Assessment of microRNA-related SNP effects in the 3′ untranslated region of the IL22RA2 risk locus in multiple sclerosis

Recent large-scale association studies have identified over 100 MS risk loci. One of these MS risk variants is single-nucleotide polymorphism (SNP) rs17066096, located ~14 kb downstream of IL22RA2. IL22RA2 represents a compelling MS candidate gene due to the role of IL-22 in autoimmunity; however, rs17066096 does not map into any known functional element. We assessed whether rs17066096 or a nearby proxy SNP may exert pathogenic effects by affecting microRNA-to-mRNA binding and thus IL22RA2 expression using comprehensive in silico predictions, in vitro reporter assays, and genotyping experiments in 6,722 individuals. In silico screening identified two predicted microRNA binding sites in the 3′UTR of IL22RA2 (for hsa-miR-2278 and hsa-miR-411-5p) encompassing a SNP (rs28366) in moderate linkage disequilibrium with rs17066096 (r ²?=?0.4). The binding of both microRNAs to the IL22RA2 3′UTR was confirmed in vitro, but their binding affinities were not significantly affected by rs28366. Association analyses revealed significant association of rs17066096 and MS risk in our independent German dataset (odds ratio ?=?1.15, P?=?3.48?×?10^?4), but did not indicate rs28366 to be the cause of this signal. While our study provides independent validation of the association between rs17066096 and MS risk, this signal does not appear to be caused by sequence variants affecting microRNA function.     

Sustainable oxidation of cyclohexane catalyzed by a VO(acac)2-oxalic acid tandem: the electrochemical motive of the process efficiency

Cyclohexane oxidation by H₂O₂ to cyclohexanol, cyclohexanone, and cyclohexylhydroperoxide under mild (40 °C, 1 atm) conditions is significantly enhanced in the system composed of VO(acac)₂ (starting catalyst) and small additives of oxalic acid (process promoter). In corroboration of this, several times higher yield of the desired products was obtained compared to that obtained in the acid-free process. The revealed advantage was addressed to elevate the electrical conductance G (or vice versa, decreasing the resistance, 1/G) of the reaction medium. On the other hand, the content of oxalic acid (20–30 mM) was compulsory to optimize the process parameters. The last value of concentration affords, besides the lowest 1/G, the utmost impact on pH, redox potential, and current–voltage relationships. Exceeding this level leads to an increase in 1/G of the reaction solution, ceases the impact on pH, ORP, and CV profiles, and is detrimental for the product yield. The putative mechanism of the revealed effects has been envisaged.

Cyclohexane oxidation by H₂O₂ to cyclohexanol, cyclohexanone, and cyclohexylhydroperoxide under mild (40 °C, 1 atm) conditions is significantly enhanced in the system composed of VO(acac)₂ (starting catalyst) and small additives of oxalic acid (process promoter).     

Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM^® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10278-022-00683-y.     

Leukemic progression as process of adaptation (theoretical model)

On the basis of an assumption that leukemic progression is a process of adaptation of normal hemopoiesis to the effect of the leukemic clone, a theoretical model of leukemia development is built. General conclusions are as follows: the possibility of reaching remission depends upon suppression of normal hemopoiesis by cytostatics in a system of two cell populations: the duration of remission depends uponsuppression of the leukemic clone; the quantity of blast cells of marrow in remission can exceed 5 per cent.     

Continuum percolation of carbon nanotubes in polymeric and colloidal media

We apply continuum connectedness percolation theory to realistic carbon nanotube systems and predict how bending flexibility, length polydispersity, and attractive interactions between them influence the percolation threshold, demonstrating that it can be used as a predictive tool for designing nanotube-based composite materials. We argue that the host matrix in which the nanotubes are dispersed controls this threshold through the interactions it induces between them during processing and through the degree of connectedness that must be set by the tunneling distance of electrons, at least in the context of conductivity percolation. This provides routes to manipulate the percolation threshold and the level of conductivity in the final product. We find that the percolation threshold of carbon nanotubes is very sensitive to the degree of connectedness, to the presence of small quantities of longer rods, and to very weak attractive interactions between them. Bending flexibility or tortuosity, on the other hand, has only a fairly weak impact on the percolation threshold.     

Pathologic correlation of transperineal in-bore 3-Tesla magnetic resonance imaging-guided prostate biopsy samples with radical prostatectomy specimen

Abdominal Radiology - To determine the accuracy of in-bore transperineal 3-Tesla (T) magnetic resonance (MR) imaging-guided prostate biopsies for predicting final Gleason grades in patients who...     

Loss of complexity characterizes the heart rate response to experimental hemorrhagic shock in swine

OBJECTIVE: To improve our ability to identify physiologic deterioration caused by critical illness, we applied nonlinear and frequency-domain analytical methods to R-to-R interval (RRI) and systolic arterial pressure (SAP) time series during hemorrhagic shock. DESIGN: Prospective, randomized, controlled trial. SETTING: Animal laboratory of a government research institute. SUBJECTS: Twenty swine (weight 36.4+/-0.11 kg). INTERVENTIONS: Fixed-volume hemorrhage followed by resuscitation; off-line analysis of RRI and SAP data. MEASUREMENTS AND MAIN RESULTS: Anesthetized swine (shock group, n=12) underwent withdrawal of 30 mL/kg blood in 10 mL/kg decrements. A control group (n=8) received maintenance fluids only. Electrocardiogram and arterial pressure waveforms were acquired at 500 Hz. Eight hundred-beat data sets were analyzed at six time points: at baseline, after each blood withdrawal, after lactated Ringer's resuscitation, and after infusion of shed blood. Nonlinear methods were used to estimate the complexity (approximate entropy, sample entropy, Lempel-Ziv entropy, normalized entropy of symbol dynamics), RRI bits per word, and fractal dimension by curve lengths and by dispersion analysis of the RRI and SAP time series. Fast Fourier transformation was used to measure the high-frequency and low-frequency powers of RRI and SAP. Baroreflex sensitivity was assessed in the time domain with the sequence method. Hemorrhagic shock caused decreases in RRI complexity as quantified by approximate entropy, sample entropy, and symbol dynamics; these changes were reversed by resuscitation. Similar but statistically insignificant changes in fractal dimension by curve lengths were seen. RRI high-frequency power decreased with hemorrhagic shock-indicating withdrawal of vagal cardiac input-and was restored by resuscitation. Similar changes in baroreflex sensitivity were seen. Hemorrhagic shock did not affect SAP complexity. CONCLUSIONS: Hemorrhagic shock caused a reversible decrease in RRI complexity; these changes may be mediated by changes in vagal cardiac control. Assessment of RRI complexity may permit identification of casualties with hemorrhagic shock.     

Development and validation of a novel fusion algorithm for continuous,accurate, and automated R-wave detection and calculation of signal-derived metrics

Purpose

Previous studies have shown that heart rate complexity may be a useful indicator of patient status in the critical care environment but will require continuous, accurate, and automated R-wave detection (RWD) in the electrocardiogram (ECG). Although numerous RWD algorithms exist, accurate detection remains a challenge. The purpose of this study was to develop and validate a novel fusion algorithm (Automated Electrocardiogram Selection of Peaks, or AESOP) that combines the strengths of several well-known algorithms to provide a more reliable real-time solution to the RWD problem.

Materials and Methods

This study involved the ECGs of 108 prehospital patient records and 32 ECGs from a conscious sedated porcine model of hemorrhagic shock. The criterion standard for validation was manual verification of R waves.

Results

For 108 human ECG records, the AESOP algorithm overall outperformed each of its component algorithms. In addition, for 32 swine ECG records, AESOP achieved an R-wave sensitivity of 97.9% and a positive predictive value of 97.5%, again outperforming its component algorithms.

Conclusion

By fusing several best algorithms, AESOP uses the strengths of each algorithm to perform more robustly and reliably in real time. The AESOP algorithm will be integrated into a real-time heart rate complexity software program for decision support and triage in critically ill patients.     

Robust and Powerful Tests for Rare Variants Using Fisher's Method to Combine Evidence of Association From Two or More Complementary Tests

Many association tests have been proposed for rare variants, but the choice of a powerful test is uncertain when there is limited information on the underlying genetic model. Proposed methods use either linear statistics, which are powerful when most variants are causal and have the same direction of effect, or quadratic statistics, which are more powerful in other scenarios. To achieve robustness, it is natural to combine the evidence of association from two or more complementary tests. To this end, we consider the minimum‐p and Fisher's methods of combining P‐values from linear and quadratic statistics. Extensive simulation studies show that both methods are robust across models with varying proportions of causal, deleterious, and protective rare variants, allele frequencies, and effect sizes. When the majority (>75%) of the causal effects are in the same direction (deleterious or protective), Fisher's method consistently outperforms the minimum‐p and the individual linear and quadratic tests, as well as the optimal sequence kernel association test, SKAT‐O. When the individual test has moderate power, Fisher's test has improved power for 90% of the ～5000 models considered, with >20% relative efficiency gain for 40% of the models. The maximum absolute power loss is 8% for the remaining 10% of the models. An application to the GAW17 quantitative trait Q2 data based on sequence data of the 1000 Genomes Project shows that, compared with linear and quadratic tests, Fisher's test has comparable power for all 13 functional genes and provides the best power for more than half of them.