Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

Objective: To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods: The study population was composed of laboratory information services data procured from a large U.S. outpatient laboratory network. The retrospective dataset was composed of 15,029 adults over a 5-year period with initial hemoglobin A1C (A1C) values between 5.0% and 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≥6.5%) within 5 years. Results: The prediabetes risk classifier model accurately predicted A1C ≥6.5% within 5 years and achieved an area under the receiver-operator characteristic curve of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions: Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.

Rationale & Objective: Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression. Study Design: Retrospective observational study. Setting & Participants: The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2. Predictors: Patient demographic and laboratory characteristics. Outcomes: Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years. Analytical Approach: Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline. Results: The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex. Limitations: The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model. Conclusions: Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression. Plain-Language Summary: Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

A Phase Ib Trial of AVID200, a TGFß 1/3 Trap, in Patients with Myelofibrosis.

PURPOSE: Myelofibrosis (MF) is a clonal myeloproliferative neoplasm characterized by systemic symptoms, cytopenias, organomegaly, and bone marrow fibrosis. JAK2 inhibitors afford symptom and spleen burden reduction but do not alter the disease course and frequently lead to thrombocytopenia. TGFß, a pleiotropic cytokine elaborated by the MF clone, negatively regulates normal hematopoiesis, downregulates antitumor immunity, and promotes bone marrow fibrosis. Our group previously showed that AVID200, a potent and selective TGFß 1/3 trap, reduced TGFß1-induced proliferation of human mesenchymal stromal cells, phosphorylation of SMAD2, and collagen expression. Moreover, treatment of MF mononuclear cells with AVID200 led to increased numbers of progenitor cells (PC) with wild-type JAK2 rather than JAK2V617F. PATIENTS AND METHODS: We conducted an investigator-initiated, multicenter, phase Ib trial of AVID200 monotherapy in 21 patients with advanced MF. RESULTS: No dose-limiting toxicity was identified at the three dose levels tested, and grade 3/4 anemia and thrombocytopenia occurred in 28.6% and 19.0% of treated patients, respectively. After six cycles of therapy, two patients attained a clinical benefit by IWG-MRT criteria. Spleen and symptom benefits were observed across treatment cycles. Unlike other MF-directed therapies, increases in platelet counts were noted in 81% of treated patients with three patients achieving normalization. Treatment with AVID200 resulted in potent suppression of plasma TGFß1 levels and pSMAD2 in MF cells. CONCLUSIONS: AVID200 is a well-tolerated, rational, therapeutic agent for the treatment of patients with MF and should be evaluated further in patients with thrombocytopenic MF in combination with agents that target aberrant MF intracellular signaling pathways.

MANIFEST: Pelabresib in Combination With Ruxolitinib for Janus Kinase Inhibitor Treatment-Naïve Myelofibrosis.

PURPOSE: Standard therapy for myelofibrosis comprises Janus kinase inhibitors (JAKis), yet spleen response rates of 30%-40%, high discontinuation rates, and a lack of disease modification highlight an unmet need. Pelabresib (CPI-0610) is an investigational, selective oral bromodomain and extraterminal domain inhibitor (BETi). METHODS: MANIFEST (ClinicalTrails.gov identifier: NCT02158858), a global, open-label, nonrandomized, multicohort, phase II study, includes a cohort of JAKi-naïve patients with myelofibrosis treated with pelabresib and ruxolitinib. The primary end point is a spleen volume reduction of ≥ 35% (SVR35) at 24 weeks. RESULTS: Eighty-four patients received ≥ 1 dose of pelabresib and ruxolitinib. The median age was 68 (range, 37-85) years; 24% of patients were intermediate-1 risk, 61% were intermediate-2 risk, and 16% were high risk as per the Dynamic International Prognostic Scoring System; 66% (55 of 84) of patients had a hemoglobin level of < 10 g/dL at baseline. At 24 weeks, 68% (57 of 84) achieved SVR35, and 56% (46 of 82) achieved a total symptom score reduction of ≥ 50% (TSS50). Additional benefits at week 24 included 36% (29 of 84) of patients with improved hemoglobin levels (mean, 1.3 g/dL; median, 0.8 g/dL), 28% (16 of 57) with ≥ 1 grade improvement in fibrosis, and 29.5% (13 of 44) with > 25% reduction in JAK2V617F-mutant allele fraction, which was associated with SVR35 response (P = .018, Fisher's exact test). At 48 weeks, 60% (47 of 79) of patients had SVR35 response. Grade 3 or 4 toxicities seen in ≥ 10% patients were thrombocytopenia (12%) and anemia (35%), leading to treatment discontinuation in three patients. 95% (80 of 84) of the study participants continued combination therapy beyond 24 weeks. CONCLUSION: The rational combination of the BETi pelabresib and ruxolitinib in JAKi-naïve patients with myelofibrosis was well tolerated and showed durable improvements in spleen and symptom burden, with associated biomarker findings of potential disease-modifying activity.

CXCL8/CXCR2 signaling mediates bone marrow fibrosis and is a therapeutic target in myelofibrosis.

Proinflammatory signaling is a hallmark feature of human cancer, including in myeloproliferative neoplasms (MPNs), most notably myelofibrosis (MF). Dysregulated inflammatory signaling contributes to fibrotic progression in MF; however, the individual cytokine mediators elicited by malignant MPN cells to promote collagen-producing fibrosis and disease evolution are yet to be fully elucidated. Previously, we identified a critical role for combined constitutive JAK/STAT and aberrant NF-κB proinflammatory signaling in MF development. Using single-cell transcriptional and cytokine-secretion studies of primary cells from patients with MF and the human MPLW515L (hMPLW515L) murine model of MF, we extend our previous work and delineate the role of CXCL8/CXCR2 signaling in MF pathogenesis and bone marrow fibrosis progression. Hematopoietic stem/progenitor cells from patients with MF are enriched for a CXCL8/CXCR2 gene signature and display enhanced proliferation and fitness in response to an exogenous CXCL8 ligand in vitro. Genetic deletion of Cxcr2 in the hMPLW515L-adoptive transfer model abrogates fibrosis and extends overall survival, and pharmacologic inhibition of the CXCR1/2 pathway improves hematologic parameters, attenuates bone marrow fibrosis, and synergizes with JAK inhibitor therapy. Our mechanistic insights provide a rationale for therapeutic targeting of the CXCL8/CXCR2 pathway among patients with MF.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING: No specific funding was provided for this study.

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Rules of engagement: Promoting academic-industry partnership in the era of digital pathology and artificial intelligence.

Academic industry partnership (AIP) represents an important alliance between academic researchers and industry that helps translate technology and complete the innovation cycle within academic health systems. Despite diverging missions and skillsets the culture for academia and industry is changing in response to the current digital era which is spawning greater collaboration between physicians and businesses in this marketplace. In the field of pathology, this is further driven by the fact that traditional funding sources cannot keep pace with the innovation needed in digital pathology and artificial intelligence. This concept article from the Digital Pathology Association (DPA) describes the rules of engagement for pathology innovators in academia and for their corporate partners to help establish best practices in this critical area. Stakeholders include pathologists, basic and translational researchers, university technology transfer and sponsored research offices, as well as industry relations officers. The article discusses the benefits and pitfalls of an AIP, reviews different partnership models, examines the role of pathologists in the innovation cycle, explains various agreements that may need to be signed, covers conflict of interest and intellectual property issues, and offers recommendations for ensuring successful partnerships.

Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia.

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. "Ground truth" classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7−99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing−Bablok slope = 0.997 (95% CI: 0.988−0.999) and intercept = 0.001 (95% CI: 0.000−0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.

Interleukin-1 Receptor Antagonism Abrogates Acute Pressure Overload-Induced Murine Heart Failure.

BACKGROUND: Recent clinical trials have suggested that blockade of interleukin-1 (IL-1) can have a favorable impact on patients with myocardial infarction and heart failure. However, the mechanism of antagonism of this specific cytokine in mediating cardiac disease remains unclear. Hence, we sought to determine the influence of IL-1 blockade on acute hypertensive remodeling. METHODS: Transverse aortic constriction was performed in C57BL mice with or without intraperitoneal administration of interleukin 1 receptor antagonism (IL-1Ra). Function, structure, and molecular diagnostics were subsequently performed and analyzed. RESULTS: Six weeks after transverse aortic constriction, a progressive decline of ejection fraction and increases in left ventricle mass and dimensions were effectively mitigated with IL-1Ra. Transverse aortic constriction resulted in an expected profile of hypertrophic markers including myosin heavy chain, atrial natriuretic peptide, and skeletal muscle actin, which were all significantly lower in IL-1Ra treated mice. Although trichrome staining 2 weeks after transverse aortic constriction demonstrated similar levels of fibrosis, IL-1ra-reduced expression of collagen-1, tissue inhibitor of metallopeptidase 1, and periostin. Investigating the angiogenic response to pressure overload, similar levels of vascular endothelial growth factor were observed, but IL-1Ra was associated with more stromal cell-derived factor-1. Immune cell infiltration (macrophages and lymphocytes) was also decreased in IL-1Ra treated mice. Similarly, cytokine concentrations of IL-1, IL-18, and IL-6 were all reduced in IL-1Ra-treated animals. CONCLUSIONS: Interleukin-1Ra prevents the progression toward heart failure associated with acute pressure overload. This functional response was associated with reductions in mediators of fibrosis, cellular infiltration, and cytokine production. These results provide mechanistic insight into recent clinical trials and could springboard future investigations in patients with pressure-overload-based cardiomyopathies.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. RESULTS: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). CONCLUSIONS: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology.

The accurate diagnosis of chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) subtypes with monocytic differentiation relies on the proper identification and quantitation of blast cells and blast-equivalent cells, including promonocytes. This distinction can be quite challenging given the cytomorphologic and immunophenotypic similarities among the monocytic cell precursors. The aim of this study was to assess the performance of convolutional neural networks (CNN) in separating monocytes from their precursors (i.e., promonocytes and monoblasts). We collected digital images of 935 monocytic cells that were blindly reviewed by five experienced morphologists and assigned into three subtypes: monocyte, promonocyte, and blast. The consensus between reviewers was considered as a ground truth reference label for each cell. In order to assess the performance of CNN models, we divided our data into training (70%), validation (10%), and test (20%) datasets, as well as applied fivefold cross validation. The CNN models did not perform well for predicting three monocytic subtypes, but their performance was significantly improved for two subtypes (monocyte vs. promonocytes + blasts). Our findings (1) support the concept that morphologic distinction between monocytic cells of various differentiation level is difficult; (2) suggest that combining blasts and promonocytes into a single category is desirable for improved accuracy; and (3) show that CNN models can reach accuracy comparable to human reviewers (0.78 ± 0.10 vs. 0.86 ± 0.05). As far as we know, this is the first study to separate monocytes from their precursors using CNN.

Important Pathologic Considerations for Establishing the Diagnosis of Myelofibrosis.

Diagnostic criteria for primary myelofibrosis as defined by the 2017 revised World Health Organization (WHO) classification system incorporate clinical and laboratory findings, including driver mutational status (JAK2, MPL, CALR. and triple negative). The WHO emphasized the role of histopathology in making an accurate diagnosis of primary myelofibrosis and successfully incorporated a fibrosis scoring system and scoring schemas for collagen fibrosis and osteosclerosis. These steps represent a significant addition to the standardization of myelofibrosis evaluation and minimize the risk for misdiagnosis. This article reviews important pathologic considerations along with highlights of potentially relevant pitfalls relevant to histopathological diagnosis of myelofibrosis.

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Phase 2 study of ruxolitinib and decitabine in patients with myeloproliferative neoplasm in accelerated and blast phase.

Myeloproliferative neoplasms (MPN) that have evolved into accelerated or blast phase disease (MPN-AP/BP) have poor outcomes with limited treatment options and therefore represent an urgent unmet need. We have previously demonstrated in a multicenter, phase 1 trial conducted through the Myeloproliferative Neoplasms Research Consortium that the combination of ruxolitinib and decitabine is safe and tolerable and is associated with a favorable overall survival (OS). In this phase 2 trial, 25 patients with MPN-AP/BP were treated at the recommended phase 2 dose of ruxolitinib 25 mg twice daily for the induction cycle followed by 10 mg twice daily for subsequent cycles in combination with decitabine 20 mg/m2 for 5 consecutive days in a 28-day cycle. Nineteen patients died during the study follow-up. The median OS for all patients on study was 9.5 months (95% confidence interval, 4.3-12.0). Overall response rate (complete remission + incomplete platelet recovery + partial remission) was 11/25 (44%) and response was not associated with improved survival. We conclude that the combination of decitabine and ruxolitinib was well tolerated, demonstrated favorable OS, and represents a therapeutic option for this high-risk patient population. This trial was registered at www.clinicaltrials.gov as #NCT02076191.

Could Bordetella pertussis vaccine protect against coronavirus COVID-19?

According to the World Health Organization (WHO), as of today, there are 2.165.500 confirmed cases of the novel coronavirus disease (COVID-19) and 145.705 deaths in over 185 countries. Unfortunately, despite the tremendous efforts to develop a vaccine initiated by various leading health institutions all over the world, it may be 18 months before a vaccine against the coronavirus is publicly available. We are proposing a theory about testing the use of the Bordetella pertussis vaccine to protect against COVID-19. We deliver this theory to the scientific community, aiming to raise the concern about it, and to provide us with support by realistic and experimental evidence.