ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset.

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization.

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process.

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available.

Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.

Objectives: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision.

Discovery of orally bioavailable phenyltetrazolium derivatives for the acute treatment and the secondary prevention of ischemic stroke.

The potential for secondary stroke prevention, which can significantly reduce the risk of recurrent strokes by almost 90%, underscores its critical importance. N-butylphthalide (NBP) has emerged as a promising treatment for acute cerebral ischemia, yet its efficacy for secondary stroke prevention is hindered by inadequate pharmacokinetic properties. This study, driven by a comprehensive structural analysis, the iterative process of structure optimization culminated in the identification of compound B4, which demonstrated exceptional neuroprotective efficacy and remarkable oral exposure and oral bioavailability.

The Combination of Gefitinib and Acetaminophen Exacerbates Hepatotoxicity via ROS-Mediated Apoptosis.

Gefitinib is the well-tolerated first-line treatment of non-small cell lung cancer. As it need for analgesics during oncology treatment, particularly in the context ofthe coronavirus disease, where patients are more susceptible to contract high fever and sore throat. This has increased the likelihood of taking both gefitinib and antipyretic analgesic acetaminophen (APAP).

Multisensory Extended Reality Applications Offer Benefits for Volumetric Biomedical Image Analysis in Research and Medicine.

3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis, commonly used visualization methods lag far behind. Recent research used extended reality (XR) for perceiving 3D images with visual depth perception and touch but used restrictive haptic devices.

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic target for leukemia treatment. In this study, we introduced a sophisticated virtual screening strategy combined with biological evaluations to discover potent LCK inhibitors.

Type B Aortic Dissection CTA Collection with True and False Lumen Expert Annotations for the Development of AI-based Algorithms.

Aortic dissections (ADs) are serious conditions of the main artery of the human body, where a tear in the inner layer of the aortic wall leads to the formation of a new blood flow channel, named false lumen. ADs affecting the aorta distally to the left subclavian artery are classified as a Stanford type B aortic dissection (type B AD). This is linked to substantial morbidity and mortality, however, the course of the disease for the individual case is often unpredictable.

Evolution of publicly available large language models for complex decision-making in breast cancer care.

Purpose: This study investigated the concordance of five different publicly available Large Language Models (LLM) with the recommendations of a multidisciplinary tumor board regarding treatment recommendations for complex breast cancer patient profiles.

Methods: Five LLM, including three versions of ChatGPT (version 4 and 3.5, with data access until September 3021 and January 2022), Llama2, and Bard were prompted to produce treatment recommendations for 20 complex breast cancer patient profiles.

Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm.

Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used.

The inhibitory effect of adenosine on tumor adaptive immunity and intervention strategies.

Adenosine (Ado) is significantly elevated in the tumor microenvironment (TME) compared to normal tissues. It binds to adenosine receptors (AdoRs), suppressing tumor antigen presentation and immune cell activation, thereby inhibiting tumor adaptive immunity. Ado downregulates major histocompatibility complex II (MHC II) and co-stimulatory factors on dendritic cells (DCs) and macrophages, inhibiting antigen presentation.

Rutin attenuates ensartinib-induced hepatotoxicity by non-transcriptional regulation of TXNIP.

Ensartinib, an approved ALK inhibitor, is used as a first-line therapy for advanced ALK-positive non-small cell lung cancer in China. However, the hepatotoxicity of ensartinib seriously limits its clinical application and the regulatory mechanism is still elusive. Here, through transcriptome analysis we found that transcriptional activation of TXNIP was the main cause of ensartinib-induced liver dysfunction.

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

Background And Objective: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm.

Single-Center Experience of Patients with Plasma Cell Leukemia in the Era of New Therapeutics.

Introduction: Plasma cell leukemia (PCL) can occur de novo as primary PCL (pPCL), or in patients with prior diagnosis of multiple myeloma (MM) as secondary PCL (sPCL). In 2021, the diagnostic criteria have been revised, establishing a new cut-off of ≥5% plasma cells in the peripheral blood. Lacking specific clinical trials, PCL is treated similarly to MM; however, outcome for patients with PCL remains poor.

Function of NEK2 in clear cell renal cell carcinoma and its effect on the tumor microenvironment.

Background: Previous studies have revealed the critical functions of NEK2 in controlling the cell cycle which is linked to poor prognosis in multiple tumor types, but less research has been devoted to clear cell renal cell carcinoma (ccRCC).

Methods: We downloaded clinical data from the gene expression omnibus (GEO) and TCGA databases together with transcriptional and mutational datasets. Strongly coexpressed genes with NEK2 were extracted from TCGA-KIRC cohort, and were submitted to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analyses.

Dabrafenib alleviates hepatotoxicity caused by lenvatinib via inhibiting the death receptor signaling pathway.

Lenvatinib is a multi-target inhibitor that exerts anti-tumor effects by inhibiting angiogenesis and is now commonly used as a first-line treatment for hepatocellular carcinoma. However, with the widespread use of lenvatinib, the problem of serious and fatal hepatotoxicity has become increasingly prominent. Currently, the mechanism behind this toxicity is not yet understood, and as a result, there is a lack of safe and effective intervention strategies with minimal side effects.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death.

SAROS: A dataset for whole-body region and organ segmentation in CT imaging.

The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso).

Design, synthesis, and antitumor activity evaluation of potent fourth-generation EGFR inhibitors for treatment of Osimertinib resistant non-small cell lung cancer (NSCLC).

Epidermal growth factor receptor (EGFR) is one of the most studied drug targets for treating non-small-cell lung cancer (NSCLC). However, there are no approved inhibitors for the C797S resistance mutation caused by the third-generation EGFR inhibitor (Osimertinib). Therefore, the development of fourth-generation EGFR inhibitors is urgent.

Neoadjuvant nivolumab with or without relatlimab in resectable non-small-cell lung cancer: a randomized phase 2 trial.

Antibodies targeting the immune checkpoint molecules PD-1, PD-L1 and CTLA-4, administered alone or in combination with chemotherapy, are the standard of care in most patients with metastatic non-small-cell lung cancers. When given before curative surgery, tumor responses and improved event-free survival are achieved. New antibody combinations may be more efficacious and tolerable.