Noncoding RNA landscape and their emerging roles as biomarkers and therapeutic targets in meningioma.

Meningiomas are among the most prevalent primary CNS tumors in adults, accounting for nearly 38% of all brain neoplasms. The World Health Organization (WHO) grade assigned to meningiomas guides medical care in patients and is primarily based on tumor histology and malignancy potential. Although often considered benign, meningiomas with complicated histology, limited accessibility for surgical resection, and/or higher malignancy potential (WHO grade 2 and WHO grade 3) are harder to combat, resulting in significant morbidity. With limited treatment options and no systemic therapies, it is imperative to understand meningioma tumorigenesis at the molecular level and identify novel therapeutic targets. The last decade witnessed considerable progress in understanding the noncoding RNA landscape of meningioma, with microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) emerging as molecular entities of interest. This review aims to highlight the commonly dysregulated miRNAs and lncRNAs in meningioma and their correlation with meningioma progression, malignancy, recurrence, and radioresistance. The role of "key" miRNAs as biomarkers and their therapeutic potential has also been reviewed in detail. Furthermore, current and emerging therapeutic modalities for meningioma have been discussed, with emphasis on the need to identify and subsequently employ clinically relevant miRNAs and lncRNAs as novel therapeutic targets and biomarkers.

The Language of Compassion: Hospital Chaplains' Compassion Capacity Reduces Patient Depression via Other-Oriented, Inclusive Language.

Objectives: Although hospital chaplains play a critical role in delivering emotional and spiritual care to a broad range of both religious and non-religious patients, there is remarkably little research on the best practices or "active ingredients" of chaplain spiritual consults. Here, we examined how chaplains' compassion capacity was associated with their linguistic behavior with hospitalized inpatients, and how their language in turn related to patient outcomes. Methods: Hospital chaplains (n = 16) completed self-report measures that together were operationalized as self-reported "compassion capacity." Next, chaplains conducted consultations with inpatients (n = 101) in five hospitals. Consultations were audio-recorded, transcribed, and analyzed using Linguistic Inquiry Word Count (LIWC). We used exploratory structural equation modeling to identify associations between chaplain-reported compassion capacity, chaplain linguistic behavior, and patient depression after the consultation. Results: We found that compassion capacity was significantly associated with chaplains' LIWC clout scores, a variable that reflects a confident leadership, inclusive, and other-oriented linguistic style. Clout scores, in turn, were negatively associated with patient depression levels controlling for pre-consult distress, indicating that patients seen by chaplains displaying high levels of clout had lower levels of depression after the consultation. Compassion capacity exerted a statistically significant indirect effect on patient depression via increased clout language. Conclusions: These findings inform our understanding of the linguistic patterns underlying compassionate and effective chaplain-patient consultations and contribute to a deeper understanding of the skillful means by which compassion may be manifest to reduce suffering and enhance well-being in individuals at their most vulnerable.

An aryl diimine cobalt(I) catalyst for carbonyl hydrosilylation.

Through the application of a redox-innocent aryl diimine chelate, the discovery and utilization of a cobalt catalyst, (Ph2PPrADI)Co, that exhibits carbonyl hydrosilylation turnover frequencies of up to 330 s-1 is described. This activity is believed to be the highest ever reported for metal-catalyzed carbonyl hydrosilylation.

Learning Compassion and Meditation: A Mixed-Methods Analysis of the Experience of Novice Meditators.

Over the last decade, numerous interventions and techniques that aim to engender, strengthen, and expand compassion have been created, proliferating an evidence base for the benefits of compassion meditation training. However, to date, little research has been conducted to examine individual variation in the learning, beliefs, practices, and subjective experiences of compassion meditation. This mixed-method study examines changes in novice meditators' knowledge and contemplative experiences before, during, and after taking an intensive course in CBCT® (Cognitively-Based Compassion Training), a contemplative intervention that is increasingly used for both inter- and intrapersonal flourishing. The participants in this study (n = 40) were Christian healthcare chaplains completing a 1-year residency in Clinical Pastoral Education (CPE) who learned CBCT as part of their professional chaplaincy training curriculum. Prior to and upon completion of training, we surveyed participants to assess their beliefs about the malleability of compassion, types of engagement in compassion meditation, and perceptions of the impact of taking CBCT. We also conducted in-depth interviews with a subset of participants to gain a qualitative understanding of their subjective experiences of learning and practicing compassion meditation, a key component of CBCT. We found that participants reported increases in the extent to which they believed compassion to be malleable after studying CBCT. We also found high levels of variability of individual ways of practicing and considered the implications of this for the study of contemplative learning processes. This multi-methodological approach yielded novel insights into how compassion practice and compassion-related outcomes interrelate, insights that can inform the basic scientific understanding of the experience of learning and enacting compassion meditation as a means of strengthening compassion itself.

Comparing the Electronic Structure of Iron, Cobalt, and Nickel Compounds That Feature a Phosphine-Substituted Bis(imino)pyridine Chelate.

It was recently discovered that (Ph2PPrPDI)Mn (PDI = pyridine diimine) exists as a superposition of low-spin Mn(II) that is supported by a PDI dianion and intermediate-spin Mn(II) that is antiferromagnetically coupled to a triplet PDI dianion, a finding that encouraged the synthesis and electronic structure evaluation of late first row metal variants that feature the same chelate. The addition of Ph2PPrPDI to FeBr2 resulted in bromide dissociation and the formation of [(Ph2PPrPDI)FeBr][Br]. Reduction of this precursor using excess sodium amalgam afforded (Ph2PPrPDI)Fe, which possesses an Fe(II) center that is supported by a dianionic PDI ligand. Similarly, reduction of a premixed solution of Ph2PPrPDI and CoCl2 yielded the cobalt analog, (Ph2PPrPDI)Co. EPR spectroscopy and density functional theory calculations revealed that this compound features a high-spin Co(I) center that is antiferromagnetically coupled to a PDI radical anion. The addition of Ph2PPrPDI to Ni(COD)2 resulted in ligand displacement and the formation of (Ph2PPrPDI)Ni, which was found to possess a pendent phosphine group. Single-crystal X-ray diffraction, CASSCF calculations, and EPR spectroscopy indicate that (Ph2PPrPDI)Ni is best described as having a Ni(II)-PDI2- configuration. The electronic differences between these compounds are highlighted, and a computational analysis of Ph2PPrPDI denticity has revealed the thermodynamic penalties associated with phosphine dissociation from 5-coordinate (Ph2PPrPDI)Mn, (Ph2PPrPDI)Fe, and (Ph2PPrPDI)Co.

Immunotherapeutics for AD: A Work in Progress.

Alzheimer's Disease (AD), often called the 'Plague of the 21st Century,' is a progressive, irreversible neurodegenerative disorder that leads to the degeneration and death of neurons. Multiple factors, such as genetic defects, epigenetic regulations, environmental factors, or cerebrovascular damage, are a manifestation of the neurodegenerative process that begins to occur decades before the onset of disease. To date, no treatment or therapeutic strategy has proven to be potent in inhibiting its progress or reversing the effects of the disease. The ever-increasing numbers and lack of sufficient therapies that can control or reverse the effects of the disease have propelled research in the direction of devising efficient therapeutic strategies for AD. This review comprehensively discusses the active and passive immunotherapies against Amyloid-ß and Tau protein, which remain the popular choice of targets for AD therapeutics. Some of the prospective immunotherapies against Aß plaques have failed due to various reasons. Much of the research is focused on targeting Tau, specifically, targeting the mid-region of extracellular Tau due to their potential to prevent seeding and hence the spread of neurofibrillary tangles (NFTs). Thus, there is a need to thoroughly understand the disease onset mechanisms and discover effective therapeutic strategies.

Phosphorous-substituted redox-active ligands in base metal hydrosilylation catalysis.

This article highlights the utilization of phosphine-containing redox-active ligands for efficient hydrosilylation catalysis. Manganese, iron, cobalt, and nickel precatalysts featuring these chelates have been described and leading activities for carbonyl, carboxylate, and ester C-O bond hydrosilylation have been achieved. Mechanistic studies have provided insight into the importance of phosphine hemilability.

Feasibility, Acceptability, and Preliminary Effectiveness of a Compassion-Centered Team Intervention to Improve Clinical Research Coordinator Resilience and Well-Being.

PURPOSE: Oncology clinical research coordinators (CRCs) and team-based coordinator care are critical for the success of clinical trials. However, CRCs typically report elevated anxiety and burnout and many oncology centers have high levels of coordinator attrition. To address the need for a team-based intervention to reduce burnout and promote resilience and cohesion among CRCs, we developed a compassion-centered, team-based intervention, Compassion-Centered Spiritual Health Team Intervention (CCSH-TI). METHODS: Participants were CRCs working in disease-specific teams within a comprehensive cancer center. CRCs were randomly assigned by team to either participate in four 60-minute sessions of CCSH-TI or receive the intervention after the study. To evaluate whether CCSH-TI is feasible and acceptable, we used a mixed-method approach including self-report questionnaires and a focus group. To evaluate the impact of CCSH-TI, we assessed self-reported resilience, well-being, burnout, and team civility before and immediately after the intervention period (ClinicalTrials.gov identifier: NCT04060901). RESULTS: Attendance varied by team, but all teams had rates more than 60%. Coordinators rated high levels of credibility of CCSH-TI to improve burnout, and the majority reported that they received benefits, particularly in resilience and stress management, indicating acceptability. Coordinators randomly assigned to CCSH-TI reported an increase in resilience compared with coordinators randomly assigned to the wait-list group (F(41) = 4.53, P = .039). CONCLUSION: Data from this pilot study indicate that CCSH-TI may be a feasible, credible, acceptable, and effective intervention to augment individual resilience among CRCs. However, the quantitative and qualitative data suggest that more comprehensive and systematic programming is necessary to truly mitigate burnout.

Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein Framework☠.

Temporal changes in medical images are often evaluated along a parametrized function that represents a structure of interest (e.g. white matter tracts). By attributing samples along these functions with distributions of image properties in the local neighborhood, we create distribution-valued signatures for these functions. We propose a novel and comprehensive framework which models their temporal evolution trajectories. This is achieved under the unifying scheme of Wasserstein distance metric. The regression problem is formulated as a constrained optimization problem and solved using an alternating projection algorithm. The solution simultaneously preserves the functional characteristics of the curve, models the temporal change in distribution profiles and forces the estimated distributions to be valid. Hypothesis testing is applied in two ways using Wasserstein based test statistics. Validation is presented on synthetic data. Detection of delayed growth is shown on DTI tracts, for a pediatric subject with respect to a healthy population of infants.

Nature's Derivative(s) as Alternative Anti-Alzheimer's Disease Treatments.

Alzheimer's disease (AD), the 'Plague of Twenty-First Century,' is a crippling neurodegenerative disease that affects a majority of the older population globally. By 2050, the incidence of AD is expected to rise to 135 million, while no treatment(s) that can reverse or control the progression of AD are currently available. The treatment(s) in use are limited in their ability to manage the symptoms or slow the progression of the disease and can lead to some severe side effects. The overall care is economically burdensome for the affected individuals as well as the caretakers or family members. Thus, there is a pressing need to identify and develop much safer alternative therapies that can better manage AD. This review discusses a multitude of such treatments borrowed from Ayurveda, traditional Chinese practices, meditation, and exercising for AD treatment. These therapies are in practice since ancient times and reported to be beneficial as anti-AD therapies. Ayurvedic drugs like turmeric, Brahmi, Ashwagandha, etc., management of stress by meditation, regular exercising, and acupuncture have been reported to be efficient in their anti-AD usage. Besides, a combination of vitamins and natural dietary intakes is likely to play a significant role in combating AD. We conclude that the use of such alternative strategies will be a stepping-stone in preventing, treating, curing, or managing the disease.

Pd-Colloids-Catalyzed/Ag2 O-Oxidized General and Selective Esterification of Benzylic Alcohols.

Palladium colloids obtained from the degradation of Hermann-Beller palladacycle proved to be an efficient catalytic system in combination with silver oxide as a selective oxidant for the oxidative esterification of differently substituted benzyl alcohols in MeOH as solvent. Excellent reactivity exhibited by the catalytic system also allowed the alcoholic coupling partner to be changed from MeOH to a wide range of alcohols having diverse functionalities. The mildness of the developed protocol also made it possible to employ propargyl alcohol as the coupling partner without any observation of any interference of the terminal alkyne. Selective oxidative coupling of a primary alcoholic functional group over secondary in the case of glycols and glycerols was also made possible using the developed catalyst system. To test the relevancy of Pd/Ag combined catalysis mixed Pd/Ag colloids were synthesized, characterized by TEM, XRD and XPS and applied to oxidative-esterification successfully.

Prospects for Irradiation in Cellulosic Ethanol Production.

Second generation bioethanol production technology relies on lignocellulosic biomass composed of hemicelluloses, celluloses, and lignin components. Cellulose and hemicellulose are sources of fermentable sugars. But the structural characteristics of lignocelluloses pose hindrance to the conversion of these sugar polysaccharides into ethanol. The process of ethanol production, therefore, involves an expensive and energy intensive step of pretreatment, which reduces the recalcitrance of lignocellulose and makes feedstock more susceptible to saccharification. Various physical, chemical, biological, or combined methods are employed to pretreat lignocelluloses. Irradiation is one of the common and promising physical methods of pretreatment, which involves ultrasonic waves, microwaves, γ-rays, and electron beam. Irradiation is also known to enhance the effect of saccharification. This review explains the role of different radiations in the production of cellulosic ethanol.

Actinomycetes: A Source of Lignocellulolytic Enzymes.

Lignocellulose is the most abundant biomass on earth. Agricultural, forest, and agroindustrial activities generate tons of lignocellulosic wastes annually, which present readily procurable, economically affordable, and renewable feedstock for various lignocelluloses based applications. Lignocelluloses are the focus of present decade researchers globally, in an attempt to develop technologies based on natural biomass for reducing dependence on expensive and exhaustible substrates. Lignocellulolytic enzymes, that is, cellulases, hemicellulases, and lignolytic enzymes, play very important role in the processing of lignocelluloses which is prerequisite for their utilization in various processes. These enzymes are obtained from microorganisms distributed in both prokaryotic and eukaryotic domains including bacteria, fungi, and actinomycetes. Actinomycetes are an attractive microbial group for production of lignocellulose degrading enzymes. Various studies have evaluated the lignocellulose degrading ability of actinomycetes, which can be potentially implemented in the production of different value added products. This paper is an overview of the diversity of cellulolytic, hemicellulolytic, and lignolytic actinomycetes along with brief discussion of their hydrolytic enzyme systems involved in biomass modification.

PARAMETRIC REGRESSION SCHEME FOR DISTRIBUTIONS: ANALYSIS OF DTI FIBER TRACT DIFFUSION CHANGES IN EARLY BRAIN DEVELOPMENT.

Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.

UNC-Utah NA-MIC framework for DTI fiber tract analysis.

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

LONGITUDINAL GROWTH MODELING OF DISCRETE-TIME FUNCTIONS WITH APPLICATION TO DTI TRACT EVOLUTION IN EARLY NEURODEVELOPMENT.

We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline functions, capturing growth with only a few descriptive parameters. An unbiased template baseline function is also jointly estimated. Solution is demonstrated via energy minimization with an extension to simultaneous modeling of trajectories for multiple subjects. The new framework is validated with synthetic data and applied to longitudinal DTI from 15 infants. Interpretation of estimated model growth parameters is facilitated by visualization in the original coordinate space of fiber tracts.

SPATIOTEMPORAL MODELING OF DISCRETE-TIME DISTRIBUTION-VALUED DATA APPLIED TO DTI TRACT EVOLUTION IN INFANT NEURODEVELOPMENT.

This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.

MODELING LONGITUDINAL MRI CHANGES IN POPULATIONS USING A LOCALIZED, INFORMATION-THEORETIC MEASURE OF CONTRAST.

Longitudinal MR imaging during early brain development provides important information about growth patterns and the development of neurological disorders. We propose a new framework for studying brain growth patterns within and across populations based on MRI contrast changes, measured at each time point of interest and at each voxel. Our method uses regression in the LogOdds space and an information-theoretic measure of distance between distributions to capture contrast in a manner that is robust to imaging parameters and without requiring intensity normalization. We apply our method to a clinical neuroimaging study on early brain development in autism, where we obtain a 4D spatiotemporal model of contrast changes in multimodal structural MRI.

Quantitative tract-based white matter development from birth to age 2years.

Few large-scale studies have been done to characterize the normal human brain white matter growth in the first years of life. We investigated white matter maturation patterns in major fiber pathways in a large cohort of healthy young children from birth to age two using diffusion parameters fractional anisotropy (FA), radial diffusivity (RD) and axial diffusivity (RD). Ten fiber pathways, including commissural, association and projection tracts, were examined with tract-based analysis, providing more detailed and continuous spatial developmental patterns compared to conventional ROI based methods. All DTI data sets were transformed to a population specific atlas with a group-wise longitudinal large deformation diffeomorphic registration approach. Diffusion measurements were analyzed along the major fiber tracts obtained in the atlas space. All fiber bundles show increasing FA values and decreasing radial and axial diffusivities during development in the first 2years of life. The changing rates of the diffusion indices are faster in the first year than the second year for all tracts. RD and FA show larger percentage changes in the first and second years than AD. The gender effects on the diffusion measures are small. Along different spatial locations of fiber tracts, maturation does not always follow the same speed. Temporal and spatial diffusion changes near cortical regions are in general smaller than changes in central regions. Overall developmental patterns revealed in our study confirm the general rules of white matter maturation. This work shows a promising framework to study and analyze white matter maturation in a tract-based fashion. Compared to most previous studies that are ROI-based, our approach has the potential to discover localized development patterns associated with fiber tracts of interest.