Loss of Dnmt3a increases self-renewal and resistance to pegIFN-α in JAK2-V617F-positive myeloproliferative neoplasms.

ABSTRACT: Pegylated interferon alfa (pegIFN-α) can induce molecular remissions in patients with JAK2-V617F-positive myeloproliferative neoplasms (MPNs) by targeting long-term hematopoietic stem cells (LT-HSCs). Additional somatic mutations in genes regulating LT-HSC self-renewal, such as DNMT3A, have been reported to have poorer responses to pegIFN-α. We investigated whether DNMT3A loss leads to alterations in JAK2-V617F LT-HSC functions conferring resistance to pegIFN-α treatment in a mouse model of MPN and in hematopoietic progenitors from patients with MPN. Long-term treatment with pegIFN-α normalized blood parameters and reduced splenomegaly and JAK2-V617F chimerism in single-mutant JAK2-V617F (VF) mice. However, pegIFN-α in VF;Dnmt3aΔ/Δ (VF;DmΔ/Δ) mice worsened splenomegaly and failed to reduce JAK2-V617F chimerism. Furthermore, LT-HSCs from VF;DmΔ/Δ mice compared with VF were less prone to accumulate DNA damage and exit dormancy upon pegIFN-α treatment. RNA sequencing showed that IFN-α induced stronger upregulation of inflammatory pathways in LT-HSCs from VF;DmΔ/Δ than from VF mice, indicating that the resistance of VF;DmΔ/Δ LT-HSC was not due to failure in IFN-α signaling. Transplantations of bone marrow from pegIFN-α-treated VF;DmΔ/Δ mice gave rise to more aggressive disease in secondary and tertiary recipients. Liquid cultures of hematopoietic progenitors from patients with MPN with JAK2-V617F and DNMT3A mutation showed increased percentages of JAK2-V617F-positive colonies upon IFN-α exposure, whereas in patients with JAK2-V617F alone, the percentages of JAK2-V617F-positive colonies decreased or remained unchanged. PegIFN-α combined with 5-azacytidine only partially overcame resistance in VF;DmΔ/Δ mice. However, this combination strongly decreased the JAK2-mutant allele burden in mice carrying VF mutation only, showing potential to inflict substantial damage preferentially to the JAK2-mutant clone.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts.

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

On the Stress-Strength Reliability of Transmuted GEV Random Variables with Applications to Financial Assets Selection.

In reliability contexts, probabilities of the type R=P(X

Influence of Ethanol Parametrization on Diffusion Coefficients Using OPLS-AA Force Field.

Molecular dynamics simulations employing the all-atom optimized potential for liquid simulations (OPLS-AA) force field were performed for determining self-diffusion coefficients (D11) of ethanol and tracer diffusion coefficients (D12) of solutes in ethanol at several temperature and pressure conditions. For simulations employing the original OPLS-AA diameter of ethanol's oxygen atom (σOH), calculated and experimental diffusivities of protic solutes differed by more than 25%. To correct this behavior, the σOH was reoptimized using the experimental D12 of quercetin and of gallic acid in liquid ethanol as benchmarks. A substantial improvement of the calculated diffusivities was found by changing σOH from its original value (0.312 nm) to 0.306 nm, with average absolute relative deviations (AARD) of 3.71% and 4.59% for quercetin and gallic acid, respectively. The new σOH value was further tested by computing D12 of ibuprofen and butan-1-ol in liquid ethanol with AARDs of 1.55% and 4.81%, respectively. A significant improvement was also obtained for the D11 of ethanol with AARD = 3.51%. It was also demonstrated that in the case of diffusion coefficients of non-polar solutes in ethanol, the original σOH=0.312 nm should be used for better agreement with experiment. If equilibrium properties such as enthalpy of vaporization and density are estimated, the original diameter should be once again adopted.

The Characteristics and Laboratory Findings of SARS-CoV-2 Infected Patients during the First Three COVID-19 Waves in Portugal-A Retrospective Single-Center Study.

Background and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients' outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients' biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients' groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients' characteristics and mortality-associated biomarkers during Portugal's first three COVID-19 waves, highlighting the importance of considering wave variations in future research due to potential significant outcome differences.

Incidental finding of accessory mitral valve tissue on routine adult echocardiography.

Accessory mitral valve tissue is a rare congenital cardiac abnormality that sometimes can cause left ventricular outflow tract obstruction. We herein present the case of a 55-year-old male with an incidental finding of accessory mitral valve tissue on transthoracic echocardiography. The patient was managed conservatively as accessory tissue was not causing left ventricular outflow obstruction and there were no hemodynamic consequences.

Biodentine and MTA modulate immunoinflammatory response favoring bone formation in sealing of furcation perforations in rat molars.

OBJECTIVES: Evaluate the tissue reaction of periodontium subjacent to furcation perforations in rat molars sealed with Biodentine or mineral trioxide aggregate (MTA). MATERIALS AND METHODS: The pulp chamber floor of right upper first molars of 60 rats was perforated and filled with Biodentine, MTA, or cotton pellet (sham); the left first molars were used as control. After 7, 15, 30, and 60 days, maxillary fragments were processed for paraffin-embedding. The periodontal space (PS), volume density of inflammatory cells (VvIC) and fibroblasts (VvFb), number of osteoclasts, and collagen content were obtained. Interleukin-6 (IL-6) and osterix (osteoblast marker) were detected by immunohistochemistry. The data were submitted to ANOVA and Tukey's test (p ≤ 0.05). RESULTS: At 7 days, high values in VvIC, IL-6-immunolabeled cells, and osteoclasts were accompanied by reduced collagen content in enlarged PS of experimental groups. At all periods, VvIC, number of osteoclasts and IL-6, and PS were higher in sham than in Biodentine and MTA (p < 0.0001). From 7 to 60 days, significant reduction in VvIC, IL-6 immunoexpression, and osteoclasts was accompanied by significant increase in VvFb, osteoblasts, and collagen in Biodentine and MTA groups. At 60 days, significant differences in VvIC, PS, IL-6, osteoclasts, and osteoblasts were not found between Biodentine and MTA. Significant differences in the osteoclast number were not observed among Biodentine, MTA, and control groups while osteoblasts number was higher in Biodentine and MTA groups. CONCLUSIONS: Despite the initial inflammatory reaction and bone resorption, the sealing of furcation perforations with Biodentine and MTA favors the repair of periodontal tissues. CLINICAL RELEVANCE: Biodentine and MTA exhibit potential as repair material in the treatment of furcation perforations.

Inherited Metabolic Disorders: From Bench to Bedside.

Inherited metabolic disorders (IMDs), commonly referred to as inborn errors of metabolism, represent a spectrum of disorders with a defined (or presumed) primary genetic cause which disrupts the normal metabolism of essential molecules in the body [...].

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool.

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

The glutaminase inhibitor CB-839 targets metabolic dependencies of JAK2-mutant hematopoiesis in MPN.

ABSTRACT: Hyperproliferation of myeloid and erythroid cells in myeloproliferative neoplasms (MPN) driven by the JAK2-V617F mutation is associated with altered metabolism. Given the central role of glutamine in anabolic and catabolic pathways, we examined the effects of pharmacologically inhibiting glutaminolysis, that is, the conversion of glutamine (Gln) to glutamate (Glu), using CB-839, a small molecular inhibitor of the enzyme glutaminase (GLS). We show that CB-839 strongly reduced the mitochondrial respiration rate of bone marrow cells from JAK2-V617F mutant (VF) mice, demonstrating a marked dependence of these cells on Gln-derived ATP production. Consistently, in vivo treatment with CB-839 normalized blood glucose levels, reduced splenomegaly and decreased erythrocytosis in VF mice. These effects were more pronounced when CB-839 was combined with the JAK1/2 inhibitor ruxolitinib or the glycolysis inhibitor 3PO, indicating possible synergies when cotargeting different metabolic and oncogenic pathways. Furthermore, we show that the inhibition of glutaminolysis with CB-839 preferentially lowered the proportion of JAK2-mutant hematopoietic stem cells (HSCs). The total number of HSCs was decreased by CB-839, primarily by reducing HSCs in the G1 phase of the cell cycle. CB-839 in combination with ruxolitinib also strongly reduced myelofibrosis at later stages of MPN. In line with the effects shown in mice, proliferation of CD34+ hematopoietic stem and progenitor cells from polycythemia vera patients was inhibited by CB-839 at nanomolar concentrations. These data suggest that inhibiting GLS alone or in combination with inhibitors of glycolysis or JAK2 inhibitors represents an attractive new therapeutic approach to MPN.

Dataset for identifying maintenance needs of home appliances using artificial intelligence.

The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of industries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capable of identifying certain patterns which can represent a malfunction or deterioration in the monitored machines. Therefore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This paper introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and washing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home appliances at a repair center and included readings of electrical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An extracted features dataset, corresponding to the collected working cycles is also made available. This dataset could benefit research and development of AI systems for home appliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption patterns of such home appliances.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy.

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

Impact of Clonal Architecture on Clinical Course and Prognosis in Patients With Myeloproliferative Neoplasms.

Myeloproliferative neoplasms (MPNs) are caused by a somatic gain-of-function mutation in 1 of the 3 disease driver genes JAK2, MPL, or CALR. About half of the MPNs patients also carry additional somatic mutations that modify the clinical course. The order of acquisition of these gene mutations has been proposed to influence the phenotype and evolution of the disease. We studied 50 JAK2-V617F-positive MPN patients who carried at least 1 additional somatic mutation and determined the clonal architecture of their hematopoiesis by sequencing DNA from single-cell-derived colonies. In 22 of these patients, the same blood samples were also studied for comparison by Tapestri single-cell DNA sequencing (scDNAseq). The clonal architectures derived by the 2 methods showed good overall concordance. scDNAseq showed higher sensitivity for mutations with low variant allele fraction, but had more difficulties distinguishing between heterozygous and homozygous mutations. By unsupervised analysis of clonal architecture data from all 50 MPN patients, we defined 4 distinct clusters. Cluster 4, characterized by more complex subclonal structure correlated with reduced overall survival, independent of the MPN subtype, presence of high molecular risk mutations, or the age at diagnosis. Cluster 1 was characterized by additional mutations residing in clones separated from the JAK2-V617F clone. The correlation with overall survival improved when mutation in such separated clones were not counted. Our results show that scDNAseq can reliably decipher the clonal architecture and can be used to refine the molecular prognostic stratification that until now was primarily based on the clinical and laboratory parameters.

The prevalence, distribution, and clinical outcomes of electrocardiographic repolarization patterns in male athletes of African/Afro-Caribbean origin.

AIMS: Athletic training in male black athletes (BAs) is associated with marked ECG repolarization changes that overlap with hypertrophic cardiomyopathy (HCM). Differentiating between the two entities is prudent since BAs exhibit a higher prevalence of exercise-related sudden death from HCM compared with white athletes (WAs). METHODS AND RESULTS: Between 1996 and 2010, 904 BAs underwent serial cardiac evaluations including ECG and echocardiography. Athletes exhibiting T-wave inversions were investigated further for HCM. Results were compared with 1819 WAs, 119 black controls (BCs), and 52 black HCM patients. Athletes were followed up for 69.7 ± 29.6 months. T-wave inversions were present in 82.7% HCM patients, 22.8% BAs, 10.1% BCs, and 3.7% WAs. In athletes, the major determinant of T-wave inversions was black ethnicity. T-wave inversions in BAs (12.7%) were predominantly confined to contiguous anterior leads (V1-V4). Only 4.1% of BAs exhibited T-wave inversions in the lateral leads. In contrast, both BCs and HCM patients exhibited lower prevalence of T-wave inversions in leads V1-V4 (4.2 and 3.8%, respectively) with most T-wave inversions in HCM patients (76.9%) involving the lateral leads. During follow-up one BA survived cardiac arrest and two athletes (one BA, one WA) were diagnosed with HCM. All three exhibited T-wave inversions in the lateral leads. CONCLUSIONS: T-wave inversions in leads V1-V4 appear to represent an ethnic variant of 'athlete's heart'. Conversely, T-wave inversions in the lateral leads may represent the initial expression of underlying cardiomyopathy and merit further evaluation and regular surveillance.

Infection Biomarkers Based on Metabolomics.

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

Predictors of in-hospital mortality in critically ill patients with COVID-19: a large dual tertiary centre study.

OBJECTIVES: The aim of this study was to investigate the relationship of echocardiographic parameters, laboratory findings and clinical characteristics with in-hospital mortality in adult patients with COVID-19 admitted to the intensive care units (ICU) in two large collaborating tertiary UK centres. DESIGN: Observational retrospective study. SETTING: The study was conducted in patients admitted to the ICU in two large tertiary centres in London, UK. PARTICIPANTS: Inclusion criteria were: (1) patients admitted to the ICU with a COVID-19 diagnosis over a period of 16 weeks. and (2) underwent a transthoracic echocardiogram on the first day of ICU admission as clinically indicated.No exclusion criteria applied.Three hundred patients were enrolled and completed the follow-up. PRIMARY AND SECONDARY OUTCOME MEASURES: The outcome measure in this study was in-hospital mortality in patients admitted to the ICU with COVID-19 infection. RESULTS: Older age (HR: 1.027, 95% CI 1.007 to 1.047; p=0.008), left ventricular (LV) ejection fraction<35% (HR: 5.908, 95% CI 2.609 to 13.376; p<0.001), and peak C reactive protein (CRP) (HR: 1.002, 95% CI 1.001 to 1.004, p=0.001) were independently correlated with mortality in a multivariable Cox regression model. Following multiple imputation of variables with more than 5% missing values, random forest analysis was applied to the imputed data. Right ventricular (RV) basal diameter (RVD1), RV mid-cavity diameter (RVD2), tricuspid annular plane systolic excursion, RV systolic pressure, hypertension, RV dysfunction, troponin level on admission, peak CRP, creatinine level on ICU admission, body mass index and age were found to have a high relative importance (> 0.7). CONCLUSIONS: In patients with COVID-19 in the ICU, both severely impaired LV function and impaired RV function may have adverse prognostic implications, but older age and inflammatory markers appear to have a greater impact. A combination of echocardiographic and laboratory investigations as well as demographic and clinical characteristics appears appropriate for risk stratification in patients with COVID-19 who are admitted to the ICU.

Are adverse effects incorporated in economic models? A survey of current practice.

BACKGROUND: Clearly the benefits of a treatment must not be outweighed by the adverse effects. If researchers fail to incorporate adverse effects adequately in models, this could limit the validity of the results obtained. In the worst case, interventions that are cost-effective may be shown not to be. The aim of this research was to review current practice when incorporating adverse effects in economic models. METHODS: A survey of HTA reports commissioned by the National Institute for Health Research (NIHR) Health Technology Assessment programme, published between 2004 and 2007 was conducted. All reports which investigated the clinical and cost-effectiveness of a health technology using a systematic review and an economic model framework were included. RESULTS: A total of eighty reports met the inclusion criteria. Of the models including adverse effects (43/80), 67 percent used a clinical adverse effects parameter, 79 percent a cost of adverse effects parameter, 86 percent used one of these, and 60 percent used both. Of the thirty-seven models that did not include adverse effects, eighteen justified this omission, most commonly lack of data; nineteen appeared to make no explicit consideration of adverse effects in the model. CONCLUSIONS: In many cases, poor reporting made it difficult to ascertain if there had been any consideration of adverse effects. We suggest that the findings of this survey support a call for much clearer and explicit reporting of adverse effects, or their exclusion, in decision models and for explicit recognition in future guidelines that "all relevant outcomes" should include some consideration of adverse events.