Low Doses of Deoxynivalenol and Zearalenone Alone or in Combination with a Mycotoxin Binder Affect ABCB1 mRNA and ABCC2 mRNA Expression in the Intestines of Pigs.

Mycotoxin binders, in combination with enzymes degrading some mycotoxins, contribute to feed detoxification. Their use reduces economic losses and the negative impacts of mycotoxins on animal health and productivity in farm animals. The aim of this study was to evaluate the efficacy of a mycotoxin detoxifier on the expression of the ATP-binding cassette efflux transporters ABCB1 mRNA and ABCC2 mRNA, which transport xenobiotics and thus have a barrier function, in the tissues of pigs exposed to low doses of deoxynivalenol (DON, 1 mg/kg feed) and zearalenone (ZEN, 0.4 mg/kg feed) for 37 days. The levels of expression were determined by an RT-PCR, and the effect of the mycotoxin detoxifier (Mycofix Plus3.E) was evaluated by a comparison of results between healthy pigs (n = 6), animals treated with DON and ZEN (n = 6), and a group that received both mycotoxins and the detoxifier (n = 6). A significant downregulation of ABCB1 mRNA and ABCC2 mRNA was observed in the jejunum (p < 0.05). A tendencies toward the downregulation of ABCB1 mRNA and ABCC2 mRNA were found in the ileum and duodenum, respectively. The mycotoxin detoxifier restored the expression of ABCB1 mRNA to the level found in healthy animals but did not restore that of ABCC2 mRNA to the level of healthy animals in the jejunum.

American College of Rheumatology and Food and Drug Administration Summit: Summary of the Meeting May 17-18, 2022.

The American College of Rheumatology and the US Food and Drug Administration co-sponsored a public meeting in May 2022 about challenges in the clinical development of drugs for rheumatoid arthritis (RA) and psoriatic arthritis (PsA), focusing on innovative clinical trial designs, outcome measures, and data collection methods. Recommendations include early dose-ranging studies and use of active comparators. Challenges and opportunities in assessing long-term safety by leveraging real-world data from electronic health records (EHRs) and claims data are discussed, along with insights from European registries and the evolving role of real-world evidence and artificial intelligence in regulatory evaluations. Endpoints for assessing disease activity and outcome measures used in RA and PsA trials are explored, emphasizing challenges in defining remission, assessing clinical response, and evaluating structural progression. The need for outcome measures that better reflect treatment targets and the potential of advanced imaging in future trials are highlighted. Challenges with placebo-controlled trials in RA are discussed and use of non-inferiority clinical trial design, in which new drugs are evaluated with active comparators, is proposed. Pragmatic trials in RA and PsA, employing decentralized approaches, are highlighted for their real-world relevance and administrative efficiencies. Strategies for identifying at-risk populations for RA and the challenges of using EHRs and insurance claims data in drug development are discussed. Registry data and digital health technologies show promise in bridging the gap between clinical trials and real-world effectiveness.

Using Machine Learning to Determine a Suitable Patient Population for Anakinra for the Treatment of COVID-19 Under the Emergency Use Authorization.

A randomized, double-blind, placebo-controlled study (SAVEMORE trial) provided data to support an Emergency Use Authorization (EUA) of anakinra in hospitalized adults with positive results of direct severe acute respiratory syndrome-coronavirus 2 viral testing with pneumonia requiring supplemental oxygen (low- or high-flow oxygen) who are at risk of progressing to severe respiratory failure and likely to have an elevated plasma soluble urokinase plasminogen activator receptor (suPAR). Currently, the suPAR assay is not commercially available in the United States. An alternative method was needed to identify patients that best reflect the population in the clinical trial selected based on suPAR level ≥ 6 ng/mL at baseline. A machine learning approach based on data from the SAVEMORE trial was used to develop a scoring rule to identify patients who are likely to have a suPAR level ≥ 6 ng/mL at baseline. External validation of the scoring rule was conducted with data from a different trial (SAVE). This clinical scoring rule with high positive predictive value, high specificity, reasonable sensitivity, and biological relevance is expected to identify patients who are likely to have an elevated suPAR level ≥ 6 ng/mL at baseline. As such, it is included in the EUA to identify patients that fall within the authorized population for whom the known and potential benefits outweigh the known and potential risks of anakinra.

Therapeutic Development in Polyarticular Course Juvenile Idiopathic Arthritis: Extrapolation, Dose Selection, and Clinical Trial Design.

OBJECTIVE: Stakeholders met to address persistent challenges facing the development of therapeutics for polyarticular juvenile idiopathic arthritis (pJIA), which result in fewer approved therapies for children with pJIA than adults with rheumatoid arthritis (RA) and long lag times from adult RA approval to pediatric labeling. Ensuring that new medications are authorized in a timely manner to meet the needs of JIA patients worldwide is critically important to multiple stakeholders. METHODS: The Food and Drug Administration in collaboration with the University of Maryland Center for Regulatory Science and Innovation held a public workshop entitled "Accelerating Drug Development for pJIA" on October 2, 2019, to address challenges surrounding access to new medications for children and adolescents with pJIA. Regulatory, academic, and industry stakeholders, as well as patient representatives, participated in the workshop, which consisted of 4 sessions, including panel discussions. RESULTS: The workshop facilitated broad public discussion of challenges facing the development of pJIA therapeutics, highlighting areas of need and outlining opportunities to expedite development, while underscoring the necessity of close collaboration between all stakeholders, including patients and families. CONCLUSION: This report summarizes key aspects of the workshop, including the appropriate application of innovative approaches to the development of pJIA therapeutics, including extrapolation, to address current challenges and provide timely access to newer safe and effective treatments. Long-term safety assessment is of pressing concern to stakeholders and cannot be fully extrapolated from adult studies but requires consistent postmarketing long-term follow-up.

Linear Interval Approximation of Sensor Characteristics with Inflection Points.

The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines.

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

Early Release Science of the exoplanet WASP-39b with JWST NIRCam.

Measuring the metallicity and carbon-to-oxygen (C/O) ratio in exoplanet atmospheres is a fundamental step towards constraining the dominant chemical processes at work and, if in equilibrium, revealing planet formation histories. Transmission spectroscopy (for example, refs. 1,2) provides the necessary means by constraining the abundances of oxygen- and carbon-bearing species; however, this requires broad wavelength coverage, moderate spectral resolution and high precision, which, together, are not achievable with previous observatories. Now that JWST has commenced science operations, we are able to observe exoplanets at previously uncharted wavelengths and spectral resolutions. Here we report time-series observations of the transiting exoplanet WASP-39b using JWST's Near InfraRed Camera (NIRCam). The long-wavelength spectroscopic and short-wavelength photometric light curves span 2.0-4.0 micrometres, exhibit minimal systematics and reveal well defined molecular absorption features in the planet's spectrum. Specifically, we detect gaseous water in the atmosphere and place an upper limit on the abundance of methane. The otherwise prominent carbon dioxide feature at 2.8 micrometres is largely masked by water. The best-fit chemical equilibrium models favour an atmospheric metallicity of 1-100-times solar (that is, an enrichment of elements heavier than helium relative to the Sun) and a substellar C/O ratio. The inferred high metallicity and low C/O ratio may indicate significant accretion of solid materials during planet formation (for example, refs. 3,4,) or disequilibrium processes in the upper atmosphere (for example, refs. 5,6).

FDA/Arthritis Foundation osteoarthritis drug development workshop recap: Assessment of long-term benefit.

OBJECTIVE: To summarize proceedings of a workshop convened to discuss the current state of science in the disease of osteoarthritis (OA), identify the knowledge gaps, and examine the developmental and regulatory challenges in bringing these products to market. DESIGN: Summary of the one-day workshop held virtually on June 22nd, 2021. RESULTS: Speakers selected by the Planning Committee presented data on the current approach to assessment of OA therapies, biomarkers in OA drug development, and the assessment of disease progression and long-term benefit. CONCLUSIONS: Demonstrated by numerous failed clinical trials, OA is a challenging disease for which to develop therapeutics. The challenge is magnified by the slow time of onset of disease and the need for clinical trials of long duration and/or large sample size to demonstrate the effect of an intervention. The OA science community, including academia, pharmaceutical companies, regulatory agencies, and patient communities, must continue to develop and test better clinical endpoints that meaningfully reflect disease modification related to long-term patient benefit.

UV absorption by silicate cloud precursors in ultra-hot Jupiter WASP-178b.

Aerosols have been found to be nearly ubiquitous in substellar atmospheres1-3. The precise temperature at which these aerosols begin to form in exoplanets has yet to be observationally constrained. Theoretical models and observations of muted spectral features indicate that silicate clouds play an important role in exoplanets between at least 950 and 2,100 K (ref. 4). Some giant planets, however, are thought to be hot enough to avoid condensation altogether5,6. Here we report the near-ultraviolet transmission spectrum of the ultra-hot Jupiter WASP-178b (approximately 2,450 K), which exhibits substantial absorption. Bayesian retrievals indicate the presence of gaseous refractory species containing silicon and magnesium, which are the precursors to condensate clouds at lower temperatures. SiO, in particular, has not previously, to our knowledge, been detected in exoplanets, but the presence of SiO in WASP-178b is consistent with theoretical expectations as the dominant Si-bearing species at high temperatures. These observations allow us to re-interpret previous observations of HAT-P-41b and WASP-121b that did not consider SiO, to suggest that silicate cloud formation begins on exoplanets with equilibrium temperatures between 1,950 and 2,450 K.

Clinical pharmacology considerations for the approval of belimumab for the treatment of adult patients with active lupus nephritis: A regulatory perspective.

On 16 December 2020, FDA approved Benlysta® (belimumab) for both the intravenous (IV) and subcutaneous (SC) administration routes for the treatment of adult patients with active lupus nephritis (LN) who are receiving standard therapy. This approval represents the first FDA approved treatment of patients with active LN.The approved IV dosing regimen (10 mg/kg dose Q2W for three doses, then 10 mg/kg Q4W thereafter) was based on a randomized double-blind placebo controlled clinical trial in adult patients with LN. For the approval of the SC dosing regimen (400 mg dose QW for four doses, then 200 mg QW thereafter), efficacy was supported solely by pharmacokinetics (PK) modeling and simulation which estimated a matched steady state average concentration and higher trough concentrations for the SC administration route, for bridging to the efficacy of IV belimumab in adults with LN. The safety and immunogenicity profile of the SC administration route has been assessed in the SLE studies.In a population PK analysis, higher proteinuria was associated with greater belimumab clearance and lower belimumab exposure. In an exposure response analysis, the efficacy of belimumab as evaluated by renal response was mainly driven by patients with lower proteinuria at baseline regardless of other baseline characteristics (e.g. baseline renal function, renal biopsy classification), induction therapies, or belimumab exposure levels (within 10 mg/kg dosing regimen), etc. However, post hoc analyses showed that belimumab had activity in LN patients with higher proteinuria at baseline. There is no adequate information to suggest that a higher dose would provide additional benefit for patients with lower exposure (e.g. higher proteinuria).

Linear Interval Approximation for Smart Sensors and IoT Devices.

In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate "lightweight" algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.

Concept End Points Informing Design Considerations for Confirmatory Clinical Trials in Osteoarthritis.

OBJECTIVE: There is an unmet need for therapies that target the underlying pathophysiology of osteoarthritis (OA). However, defining appropriate measures for clinical trials of such therapies is challenging. Our objective was to propose concept clinical end points that directly capture clinical benefit in this setting and evaluate the feasibility of their use. METHODS: This analysis used the multicenter, longitudinal, observational Osteoarthritis Initiative (OAI) database. OAI participants primarily had knee OA, with follow-up of up to 9 years and assessments of joints, surgical interventions, performance outcomes, and patient-reported outcomes. We examined this data set to identify existing outcome measures of direct clinical benefit. We evaluated the feasibility of conducting trials using these candidate end points by estimating incidence rates and resulting required sample sizes and study durations in time-to-event analyses. RESULTS: We identified candidate end points based on total knee replacement (TKR) and composite end points defined by TKR and conservative thresholds of patient-reported outcomes of pain and function. Using time to TKR as an end point, a study with an average follow-up time of 3 years requires approximately 3,000 to 18,000 subjects, depending on effect size. Alternatively, for a composite end point, such as "time to TKR or severe pain or severely impaired functioning," the required sample sizes ranged from approximately 2,000 to 11,000 for a 3-year study. CONCLUSION: The proposed concept end points can reliably and feasibly evaluate the effectiveness of therapies for this unmet need. In particular, the composite end point approach can substantially reduce sample sizes (up to approximately 40%) compared to the use of TKR alone.

Prevalence of low bone mineral density at axial sites and fracture risk in Bulgarian population.

Background: Osteoporosis is a common chronic disease characterized by low bone mineral density (BMD) and microarchitectural deterioration of the bone, which are associated with increased risk of fragility fractures. Currently the most popular tool is the fracture risk assessment model FRAX to calculate the 10-year probability of major osteoporotic fractures (MOF) and hip fractures (HF). Objective: To investigate the prevalence of low BMD at axial sites and fracture risk in Bulgarian population. Methods: We retrospectively analyzed dual energy X-ray absorptiometry (DXA) scan results of 12 478 subjects. Scan results included BMD and T-score assessments of lumbar spine and femoral neck. FRAX major osteoprotic fracture (MOF) and FRAX hip fracture (HF) were assessed in subjects between 40 and 90 years using BMD values. Results: Of total 12478 subjects, 12119 were women and 359 were men. The mean age of the subjects was 61 years (yrs.) ± 10 yrs. The overall prevalence of low BMD at the lumbar spine was 6084/9336 subjects (65.2%). 3502/9336 subjects (37.5%) were considered as osteopenic and 2582/9336 subjects (27.7%) were considered as osteoporotic. The overall prevalence of low BMD at the femoral neck was 2036/3140 (64.8%). 1641/3140 subjects (52.3%) were classified as osteopenic and 395/3 140 subjects (12.6%) were classified as osteoporotic. The mean values of FRAX MOF and FRAX HF increased significantly with increasing the age interval. Conclusion: This study is the largest epidemiological research in Bulgaria up to date about the prevalence of low BMD at axial sites.

Big Data Workflows: Locality-Aware Orchestration Using Software Containers.

The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.

Inhibition of miR-99a-5p prevents allergen-driven airway exacerbations without compromising type-2 memory responses in the intestine following helminth infection.

Acute exacerbations (AE) of asthma, remain one of the biggest concerns for patients living with asthma. As such, identifying the causes, the molecular mechanisms involved and new therapeutic interventions to prevent AE is a high priority. Immunity to intestinal helminths involves the reactivation of type-2 immune responses leading to smooth muscle contraction and mucus hypersecretion-physiological processes very similar to acute exacerbations in the airways following allergen exposure. In this study, we employed a murine model of intestinal helminth infection, using Heligmosomoides polygyrus, to identify miRNAs during active expulsion, as a system for the identification of miRNAs that may contribute to AE in the airways. Concomitant with type-2 immunity and expulsion of H. polygyrus, we identified miR-99a-5p, miR-148a-3p and miR-155-5p that were differentially regulated. Systemic inhibition of these miRNAs, alone or in combination, had minimal impact on expulsion of H. polygyrus, but inhibition of miR-99a-5p or miR-155-5p significantly reduced house dust mite (HDM)-driven acute inflammation, modelling human acute exacerbations. Immunological, pathological and transcriptional analysis identified that miR-155-5p or miR-99a-5p contribute significantly to HDM-driven AE and that transient inhibition of these miRNAs may provide relief from allergen-driven AE, without compromising anti-helminth immunity in the gut.

Response Similarity Assessment Between Polyarticular Juvenile Idiopathic Arthritis and Adult Rheumatoid Arthritis for Biologics.

Polyarticular juvenile idiopathic arthritis (pJIA) is a pediatric chronic inflammatory arthritis, much like rheumatoid arthritis (RA) in adults. Drug development for pJIA can potentially be expedited by using extrapolation of efficacy from adult RA; however, the lack of understanding of the response and exposure relationship between pJIA and RA to therapeutic interventions has been a major roadblock. To address this, the objective of our analysis was to conduct a systematic response and exposure comparison between pJIA and RA trials for biologic products. Data from registration RA and pJIA clinical trials (parallel or withdrawal design) for infliximab, tocilizumab, golimumab, and adalimumab were utilized. First, exposure was compared between the pJIA trials and RA pivotal trials. Subsequently, the pJIA vs. RA response similarity was assessed by comparing similar individual subcomponents of the American College of Rheumatology (ACR) scores between the two populations. The exposure comparison demonstrated that at the pJIA trial dose, exposure in pediatric patients was similar to or higher than adults for all biologics evaluated except infliximab, where lower exposure was observed in pJIA patients ≤ 35 kg. Response comparison for individual subcomponents indicated that in a majority of the cases, pJIA response was similar or higher as compared with response from RA trials. Overall, this analysis suggests response similarity between pJIA and RA across the biologic products when exposures are matched between the two populations. These analyses provide support for the use of pharmacokinetic exposure-matching for extrapolation of efficacy from adult RA to pediatric pJIA for the products with established mechanism(s) of action.

Immunomodulatory Therapeutic Proteins in COVID-19: Current Clinical Development and Clinical Pharmacology Considerations.

The coronavirus disease 2019 (COVID-19) pandemic caused by infection with SARS-CoV-2 has led to more than 600 000 deaths worldwide. Patients with severe disease often experience acute respiratory distress characterized by upregulation of multiple cytokines. Immunomodulatory biological therapies are being evaluated in clinical trials for the management of the systemic inflammatory response and pulmonary complications in patients with advanced stages of COVID-19. In this review, we summarize the clinical pharmacology considerations in the development of immunomodulatory therapeutic proteins for mitigating the heightened inflammatory response identified in COVID-19.

A Comprehensive Updated Review on SARS-CoV-2 and COVID-19.

This literature review aims to provide a comprehensive current summary of the pathogenesis, clinical features, disease course, host immune responses, and current investigational antiviral and immunomodulatory pharmacotherapies to facilitate the development of future therapies and measures for prevention and control.

Effect of the lumbar scoliosis on the results of dual-energy X-ray absorptiometry.

One of the most common causes of lumbar scoliosis in adults is the decreased bone mineral density (BMD). The scoliosis in the lumbar spine has a known effect over the dual-energy X-ray absorptiometry (DXA) scan results. The objective of this study is to assess the influence of the lumbar scoliosis on the results of the DXA scan of the lumbar spine. 1019 women aged ≥40 years underwent a DXA scan of the spine. Age, weight, height, total BMD, total Tscore of the lumbar spine were recorded. The angle of the lumbar scoliosis (Cobb's angle) was measured from the DXA scan image using a DICOM software. The incidence of lumbar scoliosis in the current study accounts to 12.3%. Women with scoliosis showed significantly higher incidence of discrepancy in BMD T-scores between the adjacent vertebrae by more than 1 SD compared to women without scoliosis, (p=0.046). DXA results of subjects with scoliosis require more detailed evaluation of the T-scores of each vertebra to make a prompt decision about the final diagnosis.