Search Results (6,095)

Cholangiocarcinoma (CCA) is highly prevalent in the Greater Mekong sub-region, especially northeastern Thailand, where infection with the liver fluke Opisthorchis viverrini is a major etiological factor. Limited therapeutic options and the absence of reliable early diagnosis tools impede effective disease control. Atractylodes lancea (Thunb.) DC.—long used in Thai and East Asian medicine, contains atractylodin (ATD), a potent bioactive compound with anticancer potential. Here, we developed ATD-loaded poly(lactic co-glycolic acid) nanoparticles (ATD PLGA NPs) and evaluated their antitumor efficacy against CCA. The formulated nanoparticles had a mean diameter of 229.8 nm, an encapsulation efficiency of 83%, and exhibited biphasic, sustained release, reaching a cumulative release of 92% within seven days. In vitro, ATD-PLGA NPs selectively reduced the viability of CL-6 and HuCCT-1 CCA cell lines, with selectivity indices (SI) of 3.53 and 2.61, respectively, outperforming free ATD and 5-fluorouracil (5-FU). They suppressed CL-6 cell migration and invasion by up to 90% within 12 h and induced apoptosis in 83% of cells through caspase-3/7 activation. Micronucleus assays showed lower mutagenic potential than the positive control. In vivo, ATD-PLGA NPs dose-dependently inhibited tumor growth and prolonged survival in CCA-xenografted nude mice; the high-dose regimen matched or exceeded the efficacy of 5-FU. Gene expression analysis revealed significant downregulation of pro-tumorigenic factors (VEGF, MMP-9, TGF-β, TNF-α, COX-2, PGE₂, and IL-6) and upregulation of the anti-inflammatory cytokine IL-10. Collectively, these results indicate that ATD-PLGA NPs are a promising nanotherapeutic platform for targeted CCA treatment, offering improved anticancer potency, selectivity, and safety compared to conventional therapies. Full article

Electroencephalogram (EEG) analysis is essential for diagnosing neurological disorders but typically requires expert interpretation and significant time. Purpose: This study aims to automate the classification of normal and abnormal EEG recordings to support clinical diagnosis and reduce manual workload. Automating the initial screening of EEGs can help clinicians quickly identify potential neurological abnormalities, enabling timely intervention and guiding further diagnostic and treatment strategies. Methodology: We utilized the Temple University Hospital EEG dataset to develop a DenseNet-based deep learning model. To enable a fair comparison of different EEG representations, we used three input types: signal images, spectrograms, and scalograms. To reduce dimensionality and simplify computation, we focused on two channels: T5 and O1. For interpretability, we applied Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the EEG regions influencing the model’s predictions. Key Findings: Among the input types, spectrogram-based representations achieved the highest classification accuracy, indicating that time-frequency features are especially effective for this task. The model demonstrated strong performance overall, and the integration of LIME and Grad-CAM provided transparent explanations of its decisions, enhancing interpretability. This approach offers a practical and interpretable solution for automated EEG screening, contributing to more efficient clinical workflows and better understanding of complex neurological conditions. Full article

Acromegaly is caused by an excessive secretion of growth hormone and the secondary elevation of IGF-1 levels, leading to progressive changes in multiple body systems, including the craniofacial region and oral cavity. Dental manifestations such as mandibular overgrowth, macroglossia, malocclusion, periodontal disease, and prosthetic difficulties represent not only a clinical component of the disease but also a significant therapeutic and diagnostic challenge. The aim of this review is to present the current state of knowledge on the relationship between acromegaly and oral health and to analyze the role of interdisciplinary collaboration between endocrinologists and dentists in patient care. For this narrative review, a literature search was conducted in the PubMed, Scopus, and Web of Science databases covering the period from 2000 to 2025. Sixty-two peer-reviewed publications meeting the methodological and thematic criteria were included in the analysis, including original studies, meta-analyses, systematic reviews, and case reports. The results indicate significant correlations between disease activity and the severity of periodontal and microbiological changes, while effective endocrine treatment only results in the partial regression of morphological changes. Particular attention was given to the role of the dentist in recognizing the early symptoms of the disease, planning prosthetic and surgical treatment, and monitoring therapy-related complications. Interdisciplinary collaboration models, including integrated clinics and co-managed care, were also described as optimal systemic solutions for improving treatment quality. The conclusion drawn from the analysis are as follows: there is a need for the permanent integration of dentistry into the standard of interdisciplinary care for patients with acromegaly, in both diagnostic and therapeutic dimensions. Increasing awareness among dentists and developing integrated collaboration models may reduce the time to diagnosis, improve patients’ quality of life, and enable the more effective management of craniofacial complications in the course of this rare disease. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article

Gear wear degrades transmission performance, necessitating highly reliable fault diagnosis methods. To address the limitations of existing approaches—where dynamic models rely heavily on prior knowledge, while data-driven methods lack interpretability—this study proposes an integrated bidirectional verification framework combining dynamic modeling and deep learning for interpretable gear wear diagnosis. First, a dynamic gear wear model is established to quantitatively reveal wear-induced modulation effects on meshing stiffness and vibration responses. Then, a deep network incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) enables visualized extraction of frequency-domain sensitive features. Bidirectional verification between the dynamic model and deep learning demonstrates enhanced meshing harmonics in wear faults, leading to a quantitative diagnostic index that achieves 0.9560 recognition accuracy for gear wear across four speed conditions, significantly outperforming comparative indicators. This research provides a novel approach for gear wear diagnosis that ensures both high accuracy and interpretability. Full article

Palindromic antimicrobial peptides (PAMs) constitute versatile scaffolds for the design and optimization of anticancer agents with applications in therapy, diagnosis, and/or monitoring. In the present study, fluorolabeled peptides derived from the palindromic sequence RWQWRWQWR containing fluorescent probes, such as 2-Aminobenzoyl, 5(6)-Carboxyfluorescein, and Rhodamine B, were obtained. RP-HPLC analysis revealed that the palindromic peptide conjugated to Rhodamine B (RhB-RWQWRWQWR) exhibited the presence of isomers, likely corresponding to the open-ring and spiro-lactam forms of the fluorescent probe. This equilibrium is dependent on the peptide sequence, as the RP-HPLC analysis of dimeric peptide (RhB-RRWQWR-hF-KKLG)₂K-Ahx did not reveal the presence of isomers. The antibacterial activity of the fluorescent peptides depends on the probe attached to the sequence and the bacterial strain tested. Notably, some fluorescent peptides showed activity against reference strains as well as sensitive, resistant, and multidrug-resistant clinical isolates of E. coli, S. aureus, and E. faecalis. Fluorolabeled peptides 1-Abz (MIC = 62 µM), RhB-1 (MIC = 62 µM), and Abz-1 (MIC = 31 µM) exhibited significant activity against clinical isolates of E. coli, S. aureus, and E. faecalis, respectively. The RhB-1 (IC₅₀ = 61 µM), Abz-1 (IC₅₀ = 87 µM), and RhB-2 (IC₅₀ = 35 µM) peptides exhibited a rapid, significant, and concentration-dependent cytotoxic effect on HeLa cells, accompanied by morphological changes characteristic of apoptosis. RhB-1 (IC₅₀ = 18 µM) peptide also exhibited significant cytotoxic activity against breast cancer cells MCF-7. These conjugates remain valuable for elucidating the possible mechanisms of action of these novel anticancer peptides. Rhodamine-labeled peptides displayed cytotoxicity comparable to that of their unlabeled analogues, suggesting that cellular internalization constitutes a critical early step in their mechanism of action. These findings suggest that cell death induced by both unlabeled and fluorolabeled peptides proceeds predominantly via apoptosis and is likely contingent upon peptide internalization. Functionalization at the N-terminal end of the palindromic sequence can be evaluated to develop systems for transporting non-protein molecules into cancer cells. Full article

Cardiorenal syndrome (CRS) is a term used to describe the combined dysfunction of the heart and kidneys. This complex disorder is widely acknowledged to be challenging in both its diagnosis and management, and this is the case particularly in the elderly population, due to multi-morbidity, polypharmacy, and age-related physiological changes. Given advancements in medicine and more prolonged cumulative exposure to risk factors in the elderly population, it is likely that the prevalence of chronic kidney disease (CKD) and heart failure (HF) will continue to rise going forward. Hence, understanding the mechanisms involved in the development of CRS is paramount. There are five different CRS types—they are categorised depending on the primary organ involved the acuity of disease. The pathophysiological process behind CRS is complex, involving the interplay of many processes including hemodynamic changes, neurohormonal activation, inflammation, oxidative stress, and endothelial dysfunction and vascular stiffness. The numerous diagnostic and management challenges associated with CRS are significantly further exacerbated in an elderly population. Biomarkers used to aid the diagnosis of CRS, such as serum creatinine and brain natriuretic peptide (BNP), can be challenging to interpret in the elderly population due to age-related renal senescence and multiple comorbidities. Polypharmacy can contribute to the development of CRS and therefore, before initiating treatment, coordinating a patient-centred, multi-speciality, holistic review to assess potential risks versus benefits of prescribed treatments is crucial. The overall prognosis of CRS in the elderly remains poor. Treatments are primarily directed at addressing the sequelae of the underlying aetiology, which often involves the removal of fluid through diuretics or ultrafiltration. Careful considerations when managing elderly patients with CRS is essential due to the high prevalence of frailty and functional decline. As such, in these patients, early discussions around advance care planning should be prioritised. Full article

This study assessed the seroprevalence of anti-Anisakis simplex antibodies in Norwegian patients with inflammatory bowel disease (IBD), specifically ulcerative colitis (UC) and Crohn’s disease (CD), compared with healthy controls. Associations between anti-A. simplex antibody positivity and clinical or laboratory parameters in IBD were also explored. A total of 86 UC patients, 68 CD patients, and 41 healthy controls were prospectively enrolled from four Norwegian hospitals (2013–2022). Diagnosis and disease activity were established using standard clinical, endoscopic, and biomarker criteria. Serum samples were analyzed for total Ig, IgG, IgM, IgA, and IgE antibodies against A. simplex and Pseudoterranova decipiens using ELISA. Anti-A. simplex IgG seroprevalence was 4.9% in controls and 3.2% in IBD (3.5% UC, 2.9% CD). IgM seroprevalence was 0% in all groups. IgA seroprevalence was higher in IBD (16.2%) than controls (4.9%), with 14.0% in UC and 19.1% in CD. IgE seroprevalence was low across all groups. Smoking correlated with lower antibody levels and higher surgery rates. In UC, higher anti-A. simplex IgG and IgE levels were associated with milder disease and better prognosis. Anti-TNFα and azathioprine treatments were linked to higher anti-A. simplex IgA. Norwegian UC and CD patients had significantly higher anti-A. simplex total Ig and IgA seroprevalence than healthy controls, indicating increased exposure or immune response. Anti-A. simplex IgG and IgE may serve as markers of clinical activity in UC. Further research is warranted to clarify the clinical significance of these findings. Full article

Introduction: Renal malacoplakia is a rare chronic granulomatous disease, often associated with immunosuppression and persistent Gram-negative infections, particularly Escherichia coli. Case Presentation: We present a case involving a 31-year-old woman with hypertension, gestational diabetes, and prior uterine curettage after labor induction for preeclampsia at 23 weeks. She developed urinary sepsis post-procedure. Imaging revealed bilateral nephromegaly, while laboratory tests showed acute kidney injury (KDIGO stage III), anemia, and thrombocytopenia. Blood and urine cultures grew Escherichia coli. Renal biopsy confirmed malacoplakia, demonstrating PAS-positive Michaelis–Gutmann bodies and Von Hansemann cells. The patient responded to prolonged antibiotic therapy and supportive care. Discussion and Conclusion: This case highlights the importance of considering renal malacoplakia in patients with atypical urinary tract infections and nephromegaly, particularly in obstetric settings. Histopathological confirmation is essential, and timely treatment with intracellularly active antibiotics can lead to favorable outcomes. Early diagnosis is critical to prevent irreversible renal damage. Full article

Endometriosis is a disease characterized by the presence of endometrial glands and stroma outside of the uterine corpus, often clinically presenting with pain and/or infertility. Ectopic lesions exhibit features characteristic of epithelial-to-mesenchymal transition (EMT), a process in which epithelial cells lose polarity and acquire mesenchymal traits, including migratory and invasive capabilities. During the process of EMT, epithelial traits are downregulated, while mesenchymal traits are acquired, with cells developing migratory ability, increasing proliferation, and resistance to apoptosis. EMT is promoted by exposure to hypoxia and stimulation by transforming growth factor-β (TGF-β), platelet-derived growth factor (PDGF), and estradiol. Signaling pathways that promote EMT are activated in most ectopic lesions and involve transcription factors such as Snail, Slug, ZEB-1/2, and TWIST-1/2. EMT-specific molecules present in the serum of women with endometriosis appear to have diagnostic potential. Strategies targeting EMT in animal models of endometriosis have demonstrated regression of ectopic lesions, opening the door for novel therapeutic approaches. This review summarizes the current understanding of the role of EMT in endometriosis and highlights potential targets for EMT-related diagnosis and therapeutic interventions. Full article

Lung cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. Despite advancements, the overall survival rate for lung cancer remains between 10% and 20% in most countries. However, recent progress in diagnostic tools and therapeutic strategies has led to meaningful improvements in survival outcomes, highlighting the growing importance of personalized management based on accurate disease assessment. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become essential in the management of lung cancer, serving as a key imaging modality for initial diagnosis, staging, treatment response assessment, and follow-up evaluation. Recent developments in radiomics and artificial intelligence (AI), including machine learning and deep learning, have revolutionized the analysis of complex imaging data, enhancing the diagnostic and predictive capabilities of FDG PET/CT in lung cancer. However, the limitations of FDG, including its low specificity for malignancy, have driven the development of novel oncologic radiotracers. One such target is fibroblast activation protein (FAP), a type II transmembrane glycoprotein that is overexpressed in activated cancer-associated fibroblasts within the tumor microenvironment of various epithelial cancers. As a result, FAP-targeted radiopharmaceuticals represent a novel theranostic approach, offering the potential to integrate PET imaging with radioligand therapy (RLT). In this review, we provide a comprehensive overview of FDG PET/CT in lung cancer, along with recent advances in AI. Additionally, we discuss FAP-targeted radiopharmaceuticals for PET imaging and their potential application in RLT for the personalized management of lung cancer. Full article

Pediatric high-grade glioma (pHGG) is a devastating group of childhood cancers associated with poor outcomes. Traditionally, diagnosis was based on histologic and immunohistochemical characteristics, including high mitotic activity, presence of necrosis, and presence of glial cell markers (e.g., GFAP). With advances in molecular tumor profiling, these tumors have been recategorized based on specific molecular findings that better lend themselves to prediction of treatment response and prognosis. pHGG is now categorized into four subtypes: H3K27-altered, H3G34-mutant, H3/IDH-WT, and infant-type high-grade glioma (iHGG). Molecular profiling has not only increased the specificity of diagnosis but also improved prognostication. Additionally, these molecular findings provide novel targets for individual tumor-directed therapy. While these therapies are largely still under investigation, continued investigation of distinct molecular markers in these tumors is imperative to extending event-free survival (EFS) and overall survival (OS) for patients with pHGG. Full article

MicroRNA-211 (miR-211) is a versatile regulatory molecule that plays critical roles in cellular homeostasis and disease progression through the post-transcriptional regulation of gene expression. This review comprehensively examines miR-211’s multifaceted functions across various biological systems, highlighting its context-dependent activity as both a tumor suppressor and oncogene. In physiological contexts, miR-211 regulates cell cycle progression, metabolism, and differentiation through the modulation of key signaling pathways, including TGF-β/SMAD and PI3K/AKT. miR-211 participates in retinal development, bone physiology, and protection against renal ischemia–reperfusion injury. In pathological conditions, miR-211 expression is altered in various diseases, particularly cancer, where it may be a useful diagnostic and prognostic biomarker. Its stability in serum and differential expression in various cancer types make it a promising candidate for non-invasive diagnostics. The review also explores miR-211’s therapeutic potential, discussing both challenges and opportunities in developing miRNA-based treatments. Understanding miR-211’s complex regulatory interactions and context-dependent functions is crucial for advancing its clinical applications for diagnosis, prognosis, and targeted therapy in multiple diseases. Full article

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. While most diagnoses occur in outpatient settings, a subset of cases are incidentally identified during emergency department (ED) visits. The clinical characteristics and treatment decisions of these patients, particularly in relation to social background factors such as living situation and access to primary care, remain poorly understood. Methods: We conducted a retrospective study of patients diagnosed with malignancies in the ED of a single institution between April 2018 and December 2021. Patients diagnosed with lung cancer within 60 days of an ED visit were included. Data on demographics, disease status, treatment decisions, and background factors—including whether patients lived alone or had a primary care physician (PCP)—were extracted and analyzed. Results: Among 32,108 patients who visited the ED, 148 were diagnosed with malignancy within 60 days; 23 had lung cancer. Of these, 69.6% had metastatic disease at diagnosis, and 60.9% received active treatment (surgery or chemotherapy). No significant associations were observed between the extent of disease and either living arrangement or PCP status. However, the presence of a PCP was significantly associated with the selection of best supportive care (p = 0.023). No significant difference in treatment decisions was observed based on age (cutoff: 75 years). Conclusions: Although social background factors such as living alone were not significantly associated with cancer stage or treatment choice, the presence of a primary care physician was associated with a higher likelihood of best supportive care being selected. This may indicate that patients with an established PCP have more clearly defined care goals at the end of life. These findings suggest that primary care access may play a role in shaping end-of-life care preferences, highlighting the importance of personalized approaches in acute oncology care. Full article