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AbstractObjectivesTo assess the discrepancies between real-world data and the Global Burden of Disease (GBD) 2021 estimates for six neglected tropical diseases in China. Additionally, to evaluate the applicability of the GBD model within the Chinese context and to assess the effectiveness of China's historical prevention and control policies for neglected tropical diseases.DesignComparative study of real-world data and GBD 2021 (2004-2020).Main outcome measuresDisability adjusted life years (DALYs).MethodsDALYs based on reported data for leprosy, echinococcosis, schistosomiasis, visceral leishmaniasis, dengue, and rabies from 2004 to 2020 were compared with the estimated DALYs from the GBD 2021 database. Additionally, we combined and analysed China's historical policies on prevention and control of neglected tropical diseases with real-world DALYs.Data sourcesReported data were sourced from the Chinese Center for Disease Control and Prevention’s China Public Health Science data centre and related reports. Data for GBD 2021 and GBD 2019 were obtained from GBD databases. These data included all of China’s 31 provinces (including autonomous regions and municipalities) and the Xinjiang Production and Construction Corps.ResultsThe total real-world DALYs based on reported data of six neglected tropical diseases decreased from 260 000 person years in 2004 to 19 000 person years in 2020, with a 93% (241 000/260 000 person years) reduction. The 17 year average real-world DALYs from 2004 to 2020 versus the GBD 2021 estimates for the same period were 42 v 500 for leprosy, 960 v 11 000 for echinococcosis, 64 000 v 98 000 for schistosomiasis, 56 v 16 000 for visceral leishmaniasis, 190 v 780 for dengue, and 47 000 v 67 000 for rabies. The ratios of the GBD estimates to the real-world DALYs for the six neglected tropical diseases were 17 for leprosy, 11 for echinococcosis, 1.5 for schistosomiasis, 280 for visceral leishmaniasis, 4.2 for dengue, and 1.4 for rabies.ConclusionsThe findings indicate that reliance solely on global estimates, such as those of the GBD, may not sufficiently capture the dynamics of neglected tropical diseases in China. Integrating local epidemiological data into global health assessments is crucial to develop accurate and effective public health policies. This study highlights the importance of continuously updating and improving data collection and surveillance methods to adapt public health strategies to evolving disease patterns.

Background The Global Burden of Disease (GBD) study offers influential Disability-Adjusted Life Years (DALYs) estimates for various diseases. However, discrepancies with national surveillance data raise concerns about accuracy. This study aims to promote the deep integration of the GBD model with localized data and facilitate the development of region-specific models. Methods Data for 14 notifiable infectious diseases (NIDs), grouped into intestinal infectious diseases, respiratory infectious diseases, and sexually transmitted and blood-borne infections, were obtained from the Data-center of China Public Health Science. DALYs based on national surveillance data (2010–2020) were calculated using DALY formulas, and discrepancies with GBD estimates were quantified through ratio comparisons. A historical timeline map highlighted key infectious disease control policies and certified disease elimination events in China. Results National surveillance data show a decrease in DALYs for 14 NIDs in China, from 6,529,124.62 person-years in 2010 to 6,326,497.18 person-years in 2020. Among them, sexually transmitted and blood-borne infections have the highest burden, with 78% of DALYs attributed to hepatitis B (4,864,028.29 person-years). Respiratory infectious diseases follow, with 99% of DALYs from TB (394,927.70 person-years). Intestinal infectious diseases have the relative lightest burden, with 45% of DALYs from hepatitis E (496.49 person-years). Over 11 years, 9 of the 14 NIDs showed a downward trend. Comparisons reveal that DALYs based on national surveillance data are lower than GBD 2021 estimates. Conclusions Considerable differences exist between the GBD estimates and national surveillance data regarding the burden of 14 NIDs in China. Therefore, strengthening national reporting systems and integrating localized data with the GBD model is essential for more accurate disease burden assessments and effective response strategies. Despite significant progress in infectious disease control, China still faces substantial challenges in domestic disease elimination. Graphical Abstract

Background Dengue is a major global health threat with varied clinical manifestations across age groups, countries, and regions. This study aims to estimate global dengue disability weights (DWs) based on clinical manifestations data and examine variations across different demographics and geographical areas. These findings will inform public health strategies and interventions to reduce the global burden of dengue. Methods We conducted a systematic search across six databases (Scopus, Web of Science, PubMed, China National Knowledge Infrastructure, Wanfang Data, and Database of Chinese sci-tech periodicals) for studies on human dengue clinical manifestations or infection from the establishment of each database through December 31, 2023. DWs were estimated by combining clinical manifestations frequencies with corresponding DW values derived from the Global Burden of Disease (GBD) study, using Monte Carlo simulations to generate uncertainty intervals. Odds ratios ( OR s) with 95% confidence intervals ( CI ) and Chi-square tests were performed to compare clinical manifestations between adults and children. Results A total of 35 adult studies (7109 cases) and 17 pediatric studies (2996 cases) were analysed. Adults had higher rates of muscle pain ( OR  = 9.18; 95% CI: 8.17–10.33) and weak ( OR  = 4.95; 95% CI 4.12–5.98). Children showed higher frequencies of decreased appetite ( OR  = 0.12; 95% CI: 0.11–0.14) and lymphadenectasis ( OR  = 0.04; 95% CI: 0.03–0.06). Severe dengue was more prevalent in children (8.2%) than adults (4.6%). The global DW for universal dengue was 0.3258 in adults and 0.4022 in children, with Indian children showing the highest DW for severe dengue (0.6991) and Chinese adult showing the highest DW for severe dengue (0.7214). Regionally, most studies were from South and Southeast Asia, with India contributing the largest number of publications (80 articles). Additionally, India had the highest dengue disease burden in 2021 (352,468.54 person-years). Conclusions These findings reveal important age and regional differences in dengue disease burden. There is a relative lack of research on dengue clinical manifestations in several high-burden countries in the Americas, and these gaps may affect the comprehensiveness and accuracy of global dengue disability weight estimates. These highlight the urgent need for targeted interventions and optimized resource allocation to mitigate its global impact. Graphical Abstract

Embryonic stem （ES） cells are powerful tools for generating genetically modified animals that can assist in advancing our knowledge of mammalian physiology and disease. Pigs provide outstanding models of human genetic diseases due to the striking similarities to human anatomy, physiology and genetics, but progress with porcine genetic engineering has been hampered by the lack of germline-competent pig ES ceils.

The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X\\(_2\\)YH\\(_6\\) system, perovskite MXH\\(_3\\) system, M\\(_3\\)XH\\(_8\\) system, cage-like BCN-doped metal atomic systems derived from LaH\\(_10\\) structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB\\(_2\\). The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.

The discovery of new superconducting materials, particularly those exhibiting high critical temperature (\\(T_c\\)), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop InvDesFlow, an AI search engine that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for the discovery of high-\\(T_c\\) superconductors. Utilizing InvDesFlow, we have obtained 74 dynamically stable materials with critical temperatures predicted by the AI model to be \\(T_c \\) 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B\\(_4\\)CN\\(_3\\) (at 5 GPa) and B\\(_5\\)CN\\(_2\\) (at ambient pressure) whose \\(T_c\\)s are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-\\(T_c\\) superconductors, outline its potential for accelerating discovery of the materials with targeted properties.

The discovery of new superconducting materials, particularly those exhibiting high critical temperature (\\(T_c\\)), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop InvDesFlow, an AI search engine that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for the discovery of high-\\(T_c\\) superconductors. Utilizing InvDesFlow, we have obtained 74 dynamically stable materials with critical temperatures predicted by the AI model to be \\(T_c \\) 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B\\(_4\\)CN\\(_3\\) (at 5 GPa) and B\\(_5\\)CN\\(_2\\) (at ambient pressure) whose \\(T_c\\)s are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-\\(T_c\\) superconductors, outline its potential for accelerating discovery of the materials with targeted properties.

Hydrogen-rich materials are the most promising candidates for high-temperature conventional superconductors under ambient pressure. Multinary hydrides have abundant structural configurations and are more promising to find high-temperature superconductors at ambient pressure, but searching for multinary materials in complex phase space is a great challenge. In this work, we used our developed AI search engine (InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. Several quaternary hydrides with high superconducting temperature~(\\(T_c\\)) are predicted. In particular, the superconducting \\(T_c\\) of K\\(_2\\)GaCuH\\(_6\\) and K\\(_2\\)LiCuH\\(_6\\) are calculated to be 68~K and 53~K under ambient pressure, respectively, which shows a significant enhancement in comparison with that of K\\(_2\\)CuH\\(_6\\)~(\\(T_c\\) \\(\\) 16~K). We also find that intercalating atoms could cause phonon softening and induce more phonon modes with strong electron-phonon coupling. Hence, we propose that intercalating atoms is a feasible approach in searching for superconducting quaternary hydrides.

The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X\\(_2\\)YH\\(_6\\) system, perovskite MXH\\(_3\\) system, M\\(_3\\)XH\\(_8\\) system, cage-like BCN-doped metal atomic systems derived from LaH\\(_10\\) structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB\\(_2\\). The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.

We used our developed AI search engine~(InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. A cubic structure Li\\(_2\\)AuH\\(_6\\) with Au-H octahedral motifs is identified to be a candidate. After performing thermodynamical analysis, we provide a feasible route to experimentally synthesize this material via the known LiAu and LiH compounds under ambient pressure. The further first-principles calculations suggest that Li\\(_2\\)AuH\\(_6\\) shows a high superconducting transition temperature (\\(T_c\\)) \\(\\) 140 K under ambient pressure. The H-1\\(s\\) electrons strongly couple with phonon modes of vibrations of Au-H octahedrons as well as vibrations of Li atoms, where the latter is not taken seriously in other previously similar cases. Hence, different from previous claims of searching metallic covalent bonds to find high-\\(T_c\\) superconductors, we emphasize here the importance of those phonon modes with strong electron-phonon coupling (EPC). And we suggest that one can intercalate atoms into binary or ternary hydrides to introduce more potential phonon modes with strong EPC, which is an effective approach to find high-\\(T_c\\) superconductors within multicomponent compounds.