Cardiovascular diseases pose a major health threat, with atherosclerosis being the leading cause. Free fatty acids (FFAs) can act as signaling molecules, influencing inflammation and oxidative stress, thereby potentially impacting atherosclerosis risk. This study examined the expression of FFAR2 and FFAR3 and their relationship with serum IL-1β, total antioxidant capacity (TAC), and malondialdehyde (MDA) levels in patients undergoing CT angiography.

Fasting blood samples from 113 patients were collected, dividing them into a high coronary artery disease risk group (n = 52), Low Risk of CAD (n = 37) and a control group (n = 24) based on calcium scores. FFAR2 expression was significantly lower in the case group, while FFAR3 levels showed no difference. Increased interleukine-1 beta (IL-1β) and MDA levels, alongside decreased total antioxidant capacity (TAC), were noted in the case group.

The study identified a negative correlation between FFAR2 expression and atherosclerotic indices, while serum IL-1β and MDA exhibited positive correlations. Thus, FFAR2 and IL-1β may be potential biomarkers for coronary artery disease risk.

Hyperlipidemia is a major risk factor for cardiovascular diseases, but its relationship with gut microbiota remains unclear. This study aimed to systematically assess genetic associations between gut microbiota and lipoprotein metabolism disorders using Mendelian randomization (MR) and explore their links with cardio-metabolic risk factors such as obesity, diabetes and BMI.

We performed a two-sample MR analysis integrating gut microbiota GWAS data from the MiBioGen consortium (n = 18,340) and lipoprotein metabolism disorder data from FinnGen (14,101 cases/197,259 controls). Inverse variance weighted (IVW) analysis identified five protective taxa, including Lachnoclostridium (OR = 0.748) and an unknown genus (id.826; OR = 0.789), and four risk taxa such as Actinobacteria (OR = 1.226) and Ruminiclostridium5 (OR = 1.227). Cross-phenotype analyses revealed consistent protective effects: Lachnoclostridium abundance inversely correlated with BMI (OR = 0.942), while the unknown genus (id.826) was protective against diabetes (OR = 0.874). Sensitivity analyses confirmed robustness (no heterogeneity or horizontal pleiotropy).

This study reveals the complex role of gut microbiota in metabolic diseases through MR, and its effects may depend on the host's metabolic status. The results provide a theoretical basis for precise intervention strategies targeting microbial communities, and in the future, it is necessary to combine multi omics and experimental models to validate key microbial communities and their mechanisms.

Diagnostic bioelectronics such as the electrocardiogram have become increasingly prevalent in the management of cardiovascular diseases like atrial fibrillation. While these devices provide meaningful clinical value, their complex and profuse data output requires considerable labour and training from clinicians to diagnose disease. This abundant production of complex data has made diagnostic bioelectronics a prime target for artificial intelligence (AI) integration. AI-integrated diagnostic bioelectronics have already left the clinic as widely prevalent wearable smartwatches equipped with single-lead electrocardiography sensors. Meanwhile, substantial innovation is also taking place at the intersection of other sensing modalities in the form of edge computing. Here, we overview the implementation and embedding of AI into diagnostic bioelectronics of multiple sensing modalities, including electrocardiography, photoplethysmography, echocardiography, and others, and discuss the recent advances made by medical device companies and researchers alike at the interface between the heart and AI.

Protein C receptor⁺ (Procr⁺) cells were identified as stem or progenitor cells in multiple adult tissues. However, whether mechanical stimuli fine-tune their activation and differentiation remain unknown. Here, we found rare Procr⁺ cells in the superficial layer of tibial articular cartilage and meniscus, which keep replenishing chondrocytes in postnatal knee joints. Mechanical stimulation by forced running significantly increased the frequency of Procr⁺ cells, whereas mechanical unloading by tail suspension showed opposite effects. Osteoarthritis (OA) activated Procr⁺ cells to repair cartilage erosion, whereas genetic ablation of Procr⁺ cells accelerated OA progression. Pharmacological or genetic inhibition of the mechanosensor Piezo1 significantly blunted cartilage regeneration by Procr⁺ cells and exacerbated OA. In contrast, intra-articular administration of a Piezo1 agonist ameliorated OA symptoms. Purified mouse or human Procr⁺ superficial cells robustly repair articular cartilage after expansion and in vivo transplantation. Together, we discovered a mechanosensitive chondroprogenitor population indispensable for articular cartilage maintenance and regeneration.

Spinal cord infarction (SCI) is a rare condition caused by ischemic injury leading to spinal cord cell death. Its diverse aetiologies, combined with the spinal cord's intricate vascular anatomy, present diagnostic and surgical challenges. Despite its rarity, comprehensive research is warranted to inform future diagnostic and management protocols.

A systematic review adhering to PRISMA 2020 guidelines was conducted to analyse SCIs over a 25-year period (1998-2023). Data from PubMed, Ovid MEDLINE and Google Scholar were included from case reports, case studies and literature reviews, with the paediatric population being excluded.

Of 200 articles, 21 studies were analysed, evaluating 734 patients. SCI showed a male predominance (60.1 %) and a mortality rate of 8.4 %. The most frequent complication was urinary or faecal incontinence (36.3 %). Key aetiologies included cardiovascular (13.6 %), peri-/post-procedural (12.4 %), and cryptogenic causes (5.8 %). 43.9 % of cases were treated medically, while 6.1 % were managed with lumbar drains. ASIA scores at onset revealed: A (30.6 %), B (23.8 %), C (21.4 %) and D (25.2 %).

SCI poses significant morbidity and mortality. Of note, aortic diseases and cardiovascular factors are critical contributors. MRI, particularly DWI, remains crucial for diagnosis. However, treatment lacks standardisation due to limited data and delayed diagnoses. Current therapies range from antiplatelets to innovative surgical approaches, but further research is essential to develop evidence-based practices and refined protocols for enhancing patient outcomes.

An electrocardiogram (ECG) is commonly used in clinical practice. Poor data quality, artifacts, and misplacement of electrodes have to be identified before the clinical interpretation of ECG. We aimed to develop an algorithm to automatically identify ECG artifacts and lead misplacement.

We utilized 42,743 ECGs from UK Biobank (UKB; n = 42,743 participants; age 55±8 y; cardiovascular disease 1.2 %; diabetes 0.9 %; chronic kidney disease 0.5 %; ventricular pacing 0 %) for the algorithm development and 41,495 ECGs from the Chronic Renal Insufficiency Cohort (CRIC; n = 3912 participants; age 63 ± 11 y; cardiovascular disease 78 %; diabetes 56 %; chronic kidney disease 100 %; ventricular pacing 3.5 %) for external validation. We developed a fully automated algorithm to detect non-physiological ECG artifacts, such as high or low peak-to-peak amplitude, frequency-based outliers, and misplaced electrodes. In UKB, the algorithm demonstrated a sensitivity of 84.9 %, a specificity of 100 %, an ROC AUC of 0.924, and a Kappa statistic of 0.91. We observed 98.81 % agreement between ground truth and algorithm-identified non-physiological ECG artifacts, significantly (p < 0.00001) larger than the random agreement of 86.91 % expected at the observed 7.6 % prevalence. The misplacement of limb lead electrodes in UKB affected the Wilson Central Terminal. In CRIC, we observed an agreement of 94.90 %, which was significantly (p < 0.00001) better than by chance (93.27 % at the observed 5.3 % prevalence, including pacing artifacts), 16.8 % sensitivity, 99.3 % specificity, and an ROC AUC of 0.580.

The fully automated algorithm can accurately detect ECG artifacts and potential lead misplacement, thus permitting automated quality control of ECG analysis. The code is provided at https://github.com/Tereshchenkolab/ECG-quality-control.

The purpose of the study is to assessment of the risk of secondary brainstem hemorrhages against the background of hypertensive encephalopathy in patients with intracerebral complicated hemispheric ischemic cerebral stroke using anticoagulant therapy. Was conducted a clinical and pathological study of 97 patients with intracerebral complicated hemispheric ischemic cerebral stroke aged 41 to 87 years. Of these, 55 were men (56,7%), average age 72,5±2,4; women 42 (43,3%), average age 76,5±2,1. Data on survival time and complications in the form of secondary brainstem hemorrhages directly correlated with the results of pathological changes in the brainstem. During autopsy of deceased patients with hemispheric ischemic cerebral stroke complicated by secondary hemorrhages of the brainstem who received anticoagulant therapy, in 12 cases (70,6%) out of 17, secondary massive hemorrhages in the brainstem were found, consisting of multiple hemorrhagic foci merging with each other. In individuals with hemispheric ischemic cerebral stroke who did not receive anticoagulant therapy, pathomorphologically, secondary massive hemorrhages in the brainstem were noted in only 12 (25,5%) of 47 deceased, while in 35 observations (74,5%), hemorrhages were determined in the form of individual hemorrhagic foci of small size or individual small-point hemorrhages, sometimes detected during macroscopic examination. Data on the presence of hypertensive encephalopathy in the pre-stroke anamnesis directly correlated with the results of secondary pathological changes in the brainstem. At autopsy, hypertensive encephalopathy was detected in 92,2% of deceased patients with secondary hemorrhages in the brainstem, while with ischemic nature of brainstem changes only in 42,4%. The above proves the need to take into account differentiated therapy depending not only on the nature of the damage to the hemispheric structures, but also on the pathomorphological type of secondary changes in the brainstem. In patients with hemispheric ischemic infarction who are predisposed to complicated hemorrhagic secondary brainstem syndrome, especially those with a history of hypertensive encephalopathy, the use of anticoagulant, thrombolytic, and dual antiplatelet therapy is not recommended due to the risk of developing secondary hemorrhagic stem syndrome as a consequence of secondary changes in microvessels, which always change under the influence of high blood pressure.

According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.

Ischemic stroke (IS) is the most common subtype of stroke. However, reliable blood biomarkers for early diagnosis remain unavailable. This study developed a predictive model based on peripheral blood (PB) biomarkers. PB samples from two independent cohorts including IS patients and healthy controls (CTR) were analyzed by RNA sequencing (RNA-seq). 69 mRNAs were consistently and significantly dysregulated in IS patients. Functional enrichment analysis revealed that the IS phenotype was negatively associated with NK cell-mediated cytotoxicity and single-sample gene set enrichment analysis (ssGSEA) revealed a significant reduction in Cd56^bright NK cells, Cd56^dim NK cells, and NKT cells in IS patients. A four-gene diagnostic model-BCL2A1, FAM200B, IGJ, and TXN-was identified and exhibited high diagnostic accuracy across derivation, validation, and external cohorts (AUCs: 0.94, 0.91, and 0.96, respectively). Additionally, potential small molecule compounds were screened using Enrichr database, among which cytochalasin D may represent a novel candidate drug for IS treatment.

Chronic stress (CS) is recognized as a contributing factor to the progression of atherosclerosis (AS), but the molecular mechanisms within the central nervous system (CNS) remain poorly understood. In this study, we established mouse models for CS and AS, including normal control (CON), AS, CS, and AS + CS groups, and analyzed gene expression in brain tissues. We identified Cxcr4 and Gng4 as key genes differentially expressed in response to AS and CS. Elevated expression of CXCR4 and GNG4 in the frontal cortex was observed in the CS and AS + CS groups compared to the CON and AS groups. CS not only induced neuronal damage but also exacerbated AS progression, as evidenced by larger atherosclerotic plaque areas in the AS + CS group, increased abdominal aorta intima‒media thickness (IMT), and reduced abdominal aorta lumen diameter (AUD) in the CS and AS + CS groups. The upregulation of Cxcr4 and Gng4 in brain tissue correlated positively with IMT and negatively with AUD, and their combined expression demonstrated strong predictive potential for IMT and AUD. Furthermore, Cxcr4 and Gng4 mRNA levels were significantly positively correlated. Additionally, CXCR4 and GNG4 colocalized, interacted, and formed stable complexes, both of which were detectable in neurons. Moreover, CS upregulated circulating levels of CXCL12, CXCR4, GNG4, and pro-inflammatory cytokines (IL-6, IL-1β). These findings suggest that CS-induced upregulation of CXCR4 and GNG4 in brain tissue and serum may amplify inflammatory responses and contribute to the progression of AS, highlighting potential therapeutic targets for stress-related cardiovascular diseases. KEY MESSAGES: • Chronic stress induced neuronal damage, exacerbated circulating inflammatory responses and AS progression. • Cxcr4 and Gng4 were identified as hub genes associated with both chronic stress and atherosclerosis, chronic stress increased the expression of CXCR4 and GNG4 in the brain tissue and serum. • The upregulation of Cxcr4 and Gng4 in brain tissue correlated positively with abdominal aorta intima‒media thickness (IMT) and negatively with abdominal aorta lumen diameter (AUD), and their combined expression demonstrated strong predictive potential for IMT and AUD. • Cxcr4 and Gng4 mRNA levels were significantly positively correlated, CXCR4 and GNG4 colocalized, interacted, and formed stable complexes, both of which were detectable in neurons.