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Enhancing melanoma treatment through systemic delivery of an immune boosting Staphylococcus epidermidis strain
Abstract A unique strain of Staphylococcus epidermidis, AIT01 (AIT, Airway Immune Trainer), identified in our previous research, has demonstrated immune-boosting properties. This study aimed to evaluate the systemic immune-modulatory effects and potential anti-tumor properties of this immune-enhancing skin microbiota strain. A series of ex vivo and in vivo experiments were conducted to assess immune cell proliferation, cytokine production, and anti-tumor efficacy. In ex vivo studies, splenocytes treated with the bacterial lysate or culture supernatant of the strain showed significantly increased viability in a concentration-dependent manner. Flow cytometry analysis revealed increased populations of dendritic cells, NK cells (Natural killer cells), and γδ T cells, with enhanced cytokine production, particularly IFN-γ (Interferon-γ) and perforin, in the lysate-treated group. When administered via intraperitoneal and intravenous routes in vivo, mice showed significant inhibition of melanoma growth upon receiving the bacterial lysate. Notably, pre-treatment demonstrated superior efficacy compared to post-treatment. Furthermore, the combination of the bacterial lysate with anti-PD-1 (anti-Programmed cell death protein-1) monoclonal antibody further suppressed tumor growth compared to anti-PD-1 monotherapy. These findings suggest that the AIT01 lysate enhances immune cell proliferation and cytokine production, contributing to its potent anti-tumor effects. The systemic delivery of this immune-boosting skin microbiota strain, particularly in combination with anti-PD-1 therapy, holds promise as an effective immunotherapeutic strategy against melanoma.
Correction: Numerical study of magneto convective ag (silver) graphene oxide (GO) hybrid nanofluid in a square enclosure with hot and cold slits and internal heat generation/absorption
Profiling extracellular vesicles from cerebrospinal fluid for classification of intradural spinal tumors
Abstract Extracellular vesicles (EVs) transport biomolecules that could serve as biomarkers for disease diagnosis and monitoring. The clinical utility of EVs derived from cerebrospinal fluid (CSF) in patients with intradural spinal tumors (IST) has not yet been investigated. Here, we obtained EVs from CSF of adult patients with intraspinal ependymoma (n = 9), meningioma (n = 9), hemangioma (n = 4) and schwannian tumors (n = 7), as well as comparison group (‘CG’, normal pressure hydrocephalus, n = 7), by ultrafiltration. CSF-EVs were characterized by electron microscopy and nanoparticle tracking analysis. EV populations according to the presence of tetraspanins (CD9, CD63, CD81) were measured by imaging flow cytometry (IFCM). CD81+ EVs were more prevalent in the comparison group, meningioma, ependymoma WHO grade 2, and hemangioma, whereas CD9+ EVs were predominant in ependymoma grade 1 and Schwannian tumors. CD63+ EVs per milliliter/CSF differed between ependymoma WHO grades 1 and 2 (FC = 24.6, AUC = 90%, p < 0.05). Based on results from a bead-based multiplex profiling, we selected ITGB1, CD44, CD133 and HLA-DR/DQ/DP for further phenotyping in CSF-EVs using IFCM, in combination with each tetraspanin as double-positive subpopulations. Compared to CG, CD44+ EVs were the most relevant population in CSF from IST patients, followed by ITGB1. Notable differences in absolute (EVs/mL CSF) and relative (percentages of CSF-EVs) levels were: CD44+/CD81+ for ependymoma grade 1 (FC = 196.5 and 34.5; p < 0.01) and grade 2 (%FC = 6.1, p < 0.05); CD44+/CD63+ for meningioma (abs. and %FC > 1000, p < 0.05); ITGB1+/CD81+ for hemangioma (%FC = 4.8, p < 0.05); and ITGB1+/CD9+ for schwannian tumors (abs.FC = 19.8, p < 0.01). In conclusion, we identified distinct EV subpopulations in the CSF of IST patients, potentially facilitating tumor classification.
Colistin- and cefotaxime-resistant Shiga toxin-producing Escherichia coli (STEC) in buffalo meat
Formaldehyde vapour fixation enables multiscale phase-contrast imaging and histological validation of human-sized lungs
Abstract Accurate diagnosis and characterization of lung disease increasingly rely on advanced imaging modalities capable of resolving fine microstructural details while minimizing radiation exposure. Phase-sensitive computed tomography (CT), particularly propagation-based imaging (PBI), offers superior soft tissue contrast but has historically been limited by the lack of compatible fixation techniques that preserve lung architecture post-excision. We present an adapted formaldehyde (FA) vapour fixation protocol designed to maintain human-sized lungs in a physiologically inflated and morphologically stable state. This approach prevents collapse of the delicate air–tissue interfaces, a major barrier to high-fidelity phase-contrast imaging and histological correlation. Our method enables high-resolution, multiscale imaging from whole-organ PBI at 67 µm voxel size to localized subcellular synchrotron PBI at 650 nm voxel size on the same specimen, with preserved spatial relationships critical for accurate validation of imaging findings. In porcine models, FA vapour fixation maintained alveolar integrity and radiological contrast without compromising histological detail, while also avoiding the artifacts associated with liquid fixation. Crucially, the protocol allows regulation of inflation and fixation dynamics, addressing longstanding challenges in ex vivo lung imaging and enabling consistent specimen preparation across studies. This fixation technique supports biosafe stabilization of freshly explanted human lungs–such as those from transplant procedures creating new opportunities for translational research on pathological tissue. By bridging high-resolution radiology and histopathology, our scalable fixation protocol establishes a standardized foundation for multimodal lung imaging and offers a critical tool for advancing both fundamental lung research and clinical diagnostics.
DsDNA breaks inflicted by cell-free chromatin particles selectively target telomeres
Correction: Acute effects of BFR intra-conditioning on torque and muscle activity of the rectus femoris muscle during isokinetic knee extensions
Correction: Healthcare-seeking intentions of middle-aged and elderly individuals with critical diseases: an expanded TPB model in the post-pandemic era
Intergenerational effects of early life protein restriction on adipose tissue development as revealed by sheep transcriptomic analyses
Transients in the Palomar Observatory Sky Survey (POSS-I) may be associated with nuclear testing and reports of unidentified anomalous phenomena
Abstract Transient star-like objects of unknown origin have been identified in the first Palomar Observatory Sky Survey (POSS-I) conducted prior to the first artificial satellite. We tested speculative hypotheses that some transients are related to nuclear weapons testing or unidentified anomalous phenomena (UAP) reports. A dataset comprising daily data (11/19/49—4/28/57) regarding identified transients, nuclear testing, and UAP reports was created (n = 2,718 days). Results revealed significant ( p = .008) associations between nuclear testing and observed transients, with transients 45% more likely on dates within + /- 1 day of nuclear testing. For days on which at least one transient was identified, significant associations were noted between total number of transients and total number of independent UAP reports per date ( p = 0.015). For every additional UAP reported on a given date, there was an 8.5% increase in number of transients identified. Small but significant ( p = .008) associations between nuclear testing and number of UAP reports were also noted. Findings suggest associations beyond chance between occurrence of transients and both nuclear testing and UAP reports. These findings may help elucidate the nature of POSS-I transients and strengthen empirical support for the UAP phenomenon.
Automated assessment of periapical health based on the radiographic periapical index using YOLOv8, YOLOv11, and YOLOv12 one-stage object detection algorithms
Abstract This study investigates the application of recent YOLO (You Only Look Once) algorithms for automated detection and classification of apical periodontitis using the Periapical Index (PAI) scoring system (1–5). A dataset of 699 digital periapical radiographs was collected from diverse sources, de-identified, and annotated by calibrated experts before splitting into training, validation, and testing sets. Three deep learning models (YOLOv8m, YOLOv11m, YOLOv12m) were trained and their performance was evaluated using Precision, Recall, F1 score, mean average precision (mAP50), Intersection over union (IoU), and confusion matrices. Two-sided McNemar’s exact test was further conducted to compare models on lesion-level outcomes. The results showed comparable mAP50 scores among the tested models. YOLOv11m and YOLOv12m demonstrated higher Precision (88.5% and 89.1%, respectively) compared to YOLOv8m (86.8%). YOLOv11m exhibited the highest Recall (86.2%) and maximum F1 score (87.1%). Confusion matrix analysis indicated superior prediction for PAI scores 3–5 across all models, with YOLOv11m excelling in scores 1 and 2, YOLOv8m performing best for score 4, and equal performance for score 5. The findings demonstrate the prospective of YOLO algorithms in automating the detection and classification of apical periodontitis using PAI. Accuracy and efficiency of tested models suggest their potential for integration in clinical workflows.
Safety and efficacy of different therapeutic regimens in Egyptian adults with moderate COVID-19 infection (EVEREST): a real-world retrospective study
Abstract This study seeks to disseminate insights from Egypt’s management of COVID-19 patients by evaluating the effectiveness and safety of various treatment regimens using combined repurposed antivirals. A retrospective cohort study was conducted on 310 moderate hospitalized COVID-19 cases. Patients were divided into four treatment arms: standard care, sofosbuvir/daclatasvir (sovodak) plus ivermectin, sofosbuvir/ledipasvir (SOF/LED) plus hydroxychloroquine, and SOF/LED plus ivermectin. The study analyzed parameters such as hospitalization days, total clinical recovery percentage, and progressive CT chest changes. The median hospitalization days significantly differed in arm 1 versus arms 2 and 3 ( p < 0.001 and p = 0.025, respectively). There was a difference between arms 1 and 4 in the days till clinical improvement ( p = 0.007). Complete normalization of vital signs occurred in 26% of arm 4 patients versus 43% in arm 1 ( p = 0.023), and a statistically significant difference in the proportion of patients with total clinical recovery was found between arms 1 and 2 ( p = 0.009). All arms displayed a statistically significant lower proportion of patients with progressive CT scans compared to arm 1. Our study reveals that most tested antiviral combinations effectively reduced hospitalization days and progressive CT scans. These regimens demonstrated efficacy in treating moderate COVID-19 to prevent disease progression and complications.
A novel approach to predict the arctic stratospheric ozone from stratospheric polar vortex dynamics using explainable machine learning
Abstract A significant decreasing trend of Arctic stratospheric ozone has been observed since 2019, with the first reported ozone hole in the Arctic Stratospheric Polar Vortex (SPV) in 2020, raising concerns for humanity. This underlines that it is essential to develop an algorithm capable of predicting Arctic ozone levels, preferably using minimal computing resources. This study presents a novel approach for ozone prediction based on the morphological and dynamical properties of the SPV utilizing a explainable machine learning approach. XGBoost exhibits good agreement with the observations, achieving an $$R^2$$ score of 0.80 and a correlation of 0.91. The algorithm accurately predicts the daily and seasonal patterns of ozone variations. It successfully captures the pattern of the lowest recorded ozone levels in 2020, though it overestimates ozone values by approximately 20 Dobson units. Moreover, in some years the predicted ozone values also show a strong alignment with the observations. Notably, the algorithm relies solely on physics based features of the SPV to predict chemical ozone loss, demonstrating the potential of dynamical parameters in predicting the ozone variability. It could serve as a tool for projecting future Arctic ozone variability by utilizing input from climate models that lack interactive chemistry.
Narrow tuning and sensitivity of the pheromone-specific olfactory neuron in male European grapevine moth (Lobesia botrana)
Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study
Abstract Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and current predictors such as lipoprotein (a) [Lp(a)] and risk scores have limitations. Automated machine learning (AutoML) offers the potential to improve CVD risk prediction by processing large datasets and developing tailored models without the need for extensive data science expertise. Using clinical datasets from the LURIC ( n = 3316) and UMC/M ( n = 423) studies, we built AutoML models to predict Lp(a), specific CVDs and CVD-related mortality in three phases. Phase 1 identified key CVD determinants such as age, Lp(a), troponin T, BMI and cholesterol with good accuracy (AUC 0.6249 to 0.9101). Phase 2 validated models in the UMC/M dataset and showed robust performance (AUC 0.7224 to 0.8417), with SHAP analysis highlighting predictors like statin therapy, age and NTproBNP. Phase 3 focused on cardiovascular mortality prediction, achieving high AUC values (0.74 to 0.85) and showed data drift, highlighting the need for model adjustment.
Ellagic acid mediates cardioprotection against adrenaline-induced toxicity via PI3K/AKT and Keap1-NRF2 axes
Abstract Ellagic acid (Ea) is an example of a bioactive polyphenolic compound with numerous beneficial effects; therefore, it is used to counteract adrenaline toxicity in this study. Thirty-six male rats were categorized into 6 groups of 6 rats. Group (1) was given oral purified distilled water for thirty successive days and injected with a saline for the next two days. Groups (2) and (3) received 7.5 and 15 mg/kg body weight Ea orally, followed by saline injection for two days. Group (4) was given distilled water orally for 30 uninterrupted days, followed by adrenaline injections for the next two days. Groups (5) and (6) received 7.5 and 15 mg/kg Ea for 30 days, followed by adrenaline injections for the next two days. Electrocardiogram (ECG) changes, oxidative stress, inflammation, immunohistochemistry, and histopathological alterations were evaluated. Specific biomarkers associated with kidney, liver, and heart injuries were recorded. At the higher dose, the Ea counteracted adrenaline-induced heart rate decrease, prolongation of the QT interval, and elevation of the ST interval in rats. It also enhanced kidney, liver, and heart function, ameliorating abnormal ECG patterns and tissue architecture changes. Ea suppressed PI3K/AKT signaling pathway and promoted nuclear factor erythroid 2-related factor 2 (NRF2) expression in the heart, possibly due to its antioxidative and anti-inflammation potential. Additionally, the study suggested a mechanistic aspect regarding the Ea’s antioxidant activity through modulating the Keap1-NRF2 axis based on a validated computational approach that warrants further investigation. This study highlights the potential benefits of Ea in reducing heart injury.
Supercritical miniaturization of turbulence in microsystems
Novel Soliton and Periodic Wave Solutions of the (3+1)-Dimensional Shallow Water Wave Equation with Bifurcation Analysis
Abstract This study derives novel exact traveling wave solutions for the $$(3 + 1)-$$ dimensional shallow water wave equation–a pivotal model in coastal hydrodynamics for tsunami prediction and tidal analysis. By employing an enhanced tanh-function method, we obtain a diverse spectrum of solutions, including dark, singular, and periodic solitons, as well as hyperbolic, Jacobi elliptic, rational, and exponential forms, which surpass the variety and generality reported in previous studies. These solutions uncover previously unexplored wave propagation patterns and interaction dynamics. A comprehensive bifurcation analysis elucidates the stability and phase transitions of the wave solutions, providing deeper analytical insight into their behavior. High-resolution graphical visualizations quantitatively demonstrate wave amplification and nonlinear interactions, confirming the superiority of our method in capturing complex physical phenomena. The results not only advance nonlinear wave theory but also enhance predictive models for marine hazard prevention and environmental monitoring strategies.