Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a robust machine-learning model for a reliable diagnosis of neurological diseases. We used miRNA sequence descriptors and gene target data to create machine-learning models that can be used as diagnostic tools.

Phase II trial of blood-brain barrier permeable peptide-paclitaxel conjugate ANG1005 in patients with recurrent high-grade glioma.

Background: This study is a phase II clinical trial to evaluate the efficacy, safety, and tolerability of the blood-brain barrier (BBB) permeable peptide-paclitaxel conjugate ANG1005 in patients with recurrent high-grade glioma (HGG) (NCT01967810).

Methods: Seventy-three patients were enrolled in 3 separate arms-recurrent glioblastoma (GBM) (Arm 1), bevacizumab refractory GBM (Arm 2), and grade 3 anaplastic gliomas (AGs) (Arm 3). The study was started in October 2013, and the data were locked on September 29, 2017.

Neoadjuvant clinical trials in adults with newly diagnosed high-grade glioma: A systematic review.

Background: High-grade gliomas are devastating cancers that remain incurable with standard surgical resection and radiochemotherapy. Although beneficial against neoplasms, radiation lowers lymphocyte counts, weakens immune activation, and recruits suppressive myeloid cells impairing immune responses. Tumor environments treated with radiation experience long-term immunosuppression, reducing immunotherapy effectiveness and contributing to recurrence.

Mesenchymal stem cells - the secret agents of cancer immunotherapy: Promises, challenges, and surprising twists.

Mesenchymal stem cells (MSCs) are recognized for their immunomodulatory capabilities, tumor-homing abilities, and capacity to serve as carriers for therapeutic agents. This review delves into the role of adoptively transferred MSCs in tumor progression, their interactions with the tumor microenvironment, and their use in delivering anti-cancer drugs, oncolytic viruses, and genetic material. It also addresses the challenges and limitations associated with MSC therapy, such as variability in MSC preparations and potential tumorigenic effects emphasizing the need for advanced genetic engineering and personalized approaches to enhance therapeutic efficacy.

Pre-radiation Nivolumab plus ipilimumab in patients with newly diagnosed high-grade gliomas.

The limited success of immune checkpoint inhibitors (ICIs) in the adjuvant setting for glioblastoma highlights the need to explore administering ICIs prior to immunosuppressive radiation. To address the feasibility and safety of this approach, we conducted a phase I study in patients with newly diagnosed Grade 3 and Grade 4 gliomas. Patients received nivolumab 300 mg every 2 weeks and ipilimumab 1 mg/kg every 6 weeks until disease progression or unacceptable toxicity.

Proton pump inhibitors are detrimental to overall survival of patients with glioblastoma: Results from a nationwide real-world evidence database.

Background: Proton pump inhibitors (PPIs) are often prescribed to manage corticosteroid-induced gastrointestinal toxicity during glioblastoma (GBM) treatment, but were recently identified as strong inducers of aldehyde dehydrogenase-1A1 (ALDH1A1). ALDH1A1 is a primary metabolic enzyme impacting the outcome of chemotherapy, including temozolomide. High expression of ALDH1A1 is associated with poor prognosis in multiple cancers, suggesting PPIs may have a negative impact on survival.

Biological and prognostic relevance of epigenetic regulatory genes in high-grade gliomas.

Background: High-grade gliomas (HGGs) are the most aggressive type of gliomas and have the poorest outcomes. Chromatin remodeling (CR) genes have been implicated in multiple oncogenic pathways in numerous cancer types. In gliomagenesis, CR genes have been implicated in regulating the stemness of glioma cells, the tumor microenvironment (TME), and resistance to therapies.

A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials.

The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near real-time access. The platform generates data in a format to support clinical care, accelerate research, and promote artificial intelligence/ machine learning algorithm development, highlighted by a clinical decision support algorithm for precision oncology.

A phase 1 dose escalation of pritumumab in patients with refractory or recurrent gliomas or brain metastases.

Background: This phase 1 (NCT04396717) open-label, multicenter study, evaluated Pritumumab, a IgG1 monoclonal antibody, in patients with gliomas and brain metastases. The primary objective was to evaluate the safety and/or tolerability and to identify a recommended phase 2 dose (RP2D) of Pritumumab.

Methods: Adult patients with recurrent gliomas or brain metastases were enrolled in the dose cohort that was open at the time of their consent.

piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs-piwi-RNAs (piRNAs)-and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers.

Segmentation of pre- and posttreatment diffuse glioma tissue subregions including resection cavities.

Background: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities.

Efficient use of binned data for imputing univariate time series data.

Time series data are recorded in various sectors, resulting in a large amount of data. However, the continuity of these data is often interrupted, resulting in periods of missing data. Several algorithms are used to impute the missing data, and the performance of these methods is widely varied.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC.

Mesenchymal Stromal Cell Implants for Chronic Motor Deficits After Traumatic Brain Injury: Post Hoc Analysis of a Randomized Trial.

Background And Objectives: Traumatic brain injury (TBI) is frequently characterized by chronic motor deficits. Therefore, this clinical trial assessed whether intracranial implantation of allogeneic modified mesenchymal stromal (SB623) cells can improve chronic motor deficits after TBI.

Methods: Post hoc analysis of the double-blind, randomized, prospective, surgical sham-controlled, phase 2, STEMTRA clinical trial (June 2016 and March 2019) with 48 weeks of follow-up was conducted.

Structure and Stability of Charge-Coupled Lanthanide-Substituted Ca(PO)F as a Potential Fluoride Bearing Nuclear Waste Form.

The structure and stability of charge-coupled lanthanide-substituted Ca(PO)F as a potential fluoride-bearing nuclear waste form for the back-end fuel cycle of Gen-IV molten salt reactor have been studied in detail. Here, calcium fluorapatite (CaFAp) as a model structure was taken for incorporation of trivalent lanthanides (Lns, La-Lu except Pm) in a charge-coupled fashion, i.e.

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings.

A real-world observation of patients with glioblastoma treated with a personalized peptide vaccine.

Current treatment outcome of patients with glioblastoma (GBM) remains poor. Following standard therapy, recurrence is universal with limited survival. Tumors from 173 GBM patients are analysed for somatic mutations to generate a personalized peptide vaccine targeting tumor-specific neoantigens.

Neoadjuvant combination treatment with checkpoint inhibitors, chemotherapy, and BRAF/MEK inhibitors for BRAF glioblastoma results in sustained response: A case report.

Radiation's confounding and adverse effects on tumor microenvironment and normal brain could potentially be delayed by upfront combination treatment. We present a patient with newly diagnosed -mutant, PD-L1-positive glioblastoma treated with off-label RAF/MEK inhibitors encorafenib/binimetinib after progressing on postoperative immune checkpoint blockade and temozolomide (no radiation administered: NCT03425292). Complete response occurred 6 months after adding encorafenib/binimetinib, and clinical benefit was sustained for over 20 months.

3D Brain Vascular Niche Model Captures Invasive Behavior and Gene Signatures of Glioblastoma.

Glioblastoma (GBM) is a lethal brain cancer with no effective treatment; understanding how GBM cells respond to tumor microenvironment remains challenging as conventional cell cultures lack proper cytoarchitecture while animal models present complexity all at once. Developing a culture system to bridge the gap is thus crucial. Here, we employed a multicellular approach using human glia and vascular cells to optimize a 3-dimensional (3D) brain vascular niche model that enabled not only long-term culture of patient derived GBM cells but also recapitulation of key features of GBM heterogeneity, in particular invasion behavior and vascular association.

Drug delivery in leptomeningeal disease: Navigating barriers and beyond.

Leptomeningeal disease (LMD) refers to the infiltration of cancer cells into the leptomeningeal compartment. Leptomeninges are the two membranous layers, called the arachnoid membrane and pia mater. The diffuse nature of LMD poses a challenge to its effective diagnosis and successful management.

A Microfluidic, Multi-Antibody Cell Capture Method to Evaluate Tumor Cells in Cerebrospinal Fluid in Patients With Suspected Leptomeningeal Metastases.

Context.—: Leptomeningeal disease (LMD) is a clinical sequela of central nervous system metastasis involving the cerebrospinal fluid (CSF), often seen in late-stage solid tumors. It has a grave prognosis without urgent treatment.

Colorectal Cancer Detection via Metabolites and Machine Learning.

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body.

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Background: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC.