Search Results (7,243)

Background/Objectives: Epilepsy is a common neurological disorder with pathological mechanisms closely associated with the spatiotemporal dynamic characteristics of electroencephalogram (EEG) signals. Although significant progress has been made in epileptic seizure detection methods using time–frequency analysis, current research still faces challenges in terms of an insufficient utilization of phase information. Methods: In this study, we propose an effective epileptic seizure detection framework based on continuous wavelet transform (CWT) and a hybrid network consisting of convolutional neural network (CNN) and vision transformer (ViT). First, the raw EEG signals are processed by the CWT. Then, the phase spectrogram and power spectrogram of the EEG are generated, and they are sent into the designed CNN and ViT branches of the network to extract more discriminative EEG features. Finally, the features output from the two branches are fused and fed into the classification network to obtain the detection results. Results: Experimental results on the CHB-MIT public dataset and our SH-SDU clinical dataset show that the proposed framework achieves sensitivities of 98.09% and 89.02%, specificities of 98.21% and 95.46%, and average accuracies of 98.45% and 94.66%, respectively. Furthermore, we compared the spectral characteristics of CWT with other time–frequency transforms within the hybrid architecture, demonstrating the advantages of the CWT-based CNN-ViT architecture. Conclusions: These results highlight the outstanding epileptic seizure detection performance of the proposed framework and its significant clinical feasibility. Full article

To study the pathological contribution of fatty acid (FA) metabolism regulators including fatty acid binding protein 4 (FABP4), FABP5, peroxisome proliferator-activated receptor alpha (PPARα), and PPARγ in ovarian carcinoma, non-cancerous human ovarian surface epithelium (HOSE) cells and two epithelial ovarian carcinoma (EOC) cell lines, AMOC-2 and ES2 established from ovarian serous adenocarcinoma and ovarian clear cell carcinoma, respectively, were subjected to (1) an analysis of the physical properties of spheroids, (2) qPCR analysis, (3) cellular metabolic analysis, and (4) multiomic pan-cancer analysis using the Cancer Genome Atlas (TCGA). In contrast to globe-shaped spheroids of HOSE cells, AMOC-2 and ES2 cells formed non-globe-shaped spheroids and ES2 spheroids were much more fragile than AMOC-2 spheroids. Gene expression levels of FABP4 and FABP5 in AMOC-2 cells and those of PPARγ in AMOC-2 cells were significantly higher than those in HOSE cells. Metabolic phenotypes and the effectiveness against antagonists for regulators were significantly different in the two types of cancerous cells. Those regulators were identified by a multiomic pan-cancer analysis as novel factors for the prediction of the prognosis of ovarian serous adenocarcinoma. The results show that dysregulated FA metabolism in AMOC-2 and ES2 suggests that the regulation of FA metabolism may be a critical factor in the pathogenesis of EOC. Full article

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses. Full article

Background/Objectives: Postoperative hemorrhage is the most common complication after Trans-Oral Robotic Surgery (TORS) described in the literature. The aim of this study is to assess the presence of any risk factors that may impact postoperative bleeding. Methods: This was a retrospective study based on the analysis of patient data. Patients undergoing TORS procedures at the ENT Unit of Forlì Hospital from 2008 to 2022 for OSA (obstructive sleep apnea) or oncological disease and with a minimum follow-up of 30 days were included. The comorbidities analyzed were perioperative anticoagulant/antiplatelet therapy and clinicopathological features concerning the pathology. Total bleeding and severe bleeding (which required management in the operating room) were included. Results: A total of 414 patients (106 oncological TORS and 308 OSA TORS patients) were included. Post-TORS bleeding occurred in 47 cases (11.3%) and severe bleeding in 18 cases (4.3%). The pathology (oncology vs. OSA) treated with TORS did not represent a risk factor (p = 0.466). Antiplatelet intake represented an important risk factor (p = 0.002). Postoperative hemorrhage for oncological TORS occurred in 11.3% patients; of these, 6.6% had severe bleeding. Artery ligation during neck dissection prevented the risk of severe bleeding (p < 0.001). In TORS for OSA, postoperative hemorrhage was found in 11.4% cases, of which 3.6% were major bleeding. Neither the degree of OSA nor the association with other concurrent procedures were risk factors for postoperative bleeding in this study. Conclusions: Patients taking perioperative antiplatelet therapy have an almost 5-fold increased risk of developing postoperative bleeding. The pathology (oncology vs. OSA) does not influence the risk of bleeding. Prophylactic arterial ligation during neck dissection significantly decreases the risk of severe bleeding. Full article

Ovarian cancer (OC) is a gynecological cancer characterized by high morbidity and mortality associated with poor survival outcomes. Bisphenol F (BPF), a widely used analog of bisphenol A (BPA), has recently gained attention due to its potential endocrine-disrupting properties and ubiquitous environmental presence. However, the carcinogenic potential of BPF in OC has not been well explored. This study investigates the effects of BPF on ovarian carcinogenesis by assessing its pathological impact on cellular processes, including cell proliferation, wound healing, and cell invasion. OC cells, SKOV3 were treated with varying concentrations of BPF (0.01 µM–250 µM). Cell viability was assessed using Alamar Blue assay, and migration ability was analyzed using wound-healing assay. Further, the total antioxidative capability (T-AOC) was measured. Statistical analysis was performed using student’s-t-test/ ANOVA, with a significance set at p < 0.05. BPF exhibited a dual role in cell viability, enhancing cell proliferation at low concentrations (1 µM: p = 0.034; 10 µM: p = 0.012) while exerting cytotoxic effects at higher concentrations (250 µM: p = 0.021). Further, a wound-healing assay demonstrated that a lower concentration, 1 µM BPF promoted cell migration (p = 0.0345), indicating its involvement in OC. However, a non-significant difference was observed in the invasive potential and T-AOC of BPF-treated SKOV3 cells. Our findings provide key insights into the effects of BPF on cellular processes linked with ovarian carcinogenesis, emphasizing the need for future experiments to comprehend its mechanisms of action. Full article

Bovine viral diarrhea virus (BVDV) is a globally prevalent pathogen causing severe detriment to the cattle industry. Vertical infection occurring before the development of the fetal adaptive immune response, before 125 days of gestation, results in an immunotolerant, persistently infected (PI) calf. It was hypothesized that epigenetic alterations observed in the splenic tissue of PI fetuses at gestational day 245 would persist into the postnatal period. White blood cell DNA from five PI and five control heifers at 4 months of age was subjected to reduced representation bisulfite sequencing and interpreted within the context of complete blood count and flow cytometry data herein. Analysis revealed 8367 differentially methylated sites contained within genes associated with the immune and cardiac system, as well as hematopoiesis. Differences observed in the complete blood counts of PI heifers include increased monocytes, microcytic anemia, and elevated platelets with decreased mean platelet volume. Flow cytometry revealed increased classical monocytes, B cells, and CD4⁺/CD8B⁺ and CD25⁺/CD127⁻ T cells, as well as decreased γδ⁺, CD4⁺, and CD4⁻/CD8B⁻ T cells. Investigation of the PI methylome provides a new perspective on the mechanisms of pathologies and provides potential biomarkers for the rapid identification of PI cattle. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive impairment with amyloid-β (Aβ) accumulation, tau hyperphosphorylation, and neuroinflammation. Among these pathological features, microglial activation is hallmark of neuroinflammation. Chaga (Inonotus obliquus) extract has been traditionally used for its diverse pharmacological properties, including anti-inflammatory and neuroprotective effects. This study aimed to evaluate the therapeutic potential of INO10, an inotodiol-rich chaga extract, in murine BV2 microglial cells and a 3xTg-AD mouse model. In BV2 cells, INO10 significantly reduced LPS-induced expression of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α), indicating its potent anti-inflammatory effects. Oral administration of INO10 significantly improved spatial memory in 3xTg-AD mice, as evidenced by increased spontaneous alternation in the Y-maze test. Furthermore, INO10 treatment attenuated neuroinflammation, as indicated by reduced microglial activation and downregulated expression of pro-inflammatory cytokines. In addition, immunohistochemical analysis confirmed that INO10 exhibited favorable bioavailability, supporting its potential as a neuroprotective agent. Histological analysis further revealed a reduction in Ab accumulation and tau phosphorylation in the hippocampus, accompanied by a marked decrease in neuroinflammatory markers. These findings suggest that INO10 effectively mitigates AD-related pathology by reducing Aβ deposition, tau hyperphosphorylation, and neuroinflammation, ultimately leading to cognitive enhancement. Given its multi-target neuroprotective properties, INO10 may serve as a promising natural compound for AD treatment. Further investigations are warranted to elucidate its precise mechanisms and clinical applicability. Full article

Background/Objectives: Lipoxins, particularly Lipoxin A4 (LXA4), are endogenous lipid mediators with potent anti-inflammatory and pro-resolving properties, making them promising candidates for the treatment of inflammatory and neurodegenerative disorders. However, their therapeutic application is limited by poor stability and bioavailability. This study aimed to develop and characterize nanomicelles encapsulating LXA4 (nano-lipoxin A4) to improve its pharmacological efficacy against Alzheimer’s disease (AD), a neurodegenerative condition marked by chronic inflammation and beta-amyloid (Aβ) accumulation. Methods: Nano-lipoxin A4 was synthesized using Pluronic F-127 as a carrier and characterized in terms of morphology, physicochemical stability, and in vitro activity against Aβ fibrils. Dissociation of Aβ fibrils was assessed via Thioflavin-T fluorescence assays and transmission electron microscopy. In vivo biodistribution and pharmacokinetic profiles were evaluated using technetium-99m-labeled nano-lipoxin A4 in rodent models. Hepatic biochemical parameters were also measured to assess potential systemic effects. Results: In vitro studies demonstrated that nano-lipoxin A4 effectively dissociated Aβ fibrils at concentrations of 50 nM and 112 nM. Electron microscopy confirmed the disruption of fibrillar structures. In vivo imaging revealed predominant accumulation in the liver and spleen, consistent with reticuloendothelial system uptake. Pharmacokinetic analysis showed a prolonged half-life (63.95 h) and low clearance rate (0.001509 L/h), indicating sustained systemic presence. Biochemical assays revealed elevated liver enzyme levels, suggestive of increased hepatic metabolism or potential hepatotoxicity. Conclusions: Nano-lipoxin A4 exhibits significant therapeutic potential for Alzheimer’s disease through effective modulation of Aβ pathology and favorable pharmacokinetic characteristics. However, the elevation in liver enzymes necessitates further investigation into systemic safety to support clinical translation. Full article

Glaesserella parasuis (G. parasuis) causes vascular inflammation in piglets, resulting in vascular damage. However, the mechanism causing vascular inflammation remains unclear. Baicalin possesses an anti-inflammatory function. Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) has been implicated in immunosuppression. CD163, a scavenger receptor expressed on macrophages that acts as a decoy receptor for TWEAK, plays a crucial role in the regulation of autophagy and inflammation. This research investigated the efficacy of baicalin in reducing immunosuppression elicited by G. parasuis through the regulation of CD163/TWEAK-mediated autophagy. The data demonstrated that G. parasuis altered routine blood indicators and biochemical parameters, increased cytokine production, and induced blood vessel tissue damage. G. parasuis reduced the CD3+ T cell proportion, CD3+CD4+ T cell proportion, and CD3+CD8+ T cell proportion in piglet blood. The proteomic analysis revealed that CD163 was differentially expressed in the blood vessels of challenged piglets. Baicalin was found to regulate CD163/TWEAK axis expression, inhibit Notch/Wnt signaling pathway activation, promote autophagy, and reduce NLRP3/Caspase 1 signaling pathway activation. Baicalin also decreased cytokine production and alleviated pathological tissue damage in the blood vessels of G. parasuis-challenged piglets. Taken together, this study indicates that baicalin alleviates G. parasuis-induced immunosuppression and might promote CD163/TWEAK-mediated autophagy. This finding suggests that baicalin could serve as a potential therapeutic agent to control G. parasuis infection and related vascular inflammation. Full article

Background/Objectives: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a chronic hepatic condition marked by lipid buildup, lipotoxicity, and inflammation. Prior research indicates that 3,3′-Diindolemethane (DIM), a natural indole-type phytochemical that is abundant in brassicaceae vegetables, has been reported to reduce body weight and improve lipid metabolism in mice subjected to a high-fat diet (HFD). The aryl hydrocarbon receptor (AhR), a nuclear receptor implicated in lipid metabolism and immune regulation, serves as a functional receptor for DIM. However, the underlying signaling pathways that regulate MAFLD remain elusive. Our objective is to ascertain the beneficial impact of DIM on MAFLD and the associated mechanisms. Methods: Hematoxylin and eosin staining, together with Oil Red O staining, were utilized to assess the pathological changes and lipid deposition in the liver. Biochemical analysis was employed to measure levels of triglyceride (TG), total cholesterol (TC), free fatty acid (FFA), aspartate transaminase (AST), alanine transaminase (ALT), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). The cell survival rate of HepG2 cells treated with palmitic acid (PA) and DIM was assessed using the CCK-8 assay. Flow cytometry was employed to measure the fluorescence intensity emitted by lipid droplets within cells. Western blotting analysis was performed to assess AhR pathway and fatty acid transporter expression levels in hepatic tissue. Results: Our results showed that DIM significantly attenuated body weight gain and hepatic injury brought on by HFD, decreased lipid droplet accumulation in HepG2 cells, and effectively suppressed the phosphorylation of p38 MAPK and the protein expression levels of fatty acid transporters CD36 and FATP4. Conclusions: DIM reduced lipid accumulation by activating AhR and suppressing p38 MAPK phosphorylation, thereby inhibiting fatty acid transport and inflammatory responses. These findings suggest that DIM may represent a promising therapeutic candidate for MAFLD, warranting further exploration for clinical applications. Full article

Introduction: Coronavirus (CoV) is an extremely contagious, enveloped positive-single-stranded RNA virus, which has become a global pandemic that causes several illnesses in humans and animals. Hence, it is necessary to investigate viral-induced reactions across diverse hosts. Herein, we propose utilizing naturally secreted extracellular vesicles (EVs), mainly focusing on exosomes to examine virus–host responses following CoV infection. Exosomes are small membrane-bound vesicles originating from the endosomal pathway, which play a pivotal role in intracellular communication and physiological and pathological processes. We suggested that CoV could impact EV formation, content, and diverse immune responses in vitro. Methods: In this study, we infected A-72, which is a canine fibroblast cell line, with a feline coronavirus (FCoV) and canine coronavirus (CCoV) independently in an exosome-free media at 0.001 multiplicity of infection (MOI), with incubation periods of 48 and 72 h. The cell viability was significantly downregulated with increased incubation time following FCoV and CCoV infection, which was identified by performing the 3-(4,5-dimethylthiazo-1-2yl)-2,5-diphenyltetrazolium bromide (MTT) assay. After the infection, EVs were isolated through ultracentrifugation, and the subsequent analysis involved quantifying and characterizing the purified EVs using various techniques. Results: NanoSight particle tracking analysis (NTA) verified that EV dimensions fell between 100 and 200 nm at both incubation periods. At both periods, total protein and RNA levels were significantly upregulated in A-72-derived EVs following FCoV and CCoV infections. However, total DNA levels were gradually upregulated with increased incubation time. Dot blot analysis indicated that the expression levels of ACE2, IL-1β, Flotillin-1, CD63, caspase-8, and Hsp90 were modified in A-72-derived EVs following both CoV infections. Conclusions: Our results indicated that FCoV and CCoV infections could modulate the EV production and content, which could play a role in the development of viral diseases. Investigating diverse animal CoV will provide in-depth insight into host exosome biology during CoV infection. Hence, our findings contribute to the comprehension and characterization of EVs in virus–host interactions during CoV infection. Full article

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by Helicobacter pylori (H. pylori) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. Therefore, when diagnosing AIG based on endoscopic images, it is essential not only to distinguish between normal and atrophic gastric mucosa but also to accurately identify the anatomical region in which the atrophic mucosa is located. In this study, we propose a patient-based multitask gastroscopy image classification network that analyzes all images obtained during the endoscopic procedure. First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. Next, we use a hierarchical clustering algorithm to group images based on this matrix. Finally, we apply the RepLKNet model, which utilizes large-kernel convolution, to each image group to perform two tasks: anatomical region classification and lesion recognition. Our method achieves an accuracy of 93.4 ± 0.5% (95% CI) and a precision of 92.6 ± 0.4% (95% CI) in the anatomical region classification task, which categorizes images into the fundus, body, and antrum. Additionally, it attains an accuracy of 90.2 ± 1.0% (95% CI) and a precision of 90.5 ± 0.8% (95% CI) in the lesion recognition task, which identifies the presence of gastric mucosal atrophic lesions in gastroscopy images. These results demonstrate that the proposed multitask patient-based gastroscopy image analysis method holds significant practical value for advancing computer-aided diagnosis systems for atrophic gastritis and enhancing the diagnostic accuracy and efficiency of AIG. Full article

Background/Objectives: With the number of detected breast cancer cases growing every year, there is a need to augment histopathological analysis with fast preliminary screening. We examine the feasibility of using X-ray diffraction measurements for this purpose. Methods: In this work, we obtained more than 6000 diffraction patterns from 211 patients and examined both standard and custom-developed methods, including Fourier coefficient analysis, for their interpretation. Various preprocessing steps and machine learning classifiers were compared to determine the optimal combination. Results: We demonstrated that benign and cancerous clusters are well separated, with specificity and sensitivity exceeding 0.9. For wide-angle scattering, the two-dimensional Fourier method is superior, while for small angles, the conventional analysis based on azimuthal integration of the images provides similar metrics. Conclusions: X-ray diffraction of biopsy tissues, supported by machine learning approaches to data analytics, can be an essential tool for pathological services. The method is rapid and inexpensive, providing excellent metrics for benign/cancer classification. Full article

Background: The current animal models of multiple sclerosis (MS) predominantly emphasize white matter inflammation, reflecting early-stage disease. However, progressive MS (PMS) is characterized by cortical pathology, including subpial demyelination, chronic meningeal inflammation, and microglial activation, which are underrepresented in the existing models. While alternative mouse models replicate the relapsing–remitting phenotype and gray matter pathology, pathology is frequently dispersed throughout the brain, complicating the analysis of the specific lesion sites. Methods: To address this gap, we developed a novel model that integrates laser-induced focal demyelination with cytokine-driven meningeal inflammation to replicate the key aspects of PMS cortical pathology. Results: Using two-photon laser irradiation, we induced controlled subpial cortical lesions in CX3CR1-GFP mice, leading to microglial activation, astrocytosis, and focal demyelination. The addition of IFNγ-expressing adenovirus to promote meningeal inflammation which resulted in prolonged glial responses, increased immune cell infiltration, and exacerbated demyelination, mimicking the PMS-associated pathology. Conclusions: This model provides a powerful tool to investigate the mechanisms underlying the cortical lesion development and immune-mediated neurodegeneration in PMS. By capturing the critical aspects of cortical pathology, it enables the evaluation of therapeutic strategies targeting neuroinflammation and demyelination, ultimately aiding in the development of new treatments of progression in PMS patients. Full article

Background: Physical activity is vital for promoting health and rehabilitation, and ensuring cardiovascular safety during such activities is paramount. Electrocardiography (ECG) and its longitudinal monitoring remain crucial for the early detection of cardiac diseases. Recent advancements in nonlinear RR analysis and machine learning offer promising approaches to identifying subtle precursors of cardiac pathologies in monitoring systems using simple heart rate (HR) wearable sensors. Therefore, using HR sensors in human activity recognition (HAR) is recommendable. After defining fatigue in a cardiological context, and focusing on an AI-based methods suite for HAR, the main research question of this scoping review is as follows: “Can RR time series be successfully used as physiological biomarkers for the early detection of cardiac fatigue?” The reported data on assessment of fatigue are focused on the last two decades. The aim of this scoping review was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in adult populations. Methods: Queries were conducted in the PubMed, Scopus and Google Scholar databases for the time period 2005–2025. Only research articles and review papers were considered suitable candidates. Results: Data from 10 papers were considered, related to the information researched. Conclusions: Information on HRV-based objective measures is quite scarce and there is an urgent need to adopt a multidisciplinary approach and to improve advanced AI-based nonlinear analyses to differentiate cardiac physiological status from cardiac pathological status. Full article