Search Results (7,665)

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

Background/Objectives: The possibilities of endovascular and minimally invasive treatment of visceral and renal artery aneurysms require precise procedure planning. Accurate visualization of vascular pathologies is crucial in this regard. Expanding diagnostic imaging with real 3D models is one of these methods. The objective of our study was to evaluate the utility of 3D printing and virtual 3D models in visualizing visceral and renal artery aneurysms. Methods: A group of 30 patients with true aneurysms of the visceral and renal arteries was selected based on computed tomography angiography (CTA). Aneurysm morphology, diameters, arterial diameters, and anatomical vessel variants were analyzed. Imaging data were processed and 3D-printed using Fused Filament Fabrication (FFF) technology. The resulting 3D models were measured, and dimensional deviations were compared to radiological images. Results: The cohort included 51 aneurysms across arteries supplying abdominal organs, with splenic artery aneurysms (49%) and renal artery aneurysms (25.5%) predominating. Half of the patient group had multiple aneurysms, and 36.7% exhibited anatomical arterial variants. Forty-three 3D models of visceral and renal artery aneurysms were generated, accurately depicting isolated vascular pathologies and the course of visceral arteries in regions of interest. Measurement analysis confirmed that the 3D-printed models showed a mean dimensional deviation of 0.24 mm compared to radiological images. Conclusions: 3D-printed and virtual models enhance the analysis of vascular pathologies, significantly improving the assessment of pathological changes and visualization of anatomical details, especially in hilar aneurysms and aneurysm branches. Full article

Background: Despite pre-transplantation vaccination against invasive pneumococcal disease (IPD) and hepatitis B virus (HBV), most solid organ transplant (SOT) recipients are without post-transplantation seroprotection against IPD and HBV. We aimed to determine the seroprotection rates and changes in antibody concentrations after booster vaccination against IPD and HBV in SOT recipients without post-transplantation seroprotection after pre-transplantation vaccination. Furthermore, we aimed to identify risk factors associated with non-response to booster vaccination. Methods: In this prospective cohort study, we included adult SOT recipients without post-transplantation seroprotection against IPD who then received the 23-valent pneumococcal polysaccharide vaccine (PPSV23) booster, as well as adult SOT recipients without seroprotection against HBV who then received the Engerix-B^® booster after pre-transplantation vaccination. Logistic regression models were used to analyze risk factors for non-response to booster vaccination. Results: We included 50 SOT recipients in analyses of booster vaccination against IPD and 52 SOT recipients in analyses of booster vaccination against HBV. Seroprotection rates were 52% after booster vaccination against IPD and 7.7% after booster vaccination against HBV. The median geometric mean concentration of pneumococcal antibodies increased from 0.54 µg/mL IgG (interquartile range, IQR: 0.35–0.77) to 1.21 µg/mL IgG (IQR: 0.87–1.62) after booster vaccination (p < 0.001). Having pre-transplantation seroprotection against IPD at time of listing was associated with lower odds of non-response to booster vaccination. We were not able to identify risk factors for non-response to HBV booster vaccination. Conclusions: Booster vaccination improved seroprotection against IPD, but not HBV. Further studies are needed to examine optimal vaccination strategies for SOT recipients. Full article

Background: Deep learning models have been used in the past for non-invasive liver fibrosis classification based on liver ultrasound scans. After numerous improvements in the network architectures, optimizers, and development of hybrid methods, the performance of these models has barely improved. This creates a need for a sophisticated method that helps improve this slow-improving performance. Methods: We propose LivSCP, a method to train liver fibrosis classification models for better accuracy than the traditional supervised learning (SL). Our method needs no changes in the network architecture, optimizer, etc. Results: The proposed method achieves state-of-the-art performance, with an accuracy, precision, recall, and F1-score of 98.10% each, and an AUROC of 0.9972. A major advantage of LivSCP is that it does not require any modification to the network architecture. Our method is particularly well-suited for scenarios with limited labeled data and computational resources. Conclusions: In this work, we successfully propose a training method for liver fibrosis classification models in low-data and computation settings. By comparing the proposed method with our baseline (Vision Transformer with SL) and multiple models, we demonstrate the state-of-the-art performance of our method. Full article

Marine algae are a prolific bioactive peptide source with a broad pharmacological potential. We characterized MP28, a cationic peptide isolated from the green alga Bryopsis plumosa. Structural modeling indicated a predominantly amphipathic α-helix (residues 3–16) flanked by flexible termini and stabilized by intramolecular disulfide bonds, a motif typical of membrane-active anticancer peptides. Functionally, MP28 demonstrated potent activity against non-small-cell lung cancer cell lines (A549, H460, H1299) without affecting non-tumorigenic lung fibroblasts (MRC-5). In vitro, MP28 decreased cell viability and clonogenic growth and suppressed migration and invasion in a dose-dependent manner. Flow cytometry revealed increased early/late apoptotic fractions, accompanied by caspase-9 activation, consistent with engagement of the intrinsic apoptotic pathway. In a mouse xenograft model, MP28 treatment significantly reduced tumor size compared with that of controls. Collectively, MP28 may be a potent anticancer peptide that exhibits selective cytotoxicity and low toxicity toward normal cells. Full article

The Bactrocera minax (Enderlein) (Diptera: Tephritidae) and Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae) are the only members of the subgenus of the Tetradacus of Bactrocera. They share nearly identical morphological characteristics and occupy highly overlapping ecological niches, specifically harming citrus crops and causing substantial damage to citrus production in China. To determine the suitable habitat of the two pests and how the citrus coverage affects this distribution. This study employed the Maximum Entropy model (MaxEnt) to predict the potential geographic distributions (PGDs) of B. minax and B. tsuneonis under current and future climate scenarios, using species occurrence data and key environmental variables. The result indicate that the MaxEnt model performed well, with an area under the curve value (AUC) of 0.969. The citrus distribution index, precipitation of driest month (BIO 14), min temperature of coldest month (BIO 6), and elevation were identified as the primary environmental factors affecting their PGDs. The PGDs for these pests are mainly concentrated in southern China, where citrus is extensively cultivated. Guizhou and Hunan identified as the most significant high-suitability habitat. The projected distribution of B. minax and B. tsuneonis show minimal changes under the future climate conditions estimated by the MaxENT model. However, under global warming scenarios, their PGDs are projected to gradually shrink, although eastern Sichuan remains at high risk of invasion by B. tsuneonis. Prevention, quarantine, and control measures for B. tsuneonis require continued attention. The findings of this study offer a more robust theoretical basis for the targeted monitoring and control of B. minax and B. tsuneonis in China. Full article

Nuclear factor erythroid 2-related factor 2 (NRF2) orchestrates redox balance, metabolism, and cellular stress responses, acting as both a tumor suppressor and promoter depending on the disease stage. In advanced cancers, persistent NRF2 activation—through KEAP1/NFE2L2 mutations or oxidative adaptation—drives epithelial-to-mesenchymal transition, metabolic reprogramming, and immune evasion, promoting tumor invasion (T) and metastasis (M). Recent pharmacologic efforts seek to exploit this duality. NRF2 inhibitors such as brusatol, halofuginone, and ML385 suppress NRF2 transcriptional activity or disrupt DNA binding, reducing motility, invasion, and metastatic dissemination in preclinical models. In contrast, NRF2 activators, such as bardoxolone methyl (CDDO-Me), sulforaphane, and dimethyl fumarate, exhibit chemopreventive effects by enhancing detoxification and mitigating oxidative DNA damage during early tumorigenesis. Furthermore, metabolic interventions, such as glutaminase or G6PD inhibitors, target NRF2-driven anabolic and antioxidant pathways essential for metastatic fitness. Therefore, understanding the temporal and contextual effects of NRF2 signaling is crucial for therapeutic design. The aim of this review is to examine how pharmacological modulation of NRF2 influences the invasive and metastatic dimensions of tumor progression, in addition to discussing its potential integration into TNM-based prognostic and treatment frameworks. Full article

This paper investigates the phenomenon of accelerating propagation for nonlocal reaction-diffusion models with spatial and trait structure in a cylinder domain $\mathbb{R}\times \Omega$. Unlike previous studies focusing on exponentially decaying or compactly supported initial data, we consider initial functions that decay more slowly than any exponential function—such as algebraic or sub-exponential decay. By constructing a pair of super- and sub-solutions via the principal eigenfunction ${\psi }_0$ of the trait operator, we prove that the solution propagates with infinitely increasing speed in the spatial direction. Explicit upper and lower bounds for the locations of level sets are derived, illustrating how the decay rate of the initial data determines the acceleration profile. The results are extended to a more general model with space- and trait-dependent competition kernels under a boundedness assumption (H3). This work highlights the crucial role of slowly decaying tails in the initial distribution in driving accelerated invasion fronts, providing a theoretical foundation for assessing propagation risks in ecology and population dynamics. Full article

A predominant etiological factor in implantation failure and early pregnancy loss is embryonic chromosomal abnormalities. The current clinical standard for determining embryonic ploidy is invasive preimplantation genetic testing. This procedure imposes mechanical stress on embryonic cells during trophectoderm biopsy and fails to significantly improve live birth rates per transfer, likely due to its inability to evaluate the embryo’s implantation potential. Consequently, there is a clear need to develop a non-invasive method, suitable for routine clinical practice, that can simultaneously assess both the ploidy and implantation competence of a blastocyst prior to uterine transfer. Our research group was the first to achieve this by quantifying specific piwiRNAs (piR_016677, piR_017716, piR_020497, piR_015462) in spent culture medium. These data served as the foundation for logistic regression models tailored for day 5 blastocysts, day 6 blastocysts, and blastocysts irrespective of their developmental rate. These models demonstrated high diagnostic accuracy, with specificity ranging from 68% to 100% and sensitivity from 71% to 100%. The rationale for employing these molecules as biomarkers lies in their potential biological roles, which encompass maintaining genomic stability through LINE-1 regulation, as well as direct involvement in critical processes such as cell cycle control, spindle assembly, and cellular adhesion—all of which are imperative for successful implantation. Full article

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

The oral cavity acts as the anatomical gateway to the respiratory tract, sharing both microbiological and pathophysiological links with the lower airways. Although radiotherapy is a cornerstone treatment for lung cancer, reliable oral microbiome biomarkers for predicting patient outcomes remain lacking. We analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls across discovery and two validation cohorts via 16S rRNA sequencing. Healthy controls exhibited a significantly higher abundance of Streptococcus compared to patients (p = 0.049, p < 0.001, p < 0.001, respectively). The structure of the microbial community exhibited substantial dynamic changes during treatment. Responders showed enrichment of Rothia aeria (p = 0.027) and Prevotella salivae (p = 0.043), associated with prolonged overall survival (OS) and progression-free survival (PFS), whereas non-responders exhibited elevated Porphyromonas endodontalis (p = 0.037) correlating with shorter OS and PFS. According to Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) analysis, Akkermansia and Alistipes were nearly absent in non-responders, while Desulfovibrio and Moraxella were virtually absent in responders. A diagnostic model based on Streptococcus achieved area under the curve (AUC) values of 0.85 (95% CI: 0.78–0.91) and 0.99 (95% CI: 0.98–1) in the validation cohorts, and a response prediction model incorporating Prevotella salivae and Neisseria oralis yielded an AUC of 0.74 (95% CI: 0.58–0.90). Furthermore, in small cell lung cancer, microbiota richness and diversity were inversely correlated with Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.008, p < 0.001, respectively) and pro-gastrin-releasing peptide (ProGRP) levels (p = 0.065, p = 0.084, respectively). These results demonstrate that lung cancer-associated oral microbiota signatures dynamically reflect therapeutic response and survival outcomes, supporting their potential role as non-invasive biomarkers for diagnosis and prognosis. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, representing a major public health challenge. Despite advances in screening strategies, surgical techniques, and systemic therapies, patient prognosis is often compromised by late diagnosis, tumor heterogeneity, and therapeutic resistance. In recent years, the integration of advanced imaging analytics and artificial intelligence (AI) has opened new avenues for precision oncology. Radiomics, defined as the high-throughput extraction of quantitative features from medical images, has emerged as a promising tool to capture intratumoral heterogeneity and predict clinical outcomes in a non-invasive manner. When combined with AI, particularly machine learning and deep learning approaches, radiomics enables the development of predictive and prognostic models that may support treatment personalization. This narrative review provides a comprehensive overview of CRC epidemiology and risk factors, summarizes current diagnostic and clinical management strategies, and focuses extensively on radiomics and AI applications in CRC, including workflow standardization, feature extraction, clinical applications, and challenges for implementation in daily practice. Full article

Background:The presence of visceral pleural invasion (VPI) is associated with increased risk of recurrence and reduced overall survival following surgical resection. We aimed to develop machine learning (ML)-based classification models that integrate clinical variables and both tumoral and peritumoral radiomic features to predict VPI in patients with lung adenocarcinoma before surgery. Methods: We retrospectively enrolled 118 patients, including 80 (68%) without VPI and 38 (32%) with histologically confirmed VPI. All patients underwent preoperative contrast-enhanced CT scans. Tumor volumes were manually segmented, and isotropic expansions of 3, 5, and 10 mm were automatically generated to define peritumoral regions. The dataset was randomly split into training (70%) and validation (30%) cohorts. Radiomic features and clinical data were used to train multiple ML algorithms. Results: Pleural Tag Sign and the Worst Histotype were identified as the strongest clinical predictors of VPI. The combined model, integrating radiomics from the lesion and clinical variables, achieved the highest training accuracy of 0.88 (95% CI: 0.80–0.92) and validation accuracy of 0.83 (95% CI: 0.68–0.92). Conclusions: VPI is associated with detectable alterations in both tumoral and peritumoral microenvironment on contrast-enhanced CT. Incorporating radiomic features with clinical data enabled improved model performance compared to clinical-only models, yielding very good accuracies. This approach may support surgical planning and patient risk stratification. Further prospective studies are needed to validate these findings and assess their clinical impact. Full article

Non-invasive brain stimulation (NIBS) techniques—including repetitive transcranial magnetic stimulation (rTMS), theta-burst stimulation (TBS), paired associative stimulation (PAS), transcranial direct current stimulation (tDCS), and transcranial alternating current stimulation (tACS)—have emerged as valuable tools for modulating neural activity and promoting plasticity. Traditionally, their effects have been interpreted within a binary framework of long-term potentiation (LTP)-like and long-term depression (LTD)-like plasticity, largely inferred from changes in motor evoked potentials (MEPs). However, existing models do not fully capture the complexity of the biological processes engaged by these techniques and despite extensive clinical application, the cellular and molecular mechanisms underlying NIBS remain only partially understood. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, synthesizes evidence from in vivo, in vitro, and ex vivo studies to delineate how NIBS influences neurotransmission through intracellular signaling, gene expression, and protein synthesis at the cellular level. Emphasis is placed on the roles of classical synaptic models, grounded in Ca²⁺-dependent glutamatergic signaling and receptor phosphorylation dynamics, as well as broader forms of plasticity involving BDNF–TrkB signaling, epigenetic modifications, neuroimmune and glial interactions, anti-inflammatory pathways, and apoptosis- and survival-related cascades. By integrating findings in humans with those in animal and cellular models, we identify both shared and technique-specific molecular mechanisms underlying NIBS-induced effects, highlighting emerging evidence for multi-pathway, non-binary plasticity mechanisms. Understanding these convergent pathways provides a mechanistic foundation for refining stimulation paradigms and improving their translational relevance for treatment of neurological and psychiatric disorders. Full article

Rare genetic ocular diseases represent a heterogeneous group of disorders that significantly impair visual function and quality of life. Despite their clinical relevance, many of these conditions remain insufficiently characterized due to complex molecular mechanisms and diagnostic limitations. Recent advances in molecular diagnostics, particularly Next-Generation Sequencing (NGS), have enabled comprehensive and accurate identification of pathogenic variants, offering novel insights into genotype–phenotype correlations and supporting precision medicine approaches. In parallel, the use of alternative biological matrices such as tear fluid has emerged as a promising non-invasive strategy for biomarker discovery and disease monitoring. Tear-based omics, including proteomics and transcriptomics, have identified diagnostic signatures and pathogenic mediators such as non-coding RNAs, microRNAs, and tRNA-derived fragments (tRFs). Among these, tRF-1001 has shown potential both as a biomarker and therapeutic target in ocular neovascular conditions through its modulation of angiogenic pathways. The objective of this review is to show the integration of two rapidly advancing yet frequently isolated fields: next-generation sequencing-based genomics and tear-fluid molecular profiling, positioning them as complementary foundations of precision ophthalmology for rare inherited retinal and optic nerve disorders. Previous reviews have mainly concentrated on either genetic diagnosis or ocular surface biomarkers separately; however, we have introduced a convergent model wherein genomic data furnish diagnostic and prognostic clarity, while tear-omics deliver dynamic, minimally invasive assessments of disease activity, treatment efficacy, and persistent neurovascular stress. By explicitly connecting these two aspects, we have delineated how multi-matrix, multi-omics approaches can expedite early diagnosis, facilitate personalized longitudinal monitoring, and direct focused treatment interventions in rare ocular genetic illnesses. Full article