UNASSIGNED: Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response.
UNASSIGNED: To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches.
UNASSIGNED: Cross-sectional study.
UNASSIGNED: The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed.
UNASSIGNED: Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT\'s descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT\'s explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval.
UNASSIGNED: This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content\'s depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

We analyze whether and how internet searching impacts stock price informativeness. Using the 2010 Google withdrawal in China as a quasi-natural experiment, we establish a causal effect between internet searching and stock price informativeness using a difference-in-difference framework. We find that firms with higher Google search volume experience a 10% decrease in stock price informativeness after the Google withdrawal. The negative effect of the Google withdrawal on stock price informativeness is pronounced in firms with more retail investors, larger state-ownership, and poor analysts\' earnings forecasts. Our results suggest that retail investors can benefit from internet searching to collect and process firm-specific information more efficiently.

Ecosystem Services Value (ESV) are the various beneficial functions and products that natural ecosystems provide to humans, and are important indicators for evaluating ecosystem conditions and human well-being. Opencast mining is one of the human activities that severely damage the surface environment, but its long-term impact on ecosystem services lacks systematic assessment. This study takes the Ordos opencast mining area as an example, and calculates the value of ESV from 1990 to 2020 based on the Google Earth Engine platform. Mann-Kendall Tau-b with Sen\'s Method (Sen + mk test) and Joinpoint regression model were used to analyzes its spatiotemporal variation characteristics. Further revealed the impacts of opencast mining on ESV as well as the trend of ESV changes. The results show that: (1) The dynamic ESV levels in the study area fluctuated considerably from 1990 to 2020 with an overall decreasing trend of 89.45%. (2) Among nine types ecosystem services, most of them were significantly different (p < 0.001) between mining areas and control areas, with biodiversity protection (BP), climate regulation (CR), gas regulation (GR), soil formation and retention (SFR), water supply (WS) and waste treatment (WT) showed a significant decrease between 1990 and 2020. (3) In the past 30 years, the ESV of the study area showed an overall improvement trend, where the improved area accounted for 48.45% of the total area of the study area. However, the degraded area also accounted for 21.28, and 17.19% of the area belonged to severe degradation. With 67% of the significantly degraded areas distributed within mining concessions. (4) The trend of ESV changes in the mining impact areas and the control area showed significant differences. The ESV of the control area increased continuously, with an average annual percentage change (AAPC) of 0.7(95%CI:0.50 ~ 0.9, P < 0.001) from 1990 to 2020; while the ESV of the mining impact areas first stabilized and then decreased significantly, with an AAPC of - 0.2(95%CI:- 0.3 ~ - 0.1,P < 0.001) from 1990 to 2020. This study provides scientific support for formulating ecosystem management, restoration plans, and payment for ecosystem service policies, which is conducive to achieving regional sustainable development and improving human well-being.

Traditional assessments of anxiety and depression face challenges and difficulties when it comes to understanding trends in-group psychological characteristics. As people become more accustomed to expressing their opinions online, location-based online media and cutting-edge algorithms offer new opportunities to identify associations between group sentiment and economic- or healthcare-related variables. Our research provides a novel approach to analyzing emotional well-being trends in a population by focusing on retrieving online information. We used emotionally enriched texts on social media to build the Public Opinion Dictionary (POD). Then, combining POD with the word vector model and search trend, we developed the Composite Anxiety and Depression Index (CADI), which can reflect the mental health level of a region during a specific time period. We utilized the representative external data by CHARLS to validate the effectiveness of CADI, indicating that CADI can serve as a representative indicator of the prevalence of mental disorders. Regression and subgroup analysis are employed to further elucidate the association between public mental health (measured by CADI) with economic development and medical burden. The results of comprehensive regression analysis show that the Import-Export index (-16.272, p < 0.001) and average cost of patients (4.412, p < 0.001) were significantly negatively associated with the CADI, and the sub-models stratificated by GDP showed the same situation. Disposable income (-28.389, p < 0.001) became significant in the subgroup with lower GDP, while the rate of unemployment (2.399, p < 0.001) became significant in the higher subgroup. Our findings suggest that an unfavorable economic development or unbearable medical burden will increase the negative mental health of the public, which was consistent across both the full and subgroup models.

People are increasingly using the web for fact-checking and other forms of information seeking. The \"Google effects\" refers to the idea that individuals rely on the Internet as a source of knowledge rather than remembering it for themselves. However, few literature review have yet comprehensively examined the media effects of this intensive Internet search behavior. In this study, by carrying out meta-analysis, we found that google effects is closely associated with cognitive load, behavioral phenotype and cognitive self-esteem. And this phenomenon is also more likely to happen while using a mobile phone to browse the Internet rather than a computer. People with a larger knowledge base are less susceptible to the consequences of Internet use than those with a smaller knowledge base. The media effect was stronger for persons who had used the Internet before than for those who had not. And meta-analyses show that participants in North America (parameter = -1.0365, 95%CI = [-1.8758, -0.1972], p < 0.05) are more susceptible to frequent Internet search behavior relative to other regions. Overall, google effects on memory challenges the way individuals seek and read information, and it may lead to changes in cognitive and memory mechanisms.

Health information websites can be useful for information seekers, and their design is crucial for the success of accessing the needed information. While web analytical tools (e.g. Google Analytics) used by such websites can provide descriptive measures of users, there is a disconnection between this data and the current understanding of health information-seeking behaviour. In this work, we leverage a theoretical model to interpret the Google Analytics data. Drawn on the visualisation of user behaviours based on this model, our research shows that better website design can be informed, and the evaluation of health websites can be performed on the basis of different user profiles.

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

OBJECTIVE: Since its introduction, the use of ChatGPT has increased significantly for medically related purposes. However, current research has not captured its applications in providing information on stem cell therapy. To address this gap, the present study compared the effectiveness of ChatGPT to Google in answering medical questions related to stem cell therapy.
METHODS: The search term \"stem cell therapy\" was used to perform a Google web search, and the top 20 frequently asked questions along with answers were recorded together with relevant website sources. Of these questions, the top 10 questions were separately entered into ChatGPT, and the answers and the sources were recorded. Then, the following statement was entered into ChatGPT: \"Do a Google search with the search term \'stem cell therapy\' and record 20 common questions related to the search term.\" After obtaining these questions, each question was separately entered into ChatGPT for an answer and source.
RESULTS: A majority of the top 20 questions provided by Google were related to fact, whereas a majority of the questions provided by ChatGPT were related to policy. The answer sources used by Google were mostly drawn from medical practice, while those used by ChatGPT were mostly drawn from academic information.
CONCLUSIONS: Compared to Google, ChatGPT exhibits stronger capabilities in promoting awareness of stem cell therapy. ChatGPT has the ability to eliminate misleading information by providing accurate and reliable answers. However, the responses provided by ChatGPT are still general in nature and cannot substitute academic sources for providing specialized knowledge.

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