How far has the globe gone in achieving One Health?Current evidence and policy implications based on global One Health index

Background:In the 21st century,as globalization accelerates and global public health crises occur,the One Health approach,guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues,has been strongly advocated by the international community.An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales.Methods:Built upon extensive expert consultations and dialogues,this follow-up study enhances the 2022 global One Health index(GOHI)indicator system.The GOHI framework is enriched by covering three indices,e.g.external drivers index(EDI),intrinsic drivers index(IDI),and core drivers index(CDI).The comprehensive indicator system incorporates 13 key indicators,50 indicators,and 170 sub I-indicators,utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator.Weighted and summed,the EDI,IDI,and CDI scores contribute to the computation of the overall GOHI 2022 score.By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories,we have not only derived an overall profile of the GOHI 2022 scores,but also assessed the GOHI framework.We also compared rankings of indicators and sub Iindicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains.Results:The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61.The global average score of the GOHI 2022 is 54.82.The average score for EDI,IDI,and CDI are 46.57,58.01,and 57.25,respectively.In terms of global rankings,countries from North America,Europe and Central Asia,East Asia and Pacific present higher scores.In terms of One Health domains of CDI,the lowest scores are observed in antimicrobial resistance(median:43.09),followed by food security(median:53.78),governance(median:54.77),climate change(median:64.12)and zoonotic diseases(median:69.23).Globally,the scores of GOHI vary spatially,with the highest score in North America while lowest in sub-Saharan Africa.In addition,evidence shows associations between the socio-demographic profile of countries/territories and their GOHI performance in certain One Health scenarios.Conclusion:The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health.With advanced technology and an annually updated database,intensifying efforts to refine GOHI's data-mining methodologies become imperative.The goal is to offer profound insights into disparities and progressions in practical One Health implementation,particularly in anticipation of future pandemics.

Disruption of KDM4C-ALDH1 A3 feed-forward loop inhibits sternness,tumorigenesis and chemoresistance of gastric cancer stem cells

Dear Editor,Gastric cancer(GC)is a considerable global health burden;the median survival of advaneed GC is less than 1 year.^(1)Cancer stem cells(CSCs),a small population of cancer cells with stem cell-like properties,are the major cause of treatme nt failure,in eluding GC,2 however,the mechanisms underlying stemness maintenanee of GC stem cells(GCSCs)are still poorly understood.

Efficient Preference Clustering via Random Fourier Features

Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks.Unlike approaches using the Nystr?m method,which randomly samples the training examples,we make use of random Fourier features,whose basis functions(i.e.,cosine and sine)are sampled from a distribution independent from the training sample set,to cluster preference data which appears extensively in recommender systems.Firstly,we propose a two-stage preference clustering framework.In this framework,we make use of random Fourier features to map the preference matrix into the feature matrix,soon afterwards,utilize the traditional k-means approach to cluster preference data in the transformed feature space.Compared with traditional preference clustering,our method solves the problem of insufficient memory and greatly improves the efficiency of the operation.Experiments on movie data sets containing 100000 ratings,show that the proposed method is more effective in clustering accuracy than the Nystr?m and k-means,while also achieving better performance than these clustering approaches.

Establishment of an indicator framework for global One Health Intrinsic Drivers index based on the grounded theory and fuzzy analytical hierarchy-entropy weight method

Background:One Health has become a global consensus to deal with complex health problems.However,the pro‑gress of One Health implementation in many countries is still relatively slow,and there is a lack of systematic evalua‑tion index.The purpose of this study was to establish an indicator framework for global One Health Intrinsic Drivers index(GOH-IDI)to evaluate human,animal and environmental health development process globally.Method:First,82 studies were deeply analyzed by a grounded theory(GT)method,including open coding,axial coding,and selective coding,to establish a three-level indicator framework,which was composed of three selective codes,19 axial codes,and 79 open codes.Then,through semi-structured interviews with 28 health-related experts,the indicators were further integrated and simplifed according to the inclusion criteria of the indicators.Finally,the fuzzy analytical hierarchy process combined with the entropy weight method was used to assign weights to the indi‑cators,thus,forming the evaluation indicator framework of human,animal and environmental health development process.Results:An indicator framework for GOH-IDI was formed consisting of three selective codes,15 axial codes and 61 open codes.There were six axial codes for“Human Health”,of which“Infectious Diseases”had the highest weight(19.76%)and“Injuries and Violence”had the lowest weight(11.72%).There were four axial codes for“Animal Health”,of which“Animal Epidemic Disease”had the highest weight(39.28%)and“Animal Nutritional Status”had the low‑est weight(11.59%).Five axial codes were set under“Environmental Health”,among which,“Air Quality and Climate Change”had the highest weight(22.63%)and“Hazardous Chemicals”had the lowest weight(17.82%).Conclusions:An indicator framework for GOH-IDI was established in this study.The framework were universal,balanced,and scientifc,which hopefully to be a tool for evaluation of the joint development of human,animal and environmental health in diferent regions globally.

Development of a molecular feature-based survival prediction model of ovarian cancer using the deep neural network

Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy of the primary tumor which is tightly linked with the expression profile of the molecular features.^(2)Therefore,in this study,we developed a molecular feature-based survival prediction model of OC using a deep neural network(DNN).