Lesion Topography and Clinical Features Associated With Respiratory Failure in Patients With Medullary Infarction.

Background: Respirator failure (RF) is a severe malignant complication in both lateral medullary infarction (LMI) and medial medullary infarction (MMI) patients. However, the differences in clinical and radiological manifestations associated with RF between patients with LMI and MMI have not been fully elucidated.

Methods: A total of 435 consecutive patients with MI within 7 days of onset in our institute were retrospectively enrolled from January 2017 to January 2024.

The use of machine learning for the prediction of response to follow-up in spine registries.

Background: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the characteristics of patients who are most likely to drop out, and to evaluate the potential cost effectiveness of the implementation of a follow-up system based on the obtained data.

Methods: All patients recruited in the local spine surgery registry were included and demographic, peri- and postoperative data were collected.

Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods.

Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic methods.

The Emerging Role of PCSK9 in the Pathogenesis of Alzheimer's Disease: A Possible Target for the Disease Treatment.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease mainly caused by β-amyloid (Aβ) accumulation in the brain. Among the several factors that may concur to AD development, elevated cholesterol levels and brain cholesterol dyshomeostasis have been recognized to play a relevant role. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a protein primarily known to regulate plasma low-density lipoproteins (LDLs) rich in cholesterol and to be one of the main causes of familial hypercholesterolemia.

Molecular Subgroups of HRD Positive Ovarian Cancer and Their Prognostic Significance.

Homologous recombination repair deficiency (HRD) is involved in the development of high-grade serous ovarian carcinoma (HGSOC) and its elevated sensitivity to platinum-based chemotherapy. To investigate the heterogeneity of the HRD-positive HGSOC we evaluated the HRD status, including BRCA mutations, genomic scar score, and methylation status of genes in 352 HGSOC specimens. We then divided the HRD-positive cohort into three molecular subgroups, the BRCA mutation cohort (BRCA+), BRCA1 methylation cohort (Meth+), and the rest of the HRD+ cohort (HRD+BRCA-Meth-), and evaluated their first-line chemotherapy response, benefit from olaparib, and progression-free survival (PFS).

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilized to identify SNSCC-associated proteins, which could be used as biomarkers. In this study, we aimed to discover a biomarker to predict SNSCC patients using proteomic analysis integrated with machine learning models.

Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction.

: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities.

Expanding the Genotypic and Phenotypic Spectrum of -Related Conditions: Three More Cases.

Introduction: Pathogenic variants in the gene are linked to a spectrum of syndromes that exhibit partial clinical overlap. Hemizygous loss-of-function variants are considered lethal in males, while heterozygous loss-of-function variants generally result in oro-facial-digital syndrome type 1. A reported phenotype, Simpson-Golabi-Behmel syndrome type 2, was published once but remains controversial, with many specialists questioning its validity and arguing about its continued listing in the OMIM database.

Combined transrectal ultrasound and radiomics model for evaluating the therapeutic effects of neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Purpose: This study aimed to explore a combined transrectal ultrasound (TRUS) and radiomics model for predicting tumor regression grade (TRG) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC).

Methods: Among 190 patients with LARC, 53 belonged to GRG and 137 to PRG. Eight TRUS parameters were identified as statistically significant (P < 0.

Factor Retention in Exploratory Multidimensional Item Response Theory.

Multidimensional Item Response Theory (MIRT) is applied routinely in developing educational and psychological assessment tools, for instance, for exploring multidimensional structures of items using exploratory MIRT. A critical decision in exploratory MIRT analyses is the number of factors to retain. Unfortunately, the comparative properties of statistical methods and innovative Machine Learning (ML) methods for factor retention in exploratory MIRT analyses are still not clear.

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.

Belzutifan plus cabozantinib as first-line treatment for patients with advanced clear-cell renal cell carcinoma (LITESPARK-003): an open-label, single-arm, phase 2 study.

Background: Belzutifan, a first-in-class HIF-2α inhibitor, has shown antitumour activity as monotherapy and in combination with cabozantinib in patients with previously treated advanced kidney cancer. The phase 2 LITESPARK-003 study was designed to determine the antitumour activity and safety of belzutifan in combination with cabozantinib in patients with advanced clear-cell renal cell carcinoma that was previously untreated (cohort 1) or previously treated with immunotherapy (cohort 2). Here, we report results from cohort 1 of this clinical trial.

Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.

Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.

Preclinical and clinical study on type 3 metabotropic glutamate receptors in Parkinson's disease.

Metabotropic glutamate (mGlu) receptors are candidate drug targets for therapeutic intervention in Parkinson's disease (PD). Here we focused on mGlu3, a receptor subtype involved in synaptic regulation and neuroinflammation. mGlu3 mice showed an enhanced nigro-striatal damage and microglial activation in response to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP).

Noninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models.

This study aims to construct and validate noninvasive diagnosis models for evaluating significant liver fibrosis in patients with chronic hepatitis B (CHB). A cohort of 259 CHB patients were selected as research subjects. Through random grouping, 182 cases were included in the training set and 77 cases in the validation set.

Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.

Background And Objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).

Novel Orthosteric/Allosteric Ligands of Cannabinoid Receptors: An Unexpected Pharmacological Profile.

The design of dualsteric/bitopic receptor ligands as compounds capable of simultaneously interacting with both the orthosteric and an allosteric binding site has gained importance to achieve enhanced receptor specificity and minimize off-target effects. In this work, we reported the synthesis and biological evaluation of a new series of compounds, namely, the series, obtained by chemically combining the CB1R ago-positive allosteric modulators (PAM) with the cannabinoid receptors (CBRs) orthosteric agonist . Therefore, compounds were designed as dualsteric/bitopic ligands for CB1R with the aim of obtaining stronger CB1R agonists or ago-PAMs, with improved receptor subtype selectivity and reduction of central side effects.

Maximizing the clinical utility and performance of cytology samples for comprehensive genetic profiling.

Comprehensive molecular profiling by next-generation sequencing has revolutionized tumor classification and biomarker evaluation. However, routine implementation is challenged by the scant nature of diagnostic material obtained through minimally invasive procedures. Here, we describe our long-term experience in profiling cytology samples with an in-depth assessment of the performance, quality metrics, biomarker identification capabilities, and potential pitfalls.

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions.

TRACERx analysis identifies a role for FAT1 in regulating chromosomal instability and whole-genome doubling via Hippo signalling.

Chromosomal instability (CIN) is common in solid tumours and fuels evolutionary adaptation and poor prognosis by increasing intratumour heterogeneity. Systematic characterization of driver events in the TRACERx non-small-cell lung cancer (NSCLC) cohort identified that genetic alterations in six genes, including FAT1, result in homologous recombination (HR) repair deficiencies and CIN. Using orthogonal genetic and experimental approaches, we demonstrate that FAT1 alterations are positively selected before genome doubling and associated with HR deficiency.

Role of homeostatic plasticity in critical brain dynamics following focal stroke lesions.

Can focal brain lesions, such as those caused by stroke, disrupt critical brain dynamics? What biological mechanisms drive its recovery? In a recent study, we showed that focal lesions generate a sub-critical state that recovers over time in parallel with behavior (Rocha et al., Nat. Commun.

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4.

Background: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.

Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction.

Plasma phosphorylated tau and neuropsychiatric symptoms in dementia with Lewy bodies.

Introduction: Neuropsychiatric symptoms (NPSs) are common in dementia with Lewy bodies (DLB) but their neurobiological mechanisms are poorly understood.

Methods: NPSs and cognition were assessed annually in participants (DLB n = 222; Alzheimer's disease [AD] n = 125) from the European DLB (E-DLB) Consortium, and plasma phosphorylated tau-181 (p-tau181) and p-tau231 concentrations were measured at baseline.

Results: Hallucinations, delusions, and depression were more common in DLB than in AD and, in a subgroup with longitudinal follow-up, persistent hallucinations and NPSs were associated with lower p-tau181 and p-tau231 in DLB.