Brain ischemia, a condition in which the brain is deprived of blood flow, can lead to a stroke due to blocked or unstable blood vessels. Global cerebral ischemia (GCI), characterized by an interruption in blood flow, deprives the brain of oxygen and nutrients, producing reactive oxygen species (ROS) that trigger cell death, which kills nerve cells. Microplastics (MPs), tiny environmental pollutants, can enter the human body through contaminated food, water, disposable items, cosmetics, and more. Once in the brain, MPs can increase neuroinflammation by overstimulating inflammatory factors such as microglia. MPs can also damage neurons by scratching myelin and microtubules, slowing signal transduction, causing cognitive impairment, and leading to neuronal death. Furthermore, microtubule damage may result in the release of phosphorylated tau proteins, potentially linked to Alzheimer’s disease. We hypothesized that MPs could exacerbate neuroinflammation and microtubule destruction after GCI, leading to increased neuronal death. To test this hypothesis, we administered MPs (0.5 µm) orally at a dose of 50 mg/kg before and after inducing GCI. Staining techniques such as Fluoro-Jade B (FJB), ionized calcium-binding adaptor molecule 1 (Iba-1), cluster of differentiation 68 (CD68), myelin basic protein (MBP), and microtubule-associated protein 2 (MAP2) were used, along with Western blot analysis for interleukin-6 (IL-6), TNF-α, tau-5, and phospho-tau (S396) to evaluate the effects of MPs on neuronal cell death, neuroinflammation, and microtubule destruction. The results showed that MP accumulation significantly increased neuroinflammation, microtubule disruption, and neuronal cell death in the GCI-MP group compared to the GCI-vehicle group. Therefore, this study suggests that MP accumulation in daily life may contribute to the exacerbation of the disease, potentially leading to severe neuronal cell death after GCI.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Clinical sequencing data should be shared so as to achieve the sufficient scale and diversity required for providing strong evidence toward improving patient care. A distributed research network (DRN) allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM), currently used in DRNs, has low coverage of sequencing data and does not reflect the latest trend of precision medicine. Objective: The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice. Methods: Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) and Human Genome Variation Society (HGVS) nomenclature was adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine (AUSOM) database of Ajou University Hospital and The Cancer Genome Atlas (TCGA), respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations. Results: The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with TCGA data, a clinically actionable mutation, ‘p.Leu858Arg’, in the EGFR gene was 6.64-times more frequent in the AUSOM database, while the ‘p.Gly12Xaa’ mutation in the KRAS gene was 2.02-times more frequent in the TCGA dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and HGVS nomenclature to calculate the proportion of patients with a given mutation. Conclusions: We developed the G-CDM for effective integration of genomic data with standardized clinical data allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool, GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and thereby enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Phosphodiesterase (PDE) enzymes regulate intracellular signaling pathways crucial for brain development and the pathophysiology of neurological disorders. Among the 11 PDE subtypes, PDE4 and PDE5 are particularly significant due to their regulation of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) signaling, respectively, which are vital for learning, memory, and neuroprotection. This review synthesizes current evidence on the roles of PDE4 and PDE5 in neurological health and disease, focusing on their regulation of second messenger pathways and their implications for brain function. Elevated PDE4 activity impairs synaptic plasticity by reducing cAMP levels and protein kinase A (PKA) activity, contributing to cognitive decline, acute brain injuries, and neuropsychiatric conditions such as bipolar disorder and schizophrenia. Similarly, PDE5 dysregulation disrupts nitric oxide (NO) signaling and protein kinase G (PKG) pathways, which are involved in cerebrovascular homeostasis, recovery after ischemic events, and neurodegenerative processes in Alzheimer’s, Parkinson’s, and Huntington’s diseases. PDE4 and PDE5 are promising therapeutic targets for neurological disorders. Pharmacological modulation of these enzymes offers potential to enhance cognitive function and mitigate pathological mechanisms underlying brain injuries, neurodegenerative diseases, and psychiatric disorders. Further research into the regulation of PDE4 and PDE5 will advance therapeutic strategies for these conditions.

Background: Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. Objective: The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. Methods: We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. Results: Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. Conclusions: This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.

The aim of this study was to investigate the clinical utility of minimal specimens acquired from endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) and perform targeted deep sequencing as a prognosis prediction tool for pancreatic ductal adenocarcinoma (PDAC). A total of 116 specimens with pathologically confirmed PDAC via EUS-FNB were tested using CancerSCAN^® panel for a customized targeted deep sequencing. Clinical prognostic factors significantly associated with survival in PDACs were as follows: stage, tumor mass size, tumor location, metastasis, chemotherapy, and initial CA19-9 level. A total of 114 patients (98.3%) had at least a single genetic alteration, and no mutations were detected in two patients, although they were qualified for the targeted deep sequencing. The frequencies of major gene mutations responsible for PDACs were KRAS 90%, CDKN2A 31%, TP53 77%, and SMAD4 29%. A somatic point mutation of NF1, copy number alteration of SMAD4, and loss-of-function of CDKN2A were significantly associated genetic factors for overall survival. Moreover, BRCA2 point mutation was related to liver metastasis. Finally, a clinico-genomic model was developed to estimate the prognosis of patients with PDAC based on clinical parameters and genetic alterations affecting survival in patients; 20 single nucleotide variants and three copy number variations were selected. Targeted deep sequencing on minimal specimens of PDACs was performed, and it was applied to establish a clinico-genomic model for prognosis prediction.

Background/Objectives: Numerous studies have explored the association between chronic rhinosinusitis (CRS) and cognitive decline. However, whether CRS is an independent risk factor for the development of dementia remains unclear. Thus, this retrospective cohort study sought to examine the potential association between CRS and increased incidence and risk of dementia by utilizing a representative population-based cohort dataset. Methods: In this study, we identified 2126 patients with CRS aged >55 years and matched them with 8504 controls to assess the incidence and risk of dementia. Results: We found that the incidence of all-cause dementia in CRS patients was 0.125 per 1000 person-years. The risk of developing all-cause dementia events (adjusted hazard ratio [HR] = 1.0, 95% confidence interval = 0.8–1.3) also did not differ significantly between the control group and the CRS group, irrespective of the CRS phenotype. Subgroup analysis also showed no increased adjusted HR for developing Alzheimer’s disease (0.9, 0.7–1.2), Parkinson’s disease (0.9, 0.5–1.4), and other types of dementia (1.0, 0.7–1.4) in the CRS group compared to the control group. Conclusions: Therefore, the present study demonstrated that patients over 55 years of age with CRS did not exhibit an increased incidence or risk of dementia compared to individuals without CRS.