Frontiers in Physiology最新文献

Prolonged sitting leads to a slumped posture, which indirectly influences spinal curvature and increases low back and hamstring stiffness. Active rather than passive recovery is an effective way to reduce the risks associated with such prolonged inactivity. However, it remains to be investigated which of the exercises frequently used for this purpose, the trunk stability and foam rolling exercise, is more beneficial. This protocol study will compare the effects of foam rolling exercises on the recovery of impaired core and postural stability induced by core muscle fatigue and hamstring muscle stiffness with those of trunk stabilization exercises in sedentary adults. Twenty sedentary adults ranging in age from 25 to 44 years old, comprising 50% men and 50% women, will participate in a modified Abt's trunk muscle fatigue protocol, then proceed with (1) active recovery in the form of trunk stabilization exercises, (2) active recovery in the form of foam rolling exercises, and (3) passive recovery, entails lying on a bed, respectively. Pre-fatigue, post-fatigue, and after all three recovery modalities, core and postural stability, and back and hamstring muscle flexibility will be evaluated using an inertial sensor system, and a posturography system. Muscle-fatigue conditions will be determined using electromyogram signals. Although the effects of foam rolling and trunk stabilization exercises can be attributed to different physiological mechanisms, the former releasing myofascial to improve flexibility and reduce muscle tension, the latter strengthening core muscles to stabilize posture, we hypothesize that both are equivalently effective in reducing the consequences of prolonged sitting.

Objective: To explore the feasibility of post-exercise heart rate recovery indicators for predicting maximum oxygen uptake (VO2max) in healthy adults aged 30-60 years.

Methods: 260 healthy adults who did not perform regular exercise were randomly recruited and divided into a model group (n = 200) and a verification group (n = 60). Measure body fat percentage, weight, height and other indicators, and complete a cardiopulmonary exercise test as required to measure VO2max and heart rate recovery (HRR1, HRR2) in the first and second minutes after exercise. Equations are established through stepwise regression method, and the selected optimal equation is tested for back substitution.

Results: The optimal equation is: $\text{Absolute\hspace{0.17em}VO}2\max =-0.528+0.039*\text{weight}-3.463*\text{body\hspace{0.17em}fat\hspace{0.17em}rate}+0.042*\text{HRR}2-0.180*\text{gender\hspace{0.17em}}\left(\text{male}=1,\text{female}=2\right)$ . Analysis of variance, goodness-of-fit test, VIF test, Shapiro-Wilk test, and Durbin-Watson test indicate that the equation is more reliable; Pearson product-moment correlation analysis, paired t test, and Bland-Altman consistency test indicate that the equation is more valid good.

Conclusion: The regression equation established through heart rate recovery after exercise can be used to predict VO2max in healthy adults aged 30-60 years.

Background: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.

Objective: This study aims to leverage DL techniques in the analysis of routine clinical and ultrasound examination data to refine ER assessment within RPL management.

Methods: Employing a retrospective, controlled design, this study included 346 individuals with unexplained RPL and 369 controls to assess ER. Participants were allocated into training (n = 485) and testing (n = 230) datasets for model construction and performance evaluation, respectively. DL techniques were applied to analyze conventional grayscale ultrasound images and clinical data, utilizing a pre-trained ResNet-50 model for imaging analysis and TabNet for tabular data interpretation. The model outputs were calibrated to generate probabilistic scores, representing the risk of RPL. Both comparative analyses and ablation studies were performed using ResNet-50, TabNet, and a combined fusion model. These were evaluated against other state-of-the-art DL and machine learning (ML) models, with the results validated against the testing dataset.

Results: The comparative analysis demonstrated that the ResNet-50 model outperformed other DL architectures, achieving the highest accuracy and the lowest Brier score. Similarly, the TabNet model exceeded the performance of traditional ML models. Ablation studies demonstrated that the fusion model, which integrates both data modalities and is presented through a nomogram, provided the most accurate predictions, with an area under the curve of 0.853. The radiological DL model made a more significant contribution to the overall performance of the fusion model, underscoring its superior predictive capability.

Conclusion: This investigation demonstrates the superiority of a DL-enhanced fusion model that integrates routine ultrasound and clinical data for accurate stratification of RPL risk, offering significant advancements over traditional methods.

Myocardial ischemia causes the production and release of metabolites such as bradykinin, which stimulates cardiac spinal sensory afferents, causing chest pain and an increase in sympathetic activity referred to as the cardiogenic sympathetic afferent reflex. While the brain stem nuclei, such as the nucleus tractus solitarius and rostral ventrolateral medulla, are essential in the cardiogenic sympathetic afferent reflex, the role of other supramedullary nuclei in the cardiogenic sympathetic afferent reflex are not clear. The dorsomedial hypothalamic nucleus (DMH) is involved in cardiovascular sympathetic regulation and plays an important role in the sympathetic response to stressful stimuli. In this study, we determined the role of DMH in the cardiogenic sympathetic afferent reflex. To do this we measured arterial pressure, heart rate, and renal sympathetic nerve activity (RSNA) responses to epicardial bradykinin (10 μg/mL) in anesthetized Sprague Dawley rats before and after bilateral DMH microinjection (50 nL) of either the GABAA agonist muscimol (0.5 nmol) to inhibit or the antagonist bicuculline (40 pmol) to disinhibit activity. Muscimol inhibition elicited a modest, albeit significant, reduction in basal arterial pressure and heart rate and attenuated the arterial pressure and heart rate reflex response to epicardial bradykinin. However, it did not change the magnitude of the reflex. Bicuculline disinhibition of the DMH increased basal arterial pressure, heart rate, and RSNA but did not augment the response to epicardial bradykinin. These results suggest that sympathetic activity derived from the DMH does not play an important role in the cardiogenic sympathetic afferent reflex in Sprague Dawley rats.

The assessment of vascular accessibility in patients undergoing hemodialysis is predominantly reliant on manual inspection, a method that is associated with several limitations. In this study, we propose an alternative approach by recording the acoustic signals produced by the arteriovenous fistula (AVF) and employing deep learning techniques to analyze these sounds as an objective complement to traditional AVF evaluation methods. Auscultation sounds were collected from 800 patients, with each recording lasting between 24 and 30 s. Features were extracted by combining Mel-Frequency Cepstral Coefficients with Mel-Spectrogram data, generating a novel set of feature parameters. These parameters were subsequently used as input to a model that integrates the Convolutional Block Attention Module and a Long Short-Term Memory neural network, designed to classify the severity of AVF stenosis based on two sound categories (normal and abnormal). The experimental results demonstrate that the CBAM-LSTM model achieves an Area Under the Receiver Operating Characteristic curve of 99%, Precision of 99%, Recall of 97%, and F1 Score of 98%. Comparative analysis with other models, including VGG, Bi-LSTM, DenseNet121, and ResNet50, indicates that the proposed CBAM-LSTM model outperforms these alternatives in classifying AVF stenosis severity. These findings suggest the potential of the CBAM-LSTM model as a reliable tool for monitoring AVF maturation.

Objective: Meta-analysis was used to evaluate the effects of an exercise intervention on bone mineral content (BMC), bone mineral density (BMD), and bone metabolism in adolescents.

Methods: A systematic search of PubMed, Embase, Cochrane Library, and Web of Science for RCTs on "exercise, adolescents, BMD, bone metabolism" up to 10 September 2024. Included RCTs focused on effects of exercise on BMC, BMD, and bone metabolism in 10-19 years old, with physical activity as exercise group and daily living/primal exercise as control group. Outcome metrics included lumbar spine, femoral neck, whole body BMC and BMD, bone alkaline phosphatase (BALP), procollagen type 1N-terminal propeptide (PINP), osteocalcin (OC), and type I collagen carboxy-terminal peptide (CTX). Exclusion criteria included duplicates, non-RCTs, non-adolescent studies, and non-compliance with indicators. Meta-analyses were performed using RevMan 5.4, and quality assessed by Cochrane's tool. Effect sizes were estimated using standardized mean differences (SMDs) and 95% confidence intervals (CIs), and heterogeneity was assessed using the I ² statistic to determine fixed or random effects models.

Results: Fifteen articles with a total of 723 subjects were included. The meta-analysis showed that, compared to the control group, (i) exercise was effective in increasing adolescents' overall BMC (SMD = 0.16, 95% CI: 0.06-0.27, p = 0.003) and BMD (SMD = 0.26, 95% CI: 0.13-0.40, p = 0.0001). (ii) Subgroup analyses showed that exercise significantly increased adolescents' lumbar spine BMC (SMD = 0.17, 95% CI: 0.01-0.34, p = 0.04), femoral neck BMC (SMD = 0.23, 95% CI: 0.05-0.42, p = 0.01), lumbar spine BMD (SMD = 0.34, 95% CI: 0.12-0.56, p = 0.003) and femoral neck BMD (SMD = 0.31, 95% CI: 0.09-0.53, p = 0.007), whereas there was no statistically significant effect on whole body BMC and BMD (p > 0.05). (iii) Exercise increased BALP, and decreased PINP, OC and CTX in adolescents. but none of the differences between the exercise groups and the control group were significant (p > 0.05).

Conclusion: Exercise is effective in improving overall BMC and BMD in adolescents and elevating BMC and BMD of the lumbar spine and femoral neck. Due to the limitation of the number and quality of the included studies, the above conclusions are yet to be validated by more high-quality empirical studies.

Systematic review registration: https://www.crd.york.ac.uk/, identifier CRD42024593399.

Introduction: Temperature and oxygen are two factors that profoundly affect survival limits of animals; too much or too little of either is lethal. However, humans and other animals can exhibit exceptional survival when oxygen and temperature are simultaneously low. This research investigates the role of oxygen in the cold shock death of Caenorhabditis elegans.

Methods: The survival of C. elegans populations in combinations of oxygen concentrations and was assayed. Additionally, the effect of cold acclimatization, mutations in the cold acclimatization pathway, compounds, and antioxidant proteins on survival in low temperatures and high oxygen were investigated.

Results: We demonstrate that C. elegans have increased survival in 2°C when deprived of oxygen, and an increase to just 0.25 kPa of oxygen decreased survival. Additionally, we show that oxygen toxicity produced by a 35-fold increase above atmospheric oxygen levels was fatal for nematodes in 8 h at room temperature and 2 h at 2°C. We found that cold acclimatization and mutations in the cold acclimatization pathway improve survival in room temperature oxygen toxicity. Furthermore, we found that the compounds glucose, manganese (II), and ascorbate improve both cold shock and high oxygen survival, while the antioxidant proteins catalase and peroxiredoxin are essential to wild type survival in these conditions.

Discussion: Our results suggest that oxygen toxicity contributes to the death of C. elegans during cold shock. The changes in survival induced by cold acclimatization and mutations in the cold acclimatization pathway suggest that oxygen toxicity in the cold exerts evolutionary pressure, leading to the development of protections against it. Additionally, the resistance provided by diverse compounds and antioxidant proteins in both low temperature and high oxygen suggests these conditions have similar chemical environments. We discuss evidence that similar phenomena may function in humans.

Objectives: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.

Methods: This retrospective study collected data from 360 patients with uterine fibroids who underwent HIFU treatment. The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60). Patients were categorized into favorable and unfavorable prognosis groups based on the post-treatment non-perfused volume ratio. Automated segmentation of uterine fibroids was performed using a V-net deep learning models. Radiomics features were extracted from T2WI and CE-T1WI, followed by data preprocessing including normalization and scaling. Feature selection was performed using t-test, Pearson correlation, and LASSO to identify the most predictive features for preoperative prognosis Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) were employed as base learners to construct base predictive models. These models were integrated into a stacking ensemble model, with Logistic Regression serving as the meta-learner to combine the outputs of the base models. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC).

Results: Among the base models developed using T2WI and CE-T1WI features, the MLP model exhibited superior performance, achieving an AUC of 0.858 (95% CI: 0.756-0.959) in the internal test set and 0.828 (95% CI: 0.726-0.930) in the external test set. It was followed by the SVM, LightGBM, and RF, which obtained AUC values of 0.841 (95% CI: 0.737-0.946), 0.823 (95% CI: 0.711-0.934), and 0.750 (95% CI: 0.619-0.881), respectively. The stacking ensemble learning model, which integrated these five algorithms, demonstrated a notable enhancement in performance, with an AUC of 0.897 (95% CI: 0.818-0.977) in the internal test set and 0.854 (95% CI: 0.759-0.948) in the external test set.

Conclusion: The DL based automatic segmentation MRI radiomics stacking ensemble learning model demonstrated high accuracy in predicting the prognosis of HIFU ablation of uterine fibroids.

Impaired intestinal integrity in broilers reduces performance and health, highlighting the importance of accurately measuring intestinal permeability (IP) to maintain gut health. The objective of this study was to evaluate the efficiency of iohexol as an IP marker in broilers challenged with Eimeria maxima, Clostridium perfringens, or both during both peak challenge (day [d] 21) and recovery (d 28) periods. One-day-old male Ross 708 birds (n = 56) were distributed into 4 treatment groups: NC (no-challenge control); EM (challenged with 5,000 E. maxima sporulated oocysts/bird on d 15); CP (challenged with 1.0 × 10⁸ CFUs/bird of C. perfringens on d 19 and d 20); and EM + CP (challenged by co-infection of E. maxima and C. perfringens as described). On d 21 and d 28, each bird received an iohexol dose of 64.7 mg/kg body weight via oral gavage. One hour later, blood samples were collected from 14 birds (12 in EM) per group on d 21 and from 7 birds (6 in EM) on d 28. For lesion scoring and ileum collection, 7 birds per group (6 birds in EM) were sampled on each d 21 and d 28. Birds in the EM and EM + CP groups had lower body weight gain (BWG) compared to the NC and CP groups on d 19-21 (P ≤ 0.05). These birds also exhibited significantly greater lesion scores and markedly higher serum iohexol levels on d 21 (P ≤ 0.05). However, no significant differences in serum iohexol levels were observed among treatment groups following recovery on d 28. Moreover, significant differentials were observed in the mRNA abundance of key tight junction proteins (CLDN1, CLDN2, and ZO3), pro-inflammatory cytokines (IL-1β, IFNγ, and IL-22), and gut health markers (GLP2, OLFM4, and MUC2) in the EM and EM + CP groups compared to the NC and CP groups on d 21. In conclusion, this study demonstrates that iohexol is an effective marker for assessing IP in broilers under different enteric challenge conditions.

Introduction: Exercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise.

Method: In this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20-30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps.

Results: The characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics.

Discussion: The variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA.