and representatives of the sponsor. Based on these, we developed an online survey and a mobile app for iOS and Android. A user group evaluated the final product using the System Usability Scale (SUS) and an open feedback framework. ResultsThe online survey was updated, and the myPROBE app for mobile devices and a new application programming interface were developed. The app was tested and modified according to user feedback over multiple cycles. The final version of the app was released in July 2019. Seventeen users aged 23 to 67 years evaluated the final version of the app using the SUS. The median (first, third quartile) SUS score for the app was 85 (68, 88) out of 100. The newly introduced functionalities were as follows: (1) capability to longitudinally track repeated fillings of the questionnaire at different time points by the same participant (as opposed to anonymous completion); (2) linking of the questionnaire with hemophilia registries, starting with the Canadian Bleeding Disorders Registry as a proof of concept; (3) removing or adding questions as needed; and (4) sending notifications to the users (eg, reminders). A new secure database was built for securely storing personal information separately from the questionnaire data. The PROBE online survey is currently available in 96 countries and 34 languages. ConclusionsThe online survey was updated successfully, and the myPROBE app was developed, with a SUS score of 85 (out of 100). The app has been released in 81 countries and 34 languages. This will facilitate data collection for research and advocacy purposes, and the use of this tool in everyday clinical practice.

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Abstract Adenoviral‐vector based vaccines for coronavirus disease 2019 (COVID‐19) have been linked with a thrombotic syndrome, vaccine‐induced thrombotic thrombocytopenia (VITT). A key clinical question is whether VITT can be reliably ruled out by the absence of thrombocytopenia. We report on three patients who presented to our institute with this syndrome. Noteworthy in our presentations are two patients who presented for medical assessment of thrombotic symptoms with a normal platelet count, one preceding and one following a period of thrombocytopenia. Prompt diagnosis of VITT is critical to prevent rapid patient decline. We provide herein a new diagnostic algorithm that we believe will help optimally capture case presentations of VITT. These cases broaden and refine our understanding of the disease process and highlight to practitioners that VITT cannot be adequately ruled out by thrombocytopenia alone.

Abstract Background Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance. Methods Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured. Results Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies. Conclusions: Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

Abstract Objective Paramedic students in the US are required to complete clinical placements to gain supervised experience with real patient encounters. Given wide variation in clinical placement practices, an evidence‐based approach is needed to guide programs in setting realistic and attainable goals for students. This study's goal was to describe patient encounters and hours logged by paramedic students during clinical placements. Methods A retrospective review of prospectively collected quality assurance data entered by US paramedic students between 2010 and 2014 was conducted. De‐identified electronic records entered in the Field Internship Student Data Acquisition Project (FISDAP) Skill Tracker database were included from consenting paramedic students whose records were audited and approved by instructors. Descriptive statistics were calculated. Results A total of 10,645 students encountered 2,239,027 patients; most encounters occurred in hospital settings (n = 1,311,967, 59%). The median total number of patient encounters per paramedic student was 206 (142–269) and the median total clinical placement hours per student was 626 (504–752). The median number of team leads per student was 56 (30–84). Students encountered a median of 22 (12–31) pediatric patients, ages 0–12 years, and 181 (126–238) adolescent or adult patients. For pediatric patient encounters, the most common clinical impressions were respiratory distress, other medical complaints, and extremity trauma. Among adult patient encounters, the most common clinical impressions included other medical, trauma, and cardiac conditions. Conclusions US paramedic students experienced a variable range of patient encounter types and volumes. The findings of this study offer an evidence base from which programs can set realistic and attainable clinical placement requirements.

Our objective was to identify the upper ambient temperature threshold that triggers an increase in cortisol in response to increased thermoregulatory demands in polar bears. The results reported here include endocrine data collected over two years from 25 polar bears housed in 11 accredited zoological institutions across North America. The effects of ambient temperature, sex, age group (juvenile, adult, elderly), breeding season and humidity on fecal cortisol metabolite (FCM) concentrations (N = 8439 samples) were evaluated using linear mixed models. Ambient temperatures were placed into five different categories: 20 °C. Ambient temperature and humidity had a significant (p < 0.05) effect on FCM concentrations with significant (p < 0.05) interactions of sex, age and breeding season. Once biotic factors were accounted for, there was a significant (p < 0.05) increase in FCM concentrations associated with ambient temperatures above 20 °C in adult polar bears. The implications of these findings for the management of both zoo and wild polar bears are discussed.

INTRODUCTION:The ways in which social determinants of health affect patients with inherited bleeding disorders remains unclear. The objective of this study was to understand healthcare provider perspectives regarding access to care and diagnostic delay amongst this patient population. METHODS:A healthcare provider survey comprising 24 questions was developed, tested, and subsequently disseminated online with recruitment to all members of The Association of Hemophilia Clinic Directors of Canada (N = 73), members of the Canadian Association of Nurses in Hemophilia Care (N = 40) and members of the Canadian Physiotherapists in Hemophilia Care (N = 44). RESULTS:There were 70 respondents in total, for a total response rate of 45%. HCPs felt that there were diagnostic delays for patients with mild symptomatology (71%, N = 50), women presenting with abnormal uterine bleeding as their only or primary symptom (59%, N = 41), and patients living in rural Canada (50%, N = 35). Fewer respondents felt that factors such as socioeconomic status (46%, N = 32) or race (21%, N = 15) influenced access to care, particularly as compared to the influence of rural location (77%, N = 54). DISCUSSION:We found that healthcare providers identified patients with mild symptomatology, isolated abnormal uterine bleeding, and residence in rural locations as populations at risk for inequitable access to care. These factors warrant further study, and will be investigated further by our group using our nation-wide patient survey and ongoing in-depth qualitative patient interviews.

Over the last few decades, the improvement of tomato aroma and flavour has gained growing attention among breeders, agronomists and food technologists, as it is reputed as a key tool to further consolidate tomato consumption in the global market. However, this represents a difficult task, since more than 400 volatile organic compounds (VOCs) differently contribute to tomato aroma and its flavour perception. The recent development of molecular tools has allowed us to identify some of the major alleles influencing the volatile composition of tomatoes. Nonetheless, such composition involves an intricate weave of polygenic systems, highly influenced by external, pre and postharvest factors. With few exceptions, our knowledge about the effects of these determinants is still scarce, making it difficult to draw sound conclusions to best manage the future genetic potential. The present review examines the existing knowledge on major VOCs influencing tomato organoleptic quality, including their classification, metabolic pathways, genetics, and technical features that affect their concentrations in fruits. Particular emphasis is placed on preharvest and postharvest factors influencing their quantitative and qualitative expression, whose integrated study with omic tools will be increasingly necessary for the perspective of disseminating reliable operational indications throughout the production/distribution chain.

Abstract Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer’s disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

Abstract Background Providing supplemental oxygen is fundamental in the management of mechanically ventilated patients. Increasing amounts of data show worse clinical outcomes associated with hyperoxia. However, these previous data in the critically ill have not focused on outcomes associated with brief hyperoxia exposure immediately after endotracheal intubation. Therefore, the objectives of this study were to evaluate the impact of isolated early hyperoxia exposure in the emergency department (ED) on clinical outcomes among mechanically ventilated patients with subsequent normoxia in the intensive care unit (ICU). Methods This was an observational cohort study conducted in the ED and ICUs of an academic center in the USA. Mechanically ventilated normoxic (partial pressure of arterial oxygen (PaO2) 60–120 mm Hg) ICU patients with mechanical ventilation initiated in the ED were studied. The cohort was categorized into three oxygen exposure groups based on PaO2 values obtained after ED intubation: hypoxia, normoxia, and hyperoxia (defined as PaO2 120 mm Hg, respectively, based on previous literature). Results A total of 688 patients were included. ED normoxia occurred in 350 (50.9%) patients, and 300 (43.6%) had exposure to ED hyperoxia. The ED hyperoxia group had a median (IQR) ED PaO2 of 189 mm Hg (146–249), compared to an ED PaO2 of 88 mm Hg (76–101) in the normoxia group, P

The FMR1 premutation is of increasing interest to the FXS community, as questions about a primary premutation phenotype warrant research attention. 100 FMR1 premutation carrier mothers (mean age = 58; 67–138 CGG repeats) of adults with fragile X syndrome were studied with respect to their physical and mental health, motor, and neurocognitive characteristics. We explored the correlates of CGG repeat mosaicism in women with expanded alleles. Mothers provided buccal swabs from which DNA was extracted and the FMR1 CGG genotyping was performed (Amplidex Kit, Asuragen). Mothers were categorized into three groups: Group 1: premutation non-mosaic (n = 45); Group 2: premutation mosaic (n = 41), and Group 3: premutation/full mutation mosaic (n = 14). Group 2 mothers had at least two populations of cells with different allele sizes in the premutation range besides their major expanded allele. Group 3 mothers had a very small population of cells in the full mutation range (>200 CGGs) in addition to one or multiple populations of cells with different allele sizes in the premutation range. Machine learning (random forest) was used to identify symptoms and conditions that correctly classified mothers with respect to mosaicism; follow-up comparisons were made to characterize the three groups. In categorizing mosaicism, the random forest yielded significantly better classification than random classification, with overall area under the receiver operating characteristic curve (AUROC) of 0.737. Among the most important symptoms and conditions that contributed to the classification were anxiety, menopause symptoms, executive functioning limitations, and difficulty walking several blocks, with the women who had full mutation mosaicism (Group 3) unexpectedly having better health. Although only 14 premutation carrier mothers in the present sample also had a small population of full mutation cells, their profile of comparatively better health, mental health, and executive functioning was unexpected. This preliminary finding should prompt additional research on larger numbers of participants with more extensive phenotyping to confirm the clinical correlates of low-level full mutation mosaicism in premutation carriers and to probe possible mechanisms.

Changing the balance between ascorbate, monodehydroascorbate, and dehydroascorbate in plant cells by manipulating the activity of enzymes involved in ascorbate synthesis or recycling of oxidized and reduced forms leads to multiple phenotypes. A systems biology approach including network analysis of the transcriptome, proteome and metabolites of RNAi lines for ascorbate oxidase, monodehydroascorbate reductase and galactonolactone dehydrogenase has been carried out in orange fruit pericarp of tomato (Solanum lycopersicum). The transcriptome of the RNAi ascorbate oxidase lines is inversed compared to the monodehydroascorbate reductase and galactonolactone dehydrogenase lines. Differentially expressed genes are involved in ribosome biogenesis and translation. This transcriptome inversion is also seen in response to different stresses in Arabidopsis. The transcriptome response is not well correlated with the proteome which, with the metabolites, are correlated to the activity of the ascorbate redox enzymes—ascorbate oxidase and monodehydroascorbate reductase. Differentially accumulated proteins include metacaspase, protein disulphide isomerase, chaperone DnaK and carbonic anhydrase and the metabolites chlorogenic acid, dehydroascorbate and alanine. The hub genes identified from the network analysis are involved in signaling, the heat-shock response and ribosome biogenesis. The results from this study therefore reveal one or several putative signals from the ascorbate pool which modify the transcriptional response and elements downstream.

Industry tomatoes are produced under a range of climatic conditions and practices which significantly impact on main quality traits of harvested fruits. However, the quality of tomato intended for processing is currently addressed on delivery through color and Brix only, whereas other traits are overlooked. Very few works provided an integrated view of the management of tomato puree quality throughout the chain. To gain insights into pre- and post-harvest interactions, four genotypes, two water regimes, three maturity stages, and two processes were investigated. Field and glasshouse experiments were conducted near Avignon, France, from May to August 2016. Two irrigation regimes were applied: control plants were irrigated in order to match 100% of evapotranspiration (ETP); water deficit (WD) plants were irrigated as control plants until anthesis of the first flowers, then irrigation was reduced to 60 and 50% ETP in field, and glasshouse respectively. Fruits were collected at three stages during ripening. Their color, fresh weight, dry matter content, and metabolite contents were determined before processing. Pericarp cell size was evaluated in glasshouse only. Two laboratory-scaled processing methods were applied before structural and biochemical analyses of the purees. Results outlined interactive effects between crop and process management. WD hardly reduced yield, but increased dry matter content in the field, in contrast to the glasshouse. The puree viscosity strongly depended on the genotype and the maturity stage, but it was disconnected from fruit dry matter content or Brix. The process impact on puree viscosity strongly depended on water supply during fruit production. Moreover, the lycopene content of fresh fruit may influence puree viscosity. This work opens new perspectives for managing puree quality in the field showing that it was possible to reduce water supply without affecting yield and to improve puree quality.

The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. Establishing a product’s potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking. Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website (INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.

Because the benefits of immune checkpoint blockade may be restricted to tumors with pre-existing immune recognition, novel therapies that facilitate de novo immune activation are needed. DRibbles is a novel multi-valent vaccine that is created by disrupting degradation of intracellular proteins by the ubiquitin proteasome system. The DRibbles vaccine is comprised of autophagosome vesicles that are enriched with defective ribosomal products and short-lived proteins, known tumor-associated antigens, mediators of innate immunity, and surface markers that encourage phagocytosis and cross-presentation by antigen presenting cells. Here we summarize the rationale and preclinical development of DRibbles, translational evidence in support of DRibbles as a therapeutic strategy in humans, as well as recent developments and expected future directions of the DRibbles vaccine in the clinic.

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Background: The rapid spread of illness and death caused by the severe respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated coronavirus disease 2019 (COVID-19) demands a rapid response in treatment development. Limitations of de novo drug development, however, suggest that drug repurposing is best suited to meet this demand. Methods: Due to the difficulty of accessing electronic health record data in general and in the midst of a global pandemic, and due to the similarity between SARS-CoV-2 and SARS-CoV, we propose mining the extensive biomedical literature for treatments to SARS that may also then be appropriate for COVID-19. In particular, we propose a method of mining a large biomedical word embedding for FDA approved drugs based on drug-disease treatment analogies. Results: We first validate that our method correctly identifies ground truth treatments for well-known diseases. We then use our method to find several approved drugs that have been suggested or are currently in clinical trials for COVID-19 in our top hits and present the rest as promising leads for further experimental investigation. Conclusions: We find our approach promising and present it, along with suggestions for future work, to the computational drug repurposing community at large as another tool to help fight the pandemic. Code and data for our methods can be found at https://github.com/finnkuusisto/covid19_word_embedding.