Estimating a 3D Human Skeleton from a Single RGB Image by Fusing Predicted Depths from Multiple Virtual Viewpoints.

In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints' relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application.

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier towards improving care. Multi-parametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing reproducibility and representativeness of findings, while lessening data fragmentation towards harmonization.

Risk stratification of potential drug interactions involving common over-the-counter medications and herbal supplements by a large language model.

Background: As polypharmacy, the use of over-the-counter (OTC) drugs, and herbal supplements becomes increasingly prevalent, the potential for adverse drug-drug interactions (DDIs) poses significant challenges to patient safety and health care outcomes.

Objective: This study evaluates the capacity of Generative Pre-trained Transformer (GPT) models to accurately assess DDIs involving prescription drugs (Rx) with OTC medications and herbal supplements.

Methods: Leveraging a popular subscription-based tool (Lexicomp), we compared the risk ratings assigned by these models to 43 Rx-OTC and 30 Rx-herbal supplement pairs.

Resolving Atomic-Scale Structure and Chemical Coordination in High-Entropy Alloy Electrocatalysts for Structure-Function Relationship Elucidation.

The recent breakthrough in confining five or more atomic species in nanocatalysts, referred to as high-entropy alloy nanocatalysts (HEAs), has revealed the possibilities of multielemental interactions that can surpass the limitations of binary and ternary electrocatalysts. The wide range of potential surface configurations in HEAs, however, presents a significant challenge in resolving active structural motifs, preventing the establishment of structure-function relationships for rational catalyst design and optimization. We present a methodology for creating sub-5 nm HEAs using an aqueous-based peptide-directed route.

Defect-Promoted Ni-Based Layer Double Hydroxides with Enhanced Deprotonation Capability for Efficient Biomass Electrooxidation.

Ni-based hydroxides are promising electrocatalysts for biomass oxidation reactions, supplanting the oxygen evolution reaction (OER) due to lower overpotentials while producing value-added chemicals. The identification and subsequent engineering of their catalytically active sites are essential to facilitate these anodic reactions. Herein, the proportional relationship between catalysts' deprotonation propensity and Faradic efficiency of 5-hydroxymethylfurfural (5-HMF)-to-2,5 furandicarboxylic acid (FDCA, FE ) is revealed by thorough density functional theory (DFT) simulations and atomic-scale characterizations, including in situ synchrotron diffraction and spectroscopy methods.

Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study.

Background: Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated.

Objective: This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes.

Optimizing Arsenic Therapy by Selectively Targeting Leukemia Cells.

Arsenic, in the simple form of arsenic trioxide, is currently marketed for the treatment of acute promyelocytic leukemia. Due to the multifaceted mechanisms of action of arsenic, it has also shown promise in other types of leukemias but is hindered by its toxic effects toward normal cells. This research has aimed to determine whether tumor-homing peptide complexes of arsenic can be designed and developed to strategically target specific cancers.

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera.

This paper presents an RGB-NIR (Near Infrared) dual-modality technique to analyze the remote photoplethysmogram (rPPG) signal and hence estimate the heart rate (in beats per minute), from a facial image sequence. Our main innovative contribution is the introduction of several denoising techniques such as Modified Amplitude Selective Filtering (MASF), Wavelet Decomposition (WD), and Robust Principal Component Analysis (RPCA), which take advantage of RGB and NIR band characteristics to uncover the rPPG signals effectively through this Independent Component Analysis (ICA)-based algorithm. Two datasets, of which one is the public PURE dataset and the other is the CCUHR dataset built with a popular Intel RealSense D435 RGB-D camera, are adopted in our experiments.

Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 in a Breast Imaging Pilot.

Objective: Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, San Francisco, California) capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain.

Solvent control of water O-H bonds for highly reversible zinc ion batteries.

Aqueous Zn-ion batteries have attracted increasing research interest; however, the development of these batteries has been hindered by several challenges, including dendrite growth, Zn corrosion, cathode material degradation, limited temperature adaptability and electrochemical stability window, which are associated with water activity and the solvation structure of electrolytes. Here we report that water activity is suppressed by increasing the electron density of the water protons through interactions with highly polar dimethylacetamide and trimethyl phosphate molecules. Meanwhile, the Zn corrosion in the hybrid electrolyte is mitigated, and the electrochemical stability window and the operating temperature of the electrolyte are extended.

Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow.

Importance: Large language model (LLM) artificial intelligence (AI) chatbots direct the power of large training datasets towards successive, related tasks, as opposed to single-ask tasks, for which AI already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as virtual physicians, has not yet been evaluated.

Objective: To evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes.

Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making.

Background: ChatGPT, a popular new large language model (LLM) built by OpenAI, has shown impressive performance in a number of specialized applications. Despite the rising popularity and performance of AI, studies evaluating the use of LLMs for clinical decision support are lacking.

Purpose: To evaluate ChatGPT's capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain.

Hematopoietic cell transplantation donor-derived memory-like NK cells functionally persist after transfer into patients with leukemia.

Natural killer (NK) cells are innate lymphoid cells that eliminate cancer cells, produce cytokines, and are being investigated as a nascent cellular immunotherapy. Impaired NK cell function, expansion, and persistence remain key challenges for optimal clinical translation. One promising strategy to overcome these challenges is cytokine-induced memory-like (ML) differentiation, whereby NK cells acquire enhanced antitumor function after stimulation with interleukin-12 (IL-12), IL-15, and IL-18.

Diagnosing intake and rationalizing toxicities associated with 5F-MDMB-PINACA and 4F-MDMB-BINACA abuse.

5F-MDMB-PINACA and 4F-MDMB-BINACA are synthetic cannabinoids (SCs) that elicit cannabinoid psychoactive effects. Defining pharmacokinetic-pharmacodynamic (PK-PD) relationships governing SCs and their metabolites are paramount to investigating their in vivo toxicological outcomes. However, the disposition kinetics and cannabinoid receptor (CB) activities of the primary metabolites of SCs are largely unknown.

Multi-Focus Image Fusion and Depth Map Estimation Based on Iterative Region Splitting Techniques.

In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g.

Quantitative Multiplex Immunoassay for Profiling Bone Turnover Biomarkers in Human Bone Tissue Culture Supernatants.

Bone is a metabolically dynamic tissue that is continuously built up and broken down through anabolic and catabolic processes regulated by a variety of systemic and local signaling molecules. Here, we describe quantitative multiplex immunoassay analysis of supernatants collected from cultured human bone tissue fragments to profile local factors associated with the bone turnover process.