Identification of key bioactive anti-migraine constituents of Asari radix et rhizoma using network pharmacology and nitroglycerin-induced migraine rat model

Purpose: To elucidate the bioactive constituents of Asari radix et rhizoma (ARR) in treating migraine based on network pharmacology and nitroglycerin-induced migraine rat model. Methods: The potential bioactive constituents of ARR were identified with the aid of literature retrieval and virtual screening, and the migraine-related hub genes were identified using protein-protein interaction and topology analyses. Then, the interaction between the potential bioactive constituents and hub genes was determined with molecular docking and topology, leading to the prediction of the anti-migraine constituents of ARR. Moreover, a rat model of nitroglycerin-induced migraine was used to confirm the prediction by measuring the frequency of head-scratching and head-shaking behavior (FHHB) in the rats. In addition, levels of nitric oxide (NO) and calcitonin gene-related peptide (CGRP) in blood, norepinephrine (NE) and 5-hydroxytryptamine (5-HT) in brain were measured using appropriate commercial kits. Results: Network pharmacology revealed that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key anti-migraine bioactive constituents of ARR. On addition of naringenin-7-O-β-D- glucopyranoside or higenamine to ARR, there was marked enhancement of the mitigating effect of ARR on nitroglycerin-induced abnormalities in levels of NO, CGRP, 5-HT and NE, as well as FHHB in rats (p < 0.05 or 0.01). Conclusion: These findings indicate that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key bioactive and anti-migraine constituents of ARR. However, in addition to naringenin-7-O-β-D- glucopyranoside and higenamine, there were many other anti-migraine constituents in ARR. Therefore, there is need for further investigations on the actual contributions of these two constituents of ARR in treating migraine

Prioritizing disease candidate genes by a gene interconnectedness-based approach

<p>Abstract</p> <p>Background</p> <p>Genome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried.</p> <p>Results</p> <p>We developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone.</p> <p>Conclusions</p> <p>ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.</p

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts. However, this progress poses security and privacy concerns, necessitating effective solutions for distinguishing deepfake texts from human-written ones. Although prior works studied humans' ability to detect deepfake texts, none has examined whether "collaboration" among humans improves the detection of deepfake texts. In this study, to address this gap of understanding on deepfake texts, we conducted experiments with two groups: (1) nonexpert individuals from the AMT platform and (2) writing experts from the Upwork platform. The results demonstrate that collaboration among humans can potentially improve the detection of deepfake texts for both groups, increasing detection accuracies by 6.36% for non-experts and 12.76% for experts, respectively, compared to individuals' detection accuracies. We further analyze the explanations that humans used for detecting a piece of text as deepfake text, and find that the strongest indicator of deepfake texts is their lack of coherence and consistency. Our study provides useful insights for future tools and framework designs to facilitate the collaborative human detection of deepfake texts. The experiment datasets and AMT implementations are available at: https://github.com/huashen218/llm-deepfake-human-study.gitComment: Accepted at The 11th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2023