A hybrid statistical morphometry free-form deformation approach to 3D personalized foot-ankle models.

Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes. The derived surface shapes from SSM then guide the FFD process to accurately reconstruct the internal bone structures. The workflow accuracy was established by comparing three model-generated foot models against corresponding skin and bone geometries manually segmented and not part of the original training set. We used the top ten principal components representing 85 % of the population variation to create the model. For prediction validation, the average Dice similarity coefficient, Hausdorff distance error, and root mean square error were 0.92 ± 0.01, 2.2 ± 0.19 mm, and 2.95 ± 0.23 mm for soft tissues, and 0.84 ± 0.03, 1.83 ± 0.1 mm, and 2.36 ± 0.12 mm for bones, respectively. This study presents an efficient approach for 3D personalized foot model reconstruction via SSM generation of the foot surface that informs bone reconstruction based on FFD. The proposed workflow is part of the open-source Musculoskeletal Atlas Project linked to OpenSim and makes it feasible to accurately generate foot models informed by population anatomy, and suitable for rigid body analysis and finite element simulation.

Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors.

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors.

The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.

A Data-Driven Approach for Fatigue Detection during Running Using Pedobarographic Measurements.

Background: Detecting fatigue at the early stages of a run could aid training programs in making adjustments, thereby reducing the heightened risk of injuries from overuse. The study aimed to investigate the effects of running fatigue on plantar force distribution in the dominant and nondominant feet of amateur runners. Methods: Thirty amateur runners were recruited for this study. Bilateral time-series plantar forces were employed to facilitate automatic fatigue gait recognition using convolutional neural network (CNN) and CNN-based long short-term memory network (ConvLSTM) models. Plantar force data collection was conducted both before and after a running-induced fatigue protocol using a FootScan force plate. The Keras library in Python 3.8.8 was used to train and tune deep learning models. Results: The results demonstrated that more mid-forefoot and heel force occurs during bilateral plantar and less midfoot fore force occurs in the dominant limb after fatigue (p < 0.001). The time of peak forces was significantly shortened at the midfoot and sum region of the nondominant foot, while it was delayed at the hallux region of the dominant foot (p < 0.001). In addition, the ConvLSTM model showed higher performance (Accuracy = 0.867, Sensitivity = 0.874, and Specificity = 0.859) in detecting fatigue gait than CNN (Accuracy = 0.800, Sensitivity = 0.874, and Specificity = 0.718). Conclusions: The findings of this study could offer empirical data for evaluating risk factors linked to overuse injuries in a single limb, as well as facilitate early detection of fatigued gait.

Understanding the form and function in Chinese bound foot from last-generation cases.

Purpose: Foot adaptation in the typically developed foot is well explored. In this study, we aimed to explore the form and function of an atypical foot, the Chinese bound foot, which had a history of over a thousand years but is not practised anymore. Methods: We evaluated the foot shape and posture via a statistical shape modelling analysis, gait plantar loading distribution via gait analysis, and bone density adaptation via implementing finite element simulation and bone remodelling prediction. Results: The atypical foot with binding practice led to increased foot arch and vertically oriented calcaneus with larger size at the articulation, apart from smaller metatarsals compared with a typically developed foot. This shape change causes the tibia, which typically acts as a load transfer beam and shock absorber, to extend its function all the way through the talus to the calcaneus. This is evident in the bound foot by i) the reduced center of pressure trajectory in the medial-lateral direction, suggesting a reduced supination-pronation; ii) the increased density and stress in the talus-calcaneus articulation; and iii) the increased bone growth in the bound foot at articulation joints in the tibia, talus, and calcaneus. Conclusion: Knowledge from the last-generation bound foot cases may provide insights into the understanding of bone resorption and adaptation in response to different loading profiles.

Regional plantar forces and surface geometry variations of a chronic ankle instability population described by statistical shape modelling.

BACKGROUND: Understanding detailed foot morphology as well as regional plantar forces could provide insight into foot function and provide recommendation for footwear design for chronic ankle instability (CAI) people. RESEARCH QUESTION: This study presented 3-dimensional statistical shape models of feet from three different populations including CAI, copers and healthy individuals, with regional plantar forces also acquired. METHODS: Sixty-six males (22 participants per group) were included in this study to capture 3-dimensional foot shapes under a standing condition and regional plantar forces during a cutting maneuver. Principal component analysis was performed to generate a mean foot shape of each group as well as modes of variations. A generalized procrustes analysis was used to achieve rapid registration of mean shapes. Besides, regional plantar forces and contact duration among these three populations were compared. RESULTS: For 3-dimensional foot shapes, although no significant differences of the average distance between each mode and mean shape were found among three populations, there were subtle variations in mean shapes. The CAI population presented a more bulging of the lateral malleolus; copers were characterized by the flexion of the lesser toes, a more bulging of the medial foot in the sagittal plane; and healthy individuals showed a greater heel width and a more bulging of the heel in the sagittal plane. In terms of plantar forces, healthy individuals had significantly greater summated plantar forces and greater plantar forces in the lateral heel area during the early contact phase compared to copers and CAI participants. SIGNIFICANCE: Overall, this study suggested that repetitive ankle sprains may lead to the bulging of the lateral malleolus. Further, CAI and copers seem to stabilize the ankle joint by medially shifting the center of pressure compared to healthy individuals under the static and less challenging dynamic conditions.

Foot Pronation Prediction with Inertial Sensors during Running: A Preliminary Application of Data-Driven Approaches.

Abnormal foot postures may affect foot movement and joint loading during locomotion. Investigating foot posture alternation during running could contribute to injury prevention and foot mechanism study. This study aimed to develop feature-based and deep learning algorithms to predict foot pronation during prolonged running. Thirty-two recreational runners have been recruited for this study. Nine-axial inertial sensors were attached to the right dorsum of the foot and the vertical axis of the distal anteromedial tibia. This study employed feature-based machine learning algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest, and deep learning, i.e., one-dimensional convolutional neural networks (CNN1D), to predict foot pronation. A custom nested k-fold cross-validation was designed for hyper-parameter tuning and validating the model's performance. The XGBoot classifier achieved the best accuracy using acceleration and angular velocity data from the foot dorsum as input. Accuracy and the area under curve (AUC) were 74.7 ± 5.2% and 0.82 ± 0.07 for the subject-independent model and 98 ± 0.4% and 0.99 ± 0 for the record-wise method. The test accuracy of the CNN1D model with sensor data at the foot dorsum was 74 ± 3.8% for the subject-wise approach with an AUC of 0.8 ± 0.05. This study found that these algorithms, specifically for the CNN1D and XGBoost model with inertial sensor data collected from the foot dorsum, could be implemented into wearable devices, such as a smartwatch, for monitoring a runner's foot pronation during long-distance running. It has the potential for running shoe matching and reducing or preventing foot posture-induced injuries.

Signal-off electrochemical sensor for matrix metalloproteinase 9 detection based on sacrificial FeMOF and host-guest strategy.

Matrix metalloproteinase-9 (MMP-9) has been implicated in various tumor cell invasions and metastases. In light of the limitations of traditional methods for MMP-9 detection, we have constructed a novel biosensor depending on cucurbit[8]uril (CB[8]) -mediated host-guest interactions and a sacrificial iron metal-organic framework (FeMOF). Herein, MMP9-specific peptides modified on the gold bare electrode are bonded to the FeMOF@AuNPs@peptide complex through CB[8] addition. The connection between MMP9-specific peptides and signal peptides via CB[8] provides stability as well as enables the immobilization of FeMOF on the electrode surface. When Fe3+ from the FeMOF interacts with electrochemical buffer K4Fe(CN)6, Prussian blue will be generated on the gold electrode surface, and a significantly enlarged current response can be detected. However, in the presence of MMP-9, their peptide substrates are specifically cleaved at the site between serine (S) and Leucine (L), which causes an abrupt decrease in the electrochemical signal. The change of signal can reflect MMP-9 concentration. This sensor can reach an ultrahigh sensitivity with a wide detection range of 0.5 pg⋅mL-1 to 500 ng⋅mL-1 and a low detection limit of 1.30 pg⋅mL-1. Importantly, this sensor is very simple, relying solely on self-sacrificial label of FeMOF, rather than complex functional materials. Additionally, it has been well used in serum samples, showing attractive potential for practical applications.

Integrated microbiome and metabolome analysis reveals the interaction between intestinal flora and serum metabolites as potential biomarkers in hepatocellular carcinoma patients.

Globally, liver cancer poses a serious threat to human health and quality of life. Despite numerous studies on the microbial composition of the gut in hepatocellular carcinoma (HCC), little is known about the interactions of the gut microbiota and metabolites and their role in HCC. This study examined the composition of the gut microbiota and serum metabolic profiles in 68 patients with HCC, 33 patients with liver cirrhosis (LC), and 34 healthy individuals (NC) using a combination of metagenome sequencing and liquid chromatography-mass spectrometry (LC-MS). The composition of the serum metabolites and the structure of the intestinal microbiota were found to be significantly altered in HCC patients compared to non-HCC patients. LEfSe and metabolic pathway enrichment analysis were used to identify two key species (Odoribacter splanchnicus and Ruminococcus bicirculans) and five key metabolites (ouabain, taurochenodeoxycholic acid, glycochenodeoxycholate, theophylline, and xanthine) associated with HCC, which then were combined to create panels for HCC diagnosis. The study discovered that the diagnostic performance of the metabolome was superior to that of the microbiome, and a panel comprised of key species and key metabolites outperformed alpha-fetoprotein (AFP) in terms of diagnostic value. Spearman's rank correlation test was used to determine the relationship between the intestinal flora and serum metabolites and their impact on hepatocarcinogenesis and progression. A random forest model was used to assess the diagnostic performance of the different histologies alone and in combination. In summary, this study describes the characteristics of HCC patients' intestinal flora and serum metabolism, demonstrates that HCC is caused by the interaction of intestinal flora and serum metabolites, and suggests that two key species and five key metabolites may be potential markers for the diagnosis of HCC.

Nucleolin recognizing silica nanoparticles inhibit cell proliferation by activating the Bax/Bcl-2/caspase-3 signalling pathway to induce apoptosis in liver cancer.

Multifunctional nanocarrier platforms have shown great potential for the diagnosis and treatment of liver cancer. Here, a novel nucleolin-responsive nanoparticle platform was constructed for the concurrent detection of nucleolin and treatment of liver cancer. The incorporation of AS1411 aptamer, icaritin (ICT) and FITC into mesoporous silica nanoparticles, labelled as Atp-MSN (ICT@FITC) NPs, was the key to offer functionalities. The specific combination of the target nucleolin and AS1411 aptamer caused AS1411 to separate from mesoporous silica nanoparticles surface, allowing FITC and ICT to be released. Subsequently, nucleolin could be detected by monitoring the fluorescence intensity. In addition, Atp-MSN (ICT@FITC) NPs can not only inhibit cell proliferation but also improve the level of ROS while activating the Bax/Bcl-2/caspase-3 signalling pathway to induce apoptosis in vitro and in vivo. Moreover, our results demonstrated that Atp-MSN (ICT@FITC) NPs had low toxicity and could induce CD3+ T-cell infiltration. As a result, Atp-MSN (ICT@FITC) NPs may provide a reliable and secure platform for the simultaneous identification and treatment of liver cancer.

Dual-Gene-Controlled Rolling Circle Amplification Strategy for SARS-CoV-2 Analysis.

The development of sensitive, accurate, and conveniently operated methods for the simultaneous assay of two nucleic acids is promising while still challenging. In this work, by using two genes (the N gene and RdRp gene) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as examples, we have designed an ingenious dual-gene-controlled rolling circle amplification (RCA) strategy to propose an accurate and sensitive electrochemical method. Specifically, the coexistence of the two target genes can trigger the RCA reaction to generate a number of repeated G-quadruplex (G4)-forming sequences. These sequences then switch into G4/hemin complexes with redox activity after the incubation of hemin, which can catalyze the TMB/H2O2 substrates to produce significantly enhanced current responses. Experimental results reveal that the proposed method exhibits satisfying feasibility and analytical performance, enabling the sensitive detection of SARS-CoV-2 in the range of 0.1-5000 pM, with the detection limit of 57 fM. Meanwhile, because only the simultaneous existence of the two target genes can effectively trigger the downstream amplification reaction, this method can effectively avoid false-positives and ensure specificity as well as accuracy. Furthermore, our method can distinguish the COVID-19 samples from healthy people, and the outcomes show a satisfying agreement with the results of RT-PCR, manifesting that our label-free dual-gene-controlled RCA strategy exhibits great possibility in clinical application.

Toward improved understanding of foot shape, foot posture, and foot biomechanics during running: A narrative review.

The current narrative review has explored known associations between foot shape, foot posture, and foot conditions during running. The artificial intelligence was found to be a useful metric of foot posture but was less useful in developing and obese individuals. Care should be taken when using the foot posture index to associate pronation with injury risk, and the Achilles tendon and longitudinal arch angles are required to elucidate the risk. The statistical shape modeling (SSM) may derive learnt information from population-based inference and fill in missing data from personalized information. Bone shapes and tissue morphology have been associated with pathology, gender, age, and height and may develop rapid population-specific foot classifiers. Based on this review, future studies are suggested for 1) tracking the internal multi-segmental foot motion and mapping the biplanar 2D motion to 3D shape motion using the SSM; 2) implementing multivariate machine learning or convolutional neural network to address nonlinear correlations in foot mechanics with shape or posture; 3) standardizing wearable data for rapid prediction of instant mechanics, load accumulation, injury risks and adaptation in foot tissue and bones, and correlation with shapes; 4) analyzing dynamic shape and posture via marker-less and real-time techniques under real-life scenarios for precise evaluation of clinical foot conditions and performance-fit footwear development.

Dataset of lower extremity joint angles, moments and forces in distance running.

This study presents a database of joint angles, moments, and forces of the lower extremity from distance running at a submaximal speed in recreational runners. Twenty recreational runners participated in two experimental sessions, specifically pre and post a 5k treadmill run, with a synchronous collection of markers trajectories and ground reaction forces for both limbs in walking and running trials. The raw data in C3D files could be used for musculoskeletal modelling. Extra datasets of joint angles, moments, and forces are presented ready-for-use in MAT files, which could be as reference for study of biomechanical alterations from distance running. Applying advanced data processing techniques (Machine Learning algorithms) to these datasets ( C3D & MAT ), such as Principal Component Analysis, could extract key features of variation, thus potentially being applied for correlation with accelerometric and gyroscope parameters from wearable sensors during field running. Dataset of multi-segmental foot could be another contribution for the investigation of foot complex biomechanics from distance running. The dataset from Asian males may also be used for population-based studies of running biomechanics.

Shock Acceleration and Attenuation during Running with Minimalist and Maximalist Shoes: A Time- and Frequency-Domain Analysis of Tibial Acceleration.

Tibial shock attenuation is part of the mechanism that maintains human body stabilization during running. It is crucial to understand how shock characteristics transfer from the distal to proximal joint in the lower limb. This study aims to investigate the shock acceleration and attenuation among maximalist shoes (MAXs), minimalist shoes (MINs), and conventional running shoes (CONs) in time and frequency domains. Time-domain parameters included time to peak acceleration and peak resultant acceleration, and frequency-domain parameters contained lower (3−8 Hz) and higher (9−20 Hz) frequency power spectral density (PSD) and shock attenuation. Compared with CON and MAX conditions, MINs significantly increased the peak impact acceleration of the distal tibia (p = 0.01 and p < 0.01). Shock attenuation in the lower frequency depicted no difference but was greater in the MAXs in the higher frequency compared with the MIN condition (p < 0.01). MINs did not affect the tibial shock in both time and frequency domains at the proximal tibia. These findings may provide tibial shock information for choosing running shoes and preventing tibial stress injuries.

Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.

With the emergence of wearable technology and machine learning approaches, gait monitoring in real-time is attracting interest from the sports biomechanics community. This study presents a systematic review of machine learning approaches in running biomechanics using wearable sensors. Electronic databases were retrieved in PubMed, Web of Science, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect. A total of 4,068 articles were identified via electronic databases. Twenty-four articles that met the eligibility criteria after article screening were included in this systematic review. The range of quality scores of the included studies is from 0.78 to 1.00, with 40% of articles recruiting participant numbers between 20 and 50. The number of inertial measurement unit (IMU) placed on the lower limbs varied from 1 to 5, mainly in the pelvis, thigh, distal tibia, and foot. Deep learning algorithms occupied 57% of total machine learning approaches. Convolutional neural networks (CNN) were the most frequently used deep learning algorithm. However, the validation process for machine learning models was lacking in some studies and should be given more attention in future research. The deep learning model combining multiple CNN and recurrent neural networks (RNN) was observed to extract different running features from the wearable sensors and presents a growing trend in running biomechanics.

Target-triggered cascade signal amplification for sensitive electrochemical detection of SARS-CoV-2 with clinical application.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the outbreak of the 2019 coronavirus (COVID-19) disease, which greatly challenges the global economy and health. Simple and sensitive diagnosis of COVID-19 at the early stage is important to prevent the spread of pandemics. Herein, we have proposed a target-triggered cascade signal amplification in this work for sensitive analysis of SARS-CoV-2 RNA. Specifically, the presence of SARS-CoV-2 RNA can trigger the catalytic hairpin assembly to generate plenty of DNA duplexes with free 3'-OH termini, which can be recognized and catalyzed by the terminal deoxynucleotidyl transferase (TdT) to generate long strand DNA. The prolonged DNA can absorb substantial Ru(NH3)63+ molecules via electrostatic interaction and produce an enhanced current response. The incorporation of catalytic hairpin assembly and TdT-mediated polymerization effectively lowers the detection limit to 45 fM, with a wide linear range from 0.1 pM to 3000 pM. Moreover, the proposed strategy possesses excellent selectivity to distinguish target RNA with single-base mismatched, three-base mismatched, and random sequences. Notably, the proposed electrochemical biosensor can be applied to analyze targets in complex circumstances containing 10% saliva, which implies its high stability and anti-interference. Moreover, the proposed strategy has been successfully applied to SARS CoV-2 RNA detection in clinical samples and may have the potential to be cultivated as an effective tool for COVID-19 diagnosis.

Automatic Classification of Barefoot and Shod Populations Based on the Foot Metrics and Plantar Pressure Patterns.

The human being's locomotion under the barefoot condition enables normal foot function and lower limb biomechanical performance from a biological evolution perspective. No study has demonstrated the specific differences between habitually barefoot and shod cohorts based on foot morphology and dynamic plantar pressure during walking and running. The present study aimed to assess and classify foot metrics and dynamic plantar pressure patterns of barefoot and shod people via machine learning algorithms. One hundred and forty-six age-matched barefoot (n = 78) and shod (n = 68) participants were recruited for this study. Gaussian Naïve Bayes were selected to identify foot morphology differences between unshod and shod cohorts. The support vector machine (SVM) classifiers based on the principal component analysis (PCA) feature extraction and recursive feature elimination (RFE) feature selection methods were utilized to separate and classify the barefoot and shod populations via walking and running plantar pressure parameters. Peak pressure in the M1-M5 regions during running was significantly higher for the shod participants, increasing 34.8, 37.3, 29.2, 31.7, and 40.1%, respectively. The test accuracy of the Gaussian Naïve Bayes model achieved an accuracy of 93%. The mean 10-fold cross-validation scores were 0.98 and 0.96 for the RFE- and PCA-based SVM models, and both feature extract-based and feature select-based SVM models achieved an accuracy of 95%. The foot shape, especially the forefoot region, was shown to be a valuable classifier of shod and unshod groups. Dynamic pressure patterns during running contribute most to the identification of the two cohorts, especially the forefoot region.

Gait biomechanics evaluation of the treatment effects for hallux valgus patients: A systematic review and meta-analysis.

BACKGROUND: Hallux valgus (HV) is a foot deformity characterized by lateral deviation of the big toe and medial deviation of the first metatarsal. RESEARCH QUESTION: This study aimed to shed light on the treatment effects of different interventions and surgical procedures for HV deformity to determine the effectiveness of gait biomechanics correction. METHODS: English-language searches of the electronic databases were conducted in the Cochrane Library, Web of Science, PubMed, Scopus, and Embase. Gait biomechanics evaluation before and after conservative or operative treatments was essential for inclusion in this review. Methodological quality was assessed by the Institute of Health Economics (IHE) quality appraisal tool. All pooled analysis was based on the random-effects model. RESULTS: Twenty-five articles (1003 participants) were identified in this review. Three studies chose conservative therapies for HV deformity, incorporating foot orthotics and minimalist running intervention, and surgeries were performed in twenty-two studies. For the pressure parameter alteration under the hallux, the effect size (ES) in the conservative treatment subgroup was - 0.95 with 95%CI [- 1.69, - 0.21]. It demonstrated a moderate ES of - 0.44% and 95%CI [- 0.81, - 0.07] in the surgery subgroup. The five operations' peak pressure alteration under the hallux demonstrated a moderate ES of - 0.45% and 95%CI [- 0.54, - 0.36]. SIGNIFICANCE: Both non-operative and operative treatments could achieve the forefoot pressure redistribution, decreasing loading beneath the hallux and first metatarsal regions，However, the treatment effects of surgeries were not very robust. The percutaneous DSTR-Akin technique is recommended as an adequate operative treatment, with a large ES and moderate heterogeneity. The negative gait return effect should be noticed while using Scarf osteotomy, despite positive clinical and radiographic outcomes.

Evaluating function in the hallux valgus foot following a 12-week minimalist footwear intervention: A pilot computational analysis.

Hallux valgus is a foot pathological condition showing a lateral deviation of the first phalange and medial deviation of the first metatarsal. The purpose of the current study was to evaluate a longitudinal effect of minimalist footwear running protocol for a mild/moderate hallux valgus patient. The computer tomography (CT) images from a male hallux valgus (HV) patient were respectively scanned pre and post 12-week minimalist footwear running intervention. The pre and post -intervention foot finite element (FE) models were developed from the foot three-dimensional geometries manually segmented via the MIMICS 21.0. The post-process with SolidWorks 2019 was conducted for model assembly, consisting of 24 bones, 22 cartilages, five plantar fascia, and lumped encapsulated soft tissue. The foot FE models were solved in ANSYS Workbench 2020 R1 package. The FE models were validated against the plantar pressure (pre: 0.146 MPa vs 0.155 MPa, and post: 0.156 MPa vs 0.17 MPa) and vertical displacements (pre: 2.6 mm vs 2.4 ±â€¯0.4 mm, and post 1.1 mm vs 1.3 ±â€¯0.4 mm) of navicular measured from experiments. The first metatarsophalangeal joint showed varus realignment and the von Mises stress in the first metatarsal and the second metatarsal decreased 72.1% and 51.2% compared with pre-intervention (M1: 4.41 MPa and M2: 4.18 MPa). This framework investigated the shape adjustment and functional recovery in the mild/moderate HV deformity, which may provide references and implications for future studies with a larger cohort.

Ultrasensitive fluorescent detection of telomerase activity based on tetrahedral DNA nanostructures as carriers for DNA-templated silver nanoclusters.

Precise evaluation of telomerase activity is essential for the clinical diagnosis of early tumors. Herein, we have ingeniously designed a tetrahedral DNA nanostructure, with hairpin-shaped DNA probes rich in cytosine bases at four vertices for telomerase detection. The DNA-templated silver nanoclusters can be formed after the addition of Ag. Then the introduction of telomerase adds the single-strand TTAGGG extension, which can "turn on" the fluorescence of silver nanoclusters quickly by the proximity of the resulting guanine-rich sequences to silver nanoclusters and realize accurate detection of telomerase activity. In this study, integration of high stability tetrahedral DNA nanostructure and fluorescence signal amplification of four DNA-templated silver nanoclusters offers the advantage of high sensitivity, with a low detection limit of 1 cell. More than that, this method is low-cost, facile, and feasible for practical clinical applications.