Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective:To construct of evaluation index system and theory-skill-humanity (TSH) model for core competence of radiation specialist nurses, to provide reference for clinical training of radiation specialist nurses.Methods:In January 2023, the draft of the evaluation index system of core competence for radiation specialist nurses was development by literature reviews and semi-structured interviews; from February to March 2023, 20 experts were consulted with 2 rounds of Delphi consultations to determine the index system and determine indicator weights based on the importance assigned by experts to each indicator.Results:Among 20 experts, 17 were female and 3 were male, aged 35-65 (46.48 ± 8.19) years old. The effective recovery rate of the questionnaires was 100.0% (20/20) and 95.0% (19/20) in the first and second round, respectively. The authority coefficient of the experts was 0.937 in the second round, the Kendall coefficients of concordance in the first-level indexes, second-level indexes and third-level indexes were 0.448, 0.407, 0.464, respectively, the difference was statistically significant ( χ2 = 53.60, 148.81, 58.18, all P<0.001). The final evaluation index system of the core competence of radiation specialist nurses included 3 first-level indicators, 11 second-level indicators and 56 third-level indicators. Conclusions:The evaluation index system of the core competence of radiation specialist nurses constructed based on TSH model in the present study has certain feasibility and practicality, which can be used as the basis for the training and evaluation of radiation specialized nurses.

Background and purpose:For patients with human epidermal growth factor receptor 2(HER2)-positive metastatic breast cancer,trastuzumab treatment can prolong the overall survival and significantly improve the prognosis of patients.However,the reference original research trastuzumab(Herceptin?)is more expensive.Biosimilars have comparable efficacy and safety profiles while increasing patient access to treatment.This clinical trial aimed to evaluate the efficacy,pharmacokinetics,safety and immunogenicity of the trastuzumab biosimilar AK-HER2 compared to trastuzumab(Herceptin?)in patients with HER2-positive metastatic breast cancer.Methods:This multi-center,randomised,double-blind phase Ⅲ clinical trial was conducted in 43 subcenters in China.This study complied with the research protocol,the ethical principles stated in the Declaration of Helsinki and the quality management standards for drug clinical trials.It was approved by the hospital's medical ethics committee.The clinical trial registration agency is the State Food and Drug Administration(clinical trial approval number:2015L04224;clinical trial registration number:CTR20170516).Written informed consent was obtained from subjects before enrollment.Enrolled patients were randomly assigned to the AK-HER2 group and the control group,respectively receiving AK-HER2 or trastuzumab(initial loading dose 8 mg/kg,maintenance dose 6 mg/kg,every 3 weeks as a treatment cycle,total treatment time is 16 cycles)in combination with docetaxel(75 mg/m2,treatment duration is at least 9 cycles).The primary endpoint of this clinical trial was the objective response rate(ORR9)between the AK-HER2 group and the control group in the 9th cycle.Secondary efficacy endpoints included ORR16,disease control rate(DCR),clinical benefit rate(CBR),progression-free survival(PFS)and 1-year survival rate.In this study,100 subjects(AK-HER2 group to control group=1:1)were randomly selected for blood sample collection after the 6th cycle of medication,The collection time points were 45 minutes after infusion(the end of administration),4,8,24,72,120,168,336,and 504 hours after the end of administration.After collection,blood samples were analyzed by PK parameter set(PKPS).Other evaluation parameters included safety and immunogenicity assessment.Results:A total of 550 patients with HER2-positive metastatic breast cancer were enrolled in this clinical trial between Sep.2017 and Mar.2021.In the AK-HER2 group(n=237),129 subjects in the experimental group achieved complete response(CR)or partial response(PR),and the ORR9 was 54.4%.There were 134 subjects in the control group(n=241)who achieved CR or PR,and the ORR9 was 55.6%.The ORR9 ratio between the AK-HER2 group and the control group was 97.9%[90%confidence interval(CI):85.4%-112.2%,P=0.784],which was not statistically significant.In all secondary efficacy endpoints,no statistically significant differences were observed between the two groups.We conducted a mean ratio analysis of pharmacokinetics(PK)parameters between the AK-HER2 group and the control group,and the results suggested that the pharmacokinetic characteristics of the two drugs are similar.The incidence of treatment emergent adverse event(TEAE)leading to drug reduction or suspension during trastuzumab treatment was 3.6%(10 cases)in the AK-HER2 group and 8.1%(22 cases)in the control group.There was statistically significant difference between the two groups(P=0.027).The incidence rate was significantly lower in the AK-HER2 group than in the control group,and there was no statistically significant difference among the other groups.The differences in the positive rates of anti-drug antibodies(ADA)and neutralizing antibodies(NAB)between groups were of no statistical significance(P=0.385 and P=0.752).Conclusion:In patients with HER2-positive metastatic breast cancer,AK-HER2 was comparable to the trastuzumab(Herceptin?)in terms of drug efficacy,pharmacokinetics,safety and immunogenicity.

Nucleic acid-based molecular diagnostic methods are considered the gold standard for detecting infectious pathogens.However,when applied to portable or on-site rapid diagnostics,they still face various limitations and challenges,such as poor specificity,cumbersome operation,and portability difficulties.The CRISPR(Clustered regularly interspaced short palindromic repeats)/CRISPR-associated protein(Cas)-fluorescence detection method holds the potential to significantly enhance the specificity and signal-to-noise ratio of nucleic acid detection.In this study,we developed a portable grayscale reader detection system based on loop-mediated isothermal amplification(LAMP)-CRISPR/Cas.On one hand,in the presence of CRISPR RNA(crRNA),the CRISPR/Cas12a system was employed to achieve precise fluorescent detection of self-designed LAMP amplification reactions for influenza A and influenza B viruses.This further validated the high selectivity and versatility of the CRISPR/Cas system.On the other hand,the accompanying independently developed portable grayscale reader allowed for low-cost collection of fluorescence signals and high-reliability visual interpretation.At the end of the detection process,it directly provided positive or negative results.Practical sample analyses using this detection system have verified its reliability and utility,demonstrating that this system can achieve highly sensitive and highly specific portable analysis of influenza viruses.

ObjectiveThis study aims to examine the effect of Rhei Radix et Rhizoma-Coptidis Rhizoma on reducing insulin resistance in db/db mice by regulating the adenylate activated protein kinase （AMPK）/UNC-51-like kinase 1 （ULK1）/key molecule of autophagy， benzyl chloride 1 （Beclin1） pathway and elucidate the underlying mechanism. MethodSixty 6-week-old male db/db mice were studied. They were randomly divided into the model group， metformin group （0.26 g·kg^-1）， and low-， middle-， and high-dose groups （2.25， 4.5， 9 g·kg^-1） of Rhei Radix et Rhizoma-Coptidis Rhizoma. A blank group of db/m mice of the same age was set， with 12 mice in each group. After eight weeks of continuous intragastric administration， the blank group and model group received distilled water intragastrically once a day. The survival status of the mice was observed， and fasting blood glucose （FBG） was measured using a Roche blood glucose device. Fasting serum insulin （FINS） was measured using an enzyme-linked immunosorbent assay， and the insulin resistance index （HOMA-IR） was calculated. Hematoxylin-eosin （HE） staining was performed to observe the pathological changes in the liver of the mice. The protein expression levels of AMPK， Beclin1， autophagy associated protein 5 （Atg5）， and p62 in liver tissue were determined by using Western blot. The protein expression levels of autophagy associated protein 1 light chain 3B （LC3B） and ULK1 in liver tissue were determined using immunofluorescence. Real-time fluorescence quantitative PCR （Real-time PCR） was used to measure mRNA expression levels of AMPK， Beclin1， Atg5， ULK1， and p62. ResultCompared with the blank group， the model group exhibited a significant increase in body mass （P<0.01）. Additionally， the levels of FBG， FINS， and HOMA-IR significantly changed （P<0.01）. The structure of liver cells was disordered. The protein expression levels of AMPK， Beclin1， and Atg5 in liver tissue were significantly decreased （P<0.01）， while the expression level of p62 protein was significantly increased （P<0.01）. The expression levels of mRNA and proteins were consistent. Compared with the model group， the body mass of the metformin group and high and medium-dose groups of Rhei Radix et Rhizoma-Coptidis Rhizoma was significantly decreased （P<0.05）. FBG， FINS， and HOMA-IR were significantly decreased （P<0.05，P<0.01）. After treatment， the liver structure damage in each group was alleviated to varying degrees. The protein expressions of AMPK， Beclin1， Atg5， LC3B， and ULK1 were increased （P<0.05，P<0.01）， while the protein expression of p62 was decreased （P<0.01）. The expression levels of mRNA and proteins were generally consistent. ConclusionThe combination of Rhei Radix et Rhizoma-Coptidis Rhizoma can effectively improve liver insulin resistance， regulate the AMPK autophagy signaling pathway， alleviate insulin resistance in db/db mice， and effectively prevent the occurrence and development of type 2 diabetes.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.