Bone remodeling is essential for maintaining skeletal integrity and mineral homeostasis and depends on the coordinated activities of osteoclasts, osteoblasts, and osteocytes. Osteoclast-mediated bone resorption must remain closely coupled with osteoblast-mediated bone formation, while osteocytes continuously sense mechanical loading and changes in the local and systemic environment. Reactive oxygen species are involved in these physiological processes, particularly in the regulation of osteoclast differentiation and osteoblast function. Once oxidative stress exceeds the capacity of cellular antioxidant systems, however, this balance may be disturbed, leading to enhanced bone resorption, impaired bone formation, and deterioration of bone quality. In recent years, ferroptosis has attracted increasing attention in studies of bone metabolism. Ferroptosis is characterized by iron-dependent accumulation of lipid peroxides, but ferroptosis-related alterations in bone tissue may occur before obvious cell death becomes detectable. Changes in the labile iron pool, membrane lipid composition, antioxidant capacity, and mitochondrial metabolism can already influence the biological behavior of bone cells at an earlier stage. On this basis, ferroptosis susceptibility may provide a useful way to describe a reversible metabolic state in which cells become increasingly vulnerable to lipid peroxidation while widespread membrane damage and terminal cell death have not yet occurred. Such a state should not be identified by a single molecular marker. A more appropriate assessment would combine changes in intracellular iron handling, lipid peroxidation, antioxidant defense, cell viability, bone-cell function, and the response to ferroptosis-related interventions. Measurements of Fe²⁺ or the labile iron pool, C11-BODIPY oxidation, 4-HNE and MDA, GPX4 activity or expression, GSH/GSSG balance, LDH release, and PI staining may therefore provide complementary information when interpreted together with functional changes and rescue experiments. Several metabolic processes may contribute to the development of ferroptosis susceptibility in the bone. Disturbances in iron uptake, ferritin storage, ferritinophagy, and ferroportin-mediated iron export can increase the availability of redox-active iron. At the same time, ACSL4- and LPCAT3-dependent incorporation of polyunsaturated fatty acids into membrane phospholipids increases the pool of oxidizable lipid substrates. Reduced activity of the system Xc^--GSH-GPX4 pathway further weakens the ability of cells to remove lipid hydroperoxides, while FSP1-CoQ and mitochondrial DHODH-CoQ systems provide additional antioxidant protection. Mitochondrial dysfunction, inflammation, and abnormalities in calcium-phosphate and endocrine regulation may further increase this metabolic pressure. Importantly, the consequences are unlikely to be identical among bone-cell populations. In osteoclasts, increased iron availability and oxidative stress may strengthen RANKL-NFATc1-associated differentiation and resorptive activity. In osteoblasts, persistent lipid peroxidation may interfere with RUNX2-related differentiation, collagen synthesis, matrix production, and mineralization before extensive cell loss occurs. Osteocytes, because of their long lifespan and specialized location within the mineralized matrix, may be particularly sensitive to chronic metabolic stress, resulting in impaired mechanosensing and disturbed RANKL- or FGF23-related feedback. Evidence linking these changes with osteoporosis, diabetes-related bone disorders, chronic kidney disease-mineral and bone disorder, inflammatory bone loss, and particle-induced osteolysis is increasing, although the strength of evidence differs among disease models. Changes in ROS, MDA, GPX4, or other individual indicators alone are insufficient to demonstrate ferroptosis-related susceptibility, and stronger conclusions require evidence of iron dependence, lipid peroxidation, temporal sequence, and reversibility. Emerging single-cell, spatial, proteomic, lipidomic, and metabolomic approaches may help identify susceptible cell populations and bone-remodeling regions more precisely, although the mineralized matrix and the effects of sample processing remain important technical limitations. Overall, ferroptosis susceptibility may represent an intermediate metabolic state linking disturbances in iron and redox homeostasis with progressive uncoupling of bone resorption and formation. Clarifying its cell-specific features, temporal development, and reversible window may improve our understanding of how chronic metabolic stress contributes to bone remodeling disorders and may also provide a basis for earlier and more targeted intervention.

Medical fiber endoscope is one kind of medical device that utilizes fiber technique to realize visual detection for internal organs or cavities.In clinical applications,medical fiber endoscope not only need to complete fiber optic imaging but also need possess the functions of delivering water and air,and operating device.Therefore,its routine management and maintenance for fault is more and more payed attention by the staffs of using medical fiber endoscope in department.The system of delivering water and air is a key structure for some of medical fiber endoscopes,which has higher maintenance matters and frequency of maintaining fault.Based on the above reasons,this research analyzed the working principle of medical fiber endoscopes,and summarized the ideas and methods of repairing common faults through focused on the structural composition of delivering system of water and air of medical fiber endoscopes,so as to improve the management level for this type of medical device.

Objective To investigate the effects of indirect moxibustion on the expressions of DNA methyltransferases(DNMT)and methylation of the brain-derived neurotrophic factor(BDNF)promoter region in uterine tissues of rats with primary dysmenorrhea(PD);To explore the mechanism of epigenetic regulation of indirect moxibustion on PD model rats.Methods A total of 32 female SD rats were randomly divided into blank group,model group,indirect moxibustion group and Western medicine group,with 8 rats in each group.The PD model with cold dampness stagnation syndrome was established using ice-water baths combined with estradiol benzoate and oxytocin.Starting from the first day of modeling,the indirect moxibustion group received salt-partitioned moxibustion at"Shenque"and ginger-partitioned moxibustion at"Guanyuan"for 20 min,while the Western medicine group was gavaged ibuprofen solution.Both interventions were given once a day for 10 days.On day 11,writhing responses were observed and scored after oxytocin injection,Western blot and RT-qPCR were used to detect protein and mRNA expression of BDNF,DNMT3A and DNMT3B in uterine tissue,immunohistochemical staining was used to detect the positive expressions of DNMT3A and DNMT3B in uterine tissue.The DNA methylation of BDNF promoter region in uterine tissue was detected by sulfite sequencing.Results Compared with the blank group,the writhing latency was shortened and the writhing score increased in the model group(P<0.01);the protein and mRNA expressions of BDNF,DNMT3A and DNMT3B in uterine tissue increased(P<0.01),the positive expressions of DNMT3A and DNMT3B increased(P<0.01),and the DNA methylation rate in BDNF promoter region decreased(P<0.01).Compared with the model group,the writhing latency was lengthened and the writhing score decreased in the indirect moxibustion group and Western medicine group(P<0.05,P<0.01);the protein and mRNA expressions of BDNF,DNMT3A and DNMT3B in uterine tissue decreased(P<0.05,P<0.01),the positive expressions of DNMT3A and DNMT3B decreased(P<0.01),and the DNA methylation rate in BDNF promoter region increased(P<0.01).Conclusion Indirect moxibustion at"Shenque"and"Guanyuan"may inhibit the transcription of BDNF by increasing the DNA methylation level of BDNF promoter region,and reduce the expression of BDNF,so as to relieve the pain of PD rats.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Medical fiber endoscope is one kind of medical device that utilizes fiber technique to realize visual detection for internal organs or cavities.In clinical applications,medical fiber endoscope not only need to complete fiber optic imaging but also need possess the functions of delivering water and air,and operating device.Therefore,its routine management and maintenance for fault is more and more payed attention by the staffs of using medical fiber endoscope in department.The system of delivering water and air is a key structure for some of medical fiber endoscopes,which has higher maintenance matters and frequency of maintaining fault.Based on the above reasons,this research analyzed the working principle of medical fiber endoscopes,and summarized the ideas and methods of repairing common faults through focused on the structural composition of delivering system of water and air of medical fiber endoscopes,so as to improve the management level for this type of medical device.

Objective To investigate the effects of indirect moxibustion on the expressions of DNA methyltransferases(DNMT)and methylation of the brain-derived neurotrophic factor(BDNF)promoter region in uterine tissues of rats with primary dysmenorrhea(PD);To explore the mechanism of epigenetic regulation of indirect moxibustion on PD model rats.Methods A total of 32 female SD rats were randomly divided into blank group,model group,indirect moxibustion group and Western medicine group,with 8 rats in each group.The PD model with cold dampness stagnation syndrome was established using ice-water baths combined with estradiol benzoate and oxytocin.Starting from the first day of modeling,the indirect moxibustion group received salt-partitioned moxibustion at"Shenque"and ginger-partitioned moxibustion at"Guanyuan"for 20 min,while the Western medicine group was gavaged ibuprofen solution.Both interventions were given once a day for 10 days.On day 11,writhing responses were observed and scored after oxytocin injection,Western blot and RT-qPCR were used to detect protein and mRNA expression of BDNF,DNMT3A and DNMT3B in uterine tissue,immunohistochemical staining was used to detect the positive expressions of DNMT3A and DNMT3B in uterine tissue.The DNA methylation of BDNF promoter region in uterine tissue was detected by sulfite sequencing.Results Compared with the blank group,the writhing latency was shortened and the writhing score increased in the model group(P<0.01);the protein and mRNA expressions of BDNF,DNMT3A and DNMT3B in uterine tissue increased(P<0.01),the positive expressions of DNMT3A and DNMT3B increased(P<0.01),and the DNA methylation rate in BDNF promoter region decreased(P<0.01).Compared with the model group,the writhing latency was lengthened and the writhing score decreased in the indirect moxibustion group and Western medicine group(P<0.05,P<0.01);the protein and mRNA expressions of BDNF,DNMT3A and DNMT3B in uterine tissue decreased(P<0.05,P<0.01),the positive expressions of DNMT3A and DNMT3B decreased(P<0.01),and the DNA methylation rate in BDNF promoter region increased(P<0.01).Conclusion Indirect moxibustion at"Shenque"and"Guanyuan"may inhibit the transcription of BDNF by increasing the DNA methylation level of BDNF promoter region,and reduce the expression of BDNF,so as to relieve the pain of PD rats.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Objective: To explore the effects of community building environment and sports with fitness APP usage and their interactions on teachers exercise habits in the Yangtze River Delta Region, so as to provide a scientific basis for the development of a sports and health promotion intervention program for teachers. Methods: A total of 2 530 in service teachers from four provinces and cities in the Yangtze River Delta region, namely, Shanghai, Zhejiang, Jiangsu and Anhui Province, were sampled in May-June 2023 by using convenient cluster random sampling method. Self designed questionnaire was used to collect the basic information of the surveyed teachers, Physical Activity Building Environment Evaluation Questionnaire and the Sports with Fitness APP Usage Questionnaire were used to measure the teachers subjective perception of the community building environment and the usage of sports with fitness APP, respectively. Physical Exercise Habituation Scale was used to assess the level of exercise habits. Logistic regression models were applied to analyze the effects of community building environment and sports with fitness APP usage on physical exercise, and the interaction effects were analyzed by using additive and multiplicative models. Results: Among all the teachers surveyed, 658 of them reported good physical exercise habits (26.0%), and differences in the rate of physical activity habit formation by gender, age, years of teaching, as well as subject of teaching were statistically significant ( χ 2=42.94, 39.73, 35.47, 218.23 , P <0.05). Teachers with physical exercise habits had significantly higher community building environment scores and sports and fitness APP use than teachers without exercise habits ( t =12.17,16.54, P <0.05). Adjusting for the confounders of age, gender, years of teaching experience, and subjects taught, multifactorial unconditional Logistic regression analysis showed that the probability of teachers having good physical exercise habits increased by 22% for every 1-point increase in the community building environment score on average ( OR =1.22, 95% CI =1.11-1.40), and the probability of teachers having good physical exercise habits increased by 16% for every 1-point increase in the sports with fitness APP score on average ( OR = 1.16 , 95% CI =1.03-1.31) ( P <0.05). Interaction analyses showed that there was an additive interaction between the effects of community building environment and sports and fitness APP use on teachers physical exercise habits after adjustment, and the 95% CI for RERI , API and SI were 1.17 -1.65, 0.12-0.46 and 1.78-3.33 ( P <0.05), respectively, and there was no multiplicative interaction ( P >0.05). Conclusions The community building environment and the usage of sports & fitness APP show impacts in the formation of teachers physical exercise habits in the Yangtze River Delta region, and there is an interaction effect. Enhancing the construction of smart sports centers around the community can provide a high quality external environment for the physical exercise habits formation.

In order to meet the accuracy requirements of hospitals for abnormal detection of fetal monitor data,a hospital fetal monitor data anomaly detection method based on Euclidean distance was proposed.In this method,according to the abnormal detection method of fetal monitor data in hospitals,the ultrasound Doppler detector was used to collect fetal heart rate signal data,and the fetal heart rate signal denoising method based on adaptive filtering was used to remove the noise components of the collected fetal heart rate signal.The denoised fetal heart rate signal was used as the identification sample of the abnormal fetal heart rate signal identification method based on Euclidean distance method.The Euclidean distance Euclidean distance between the denoised fetal heart rate signal and the normal fetal heart rate signal was analyzed by combining the Euclidean distance method and the strong classifier to analyze whether the virtual sinusoidal distance between the denoised fetal heart rate signal and the normal fetal heart rate signal was greater than the tuned Euclidean distance threshold.Fetal heart rate signal samples greater than the threshold were classified and diagnosed as abnormal monitoring data.The fetal heart rate information collected by the fetal monitor in the hospital was accurate,and the abnormal detection results were valid and reliable.

Objective To explore the effect of auto-segmentation based on deep learning(DL)and Atlas during online adaptive MRI-guided radiotherapy.Methods Totally 15 prostate cancer patients undergoing MRI-guided online adaptive radiotherapy at some hospital from January 2020 to September 2021 were selected and divided into a training set(12 cases)and a test set(3 cases)by random sampling method.With the training set data the models of clinical target volume(CTV)and organs at risk(OAR)by DL and Atlas segmentation were established,and with the test set data the two segmentation models were modified and the modification lengths were recorded.DL and Atlas segmentation methods were compared on segmentation efficiency and accuracy in terms of Dice similarity coefficient(DSC),Hausdorff distance(HD)and mean distance to agreement(MDA).A joint auto-segmentation scheme based on combined DL and Atlas was constructed with considerations on the advantages and characteristics of the two methods,which was compared with the schemes respectively based on DL or Atlas from the aspect of the time consumed for segmentation.Results Accuracy comparison showed Atlas segmentation model behaved better significantly than DL model for CTV(P＜0.05),while obviously worse than the latter for DSC and MDA in bladder and rectum(P＜0.05).The doctor took 9.4 min in average for CTV and OAR modification based on DL model and 12 min in average for Atlas-model-based modification.The joint auto-segmentation scheme only needed 8 min in average for CTV and OAR modification,which gained advantages over the schemes based on DL or Atlas.Conclusion The auto-segmentation based on combined DL and Atlas during online adaptive MRI-guided radiotherapy behaves well in low time consumption,high accuracy and efficiency.[Chinese Medical Equipment Journal,2024,45(6):59-64]