Genetic Contributions to Recovery following Brain Trauma: A Narrative Review.

Traumatic brain injury (TBI) is a frequently encountered form of injury that can have lifelong implications. Despite advances in prevention, diagnosis, monitoring, and treatment, the degree of recovery can vary widely between patients. Much of this is explained by differences in severity of impact and patient-specific comorbidities; however, even among nearly identical patients, stark disparities can arise. Researchers have looked to genetics in recent years as a means of explaining this phenomenon. It has been hypothesized that individual genetic factors can influence initial inflammatory responses, recovery mechanisms, and overall prognoses. In this review, we focus on cytokine polymorphisms, mitochondrial DNA (mtDNA) haplotypes, immune cells, and gene therapy given their associated influx of novel research and magnitude of potential. This discussion is prefaced by a thorough background on TBI pathophysiology to better understand where each mechanism fits within the disease process. Cytokine polymorphisms causing unfavorable regulation of genes encoding IL-1ß, IL-RA, and TNF-α have been linked to poor TBI outcomes like disability and death. mtDNA haplotype H has been correlated with deleterious effects on TBI recovery time, whereas haplotypes K, T, and J have been depicted as protective with faster recovery times. Immune cell genetics such as microglial differentially expressed genes (DEGs), monocyte receptor genes, and regulatory factors can be both detrimental and beneficial to TBI recovery. Gene therapy in the form of gene modification, inactivation, and editing show promise in improving post-TBI memory, cognition, and neuromotor function. Limitations of this study include a large proportion of cited literature being focused on pre-clinical murine models. Nevertheless, favorable evidence on the role of genetics in TBI recovery continues to grow. We aim for this work to inform interested parties on the current landscape of research, highlight promising targets for gene therapy, and galvanize translation of findings into clinical trials.

Thyroid hormone rewires cortical circuits to coordinate body-wide metabolism and exploratory drive.

Animals adapt to varying environmental conditions by modifying the function of their internal organs, including the brain. To be adaptive, alterations in behavior must be coordinated with the functional state of organs throughout the body. Here we find that thyroid hormone- a prominent regulator of metabolism in many peripheral organs- activates cell-type specific transcriptional programs in anterior regions of cortex of adult mice via direct activation of thyroid hormone receptors. These programs are enriched for axon-guidance genes in glutamatergic projection neurons, synaptic regulators across both astrocytes and neurons, and pro-myelination factors in oligodendrocytes, suggesting widespread remodeling of cortical circuits. Indeed, whole-cell electrophysiology recordings revealed that thyroid hormone induces local transcriptional programs that rewire cortical neural circuits via pre-synaptic mechanisms, resulting in increased excitatory drive with a concomitant sensitization of recruited inhibition. We find that thyroid hormone bidirectionally regulates innate exploratory behaviors and that the transcriptionally mediated circuit changes in anterior cortex causally promote exploratory decision-making. Thus, thyroid hormone acts directly on adult cerebral cortex to coordinate exploratory behaviors with whole-body metabolic state.

Evidence-Based Utility of Adjunct Antioxidant Supplementation for the Prevention and Treatment of Dermatologic Diseases: A Comprehensive Systematic Review.

Skin conditions are a significant cause of fatal and nonfatal disease burdens globally, ranging from mild irritations to debilitating diseases. Oxidative stress, which is an imbalance between reactive oxygen species and the cells' ability to repair damage, is implicated in various skin diseases. Antioxidants have been studied for their potential benefits in dermatologic health, but the evidence is limited and conflicting. Herein, we conducted a systematic review of controlled trials, meta-analyses, and Cochrane review articles to evaluate the current evidence on the utility of antioxidant supplementation for adjunct prevention and treatment of skin disease and to provide a comprehensive assessment of their role in promoting dermatologic health. The Cochrane Library, PubMed, EMBASE, and Epistemonikos databases were queried. Eligibility criteria included (1) primary focus on nanoparticle utility for skin cancer; (2) includes measurable outcomes data with robust comparators; (3) includes a number of human subjects or cell-line types, where applicable; (4) English language; and (5) archived as full-text journal articles. A total of 55 articles met the eligibility criteria for the present review. Qualitative analysis revealed that topical and oral antioxidant supplementation has demonstrated preliminary efficacy in reducing sunburns, depigmentation, and photoaging. Dietary exogenous antioxidants (namely vitamins A, C, and E) have shown chemopreventive effects against skin cancer. Antioxidant supplementation has also shown efficacy in treating non-cancer dermatoses, including rosacea, psoriasis, atopic dermatitis, and acne vulgaris. While further studies are needed to validate these findings on a larger scale, antioxidant supplementation holds promise for improving skin health and preventing skin diseases.

Spatial metabolic heterogeneity of oligodendrogliomas at single-cell resolution.

Oligodendrogliomas are a type of rare and incurable gliomas whose metabolic profiles have yet to be fully examined. The present study examined the spatial differences in metabolic landscapes underlying oligodendrogliomas and should provide unique insights into the metabolic characteristics of these uncommon tumors. Single-cell RNA-sequencing expression profiles from 4044 oligodendroglioma cells derived from tumors resected from four locations frontal, temporal, parietal, and frontotemporoinsular) and in which 1p/19q co-deletion and IDH1 or IDH2 mutations were confirmed were computationally analyzed through a robust workflow to elucidate relative differences in metabolic pathway activities among the different locations. Dimensionality reduction using metabolic expression profiles exhibited clustering corresponding to each location subgroup. From the 80 metabolic pathways examined, over 70 pathways had significantly different activity scores between location subgroups. Further analysis of metabolic heterogeneity suggests that mitochondrial oxidative phosphorylation accounts for considerable metabolic variation within the same locations. Steroid and fatty acid metabolism pathways were also found to be major contributors to heterogeneity. Oligodendrogliomas display distinct spatial metabolic differences in addition to intra-location metabolic heterogeneity.

Melanoma Brain Metastases: A Systematic Review of Opportunities for Earlier Detection, Diagnosis, and Treatment.

Introduction: Melanoma continues to represent the most serious skin cancer worldwide. However, few attempts have been made to connect the body of research on advanced melanoma. In the present review, we report on strides made in the diagnosis and treatment of intracranial metastatic melanoma. Methods: Relevant Cochrane reviews and randomized-controlled trials published by November 2022 were systematically retrieved from the Cochrane Library, EMBASE, and PubMed databases (N = 27). Search and screening methods adhered to the 2020 revision of the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Results: Although the research surrounding the earlier detection of melanoma brain metastasis is scarce, several studies have highlighted specific markers associated with MBM. Such factors include elevated BRAFV600 mutant ctDNA, high LDH concentration, and high IGF-1R. The approach to treating MBM is moving away from surgery and toward nonsurgical management, namely, a combination of stereotactic radiosurgery (SRS) and immunotherapeutic agents. There is an abundance of emerging research seeking to identify and improve both novel and established treatment options and diagnostic approaches for MBM, however, more research is still needed to maximize the clinical efficacy, especially for new immunotherapeutics. Conclusions: Early detection is optimal for the efficacy of treatment and MBM prognosis. Current treatment utilizes chemotherapies and targeted therapies. Emerging approaches emphasize biomarkers and joint treatments. Further exploration toward preliminary identification, the timing of therapies, and methods to ameliorate adverse treatment effects are needed to advance MBM patient care.

Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.

Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time. For those unfamiliar with machine learning or 3D image analysis, the rapid advancement of the field can make navigating the newest software options confusing. In this paper, two open-source machine learning algorithms, Cellpose and Stardist, are compared in their application on a 3D light sheet dataset counting fluorescently stained proliferative cell nuclei. The effects of image tiling and background subtraction are shown through image analysis pipelines for both algorithms. Based on our analysis, the relative ease of use of Cellpose and the absence of need to train a model leaves it a strong option for 3D cell segmentation despite relatively longer processing times. When Cellpose's pretrained model yields results that are not of sufficient quality, or the analysis of a large dataset is required, Stardist may be more appropriate. Despite the time it takes to train the model, Stardist can create a model specialized to the users' dataset that can be iteratively improved until predictions are satisfactory with far lower processing time relative to other methods.

Local mechanical stimuli correlate with tissue growth in axolotl salamander joint morphogenesis.

Movement-induced forces are critical to correct joint formation, but it is unclear how cells sense and respond to these mechanical cues. To study the role of mechanical stimuli in the shaping of the joint, we combined experiments on regenerating axolotl (Ambystoma mexicanum) forelimbs with a poroelastic model of bone rudiment growth. Animals either regrew forelimbs normally (control) or were injected with a transient receptor potential vanilloid 4 (TRPV4) agonist during joint morphogenesis. We quantified growth and shape in regrown humeri from whole-mount light sheet fluorescence images of the regenerated limbs. Results revealed significant differences in morphology and cell proliferation between groups, indicating that TRPV4 desensitization has an effect on joint shape. To link TRPV4 desensitization with impaired mechanosensitivity, we developed a finite element model of a regenerating humerus. Local tissue growth was the sum of a biological contribution proportional to chondrocyte density, which was constant, and a mechanical contribution proportional to fluid pressure. Computational predictions of growth agreed with experimental outcomes of joint shape, suggesting that interstitial pressure driven from cyclic mechanical stimuli promotes local tissue growth. Predictive computational models informed by experimental findings allow us to explore potential physical mechanisms involved in tissue growth to advance our understanding of the mechanobiology of joint morphogenesis.

Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare.

Objective: Clinical applications of machine learning are promising as a tool to improve patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for screening. Possible clinical applications are especially prominent in dermatology as many diseases and conditions present visually. This allows a machine learning model to analyze and diagnose conditions using patient images and data from electronic health records (EHRs) after training on clinical datasets but could also introduce bias. Despite promising applications, artificial intelligence has the capacity to exacerbate existing demographic disparities in healthcare if models are trained on biased datasets. Methods: Through systematic literature review of available literature, we highlight the extent of bias present in clinical datasets as well as the implications it could have on healthcare if not addressed. Results: We find the implications are worsened in dermatological models. Despite the severity and complexity of melanoma and other dermatological diseases as well as differing disease presentations based on skin-color, many imaging datasets underrepresent certain demographic groups causing machine learning models to train on images of primarily fair-skinned individuals leaving minorities behind. Conclusion: In order to address this disparity, research first needs to be done investigating the extent of the bias present and the implications it may have on equitable healthcare.

EXAMINATION OF LIGAND-RECEPTOR INTERACTIONS AMONGST DIFFERENT MULTIPLE SCLEROSIS LESION TYPES.

Background: Multiple sclerosis represents a debilitating disease. It has many different forms and warrants further investigation. Recent evidence has linked the importance of RNA sequencing to disease progression. Aim: The purpose of this research paper is to look at the available RNA sequencing for white matter lesion disorders in the brain and correlate to outcomes and treatment. Methods: we performed a comprehensive systematic review to correlate RNA sequence to white matter lesion accuracy. This was done in accordance with PRISMA guidelines. We looked at ligand receptor scoring. Results: The results are highlighted in a series of tables showing key significant findings. These findings were grouped to outcomes and treatment. Conclusion: The paper will serve as a catalyst for further scientific development.

Omics analysis of uveal melanoma: Leukocyte gene signatures reveal novel survival distinctions and indicate a prognostic role for cytolytic activity scoring.

Objective: The significant metastatic potential of uveal melanoma (UVM) lends to high mortality. Even with successful local tumor treatment, many patients will develop metastatic disease. The present study aims to elucidate the relationship between tumor-infiltrating immune cell (TIIC) diversity and survival to identify potential therapeutic targets and improve UVM prognosis. Methods: Bulk deconvolution was used to determine the relative proportions of 22 hematopoietic TIIC from 80 UVM tumor samples. Cytolytic activity (CYT) was determined, and associated survival probabilities were mined using time-to-event data. Nominal P-values were subjected to FDR correction. Results: High relative abundance of tumor-infiltrating naïve B cells, resting memory CD4+ T cells, and monocytes correlated with better overall and disease-free survival probability. Low relative abundance of CD8+ T cells correlated with better overall survival and disease-free survival probability. CYT correlated positively with relative abundance of naïve B cells, resting memory CD4+ T cells, and monocytes. CYT correlated negatively with relative abundance of CD8+ T cells. Conclusion: Infiltrating naïve B cells, resting memory CD4+ T cells, monocytes, and CD8+ T cells are potential therapeutic targets in UVM that warrant further investigation. High CYT estimates associate with worse UVM survival outcomes.