Ischemic stroke is one of the leading causes of morbidity and mortality worldwide, with carotid atherosclerosis being its key etiological factor. MicroRNA-21 (miR-21) regulates intracellular signal pathways responsible for vascular changes and ischemic brain injury, and is recognized as a potential diagnostic and prognostic biomarker. It modifies the activity of macrophages (MΦ) and vascular smooth muscle cells, causing inflammation and affecting the stability of atherosclerotic plaques. A deficiency of miR-21 in macrophages stimulates the inflammatory response and plaque growth. It promotes both the synthesis of extracellular matrix, stabilizing the plaque, and the degradation of the fibrin cap, which leads to plaque instability. The effect of miR-21 on endothelial cells differs: it stimulates both NO· synthesis and inflammation. During ischemic stroke, miR-21 demonstrates neuroprotective effects by modulating post-ischemic inflammation and protecting the integrity of the blood–brain barrier. Therapy targeting miR-21 shows potential in experimental models, but it requires cell-specific delivery and precise timing. Further research efforts should focus on the effects of miR-21 on different cell types, as well as the development of new technologies for diagnostic and therapeutic applications.

Background: Despite the increased use of mechanical thrombectomy (MT) in recent years, there remains a lack of research on in-hospital mortality rates following the procedure, the primary factors influencing these rates, and the potential for predicting them. This study aimed to utilize interpretable machine learning (ML) to help clarify these uncertainties. Methods: This retrospective study involved patients with anterior circulation large vessel occlusion (LVO)-related ischemic stroke who underwent MT. The patient division was made into two groups: (I) the in-hospital death group, referred to as miserable outcome, and (II) the in-hospital survival group, or favorable outcome. Python 3.10.9 was utilized to develop the machine learning models, which consisted of two types based on input features: (I) the Pre-MT model, incorporating baseline features, and (II) the Post-MT model, which included both baseline and MT-related features. After a feature selection process, the models were trained, internally evaluated, and tested, after which interpretation frameworks were employed to clarify the decision-making processes. Results: This study included 602 patients with a median age of 76 years (interquartile range (IQR) 65–83), out of which 54% (n = 328) were female, and 22% (n = 133) had miserable outcomes. Selected baseline features were age, baseline National Institutes of Health Stroke Scale (NIHSS) value, neutrophil-to-lymphocyte ratio (NLR), international normalized ratio (INR), the type of the affected vessel (‘Vessel type’), peripheral arterial disease (PAD), baseline glycemia, and premorbid modified Rankin scale (pre-mRS). The highest odds ratio of 4.504 was observed with the presence of peripheral arterial disease (95% confidence interval (CI), 2.120–9.569). The Pre-MT model achieved an area under the curve (AUC) value of around 79% utilizing these features, and the interpretable framework discovered the baseline NIHSS value as the most influential factor. In the second data set, selected features were the same, excluding pre-mRS and including puncture-to-procedure-end time (PET) and onset-to-puncture time (OPT). The AUC value of the Post-MT model was around 84% with age being the highest-ranked feature. Conclusions: This study demonstrates the moderate to strong effectiveness of interpretable machine learning models in predicting in-hospital mortality following mechanical thrombectomy for ischemic stroke, with AUCs of 0.792 for the Pre-MT model and 0.837 for the Post-MT model. Key predictors included patient age, baseline NIHSS, NLR, INR, occluded vessel type, PAD, baseline glycemia, pre-mRS, PET, and OPT. These findings provide valuable insights into risk factors and could improve post-procedural patient management.

Introduction: While the machine learning (ML) model’s black‐box nature presents a significant barrier to effective clinical application, the dynamic nature of stroke patients’ recovery further undermines the reliability of established predictive scores and models, making them less suitable for accurate prediction and appropriate patient care. This research is aimed at building and evaluating an interpretable ML‐based model, which would perform outcome prediction at different time points of patients’ recovery, giving more secure and understandable output through interpretable packages. Materials and Methods: A retrospective analysis was conducted on acute ischemic stroke (AIS) patients treated with alteplase at the Neurology Clinic of the University Clinical Center of Vojvodina (Novi Sad, Serbia), for 14 years. Clinical data were grouped into four categories based on collection time—baseline, 2‐h, 24‐h, and discharge features—serving as inputs for three different classifiers—support vector machine (SVM), logistic regression (LR), and random forest (RF). The 90‐day modified Rankin scale (mRS) was used as the outcome measure, distinguishing between favorable (mRS ≤ 2) and unfavorable outcomes (mRS ≥ 3). Results: The sample was described with 49 features and included 355 patients, with a median age of 67 years (interquartile range (IQR) 60–74 years), 66% being male. The models achieved strong discrimination in the testing set, with area under the curve (AUC) values ranging from 0.80 to 0.96. Additionally, they were compared with a model based on the DRAGON score, which showed an AUC of 0.760 (95% confidence interval (CI), 0.640–0.862). The decision‐making process was more thoroughly understood using interpretable packages: Shapley additive explanation (SHAP) and local interpretable model–agnostic explanation (LIME). They revealed the most significant features at both the group and individual patient levels. Conclusions and Clinical Implications: This study demonstrated the moderate to strong efficacy of interpretable ML‐based models in predicting the functional outcomes of alteplase‐treated AIS patients. In all constructed models, age, onset‐to‐treatment time, and platelet count were recognized as the important predictors, followed by clinical parameters measured at different time points, such as the National Institutes of Health Stroke Scale (NIHSS) and systolic and diastolic blood pressure values. The dynamic approach, coupled with interpretable models, can aid in providing insights into the potential factors that could be modified and thus contribute to a better outcome.

Background: as a relatively high number of ST-segment elevation myocardial infarction (STEMI) patients develop major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI), our aim was to determine the significance, and possible predictive value of QRS complex width and ST-segment elevation. Methods: our patient sample included 200 PCI-treated STEMI patients, which were divided into two groups based on the following duration of symptoms: (I) less than 6 h, and (II) 6 to 12 h. For every patient, an ECG was performed at six different time points, patients were followed for up to six years for the occurrence of MACE. Results: the mean age was 60.6 ± 11.39 years, and 142 (71%) were male. The 6–12 h group had significantly wider QRS complex, higher ST-segment elevation, lower prevalence of ST-segment resolution as well as MACE prevalence (p < 0.05). ECG parameters, QRS width, and magnitude of ST-segment elevation were proved to be independent significant predictors of MACE in all measured time points (p < 0.05). Even after controlling for biomarkers of myocardial injury, these ECG parameters remained statistically significant predictors of MACE (p < 0.05). Conclusion: our study highlights that wider QRS complex and a more pronounced ST-segment elevation are associated with longer total ischemic time and higher risk of long-term MACE.

Background/Aim. To date, most of the scientific attention has been aimed at the morphometric analysis of the nephrogenic zone (NZ) of the fetal renal cortex, while the quantification and analysis of the maturation zone (MZ) and other indicators of renal maturity were missing. The aim of the study was to examine the characteristics of fetal kidney cortex maturation, as well as to propose the development of a new cortical maturity index (CMI). Methods. The study included 42 paraffin molds of the fetal kidney, divided into three groups according to gestational age (GA). After hematoxylin and eosin staining, tissue sections were analyzed through the following parameters: the thickness of the NZ and MZ, the renal corpuscles area (RCa) and the glomerular capillary tuft area (GCTa), and the maturation stages of the glomeruli. In addition, a new parameter, CMI, was formed as a ratio of NZ and MZ thickness. The collected data were statistically processed. Results. Changes in NZ and MZ thickness were statistically significant, and they correlated with GA. A value of CMI higher than 0.2 was recorded in the kidney samples of fetuses younger than the 20th gestational week (GW), while a value lower than 0.1 was recorded in the samples older than the 30th GW. With an increase in GA in all zones of the renal cortex, RCa and GCTa decreased. A statistically significant reduction of GCTa was observed in the oldest group in the juxtamedullary and intermediate zones of the cortex (p < 0.01). Glomeruli located in the deeper parts of the cortex were more mature than the superficial ones. Conclusion. The measured parameters can serve as a starting point for future studies that would analyze the histomorphological characteristics of the fetal kidney cortex. In the absence of clinical data, a newly formed parameter CMI can represent assistance with the determination of GA, as it significantly correlates with GA (p < 0.01).

Intracerebral hemorrhages (ICH) during implantation of stereo‐EEG electrodes are rare. The impact of tobacco‐free nicotine consumption on periprocedural bleeding is uncertain. We present a 20+ year‐old man with drug‐resistant epilepsy who underwent stereo‐EEG with 17 depth electrodes. Within a few days after insertion, the patient developed multiple ICHs in the electrode trajectories and an intradural hemorrhage after a diagnostic spinal tap. We performed the investigation of the clotting system and whole‐exome sequencing (WES). WES identified a heterozygous mutation c.4698G>T, p.(Trp1566Cys) in COL4A2 (NM_001846.4) encoding a collagen type‐IV alpha‐2 chain inherited from his seemingly healthy mother. As COL4A2 mutations had been identified in four adult patients with ICH we postulated that the identified variant presents a potential risk factor. Notably, mutations encoding other collagens have been linked to cerebral hemorrhages (COL4A1) and increased propensity to trigger ICH upon smoking (COL1A2). Our patient consumed at least 24 oral nicotine pouches (containing 11 mg nicotine each) per day. We consider the patient's COL4A2 mutation in combination with his substantial nicotine consumption as likely predisposition to multiple ICHs precipitated by stereo‐EEG. Patients with nicotine consumption and any collagen mutation may have a substantially higher risk for hemorrhagic complications in SEEG and other neurosurgical procedures.Plain Language Summary: A young man with drug‐resistant epilepsy experienced multiple intracerebral hemorrhages after implantation of SEEG electrodes for presurgical evaluation and concomitantly a intradural hemorrhage after a lumbar spinal tap. A collagen IV mutation of unclear significance and heavy use of oral nicotine pouches were the only potential risk factors identified. As collagen mutations were previously described risk factors and smoking in particular worsens the bleeding risk in collagen mutations, further research is warranted to prevent hemorrhages in neurosurgical procedures. Nicotine consumption in any form is a preventable risk factor.