Browse Articles

N-terminal fragment shedding contributes to signaling of the full-length adhesion receptor ADGRL3

Human brain organoids identify glioma inhibitors

EXO1 is a key gene for lung-resident memory T cells and has diagnostic and predictive values for lung adenocarcinoma

Identification of ABHD6 as a lysophosphatidylserine lipase in the mammalian liver and kidneys

DNA-PKcs inhibitor causes genomic alterations

Spatiotemporal simulation of blue-green space pattern evolution and carbon storage under different SSP-RCP scenarios in Wuhan

Betagenin ameliorates diabetes by inducing insulin secretion and β-cell proliferation

Engineered T cells traverse new terrain

Study on the effects of fissure geometric characteristics on the mechanical behavior and failure mechanism of granite under uniaxial compression test

Receptor-independent regulation of Gα13 by alpha-1-antitrypsin C-terminal peptides

Lipid nanoparticle ferries therapeutic mRNA to the placenta

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

Deafness-associated mitochondrial 12S rRNA mutation reshapes mitochondrial and cellular homeostasis

Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection

Porphyromonas gingivalis gingipain potentially activates influenza A virus infectivity through proteolytic cleavage of viral hemagglutinin

2024 FDA approvals

Synergistic effect of defocus incorporated multiple segment glasses and repeated low level red light therapy against myopia progression

Abstract Defocus incorporated multiple segment (DIMS) lenses and repeated low-level red-light (RLRL) are used to retard myopia progression. However, it is currently unknown if there is a synergistic effect of the two interventions. In the current study, 190 school-aged children with myopia (380 eyes) were studied for the change in axial length (AL) over nearly one year of follow-up. Of 380 eyes, 170 eyes wore DIMS lenses, 80 eyes had RLRL therapy, and 130 eyes had both interventions (DIMS_RLRL) for myopia control. AL changes were calculated at each follow-up visit by subtracting the baseline measurements and normalized to yearly changes in mm. AL changes as a primary outcome were analyzed in a generalized linear mixed model to compare effect sizes of myopia control among three interventions while adjusting for age, sex, baseline axial length, and follow-up length. Participants had a mean age of 9.84 ± 2.63 years old, mean AL of 24.49 ± 1.20 mm, mean SER of -2.90 ± 2.08 diopters, and mean follow-up time of 301 ± 91 days. By the end of the study, the adjusted mean yearly axial change with combination therapy was − 0.13 mm, -0.04 mm for the eyes with RLRL alone, and 0.16 mm for the eyes with DIMS lenses alone (p < 0.0001). Combination therapy of DIMS and RLRL has significantly greater effect size in controlling myopia progression than either RLRL alone (p = 0.0009) or DIMS alone (p < 0.0001).

Toxin protein LukS-PV targeting complement receptor C5aR1 inhibits cell proliferation in hepatocellular carcinoma via the HDAC7–Wnt/β-catenin axis

Pairing up with GLP-1 to combat obesity

Me vs. the machine? Subjective evaluations of human- and AI-generated advice

Abstract Artificial intelligence (“AI”) has the potential to vastly improve human decision-making. In line with this, researchers have increasingly sought to understand how people view AI, often documenting skepticism and even outright aversion to these tools. In the present research, we complement these findings by documenting the performance of LLMs in the personal advice domain. In addition, we shift the focus in a new direction—exploring how interacting with AI tools, specifically large language models, impacts the user’s view of themselves. In five preregistered experiments (N = 1,722), we explore evaluations of human- and ChatGPT-generated advice along three dimensions: quality, effectiveness, and authenticity. We find that ChatGPT produces superior advice relative to the average online participant even in a domain in which people strongly prefer human-generated advice (dating and relationships). We also document a bias against ChatGPT-generated advice which is present only when participants are aware the advice was generated by ChatGPT. Novel to the present investigation, we then explore how interacting with these tools impacts self-evaluations. We manipulate the order in which people interact with these tools relative to self-generation and find that generating advice before interacting with ChatGPT advice boosts the quality ratings of the ChatGPT advice. At the same time, interacting with ChatGPT-generated advice before self-generating advice decreases self-ratings of authenticity. Taken together, we document a bias towards AI in the context of personal advice. Further, we identify an important externality in the use of these tools—they can invoke social comparisons of me vs. the machine.