Idiopathic pulmonary fibrosis (IPF), a chronic interstitial lung disease, is characterized by aberrant wound healing, excessive scarring and the formation of myofibroblastic foci. Although the role of alternative splicing (AS) in the pathogenesis of organ fibrosis has garnered increasing attention, its specific contribution to pulmonary fibrosis remains incompletely understood. In this study, we identified an up-regulation of serine/arginine-rich splicing factor 7 (SRSF7) in lung fibroblasts derived from IPF patients and a bleomycin (BLM)-induced mouse model, and further characterized its functional role in both human fetal lung fibroblasts and mice. We demonstrated that enhanced expression of Srsf7 in mice spontaneously induced alveolar collagen accumulation. Mechanistically, we investigated alternative splicing events and revealed that SRSF7 modulates the alternative splicing of pyruvate kinase (PKM), leading to metabolic dysregulation and fibroblast activation. In vivo studies showed that fibroblast-specific knockout of Srsf7 in conditional knockout mice conferred resistance to bleomycin-induced pulmonary fibrosis. Importantly, through drug screening, we identified lomitapide as a novel modulator of SRSF7, which effectively mitigated experimental pulmonary fibrosis. Collectively, our findings elucidate a molecular pathway by which SRSF7 drives fibroblast metabolic dysregulation and propose a potential therapeutic strategy for pulmonary fibrosis.

Objective To investigate the effects of transcranial direct current stimulation(tDCS)combined with resistance training on the performance of college students completing pull-ups,and explore the potential mechanisms underlying the effects of training intervention from the perspective of neuromuscular activity control.Methods A total of 25 male college student volunteers were randomly divided into the tDCS combined with resistance training group(experiment group)and resistance training group(control group).Twelve subjects in the control group received a lat pull-down strength training intervention lasting for 8 weeks,with 4 sets of 12 movement repetitions each,3 times per week.Thirteen subjects in the experimental group received a 20-minute tDCS before the lat pull-up resistance training intervention.Lat pull-down isometric maximal voluntary contraction(MVC)force,lat pull-down maximal repetitions under 80％one-repetition maximum(1RM)loading,and conventional pull-up exercise were tested before and after the training intervention.Surface electromyography(sEMG)signals of the main exertion muscles of the upper limb were recorded during the pull-up exercise test.Results After the training intervention,the number of pull-ups completed by the experimental group and control group increased by 1.74 times and 1.42 times,respectively.Subjects in both groups showed significant improvements in their MVC and lat pull-down maximal repetitions under 80％1RM loading.However,there were no statistical differences in these indicators between groups.Activation levels of the agonist muscles brachioradialis,posterior deltoid,and pectoralis major were significantly decreased after the training compared to those before training for both groups.In addition,the coactivation level of the antagonist triceps brachii muscle in the experimental group significantly decreased from 0.50±0.22 to 0.37±0.09 after the training,while there was no significant change in the control group before and after the intervention.Conclusions Eight-week tDCS combined with resistance training and resistance training alone can significantly improve the pull-up performance of college students,which may be related to the fact that both types of training can significantly improve the active muscle contraction capacity.Combined with resistance training,tDCS is more effective in decreasing the coactivation level of triceps brachii during pull-ups and increasing the contraction efficiency of elbow joint muscles.

Objective To investigate the distribution of plantar pressures and bone stresses of the foot with high,normal and low arch morphologies,and reveal the influence of arch morphology on foot biomechanical properties.Methods A total of 127 young females were recruited.The foot type was classified by collecting the morphological data of the foot with the three-dimensional(3D)foot scanner,and three types of the foot arch morphology were selected for analysis.The geometric model of foot bone was obtained by CT scanning,so as to establish the biomechanical finite element model of the foot.A load of 50％human body weight was applied to the model to simulate the state of bipedal standing.Results The calculated plantar contact area was compared with the measured results,and the relative error values were smaller than 10％,which proved the validity of the finite element model.The peak plantar pressure under three types of arch morphologies was located in the hind foot region,and the heel pressure of high-and low-arched foot was higher than that of normal-arched foot.Compared with normal-arched foot,high-arched foot showed a significant increase in stress in the hind foot area,the peak stress of soft tissues was 299.45％higher,and the peak bone stress was 93.19％higher.For low-arched foot,the plantar contact area increased by 13.28％and calcaneal stress increased by 98.09％.The peak bone stresses of high-,normal-and low-arched foot were located at the talus,which were 9.903,19.921 and 36.308 MPa,respectively.Conclusions This study supports the association between abnormal arch morphology,foot pain and foot diseases,and provides a basis and direction for the design of orthopedic insoles and arch support structures for abnormal feet.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

In order to fully consider the physiological characteristics and movement mechanism of the human body under the premise of ensuring the function and practicality of the product,the human-fabric contact finite element model based on biomechanical feedback plays an important role in the design of'people-oriented'health protection products.This review focuses on the design of protective products made of flexible materials,and discusses the application status and development trend of human finite element model in the design of protective products.The construction process of finite element models of different parts of the human body in recent years is summarized from the perspective of human biomechanics.Secondly,from the contact models established between the human head,torso,upper limbs and lower limbs and protective devices,the application status and challenges of finite element method in the design of health protective products are sorted out.Finally,the problems existing in the use of finite element method in such researches are discussed.It is pointed out that in the context of pursuing accuracy,real-time and realism,finite element contact models with the advantages of high efficiency,precision and reusability still have a broad application prospect.

Motor dysfunction seriously affects people's quality of life,and currently is one of the main health problems facing mankind.Sports and rehabilitation biomechanics focuses on motor dysfunction,providing the theoretical basis for motor function assessment,dysfunction intervention technology and equipment development.In 2024,the biomechanical research of motor dysfunction achieved fruitful results.From 3 aspects,namely,non-contact assessment of motor function,intelligent classification and grading of dysfunction,and neurobiomechanical mechanism of motor dysfunction,this review summarizes the research progress in the past year and discusses the future development direction.

Motor dysfunction seriously affects people's quality of life,and currently is one of the main health problems facing mankind.Sports and rehabilitation biomechanics focuses on motor dysfunction,providing the theoretical basis for motor function assessment,dysfunction intervention technology and equipment development.In 2024,the biomechanical research of motor dysfunction achieved fruitful results.From 3 aspects,namely,non-contact assessment of motor function,intelligent classification and grading of dysfunction,and neurobiomechanical mechanism of motor dysfunction,this review summarizes the research progress in the past year and discusses the future development direction.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

In order to fully consider the physiological characteristics and movement mechanism of the human body under the premise of ensuring the function and practicality of the product,the human-fabric contact finite element model based on biomechanical feedback plays an important role in the design of'people-oriented'health protection products.This review focuses on the design of protective products made of flexible materials,and discusses the application status and development trend of human finite element model in the design of protective products.The construction process of finite element models of different parts of the human body in recent years is summarized from the perspective of human biomechanics.Secondly,from the contact models established between the human head,torso,upper limbs and lower limbs and protective devices,the application status and challenges of finite element method in the design of health protective products are sorted out.Finally,the problems existing in the use of finite element method in such researches are discussed.It is pointed out that in the context of pursuing accuracy,real-time and realism,finite element contact models with the advantages of high efficiency,precision and reusability still have a broad application prospect.

Objective To investigate the effects of transcranial direct current stimulation(tDCS)combined with resistance training on the performance of college students completing pull-ups,and explore the potential mechanisms underlying the effects of training intervention from the perspective of neuromuscular activity control.Methods A total of 25 male college student volunteers were randomly divided into the tDCS combined with resistance training group(experiment group)and resistance training group(control group).Twelve subjects in the control group received a lat pull-down strength training intervention lasting for 8 weeks,with 4 sets of 12 movement repetitions each,3 times per week.Thirteen subjects in the experimental group received a 20-minute tDCS before the lat pull-up resistance training intervention.Lat pull-down isometric maximal voluntary contraction(MVC)force,lat pull-down maximal repetitions under 80％one-repetition maximum(1RM)loading,and conventional pull-up exercise were tested before and after the training intervention.Surface electromyography(sEMG)signals of the main exertion muscles of the upper limb were recorded during the pull-up exercise test.Results After the training intervention,the number of pull-ups completed by the experimental group and control group increased by 1.74 times and 1.42 times,respectively.Subjects in both groups showed significant improvements in their MVC and lat pull-down maximal repetitions under 80％1RM loading.However,there were no statistical differences in these indicators between groups.Activation levels of the agonist muscles brachioradialis,posterior deltoid,and pectoralis major were significantly decreased after the training compared to those before training for both groups.In addition,the coactivation level of the antagonist triceps brachii muscle in the experimental group significantly decreased from 0.50±0.22 to 0.37±0.09 after the training,while there was no significant change in the control group before and after the intervention.Conclusions Eight-week tDCS combined with resistance training and resistance training alone can significantly improve the pull-up performance of college students,which may be related to the fact that both types of training can significantly improve the active muscle contraction capacity.Combined with resistance training,tDCS is more effective in decreasing the coactivation level of triceps brachii during pull-ups and increasing the contraction efficiency of elbow joint muscles.