Increased Permeability of the Blood-Brain Barrier in a Diabetic Mouse Model (Leprdb/db Mice).

Type 2 Diabetes Mellitus (T2DM) is linked to multiple complications, including cognitive impairment, and the prevalence of memory-related neurodegenerative diseases is higher in T2DM patients. One possible theory is the alteration of the microvascular and macrovascular environment of the blood-brain barrier (BBB). In this study, we employed different approaches, including RT-PCR, functional pharmacokinetic studies using sodium fluorescein (NaFL), and confocal microscopy, to characterize the functional and molecular integrity of the BBB in a T2DM animal model, leptin receptor-deficient mutant mice (Leprdb/db mice). As a result, VCAM-1, ICAM-1, MMP-9, and S100b (BBB-related markers) dysregulation was observed in the Leprdb/db animal model compared to littermate wild-type mice. The brain concentration of sodium fluorescein (NaFL) increased significantly in Leprdb/db untreated mice compared to insulin-treated mice. Therefore, the permeability of NaFL was higher in Leprdb/db control mice than in all remaining groups. Identifying the factors that increase the BBB in Leprdb/db mice will provide a better understanding of the BBB microvasculature and present previously undescribed findings of T2DM-related brain illnesses, filling knowledge gaps in this emerging field of research.

Enzymatic strategies for selenium incorporation into biological molecules.

The trace element selenium (Se) is essential to the physiology of most organisms on the planet. The most well documented of Se's biological forms are selenoproteins, where selenocysteine often serves as the catalytic center for crucial redox processes. Se is also found in several other classes of biological molecules, including nucleic acids, sugars, and modified amino acids, although its role in the function of these metabolites is less understood. Despite its prevalence, only a small number of Se-specific biosynthetic pathways have been discovered. Around half of these were first characterized in the past three years, suggesting that the selenometabolome may be more diverse than previously appreciated. Here, we review the recent advances in our understanding of this intriguing biochemical space, and discuss prospects for future discovery efforts.

Detection of a Kinetically Competent Compound-I Intermediate in the Vancomycin Biosynthetic Enzyme OxyB.

Cytochrome P450 enzymes are abundantly encoded in microbial genomes. Their reactions have two general outcomes, one involving oxygen insertion via a canonical "oxygen rebound" mechanism and a second that diverts from this pathway and leads to a wide array of products, notably intramolecular oxidative cross-links. The antibiotic of-last-resort, vancomycin, contains three such cross-links, which are crucial for biological activity and are installed by the P450 enzymes OxyB, OxyA, and OxyC. The mechanisms of these enzymes have remained elusive in part because of the difficulty in spectroscopically capturing transient intermediates. Using stopped-flow UV/visible absorption and rapid freeze-quench electron paramagnetic resonance spectroscopies, we show that OxyB generates the highly reactive compound-I intermediate, which can react with a model vancomycin peptide substrate in a kinetically competent fashion to generate product. Our results have implications for the mechanism of OxyB and are in line with the notion that oxygen rebound and oxidative cross-links share early steps in their catalytic cycles.

Fine-tuning Protein Embeddings for Functional Similarity Evaluation.

MOTIVATION: Protein's with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it's unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks. RESULTS: We find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering. AVAILABILITY: github.com/mofradlab/go_metric. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Somatic RIT1 delins in arteriovenous malformations hyperactivate RAS-MAPK signaling amenable to MEK inhibition.

Arteriovenous malformations (AVM) are benign vascular anomalies prone to pain, bleeding, and progressive growth. AVM are mainly caused by mosaic pathogenic variants of the RAS-MAPK pathway. However, a causative variant is not identified in all patients. Using ultra-deep sequencing, we identified novel somatic RIT1 delins variants in lesional tissue of three AVM patients. RIT1 encodes a RAS-like protein that can modulate RAS-MAPK signaling. We expressed RIT1 variants in HEK293T cells, which led to a strong increase in ERK1/2 phosphorylation. Endothelial-specific mosaic overexpression of RIT1 delins in zebrafish embryos induced AVM formation, highlighting their functional importance in vascular development. Both ERK1/2 hyperactivation in vitro and AVM formation in vivo could be suppressed by pharmacological MEK inhibition. Treatment with the MEK inhibitor trametinib led to a significant decrease in bleeding episodes and AVM size in one patient. Our findings implicate RIT1 in AVM formation and provide a rationale for clinical trials with targeted treatments.

Behavioral comorbidities treatment by fecal microbiota transplantation in canine epilepsy: a pilot study of a novel therapeutic approach.

Introduction: Anxiety and cognitive dysfunction are frequent, difficult to treat and burdensome comorbidities in human and canine epilepsy. Fecal microbiota transplantation (FMT) has been shown to modulate behavior in rodent models by altering the gastrointestinal microbiota (GIM). This study aims to investigate the beneficial effects of FMT on behavioral comorbidities in a canine translational model of epilepsy. Methods: Nine dogs with drug-resistant epilepsy (DRE) and behavioral comorbidities were recruited. The fecal donor had epilepsy with unremarkable behavior, which exhibited a complete response to phenobarbital, resulting in it being seizure-free long term. FMTs were performed three times, two weeks apart, and the dogs had follow-up visits at three and six months after FMTs. Comprehensive behavioral analysis, including formerly validated questionnaires and behavioral tests for attention deficit hyperactivity disorder (ADHD)- and fear- and anxiety-like behavior, as well as cognitive dysfunction, were conducted, followed by objective computational analysis. Blood samples were taken for the analysis of antiseizure drug (ASD) concentrations, hematology, and biochemistry. Urine neurotransmitter concentrations were measured. Fecal samples were subjected to analysis using shallow DNA shotgun sequencing, real-time polymerase chain reaction (qPCR)-based Dysbiosis Index (DI) assessment, and short-chain fatty acid (SCFA) quantification. Results: Following FMT, the patients showed improvement in ADHD-like behavior, fear- and anxiety-like behavior, and quality of life. The excitatory neurotransmitters aspartate and glutamate were decreased, while the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) and GABA/glutamate ratio were increased compared to baseline. Only minor taxonomic changes were observed, with a decrease in Firmicutes and a Blautia_A species, while a Ruminococcus species increased. Functional gene analysis, SCFA concentration, blood parameters, and ASD concentrations remained unchanged. Discussion: Behavioral comorbidities in canine IE could be alleviated by FMT. This study highlights FMT's potential as a novel approach to improving behavioral comorbidities and enhancing the quality of life in canine patients with epilepsy.

Characterization of the small Arabidopsis thaliana GTPase and ADP-ribosylation factor-like 2 protein TITAN 5.

Small GTPases switch between GDP- and GTP-bound states in cell signaling. The ADP-ribosylation factor (ARF) family is involved in vesicle trafficking. Though evolutionarily well conserved, little is known about ARF and ARF-like GTPases in plants. We characterized biochemical properties and cellular localization of the essential small ARF-like GTPase TITAN 5/HALLIMASCH/ARL2/ARLC1 from Arabidopsis thaliana, including two conserved point mutants, suspected to be functional for nucleotide exchange and GTP hydrolysis, TTN5T30N and TTN5Q70L. TTN5 exhibited very rapid intrinsic nucleotide exchange and remarkably low GTP hydrolysis activity, functioning as a non-classical small GTPase being likely present in a GTP-loaded active form. We analyzed signals from YFP-TTN5 and HA3-TTN5 in situ immunolocalization in Arabidopsis seedlings and in transient expression system. Colocalization with endomembrane markers and pharmacological treatments suggests that TTN5 can be present at the plasma membrane and dynamically associated with membranes of vesicles, Golgi stacks and multivesicular bodies. While TTN5Q70L mirrored wild-type TTN5 behavior, TTN5T30N mutant differed in some aspects. Hence, the unusual rapid nucleotide exchange activity of TTN5 is linked with membrane dynamics, likely associated with vesicle transport in the endomembrane system.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction.

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

A configuration for cooling assisted organic solvent coated thin film microextraction after dispersive liquid-liquid microextraction method: A microextraction method for ultra-trace analyzing of volatile sample.

A combination of the dispersive liquid-liquid microextraction (DLLME) method based on the total vaporization procedure and cooling-assisted organic solvent-coated thin film microextraction (TFME) was applied for extracting chlorpyrifos (as the model compound). Based on the high thermal conductivity, a nickel foam thin film with the dimensions of 5.0 mm × 5.0 mm was used as a substrate for holding the organic solvent. Supporting thin film by organic solvent increases the thickness and contact area of the film relative to TFME or single drop microextraction (SDME) alone, resulting in a dramatic increase in the extraction efficiency. To protect the organic solvent and enhance the analyte distribution coefficient between the film and the vapor phase, a cooling system was applied. The proposed design was effective due to condensing the target analyte only on the uniform cooled thin film and not on the other regions in the extraction chamber. A corona discharge ionization source-ion mobility spectrometer was employed to identify the analyte. After optimizing the effective parameters, the limits of quantification (S/N = 10) and detection (S/N = 3) were calculated 0.1 and 0.03 µg L-1, respectively, and the dynamic range was measured between 0.1 and 7.0 µg L-1, with a determination coefficient of 0.9997. For three concentration levels of 0.1, 3.0, and 7.0 µg L-1, the relative standard deviations (n = 3) as the repeatability index were to be 6 %, 5 %, and 4 % for intra-day and 9 %, 6 %, and 5 % for inter-day, respectively. The enrichment factor was also calculated to be 3630 for the analyte concentration of 1.0 µg L-1. Well water, potato, and agricultural wastewater were analyzed as the real samples and the relative recovery values were measured between 92 % and 99 %. The accuracy of the proposed technique was validated by the European Standards EN 12393 method. In this approach, two steps of analyte extraction (DLLME and TFME) were used consecutively, resulting in better preconcentration and reduced matrix interference during cleaning-up.

Differential Stroke Volume between Left and Right Ventricles as a Predictor of Clinical Outcomes: The MESA Study.

Background Valvular heart disease and intracardiac shunts can disrupt the balance between left ventricular (LV) and right ventricular (RV) stroke volumes. However, the prognostic value of such imbalances has not been established among asymptomatic individuals. Purpose To assess the association between differential ventricular stroke volumes quantified using cardiac MRI and clinical outcomes in individuals without cardiovascular disease. Materials and Methods This secondary analysis of a prospective study included participants without cardiovascular disease at enrollment (July 2000 to July 2002) who underwent cardiac MRI. Differences in stroke volume were calculated as LV stroke volume minus RV stroke volume, and participants were categorized as having balanced (greater than or equal to -30 mL to ≤30 mL), negative (less than -30 mL), or positive (>30 mL) differential stroke volumes. Multivariable Cox proportional hazard regression models were used to test the association between differences in stroke volume and adverse outcomes. Results A cohort of 4058 participants (mean age, 61.4 years ± 10 [SD]; 2120 female) were included and followed up for a median of 18.4 years (IQR, 18.3-18.5 years). During follow-up, 1006 participants died, 235 participants developed heart failure, and 764 participants developed atrial fibrillation. Compared with participants who had a balanced differential stroke volume, those with an increased differential stroke volume showed a higher risk of mortality (hazard ratio [HR], 1.73 [95% CI: 1.12, 2.67]; P = .01), heart failure (HR, 2.40 [95% CI: 1.11, 5.20]; P = .03), and atrial fibrillation (HR, 1.89 [95% CI: 1.16, 3.08]; P = .01) in adjusted models. Participants in the negative group, with a decreased differential stroke volume, showed an increased risk of heart failure compared with those in the balanced group (HR, 2.09 [95% CI: 1.09, 3.99]; P = .03); however, this was no longer observed after adjusting for baseline LV function (P = .34). Conclusion Participants without cardiovascular disease at the time of study enrollment who had an LV stroke volume exceeding the RV stroke volume by greater than 30 mL had an increased risk of mortality, heart failure, and atrial fibrillation compared with those with balanced stroke volumes. ClinicalTrials.gov Identifier: NCT00005487 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Almeida in this issue.

Integrin mechanosensing relies on a pivot-clip mechanism to reinforce cell adhesion.

Cells intricately sense mechanical forces from their surroundings, driving biophysical and biochemical activities. This mechanosensing phenomenon occurs at the cell-matrix interface, where mechanical forces resulting from cellular motion, such as migration or matrix stretching, are exchanged through surface receptors, primarily integrins, and their corresponding matrix ligands. A pivotal player in this interaction is the α5ß1 integrin and fibronectin (FN) bond, known for its role in establishing cell adhesion sites for migration. However, upregulation of the α5ß1-FN bond is associated with uncontrolled cell metastasis. This bond operates through catch bond dynamics, wherein the bond lifetime paradoxically increases with greater force. The mechanism sustaining the characteristic catch bond dynamics of α5ß1-FN remains unclear. Leveraging molecular dynamics simulations, our approach unveils a pivot-clip mechanism. Two key binding sites on FN, namely the synergy site and the RGD (Arg-Gly-Asp) motif, act as active points for structural changes in α5ß1 integrin. Conformational adaptations at these sites are induced by a series of hydrogen bond formations and breaks at the synergy site. We disrupt these adaptations through a double mutation on FN, known to reduce cell adhesion. A whole-cell finite-element model is employed to elucidate how the synergy site may promote dynamic α5ß1-FN binding, resisting cell contraction. In summary, our study integrates molecular- and cellular-level modeling to propose that FN's synergy site reinforces cell adhesion through enhanced binding dynamics and a mechanosensitive pivot-clip mechanism. This work sheds light on the interplay between mechanical forces and cell-matrix interactions, contributing to our understanding of cellular behaviors in physiological and pathological contexts.

Transition between cardiometabolic conditions and body weight among women: which paths increase the risk of diabetes and cardiovascular diseases?

Previous studies investigated the association of body weight and hypertension with risk of incident cardiometabolic multimorbidity. Our aim was to estimate the risk of diabetes and cardiovascular disease later in life for subjects with different progression patterns of overweight, obesity, and hypertension in mid-life. This was a prospective cohort study in which data from 12,784 participants in the Australian Longitudinal Study on Women's Health were used. Multistate model was used to study the progression pattern of overweight, obesity, hypertension, diabetes, and cardiovascular disease over the life course. The cumulative incidence of diabetes and cardiovascular disease up to the age of 73 was estimated for women with different patterns of other conditions. The six most common paths and corresponding cumulative incidences for diabetes were overweight 5.1%, obesity 11.5%, hypertension 6.9%, progression from overweight to obesity 8.2%, overweight and hypertension 12.1%, and obesity and hypertension 36.8%. For women with diabetes and other conditions, the cumulative incidence of cardiovascular disease (heart disease or stroke) as the next immediate condition was 22.4%. The corresponding figure for women who only had a report of diabetes but did not have high body weight or hypertension was 8.3%. The higher risk of transition from healthy state to a cardiometabolic condition was associated with low education, income stress, smoking, not drinking alcohol (compared to low drinkers), physical inactivity, and high perceived stress. Women with obesity and hypertension in middle-age had a substantially higher risk of developing diabetes and cardiovascular disease than women without these potentially preventable conditions.

HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

MOTIVATION: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. RESULTS: We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

Detection of clinically significant prostate cancer following initial omission of biopsy in multiparametric MRI era.

BACKGROUND: Multiparametric prostate MRI (mpMRI) is being increasingly adopted for work-up of prostate cancer. For patients selected to omit biopsy, we identified factors associated with repeat MRI, eventual prostate biopsy, and subsequent detection of clinically significant prostate cancer (csPCa, Grade Group ≥2). METHODS: We identified biopsy-naïve men presenting with PSA 2-20 ng/mL (March 2018-June 2021) undergoing initial mpMRI with PIRADS 1-3 lesions who were not selected for biopsy with ≥6 months follow-up. We examined factors associated with repeat mpMRI, progression to biopsy, and subsequent detection of csPCa with univariable and multivariable logistic regression. RESULTS: Of 1494 men, 31% (463/1494) did not pursue biopsy. PSA density (PSAD) ≤ 0.1, prostate health index (PHI) < 55, and PIRADS 1-2 were associated with omission of prostate biopsy. csPCa diagnosis-free survival was 97.6% (326/334) with median follow up of 23.1 months (IQR 15.1-34.6 months). Black race, PSA, PHI, PSA density, and PSA and PHI velocity were significant predictors of undergoing repeat mpMRI (15.6%, 52/334) and subsequent biopsy (8.4%, 28/334). 8 men were subsequently diagnosed with csPCa (N = 7 on prostate biopsy; N = 1 incidentally on holmium enucleation of prostate). All patients diagnosed with csPCa had PIRADS 4-5 on repeat mpMRI. CONCLUSIONS: The subsequent detection rate of csPCa among patients not initially biopsied after mpMRI was low at 2.4%. Decisions to omit biopsy after initial reassuring PHI, PSAD, and mpMRI appear safe with subsequent reassuring serum biomarkers and for cause mpMRI during follow-up.

Adaptive noise-resilient deep learning for image reconstruction in multimode fiber scattering.

This research offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.

The single-cell opioid responses in the context of HIV (SCORCH) consortium.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

The impact of social isolation and loneliness on cardiovascular disease risk factors: a systematic review, meta-analysis, and bibliometric investigation.

Data on the association between social isolation, loneliness, and risk of incident coronary heart disease (CVD) are conflicting. The objective of this study is to determine the relationship between social isolation and loneliness, and the risk of developing cardiovascular disease (CVD) in middle age and elderly using meta-analysis. The purpose of the bibliometric analysis is to systematically evaluate the existing literature on the relationship between social isolation, loneliness, and the risk of developing cardiovascular disease (CVD) in middle-aged and elderly individuals. A comprehensive search through four electronic databases (MEDLINE, Google Scholar, Scopus, and Web of Science) was conducted for published articles that determined the association between social isolation and/or loneliness and the risk of developing coronary heart disease from June 2015 to May 2023. Two independent reviewers reviewed the titles and abstracts of the records. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline to conduct the systematic review and meta-analysis. Data for the bibliometric analysis was obtained from the Scopus database and analyzed using VOSviewer and Bibliometrix applications. Six studies involving 104,511 patients were included in the final qualitative review and meta-analysis after screening the records. The prevalence of loneliness ranged from 5 to 65.3%, and social isolation ranged from 2 to 56.5%. A total of 5073 cardiovascular events were recorded after follow-up, ranging between 4 and 13 years. Poor social relationships were associated with a 16% increase in the risk of incident CVD (Hazard Ratio of new CVD when comparing high versus low loneliness or social isolation was 1.16 (95% Confidence Interval (CI) 1.10-1.22). The bibliometric analysis shows a rapidly growing field (9.77% annual growth) with common collaboration (6.37 co-authors/document, 26.53% international). The US leads research output, followed by the UK and Australia. Top institutions include University College London, Inserm, and the University of Glasgow. Research focuses on "elderly," "cardiovascular disease," and "psychosocial stress," with recent trends in "mental health," "social determinants," and "COVID-19". Social isolation and loneliness increase the risk of and worsen outcomes in incident cardiovascular diseases. However, the observed effect estimate is small, and this may be attributable to residual confounding from incomplete measurement of potentially confounding or mediating factors. The results of the bibliometric analysis highlight the multidimensional nature of CVD research, covering factors such as social, psychological, and environmental determinants, as well as their interplay with various demographic and health-related variables.

Medical Extended Reality for Radiology Education and Training.

Medical extended reality (MXR), encompassing augmented reality, virtual reality, and mixed reality (MR), presents a novel paradigm in radiology training by offering immersive, interactive, and realistic learning experiences in health care. Although traditional educational tools in the field of radiology are essential, it is necessary to capitalize on the innovative and emerging educational applications of extended reality (XR) technologies. At the most basic level of learning anatomy, XR has been extensively used with an emphasis on its superiority over conventional learning methods, especially in spatial understanding and recall. For imaging interpretation, XR has fostered the concepts of virtual reading rooms by enabling collaborative learning environments and enhancing image analysis and understanding. Moreover, image-guided interventions in interventional radiology have witnessed an uptick in XR utilization, illustrating its effectiveness in procedural training and skill acquisition for medical students and residents in a safe and risk-free environment. However, there remain several challenges and limitations for XR in radiology education, including technological, economic, and ergonomic challenges and and integration into existing curricula. This review explores the transformative potential of MXR in radiology education and training along with insights on the future of XR in radiology education, forecasting advancements in immersive simulations, artificial intelligence integration for personalized learning, and the potential of cloud-based XR platforms for remote and collaborative training. In summation, MXR's burgeoning role in reshaping radiology education offers a safer, scalable, and more efficient training model that aligns with the dynamic healthcare landscape.

Hirocidins, Cytotoxic Metabolites from Streptomyces hiroshimensis, Induce Mitochondrion-mediated Apoptosis.

Recent advances in whole genome sequencing have revealed an immense microbial potential for the production of therapeutic small molecules, even from well-known producers. To access this potential, we subjected prominent antimicrobial producers to alternative antiproliferative assays using persistent cancer cell lines. Described herein is our discovery of hirocidins, novel secondary metabolites from Streptomyces hiroshimensis with antiproliferative activities against colon and persistent breast cancer cells. Hirocidin A is an unusual nine-membered carbocyclic maleimide and hirocidins B and C are relatives with an unprecedented, bridged azamacrocyclic backbone. Mode of action studies show that hirocidins trigger mitochondrion-dependent apoptosis by inducing expression of the key apoptotic effector caspase-9. The discovery of new cytotoxins contributes to scaffold diversification in anticancer drug discovery and the reported modes of action and concise total synthetic route for variant A set the stage for unraveling specific targets and biochemical interactions of the hirocidins.