Frontiers in Physiology最新文献

Introduction: Research on the effects of training programs involving small-sided games (SSG) versus high-intensity interval training (HIIT) has been increasing in recent years. However, there is limited understanding of how an individual's initial physical fitness level might influence the extent of adaptations achieved through these programs. This study aimed to compare the impacts of SSG and HIIT on male soccer players, while also considering the players' athleticism, categorized into lower and higher total athleticism score (TSA).

Methods: A prospective cohort study was conducted over a 6-week pre-season training period, involving 43 male soccer players from regional-level teams (average age 16.5 ± 0.7 years). Players were evaluated at the start and after the 6-week period. One team incorporated SSG as a core component of their aerobic-based training, while the other team used HIIT. Evaluations included a countermovement jump (CMJ) test, a 30-meter linear sprint test, and the 30-15 intermittent fitness test (30-15 IFT). TSA was calculated to assess each player's overall athleticism level (classifying them as fit and non-fit).

Results: Results revealed that non-fit players showed significantly greater CMJ improvements (mean difference: 3.0 cm; p < 0.005) and VIFT improvements (mean difference: 0.682 km/h; p = 0.002) in SSG compared to fit players. In the HIIT group, non-fit players also revealed greater improvements than fit players in CMJ (mean difference: 2.5 cm; p < 0.005) and peak speed in sprint (mean difference: 0.706 km/h; p = 0.002). No significant differences were found between groups regarding the observed improvements.

Discussion: In conclusion, this study suggests that the initial level of physical fitness significantly influences the magnitude of adaptations. Specifically, players with lower fitness levels appear to benefit more from training interventions. Improvements in CMJ and aerobic capacity in SSG seem to depend on players' fitness levels, and a similar trend is observed in HIIT for CMJ and peak speed. Individualizing training programs is recommended, with a focus on providing greater or different stimuli to more well-prepared players to ensure their continued development.

Melatonin, a pleiotropic hormone plays a vital role in enhancing livestock performance not only by regulating circadian rhythms but also by exhibiting antioxidant, immunomodulatory, and metabolic regulatory effects that collectively improve resilience, fertility, and productivity. Melatonin's synthesis is predominantly influenced by light exposure, with increased production in darkness; however, factors such as diet and health status further modulate its levels. By helping animals adapt to environmental stressors, melatonin boosts immune responses, mitigates chronic illnesses, and optimizes production efficiency. Its regulatory influence extends to the hypothalamic-pituitary-gonadal (HPG) axis, enhancing hormone secretion, synchronizing estrous cycles, and improving embryo viability. This results in improved reproductive outcomes through the protection of gametes, increased sperm motility, and enhanced oocyte quality, all of which benefit the fertilization process. Additionally, melatonin positively impacts productive performance, promoting muscle growth, development, and optimizing milk yield and composition through its interaction with metabolic and endocrine systems. As ongoing research continues to uncover its broader physiological effects, melatonin supplementation emerges as a promising approach to improving livestock welfare, productivity, and sustainability in modern animal husbandry.

Objective: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.

Methods: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT.

Results: The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions.

Conclusion: The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.

The highly developed sensitive olfactory system is essential for Picromerus lewisi Scott (Hemiptera: Pentatomidae) adults, an widely distributed natural predatory enemy, to locate host plants. During this process, odorant-binding proteins (OBPs) are thought to have significant involvement in the olfactory recognition. However, the roles of OBPs in the olfactory perception of P. lewisi are not frequently reported. Here, we conducted odor exposure and transcriptome sequencing experiments using healthy and Spodoptera litura-infested tobacco plants as odor sources. The transcriptomic data revealed that the alteration in the expression of mRNA levels upon exposure to odor was sex-dependent. As the expression profiles differed significantly between male and female adults of P. lewisi. A total of 15 P. lewisi OBPs (PlewOBPs) were identified from the P. lewisi transcriptome. Sequence and phylogenetic analysis indicated that PlewOBPs can be classified into two subfamilies (classic OBP and plus-C OBP). The qRT-PCR results showed that the transcript abundance of 8 PlewOBPs substantially altered following exposure to S. litura-infested tobacco plants, compared to the blank control or healthy plants. This implies that these PlewOBPs may have an olfactory function in detecting S. litura-infested tobacco plants. This study establishes the foundation for further understanding of the olfactory recognition mechanism of P. lewisi and helps discover novel targets for functional characterization in future research.

Background: Patients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.

Objective: To develop and validate a machine learning model for predicting PPI risk in patients with diabetes.

Methods: This retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score.

Results: The Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0.82, and NPV of 0.82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery.

Conclusion: Our study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes.

[This corrects the article DOI: 10.3389/fphys.2024.1426872.].

[This corrects the article DOI: 10.3389/fphys.2023.1067683.].

Aims: Based on the quantitative and qualitative fusion data of traditional Chinese medicine (TCM) and Western medicine, a diabetic foot (DF) prediction model was established through combining the objectified parameters of TCM and Western medicine.

Methods: The ResNet-50 deep neural network (DNN) was used to extract depth features of tongue demonstration, and then a fully connected layer (FCL) was used for feature extraction to obtain aggregate features. Finally, a non-invasive DF prediction model based on tongue features was realized.

Results: Among the 391 patients included, there were 267 DF patients, with their BMI (25.2 vs. 24.2) and waist-to-hip ratio (0.953 vs. 0.941) higher than those of type 2 diabetes mellitus (T2DM) group. The diabetes (15 years vs. 8 years) and hypertension durations (10 years vs. 7.5 years) in DF patients were significantly higher than those in T2DM group. Moreover, the plantar hardness in DF patients was higher than that in T2DM patients. The accuracy and sensitivity of the multi-mode DF prediction model reached 0.95 and 0.9286, respectively.

Conclusion: We established a DF prediction model based on clinical features and objectified tongue color, which showed the unique advantages and important role of objectified tongue demonstration in the DF risk prediction, thus further proving the scientific nature of TCM tongue diagnosis. Based on the qualitative and quantitative fusion data, we combined tongue images with DF indicators to establish a multi-mode DF prediction model, in which tongue demonstration and objectified foot data can correct the subjectivity of prior knowledge. The successful establishment of the feature fusion diagnosis model can demonstrate the clinical practical value of objectified tongue demonstration. According to the results, the model had better performance to distinguish between T2DM and DF, and by comparing the performance of the model with and without tongue images, it was found that the model with tongue images performed better.