Based on clinical epidemiological data， it is believed that qi deficiency constitution is closely related to allergic diseases. According to the fundamental principles of traditional Chinese medicine （TCM） constitution theory， the intrinsic connection between qi deficiency constitution and allergic diseases is analyzed from the perspectives of inherited endowment， life process， environmental restriction， and the interplay of form and spirit. This paper discusses the key points of regulating qi deficiency constitution to prevent allergic diseases in three stages， prevention before illness， prevention of disease progression， and prevention of recurrence after recovery. It also distinguishes the treatment directions for regulating qi deficiency constitution to treat allergic diseases based on different disease locations such as the lung， spleen， and kidney. This aims to expand new ideas for the research on qi deficiency constitution and allergic diseases as well as the prevention and treatment of allergic diseases.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

BACKGROUND:Bone microenvironment is also rich in various immune cells and cytokines,which are closely related to bone cells and form an interactive network.Therefore,bone aging is not only caused by the senescence of osteocytes,but also accelerated by age-related changes in the immune system.OBJECTIVE:To review the age-related changes of bone marrow mesenchymal stem cells,osteoblasts,osteoclasts,and immune cells in the bone microenvironment,emphasize the key role of the immune microenvironment in bone aging,and the potential of immunotherapy in the treatment of bone aging.METHODS:We searched PubMed and China National Knowledge Infrastructure for articles on the interaction between bone cell senescence and immune cell senescence using"osteocytes,bone aging,immune microenvironment,immune cells,cytokines,immunosenescence,immunotherapy"as Chinese and English search terms.According to the inclusion and exclusion criteria,128 articles were finally included in the review.RESULTS AND CONCLUSION:Bone aging is a common pathological condition in the elderly,characterized by the interaction of multiple biological processes,among which immune factors play a key role.The cells,molecules,and signaling pathways in the immune microenvironment together constitute a complex network,and the imbalance of this network will accelerate the process of bone aging.The combination of anti-cellular aging and immunotherapy may bring new methods for the treatment of bone aging diseases,including the removal of senescent cells,targeted drugs for senescence-related secretory phenotypes,targeted therapy of inflammatory cytokines,immune cell regulation therapy,stem cell therapy,and molecular therapy.To more effectively and reasonably remove senescent cells,a deeper understanding of the mechanism of senescent cells is needed,which will help to identify senescent cells more accurately.Immunotherapy shows great potential and prospects in the treatment of bone aging,but there are some potential risks.It is believed that with the advancement of science and technology,people can more accurately understand the genetic information and immune status of the human body and develop more personalized immunotherapy plans.

BACKGROUND:Bone microenvironment is also rich in various immune cells and cytokines,which are closely related to bone cells and form an interactive network.Therefore,bone aging is not only caused by the senescence of osteocytes,but also accelerated by age-related changes in the immune system.OBJECTIVE:To review the age-related changes of bone marrow mesenchymal stem cells,osteoblasts,osteoclasts,and immune cells in the bone microenvironment,emphasize the key role of the immune microenvironment in bone aging,and the potential of immunotherapy in the treatment of bone aging.METHODS:We searched PubMed and China National Knowledge Infrastructure for articles on the interaction between bone cell senescence and immune cell senescence using"osteocytes,bone aging,immune microenvironment,immune cells,cytokines,immunosenescence,immunotherapy"as Chinese and English search terms.According to the inclusion and exclusion criteria,128 articles were finally included in the review.RESULTS AND CONCLUSION:Bone aging is a common pathological condition in the elderly,characterized by the interaction of multiple biological processes,among which immune factors play a key role.The cells,molecules,and signaling pathways in the immune microenvironment together constitute a complex network,and the imbalance of this network will accelerate the process of bone aging.The combination of anti-cellular aging and immunotherapy may bring new methods for the treatment of bone aging diseases,including the removal of senescent cells,targeted drugs for senescence-related secretory phenotypes,targeted therapy of inflammatory cytokines,immune cell regulation therapy,stem cell therapy,and molecular therapy.To more effectively and reasonably remove senescent cells,a deeper understanding of the mechanism of senescent cells is needed,which will help to identify senescent cells more accurately.Immunotherapy shows great potential and prospects in the treatment of bone aging,but there are some potential risks.It is believed that with the advancement of science and technology,people can more accurately understand the genetic information and immune status of the human body and develop more personalized immunotherapy plans.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective: To understand the health literacy and relevant factors of cancer prevention consciousness in Chinese urban residents from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The self-designed questionnaire was used to collect the information of demographic characteristics and cancer prevention consciousness focusing on nine common risk factors, including smoking, alcohol, fiber food, food in hot temperature or pickled food, chewing betel nut, helicobacter pylori, moldy food, hepatitis B infection, estrogen, and exercise. The logistic regression model was adopted to identify the influencing factors. Results: The overall health literacy of the cancer prevention consciousness was 77.4% (24 980 participants), with 77.4% (12 018 participants), 79.9% (6 406 participants), 77.2% (1 766 participants) and 74.5% (4 709 participants) in each group (P<0.001). The correct response rates for nine risk factors ranged from 55.2% to 93.0%. The multivariate logistic regression analysis showed that compared with community residents, people with primary school level education or below, and the number of people living together in the family <3, the cancer risk assessment/screening intervention population, cancer patients, those with junior high school level educationor above and the number of people living in the family ≥3 had better health literacy of the cancer prevention consciousness (all P values <0.05). Compared with females, 39 years old and below, government-affiliated institutions or civil servants, from the eastern region, males, older than 40 years, company or enterprise employees, and from the middle or western region had worse health literacy of the cancer prevention consciousness (all P values <0.05). Conclusion The health literacy of the cancer prevention consciousness in Chinese urban residents should be improved. The cancer screening intervention, gender, age, education, occupation, the number of people co-living in the family, and residential region were associated with the health literacy of the cancer prevention consciousness.

Objective: To understand the consciousness of the cancer early detection among urban residents and identify the influencing factors from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. Self-designed questionnaires were used to collect population, socioeconomic indicators, self-cancer risk assessment, regular participation in physical examination and other information. The multivariate logistic regression model was used to identify the factors of people who had not regularly participated in the regular physical examination in the past five years. Results: The self-assessment results of 32 357 residents showed that there were 27.54% (8 882) of total study population with self-reported cancer risk, 45.48% (14 671) without cancer risk and 26.98% (8 704) with unclear judgement on their own cancer risk. Among population with cancer risk, 79.84% (7 091) considered physical examination accounted. In the past five years, there were 21 105 (65.43%) residents participated in regular physical examination and 11 148 (34.56%) participated in non-scheduled one, respectively. The multivariate logistic regression analysis showed that compared with unmarried and western region residents, divorced, middle and eastern region residents had a stronger consciousness to participate in the regular physical examination (P<0.05). Compare with residents with annual household income less than 20 000 CNY in 2014, cancer risk assessment/screening intervention population, and self-assessment with cancer risk, residents with annual household income between 20 000 CNY and 59 000 CNY in 2014, occupational population, community residents, cancer patients, self-reported cancer-free risk, and self-assessment with unclear judgement of cancer risk were less likely to participate in the regular physical examination (all P values <0.05). Conclusion From 2015 to 2017, the Chinese urban residents had a acceptable consciousness of the cancer early detection. The marital status, annual household income, population group and self-assessment of cancer risk were related to the consciousness of the cancer early detection of people who had not participated in the regular physical examination in the past five years.

Objective: To understand the consciousness of the cancer early diagnosis among urban residents and identify the related factors from 2015 to 2017. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The general demographic characteristics, the consciousness of the cancer early diagnosis (whether people would have a willingness or encourage their relatives/friends to confirm the abnormal results once which were detected from the physical examination) and other information were collected by using the self-designed questionnaire. The non-conditional logistic regression model was used to identify the relateol factors related to the consciousness of the cancer early diagnosis. Results: As for residents with abnormal result from the physical examination, 89.29% (28 802) of residents would choose to seek medical treatment for further diagnosis. If their relatives/friends had abnormal results from the physical examination, 89.55% (28 886) of residents would encourage their relatives/friends to confirm the diagnosis in time. The non-conditional logistic regression model analysis showed that compared with the public institution staff/civil servants, annual household income less than 20 000 CNY, the western region and the cancer risk assessment/screening intervention population, the company staff, annual household income about 40 000 CNY and more, and the residents from the middle and eastern region had a stronger consciousness to seek further diagnosis; while the unemployed residents and community residents were less likely to seek further diagnosis (P<0.05). Conclusions From 2015 to 2017, the Chinese urban residents had a good consciousness of the cancer early diagnosis. Occupation, annual household income, residential region and population group were related to the consciousness of the cancer early diagnosis.