MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.

Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM.

Reactive oxygen species responsive double-locked liposome collaborative photodynamic therapy for reducing electrical conduction recurrence after radiofrequency catheter ablation.

Radiofrequency catheter ablation (RFCA) is the preferred technique for the treatment of atrial fibrillation, but the recovery of electrical conduction after ablation seriously endangers the health of patients. This study aimed to develop reactive oxygen species (ROS) responsive double-locked liposome collaborative photodynamic therapy (PDT) to target the ablation area and reduce the recovery of electrical conduction after ablation. The successful synthesis of β-cyclodextrin modified with phenylboronic acid pinacol ester (OCD) was confirmed by H NMR and FT-IR.

Correction: Tasci et al. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. 2023, , 2672.

In the original publication [...

Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma.

Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples.

Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space.

The efficacy of coaxial percutaneous iodine-125 seed implantation combined with arterial infusion chemotherapy for advanced pancreatic cancer: a randomized clinical trial.

Background: This study aimed to evaluate the clinical efficacy of coaxial percutaneous Iodine-125 (I) seed implantation in combination with arterial infusion chemotherapy for the treatment of advanced pancreatic cancer (PC) through a randomized controlled trial.

Methods: A total of 101 patients with advanced PC were randomized into two groups: control group treated with systemic intravenous chemotherapy and experimental group that received I seed implantation in combination with arterial infusion chemotherapy. Outcomes, including tumor control, abdominal pain relief, and survival time were compared between these two groups (Trial Registration No.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring.

Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation.

Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents.

pH-Sensitive Nanoparticles for Colonic Delivery Anti-miR-301a in Mouse Models of Inflammatory Bowel Diseases.

Though the anti-miR-301a (anti-miR) is a promising treatment strategy for inflammatory bowel disease (IBD), the degradability and the poor targeting of the intestine are a familiar issue. This study aimed to develop a multifunctional oral nanoparticle delivery system loaded with anti-miR for improving the targeting ability and the therapeutic efficacy. The HA-CS/ES100/PLGA nanoparticles (HCeP NPs) were prepared using poly (lactic-co-glycolic acid) copolymer (PLGA), enteric material EudragitS100 (ES100), chitosan (CS), and hyaluronic acid (HA).

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics.

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets.

The value of the wireless stethoscope in patients with COVID-19 infection in a makeshift hospital.

Objective: When COVID-19 sweeps the world, traditional stethoscopes are seen as infectious agents and then the use of stethoscopes is limited especially when health providers were in their personal protective equipment. These reasons led to the ignoring of the values of stethoscopes during pandemics. This study aims to explore the value of wireless stethoscopes in patients of a makeshift hospital.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions.

The role of KNTC1 in the regulation of proliferation, migration and tumorigenesis in colorectal cancer.

Background: Current findings have revealed that kinetochore-associated protein 1 (KNTC1) plays a pivotal role in the carcinogenesis of numerous types of cancer. This study was undertaken to inspect the role and probable underlying mechanisms of KNTC1 during the genesis and progression of colorectal cancer.

Methods: Immunohistochemistry was implemented to determine KNTC1 expression levels in colorectal cancer tissues and para-carcinoma tissues.

The serum soluble scavenger with 5 domains levels: A novel biomarker for individuals with heart failure.

We aimed to explore the relationship between the serum Soluble Scavenger with 5 Domains (SSC5D) levels and heart failure (HF). We retrospectively enrolled 276 patients diagnosed with HF or normal during hospitalization in Shanghai General Hospital between September 2020 and December 2021. Previously published RNA sequencing data were re-analyzed to confirm the expression profile of in failing and non-failing human and mouse heart tissues.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future.

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma.

Canine model of electrical conduction recurrence after radiofrequency catheter ablation constructed by CARTO3 and preliminary application evaluation of DOX-L.

Background: Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical conduction after ablation is essential before we can explore the mechanisms involved or develop new therapeutic strategies.

Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors.

Machine learning based survival prediction in Glioma using large-scale registry data.

Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy.

The Next Frontier in Health Disparities-A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back.

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits.

Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.

DNA Walkers for Biosensing Development.

The ability to design nanostructures with arbitrary shapes and controllable motions has made DNA nanomaterials used widely to construct diverse nanomachines with various structures and functions. The DNA nanostructures exhibit excellent properties, including programmability, stability, biocompatibility, and can be modified with different functional groups. Among these nanoscale architectures, DNA walker is one of the most popular nanodevices with ingenious design and flexible function.

Automated glioma grading on conventional MRI images using deep convolutional neural networks.

Purpose: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs).

Inhibition of circHIPK3 prevents angiotensin II-induced cardiac fibrosis by sponging miR-29b-3p.

Background: Circular RNAs (circRNAs) are emerging as powerful regulators of cardiac development and disease. Nevertheless, detailed studies describing circRNA-mediated regulation of cardiac fibroblasts (CFs) biology and their role in cardiac fibrosis remain limited.

Methods: PCR and Sanger sequencing were performed to identify the expression of circHIPK3 in CFs.

A data-efficient method for local noise power spectrum (NPS) estimation in FDK-reconstructed 3D cone-beam CT.

Purpose: For computed tomography (CT) systems in which noise is nonstationary, a local noise power spectrum (NPS) is often needed to characterize its noise property. We have previously developed a data-efficient radial NPS method to estimate the two-dimensional (2D) local NPS for filtered back projection (FBP)-reconstructed fan-beam CT utilizing the polar separability of CT NPS. In this work, we extend this method to estimate three-dimensional (3D) local NPS for feldkamp-davis-kress (FDK)-reconstructed cone-beam CT (CBCT) volumes.