Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.

Evidence serves as the driving force shifting medical practice from empirical medicine towards evidence-based medicine. In the current era of information explosion, it is challenging for clinical surgeons to extract evidence from the vast pool of primary research literature to address clinical issues. Literature reviews, as a form of synthesized evidence, are particularly crucial for precise and efficient evidence utilization. A new form of review within the framework of evidence-based medicine, systematic reviews, also has widespread application in the surgical domain. With the development of methodological approaches in evidence-based medicine, the types of systematic reviews continue to diversify. This paper outlines and summarizes the common types and methodologies of systematic reviews in the surgical field, aiming to provide a clear framework for surgical practitioners to select evidence for both confirming and innovating clinical practices in specific clinical challenges.

Objective To establish the fingerprint of Xuemai Shutong granules by UPLC-ELSD and determine the contents of 9 components in the preparation simultaneously.Methods The UPLC-ELSD was used to establish the fingerprint of Xuemai Shutong granules,and determine the content of its 9 components.The similarity evaluation system,systematic,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used to evaluate the quality of different batches of preparation.Results The similarity degrees of UPLC-ELSD fingerprints of 11 batches of Xuemai Shutong granules were from 0.929 to 0.978,17 common peaks were calibrated,of which 11 peaks were identified:peak 3(notoginsenoside R1),peak 4[ginsenoside Rg,(Re)],peak 5(notoginsenoside R2),peak 6(ginsenoside Rb,),peak 9(astragaloside Ⅳ),peak 10(ginsenoside Rk3),peak 11(ginsenoside Rh4),peak 12[20(S)-ginsenoside Rg3],peak 13[20(R)-ginsenoside Rg3],peak 14(ginsenoside Rk1),peak 15(ginsenoside Rg5).The stoichiometric analysis divided 11 batches of samples into 2 classes,and the 2 principal components in PCA analysis reflected the information of 17common peaks,10 peaks which affected the quality difference are screened out.The linear relationship of the 9 components was good in their respective quality ranges in the content analysis(r＞0.999 2),the average recovery rate were between 95.02％-97.78％and the RSD were 0.69％-1.70％(n=6).The contents of notoginsenoside R1,notoginsenoside R2,ginsenoside Rb1,astragaloside Ⅳ,ginsenoside Rh4,20(S)-ginsenoside Rg3,20(R)-ginsenoside Rg3,ginsenoside Rk1 ginsenoside Rg5 in the 11 batches of Xuemai Shutong granules were 0.087 5-0.187 6,0.494 3-0.688 6,0.448 1-0.705 5,0.192 2-0.270 8,1.492 5-2.077 6,0.316 0-0.463 8,0.254 5-0.382 0,0.117 6-0.163 9,3.407 7-4.706 4 mg·g-1,respectively.Conclusions The established fingerprint and content determination method was accurate and reliable,which can improve the quality standard of Xuemai Shutong granules,and provide reference for its overall quality evaluation.

Objective To establish the fingerprint of Xuemai Shutong granules by UPLC-ELSD and determine the contents of 9 components in the preparation simultaneously.Methods The UPLC-ELSD was used to establish the fingerprint of Xuemai Shutong granules,and determine the content of its 9 components.The similarity evaluation system,systematic,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used to evaluate the quality of different batches of preparation.Results The similarity degrees of UPLC-ELSD fingerprints of 11 batches of Xuemai Shutong granules were from 0.929 to 0.978,17 common peaks were calibrated,of which 11 peaks were identified:peak 3(notoginsenoside R1),peak 4[ginsenoside Rg,(Re)],peak 5(notoginsenoside R2),peak 6(ginsenoside Rb,),peak 9(astragaloside Ⅳ),peak 10(ginsenoside Rk3),peak 11(ginsenoside Rh4),peak 12[20(S)-ginsenoside Rg3],peak 13[20(R)-ginsenoside Rg3],peak 14(ginsenoside Rk1),peak 15(ginsenoside Rg5).The stoichiometric analysis divided 11 batches of samples into 2 classes,and the 2 principal components in PCA analysis reflected the information of 17common peaks,10 peaks which affected the quality difference are screened out.The linear relationship of the 9 components was good in their respective quality ranges in the content analysis(r＞0.999 2),the average recovery rate were between 95.02％-97.78％and the RSD were 0.69％-1.70％(n=6).The contents of notoginsenoside R1,notoginsenoside R2,ginsenoside Rb1,astragaloside Ⅳ,ginsenoside Rh4,20(S)-ginsenoside Rg3,20(R)-ginsenoside Rg3,ginsenoside Rk1 ginsenoside Rg5 in the 11 batches of Xuemai Shutong granules were 0.087 5-0.187 6,0.494 3-0.688 6,0.448 1-0.705 5,0.192 2-0.270 8,1.492 5-2.077 6,0.316 0-0.463 8,0.254 5-0.382 0,0.117 6-0.163 9,3.407 7-4.706 4 mg·g-1,respectively.Conclusions The established fingerprint and content determination method was accurate and reliable,which can improve the quality standard of Xuemai Shutong granules,and provide reference for its overall quality evaluation.

Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.

Objective To deeply analyze the problems of public hospitals in medical service satisfaction,construct a more applicable and explanatory service satisfaction evaluation model,and provide reference for the improvement path of patients'medical service satisfaction.Methods Based on the ESCI model,this paper puts forward a research hypothesis,compiles the"Medical Service Satisfaction Evaluation Scale of Public Hospitals in S City",selects S public hospitals as the research object,and evaluates medical service satisfaction from the perspective of patients.Results At present,the main problems affecting the improvement of patients'satisfaction with medical service management in public hospitals in S city are as follows:it is urgent to improve the diagnosis and treatment technology in terms of hardware quality and optimize the environmental hygiene of public hos-pitals,and it is an urgent task to shorten the waiting time for medical treatment in software quality.The score of medical service satisfaction evaluation of S public hospitals was 3.76,and the satisfaction of patients in S public hospitals with their medical serv-ices was at a moderately low level.Conclusion In view of the above problems,it is recommended to strengthen the construction of key specialties,strengthen the construction of talent team,optimize the medical process,open digital linkage services,and strengthen service management,so as to promote the improvement of patients'satisfaction with medical services.

Head and neck squamous cell carcinoma(HNSCC)is characterized by high recurrence or distant metastases rate and the prognosis is challenging.There is mounting evidence that tumor-infiltrating B cells(TIL-Bs)have a crucial,synergistic role in tumor control.However,little is known about the role TIL-Bs play in immune microenvironment and the way TIL-Bs affect the outcome of immune checkpoint blockade.Using single-cell RNA sequencing(scRNA-seq)data from the Gene Expression Omnibus(GEO)database,the study identified distinct gene expression patterns in TIL-Bs.HNSCC samples were categorized into TIL-Bs inhibition and TIL-Bs activation groups using unsupervised clustering.This classification was further validated with TCGA HNSCC data,correlating with patient prognosis,immune cell infiltration,and response to immunotherapy.We found that the B cells activation group exhibited a better prognosis,higher immune cell infiltration,and distinct immune checkpoint levels,including elevated PD-L1.A prognostic model was also developed and validated,highlighting four genes as potential biomarkers for predicting survival outcomes in HNSCC patients.Overall,this study provides a foundational approach for B cells-based tumor classification in HNSCC,offering insights into targeted treatment and immunotherapy strategies.

Crawling is a significant sign of gross motor development in infants,and also an important means of rehabilitation training for patients with motor disorders.The accurate measurement of the motion state during human crawling is essential for evaluating the gross motor developmental process in infants and the rehabilitation outcome of patients with motor disorders.In recent years,many studies have attempted to quantitatively evaluate the motion state by detecting physiological signals during human crawling,but there is a lack of overview on human crawling motion signal acquisition and processing.Herein the detection and processing methods for motion signals during human crawling and relevant researches are reviewed.The mainstream methods for detecting motion signals during human crawling are introduced from the perspectives of inertial sensors,pressure sensors,and surface electromyography.Then,the signal processing and analysis such as periodic segmentation,kinematic analysis,and dynamic analysis in human crawling are summarized.Based on the theory of motion coordination,the research advances in joint synergy and muscle synergy during human crawling are elaborated.Finally,the current problems and future development directions of motion analysis for human crawling are discussed.