Objective: To explore the association between the HLA antibody positivity rate in female blood donors and pregnancy history, number of pregnancies, interval from the last pregnancy to blood donation, and age, to identify associated variables using a univariate generalized additive model (GAM), and to further analyze the predictive role of characteristic variables for HLA antibody positivity using a random forest model. Methods: HLA antibody detection was performed on 391 female blood donors using the Luminex immunomagnetic bead method. The correlation between pregnancy-related factors and HLA antibodies was analyzed using the Chi-square test. Based on R software, a univariate GAM was first constructed to analyze the association types between characteristic variables and the HLA antibody positivity rate, followed by the construction of a random forest model to evaluate the predictive value of the variables. Results: Among the 391 female blood donors without a transfusion history, the overall HLA antibody positivity rate was 26.34%. The positivity rate in donors with a pregnancy history was significantly higher than that in those without (30.09% vs 9.72%, P<0.05), and HLA antibody positivity rate increased linearly with the number of pregnancies (P<0.05). In the univariate GAM, age and number of deliveries exhibited a non-linear association with the HLA antibody positivity rate (the positivity rate increased sharply between 25-35 years of age and stabilized after 3 deliveries). Besides, the interval from the last pregnancy to blood donation showed a linear association with the HLA antibody positivity rate, and the positivity rate decreased as the interval prolonged (P<0.05). In the random forest model, age (mean decrease gini=29.26) and interval from the last pregnancy to blood donation (mean decrease gini=22.02) were core predictive variables: age was more conducive to identifying positive samples, while the interval from the last pregnancy to blood donation was more helpful for excluding negative samples. The number of deliveries (mean decrease accuracy=16.98) made a significant contribution to predicting positive samples, whereas the number of abortions had no impact. The model had an AUC of 0.583 (95% CI: 0.593 8-0.770 2), indicating a certain predictive value. Conclusion: The associated variables identified by the univariate GAM model, including age, interval from the last pregnancy to blood donation, and number of deliveries, provide a basis for key variables in the random forest model. All three variables have predictive value for HLA antibody positivity, which can provide evidence-based support for personalized transfusion management and stratified screening of female blood donors in this region.

By analyzing the problems existing in the existing medical service model,this paper explores the core health needs of people in the era of intelligent medical care,and puts forward people-oriented,"4S-PAMA"(Self-Perception,Self-Assessment,Self-Management,Self-Adaption)health service model with individual active health promotion as the core.At the same time to build a multi-level,many modules,interactive health management of traditional Chinese medicine harbor as the breakthrough point,science and technology innovation as the means,through to transform traditional medical center or create new health management,in order to further carry out"perception model of the health management"model applied to provide theoretical basis.

Objective To establish the genetic typing detection technique of matrix-assisted laser de-sorption ionization-time or flight mass spectrometry(MALDI-TOF MS),and to apply it to investigate the polymorphism of the human platelet antigen(HPA)-29-35w low-frequency gene in blood donor population from Guangxi area.Methods The RS number of 7 target gene mutation sites in dbSNP and 21 primers were designed by using Assay Design Suite(ADS)of MassARRAY platform online primer design tool.Seven over-expression vectors inserting into HPA-29-35bb mutant sequence were constructed.Twenty-nine blood donors were randomly selected among the blood donors team in Guangxi area.The samples and vectors conducted the iPLEX Pro multiple genotyping analysis and mass spectrometric detection.Meanwhile,the above samples were sequenced and the sequencing results conducted the comparison validation with the mass spectrometric detec-tion results,then the MALDI-TOF MS genotyping detection technology was established.Then adopting this technology conducted the platelet antigen HPA-29-35W genotyping detection and polymorphism analysis in the samples from 588 blood donors in Guangxi area.Results The MALDI-TOF MS HPA genotyping detec-tion results were consistent with the sequencing results.The HPA-29-35w genotyping results showed that theHPA-29-35w genotype of 588 blood donors in Guangxi area was aa homozygote.Conclusion The genotype detection method of MALDI-TOF MS for HPA-29-35w is successfully established and applied to the screening of HPA-29-35w gene among the blood donor population in Guangxi area.

By analyzing the problems existing in the existing medical service model,this paper explores the core health needs of people in the era of intelligent medical care,and puts forward people-oriented,"4S-PAMA"(Self-Perception,Self-Assessment,Self-Management,Self-Adaption)health service model with individual active health promotion as the core.At the same time to build a multi-level,many modules,interactive health management of traditional Chinese medicine harbor as the breakthrough point,science and technology innovation as the means,through to transform traditional medical center or create new health management,in order to further carry out"perception model of the health management"model applied to provide theoretical basis.

With the scientific and modernization of acupuncture,various kinds of acupuncture medical equipment continue to innovate,especially with the emergence of intelligent acupuncture diagnosis and treatment units,automatic detection of acupuncture needles in the"needle retention"stage of acupuncture clinical practice has become a hot demand.Aiming at the problems that the input image size is too large,the acupuncture needles are slender,and the acupuncture needles are densely distributed,the Acupuncture Needle Object Detection Model(ANODM),an improved YOLOv5 model for acupuncture needles,is proposed in this paper.① In the preprocessing stage,the image is divided into multiple patches for prediction,respectively.② At the model structure level,a new small object detection layer is added to the original three detection layers to improve the recognition ability of small objects.The ordinary convolution of the backbone network is replaced by the dialated convolution to increase the sensitivity field.Features of different stages are fused.③ In the post-processing stage,Soft-NMS is used to reduce the miss rate of positive samples,and cosine similarity match is used to reduce the error rate of negative samples.The experimental results show that,compared with the original YOLOv5,the detection accuracy of the improved YOLOv5 in this paper is improved by 4.2%on the acupuncture needle small object dataset,which can better complete the acupuncture needle small target detection task.

Objective To investigate the epidemiological aspects of traditional Chinese medicine(TCM)deficiency,excess syndromes and physical characteristics related to insomnia in China from 2000 to 2023.Methods A search was conducted across CNKI,WANFANG DATA,VIP,and PubMed databases to identify epidemiological studies on the deficiency-excess syndrome differentiation in TCM and TCM body constitution distribution among Chinese patients with insomnia.Descriptive statistical analysis was performed to summarize the syndrome differentiation patterns,geographical distribution,and demographic characteristics of the study populations.Results A total of 44 relevant studies were included,encompassing 22184 insomnia patients from 23 provinces,municipalities,and autonomous regions.Among these studies,75.0%employed cross-sectional research methods,while the remainder utilized retrospective analysis(6.8%),cohort studies(2.3%),and 15.9%did not specify their research methods.The primary focus of the surveys revealed that 52.3%concentrated on the distribution of syndromes,31.8%on the distribution of body constitution,and 38.6%on the distribution of primary symptoms of insomnia.The main survey results indicated that the majority of insomnia patients in China exhibited excess syndrome,accounting for 61.7%,while deficiency syndrome comprised 38.3%.The main findings indicated that the predominant TCM syndrome among insomnia patients in China was Qi deficiency of the heart and spleen,accounting for 54.6%.In the empirical population,Qi stagnation transforming into fire constituted 46.3%.Conclusion Over the past two decades,substantial data has been accumulated in China on the deficiency-excess syndrome differentiation and body constitution distribution in TCM for insomnia,providing a foundational basis for future research in this field.

Objective To construct a self-service traditional Chinese medicine(TCM)syndrome differentiation-based home sleep scenario and establish a home-based TCM sleep aid environment.Methods Based on medical principles and modern sleep-related technologies,this study divided the home sleep scenario into four dimensions:Perception,syndrome differentiation,intervention,and evaluation.It proposed a construction plan for a self-service TCM sleep aid home scenario that integrated these four dimensions.Results The proposed TCM sleep aid scenario solution enables collaborative collection data from various devices in a home setting.This involved the setup of hardware at the perception level,the development of software at the syndrome differentiation level,the integration of hardware at the intervention level,and data cleaning,fusion,and analysis at the evaluation level.These four components are interconnected,allowing for the extraction of physiological data that is valuable for TCM-based population classification and intervention,which can be used to update algorithms and improve accuracy through iterative upgrades.Conclusion The construction of a home sleep scenario based on TCM syndrome differentiation is feasible and can provide a reference for the promotion and application of appropriate TCM techniques in home settings.

Objective To investigate the epidemiological aspects of traditional Chinese medicine(TCM)deficiency,excess syndromes and physical characteristics related to insomnia in China from 2000 to 2023.Methods A search was conducted across CNKI,WANFANG DATA,VIP,and PubMed databases to identify epidemiological studies on the deficiency-excess syndrome differentiation in TCM and TCM body constitution distribution among Chinese patients with insomnia.Descriptive statistical analysis was performed to summarize the syndrome differentiation patterns,geographical distribution,and demographic characteristics of the study populations.Results A total of 44 relevant studies were included,encompassing 22184 insomnia patients from 23 provinces,municipalities,and autonomous regions.Among these studies,75.0%employed cross-sectional research methods,while the remainder utilized retrospective analysis(6.8%),cohort studies(2.3%),and 15.9%did not specify their research methods.The primary focus of the surveys revealed that 52.3%concentrated on the distribution of syndromes,31.8%on the distribution of body constitution,and 38.6%on the distribution of primary symptoms of insomnia.The main survey results indicated that the majority of insomnia patients in China exhibited excess syndrome,accounting for 61.7%,while deficiency syndrome comprised 38.3%.The main findings indicated that the predominant TCM syndrome among insomnia patients in China was Qi deficiency of the heart and spleen,accounting for 54.6%.In the empirical population,Qi stagnation transforming into fire constituted 46.3%.Conclusion Over the past two decades,substantial data has been accumulated in China on the deficiency-excess syndrome differentiation and body constitution distribution in TCM for insomnia,providing a foundational basis for future research in this field.

Objective To construct a self-service traditional Chinese medicine(TCM)syndrome differentiation-based home sleep scenario and establish a home-based TCM sleep aid environment.Methods Based on medical principles and modern sleep-related technologies,this study divided the home sleep scenario into four dimensions:Perception,syndrome differentiation,intervention,and evaluation.It proposed a construction plan for a self-service TCM sleep aid home scenario that integrated these four dimensions.Results The proposed TCM sleep aid scenario solution enables collaborative collection data from various devices in a home setting.This involved the setup of hardware at the perception level,the development of software at the syndrome differentiation level,the integration of hardware at the intervention level,and data cleaning,fusion,and analysis at the evaluation level.These four components are interconnected,allowing for the extraction of physiological data that is valuable for TCM-based population classification and intervention,which can be used to update algorithms and improve accuracy through iterative upgrades.Conclusion The construction of a home sleep scenario based on TCM syndrome differentiation is feasible and can provide a reference for the promotion and application of appropriate TCM techniques in home settings.

With the scientific and modernization of acupuncture,various kinds of acupuncture medical equipment continue to innovate,especially with the emergence of intelligent acupuncture diagnosis and treatment units,automatic detection of acupuncture needles in the"needle retention"stage of acupuncture clinical practice has become a hot demand.Aiming at the problems that the input image size is too large,the acupuncture needles are slender,and the acupuncture needles are densely distributed,the Acupuncture Needle Object Detection Model(ANODM),an improved YOLOv5 model for acupuncture needles,is proposed in this paper.① In the preprocessing stage,the image is divided into multiple patches for prediction,respectively.② At the model structure level,a new small object detection layer is added to the original three detection layers to improve the recognition ability of small objects.The ordinary convolution of the backbone network is replaced by the dialated convolution to increase the sensitivity field.Features of different stages are fused.③ In the post-processing stage,Soft-NMS is used to reduce the miss rate of positive samples,and cosine similarity match is used to reduce the error rate of negative samples.The experimental results show that,compared with the original YOLOv5,the detection accuracy of the improved YOLOv5 in this paper is improved by 4.2%on the acupuncture needle small object dataset,which can better complete the acupuncture needle small target detection task.