Search Results (3,482)

Four classes of multiple symmetric sums over cyclic products of binomial coefficients are examined. By incorporating the generating function approach and recursive construction method, they are expressed analytically as coefficients of rational functions. Several recurrence relations and generating functions are explicitly determined when the dimension of the multiple sums does not exceed five. Full article

The Farnesoid X receptor (FXR) has recently been identified as being closely associated with the progression of primary hepatocellular carcinoma. Cancer stem cells (CSCs) play a crucial role in tumor initiation, progression, invasion, metastasis, recurrence, and drug resistance. The elucidation of the role and regulatory mechanism of FXR in CSCs is therefore deemed significant. Here, bioinformatics analysis has revealed a downregulation of FXR in hepatocellular carcinoma (HCC), which showed a negative correlation with HCC malignancy. This result was further confirmed through clinical sample analysis. Subsequently, CSCs were isolated from HCC cell lines and exhibited a significant decrease in the expression of FXR. The activation of FXR resulted in a remarkable inhibition of the proliferation, invasion, and tumorigenicity of CSCs. Furthermore, activated FXR prominently upregulated the expression of SOCS3 while suppressing STAT3 phosphorylation in CSCs. To further investigate this discovery, we established a DEN-induced HCC model in mice and observed that FXR-deficient mice exhibited heightened susceptibility to HCC. This was accompanied by decreased expression levels of SOCS3 and elevated expression and phosphorylation levels of STAT3, as well as significantly enhanced HCC CSCs markers and stemness-related genes expression in DEN-induced HCC tissues of FXR-deficient mice. Additionally, we also found a significant upregulation of CSCs markers and stemness-related genes within HCC clinical samples. Based on these findings, we postulated that targeted regulation of SOCS3 by FXR inhibits STAT3 phosphorylation, thereby exerting an inhibitory effect on CSCs. Full article

Cancer heterogeneity is a major challenge in oncology, complicating diagnosis, prognostication, and treatment. The clinical heterogeneity of cancer, which leads to differential treatment outcomes between patients with histopathologically similar cancers, is attributable to molecular diversity manifesting through genetic, epigenetic, transcriptomic, microenvironmental, and host biology differences. Heterogeneity is observed between patients, individual metastases, and within individual lesions. This review discusses clinical implications of heterogeneity, emphasizing need for personalized approaches to overcome challenges posed by cancer’s diverse presentations. Understanding of emerging molecular diagnostic and analytical techniques can provide a view into the multidimensional complexity of cancer heterogeneity. With over 90% of cancer-related deaths associated with metastasis, we additionally explore the role heterogeneity plays in treatment resistance and recurrence of metastatic lesions. Molecular insights from next-generation sequencing, single-cell transcriptomics, liquid biopsy technology, and artificial intelligence will facilitate the development of combination therapy regimens that can potentially induce lasting and even curative treatment outcomes. Full article

Objectives: The primary aim of this phase 1 trial is to establish the recommended phase 2 dose (RP2D) of tazemetostat given with a fixed dose of pembrolizumab in patients with recurrent or metastatic head and neck squamous cell carcinomas (RM-HNSCCs). Methods: A 3 + 3 dose-escalation phase 1 design was used to assess three dose-levels of tazemetostat (400, 600, and 800 mg orally, twice daily) with pembrolizumab (200 mg intravenously). Cycle 1 was 35 days (tazemetostat days 1–35; pembrolizumab day 15). Subsequent cycles were 21 days (tazemetostat days 1–21; pembrolizumab day 1). Dose-limiting toxicity (DLT), assessed during cycle 1, was defined as study-drug-related grade 4 neutropenia or thrombocytopenia, grade 3 febrile neutropenia, or grade 3–4 non-hematologic adverse events (AEs). Patients had to have completed cycle 1 to be evaluable for the DLT assessment; otherwise, an equal number of additional patients were enrolled. The RP2D was defined as the highest dose level in which zero of three or less than or equal to one of six patients experienced a DLT. Results: Twelve patients were enrolled: three on 400 mg, three on 600 mg, and six on the 800 mg dose level of tazemetostat. Three patients on the 800 mg dose level did not complete cycle 1 and were not evaluable for DLT. In the other nine patients, DLTs did not occur during cycle 1. In all 12 patients, the most common AEs included anemia (10 patients), fatigue (eight), and hyponatremia (seven). Conclusions: Among the patients with RM-HNSCCs, the RP2D of tazemetostat was 800 mg and administered twice daily when given with pembrolizumab. Full article

Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain–computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on pain detection based on electroencephalography (EEG) signals. It presents the findings, methodologies, and advancements reported in 20 peer-reviewed articles that utilize machine learning and deep learning (DL) approaches for EEG-based pain detection. We analyze various ML and DL techniques, support vector machines, random forests, k-nearest neighbors, and convolution neural network recurrent neural networks and transformers, and their effectiveness in decoding pain neural signals. The motivation for combining AI with BCI technology lies in the potential for significant advancements in the real-time responsiveness and adaptability of these systems. We reveal that DL techniques effectively analyze EEG signals and recognize pain-related patterns. Moreover, we discuss advancements and challenges associated with EEG-based pain detection, focusing on BCI applications in clinical settings and functional requirements for effective pain classification systems. By evaluating the current research landscape, we identify gaps and opportunities for future research to provide valuable insights for researchers and practitioners. Full article

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data. Full article

Mediastinal hemangiomas, particularly those of thymic origin, are rare phenomena. Due to its rarity, this pathologic condition is not characterized as related to the angiogenic profile of hemangioma endothelial cells. The diagnosis is challenging clinically and radiologically, and biopsies may not yield a definitive answer. Surgical resection offers the material for histologic diagnosis, relieves symptoms, and has a favorable long-term prognosis for such benign tumors. Sometimes, such benign tumors may have aggressive behavior and repeated recurrences but the causes responsible for this unpredictable evolution are not actually known. A case of intrathymic hemangioma diagnosed in a 16-year-old girl is presented here. We focused equally on a multidisciplinary approach to this challenging diagnosis but also on the characterization of the hemangioma endothelial cells profile not previously performed for such type of vascular anomalies. To define an antibodies panel for the evaluation of intrathymic hemangiomas may help in the full characterization of this rare vascular lesion, and subsequently focus on the new therapeutic targets which may be applied for cases with aggressive behavior. Full article

In an e-learning environment, question levels are based on Bloom’s Taxonomy (BT), which normally classifies a course’s learning objectives into diverse levels. As per the previous literature, the assessment procedure lacks accuracy and results in redundant keywords when automatically assigning Bloom’s taxonomic categories using a keyword-based approach. These assessments are considered challenging as far as e-learning-based students are concerned, as the text feed is the only instrumental testing part. Student assessments are limited to multiple-choice questions and lack an evaluation of students’ text-based input. This paper proposes a natural-language processing-based intelligent deep-learning model that relies on parametric cognitive assessments. By applying class labels to students’ descriptive responses, the proposed approach helps classify a variety of questions mapped to BT levels. The first contribution of this work is a compiled dataset of the assessment items from 300 students, who were tested on 20 questions at each level. Each level is calculated by combining the responses from all students, resulting in 6000 questions per cognitive level for a total of 36,000 records. The second contribution is the development of an intelligent model based on a recurrent neural network (RNN), which not only predicts Bloom’s question level but also learns it over further iterations. The students’ text-based answers are accessed to gauge performance using a refined question pool gathered through the RNN model. The student dataset is mapped and tested using the NLP model for further classification of the students’ cognitive levels. This assessment is related to the formulation of questions and the compilation of Episode 2 for assessment. The third contribution is the comparison and demonstration of the improvements in learning using a parametric cognitive-based assessment in an episodic manner. Improved classification accuracy was attained by adding more processing layers based on the iterative, RNN-based learning model to achieve the vital threshold difference. The cognitive based questions pool classification achieved by RNN results in 98% accuracy. The resulting student assessments, based on performance, increased to an accuracy ratio of 92.16% and a precision ratio of 92.36% at an aggregate level based on the Random Forest classifier. We claim that our work serves as an initiative for effective student evaluations in interactive and e-learning-based environments when handling other types of inputs, like mathematical, graphical, and multimodal inputs. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most difficult cancers to treat, with a dismal 5-year survival rate of only 8–10%. This challenging prognosis highlights the urgent need for innovative therapeutic approaches to improve outcomes for patients with PDAC. Electrochemotherapy (ECT), which enhances intracellular chemotherapeutic uptake via electric pulses, has been explored for resectable, borderline resectable (BR), locally advanced (LA), recurrent, and metastatic PDAC, either as a complement to conventional treatments or as an alternative when these are not feasible or effective, offering possible benefits in symptomatic palliation and local tumor control. Methods: A systematic review was performed in accordance with PRISMA guidelines for studies assessing the efficacy of ECT in PDAC. After searching Embase, PubMed/MEDLINE, Scopus, and Web of Science, five studies with a combined total of 43 patients in various disease stages were identified. Results: ECT showed promise in improving tumor control, alleviating cancer-related pain, and improving quality of life. One study noted a trend towards tumor size reduction of 8.3% at one-month and 16.1% at six-months follow-up (p = 0.211 and p = 0.315), although these findings were derived from studies conducted without specific comparative control groups. Severity of complication was mainly mild (Clavien–Dindo I-II), while severe complications occurred in only 2.3% of patients. Median overall survival was reported in two studies as 8 months (range 2–19) and 11.5 months (range 1–74). ECT showed efficacy for symptom management, with 60% of patients reporting reduced pain/discomfort and 40% showing enhanced quality of life in one study, while another reported pain scores as decreasing from 6 to 3 at one month and to 2 at six months. Conclusions: ECT appears to be a new promising and safe adjunct treatment modality in PDAC management across different disease stages, with potential benefits in tumor control, cancer-related pain reduction, and quality of life. Further studies are warranted to validate these findings and identify patients who could benefit most. Full article

An algorithmic method is proposed to generate all cubic polynomials with a critical orbit relation. We generate curves (polynomials of parameters) that correspond to those functions with critical orbit relations. The irreducibility of the polynomials obtained is left as an open problem. Our approach also works to generate critical orbit relations in all families of rational functions with active critical points. Full article

Background/Objectives: Radical prostatectomy (RP) may be considered for recurrent prostate cancer (PCa) following primary curative-intended local therapy. The effect of different prior therapies on patient-reported outcome measures (PROMs) after RP is not well defined. Methods: Validated PROMs (SF-12, EPIC-26, Decision Regret Scale) were used to compare health-related quality of life (HRQOL) and functional status changes following salvage RP after radiotherapy (RT-sRP) or focal therapy (FT-sRP), relative to primary RP. Results: Among 26,515 RP patients who underwent RP between 2014 and 2024, 107 (0.4%) previously received radiotherapy (RT-sRP) and 98 (0.4%) previously received focal therapy (FT-sRP). Compared with primary patients before RP, only the sexual function of RT-sRP patients was lower (EPIC score, 51 vs. 75, p < 0.001). One year after RP, RT-sRP patients exhibited lower functional status in all EPIC-26 domains compared to primary RP patients, whereas FT-sRP patients did not differ significantly. For instance, the median 1 yr EPIC-26 urinary incontinence scores were 46 (RT-sRP), 86 (FT-sRP), and 92 (primary RP). In adjusted mixed model analyses, the detrimental effects of RT-sRP vs. primary RP were further validated. In contrast, no such association was observed for FT-sRP. Decision regret and severe complications were low. Conclusions: Prior FT had only a marginal effect on HRQOL and functional status following RP, while urinary continence and sexual function were lower for RT-sRP patients as compared to primary RP patients. However, from an overall PROM perspective, prior therapies did not exert a prohibitive effect that would preclude RP as a treatment option in those patients. Full article

In this paper, we aim to establish new inequalities of Hermite–Hadamard (H.H) type for harmonically convex functions using proportional Caputo-Hybrid (P.C.H) fractional operators. Parameterized by $\alpha$, these operators offer a unique flexibility: setting $\alpha =1$ recovers the classical inequalities for harmonically convex functions, while setting $\alpha =0$ yields inequalities for differentiable harmonically convex functions. This framework allows us to unify classical and fractional cases within a single operator. To validate the theoretical results, we provide several illustrative examples supported by graphical representations, marking the first use of such visualizations for inequalities derived via P.C.H operators. Additionally, we demonstrate practical applications of the results by deriving new fractional-order recurrence relations for the modified Bessel function of type-1, which are useful in mathematical modeling, engineering, and physics. The findings contribute to the growing body of research in fractional inequalities and harmonic convexity, paving the way for further exploration of generalized convexities and higher-order fractional operators. Full article

Background/Objectives: Issues related to the chronic venous leg ulcer (VLU) treatment and prevention of recurrences remain the subject of research, but so too do common clinical problems in daily medical practice. Due to its medicinal properties, Manuka honey is increasingly used in the treatment of wounds of various origins. The aim of the study was to investigate the effectiveness of Manuka honey for the topical treatment of non-healing, chronic, venous leg ulcers. Methods: Eighty patients with chronic VLU participated in the study and were randomized into two equinumerous groups. In group 1, patients were treated with topical Manuka honey application and short stretch bandage compression, whereas, in group 2, antimicrobial calcium alginate wound dressing + Ag was used instead of Manuka honey. The efficacy of both treatment methods was compared. Results: The ulcerations in patients from group 1 have healed completely after up to seven weeks of therapy in all cases. In contrast, in all patients from group 2, the healing process was longer but completed successfully after up to 14 weeks of the therapy. The process of wound cleaning from microorganisms was also faster in group 1, as well as the reduction in ulcer area during treatment. Conclusions: It was found that the topical administration of Manuka honey may be a promising alternative to traditional methods of non-healing VLU treatment. Full article

Background: Antiphospholipid syndrome (APS) is a rare systemic autoimmune disease characterized by persistent antiphospholipid antibodies (aPL) in combination with recurrent thrombosis in the veins and/or arteries, obstetric morbidity, and various non-thrombotic associated complications. APS can be primary, as an isolated condition, or secondary in the context of another autoimmune disease, especially systemic lupus erythematosus. This comprehensive clinical review aims to summarize the current understanding of APS pathogenesis, diagnostic approaches, and treatment strategies for this unique clinical entity. Methods: A comprehensive review of the existing literature on APS was conducted, focusing on pathophysiological mechanisms, current diagnostic criteria, and therapeutic approaches. Results: APS pathogenesis involves complex interactions between aPL, phospholipid-binding proteins, and the coagulation cascade. Apart from the cardinal features of thrombosis and APS-related obstetric morbidity, APS is associated with a wide spectrum of clinical manifestations. Diagnosis remains challenging due to overlapping symptoms with other conditions, and clinicians should maintain a high index of suspicion in order to set the diagnosis. The recently published 2023 ACR/EULAR criteria although not definitive for clinical decision-making, these criteria offer clinicians a valuable tool to aid in determining whether further investigation for APS is warranted. Continued refinement of these criteria through ongoing feedback and updates is anticipated. Treatment strategies center on anticoagulation, but individualized approaches are necessary. Conclusions: Early diagnosis and multidisciplinary management of APS are critical to reducing morbidity and improving outcomes. Moreover, familiarization with the 2023 ACR/EULAR criteria is encouraged, recognizing that ongoing feedback and updates will contribute to their ongoing refinement and improvement. While VKAs remain the mainstay of treatment for most APS patients further research is needed to optimize treatment strategies and deepen our understanding of APS’s underlying disease mechanisms. Full article

Radiomics and deep learning computer vision algorithms can extract clinically relevant information from medical images, providing valuable insights for accurate diagnosis of cancerous lesions, tumor differentiation and molecular subtyping, prediction of treatment response, and prognostication of long-term outcomes. In head and neck squamous cell carcinoma (HNSCC), growing evidence supports the potential role of radiomics and deep learning models in predicting treatment response, long-term outcomes, and treatment complications following radiation therapy. This is especially important given the pivotal role of radiotherapy in early-stage and locally advanced HNSCC, as well as in post-operative and concomitant chemoradiotherapy. In this article, we summarize recent studies highlighting the role of radiomics in predicting early post-radiotherapy response, locoregional recurrence, survival outcomes, and treatment-related complications. Radiomics-guided tools have the potential to personalize HNSCC radiation treatment by identifying low-risk patients who may benefit from de-intensified therapy and high-risk individuals who require more aggressive treatment strategies. Full article