The high frequency oscillatory ventilation (HFOV) is characterized with low tidal volume and low mean airway pressure, and can well support the breathing of the patients with respiratory diseases. Since the HFOV was proposed, it has been widely concerned by medical and scientific researchers. About the HFOV, this paper discussed its current research status and prospected its future development in technologies. The research status of ventilation model, mechanisms and ventilation mode were introduced in detail. In the next years, the technologies in developing HFOV will be focused on: to develop the branched high-order nonlinear or volume-depended resistance-inertance-compliance (RIC) ventilation model, to fully understand the mechanisms of HFOV and to achieve the noninvasive HFOV. The development in technologies of HFOV will be beneficial to the patients with respiratory diseases who failed with conventional mechanical ventilation as one of considerable ventilation methods.
PNAS-4 is a novel pro-apoptosis gene identified latetly. In recent years, there has been a large number of research reports on the basic studies about PNAS-4 in cancer gene therapy and gene therapy of PNAS-4 alone or combined with chemotherapy or radiotherapy manifested a good application prospect, but its molecular mechanisms to promote apoptosis is not clear yet. In this paper, recent research about PNAS-4 in cancer gene therapy is briefly reviewed, and recent hypotheses on its molecular mechanisms to promote apoptosis are especially elucidated. Based on its newly identified characteristics of structural domain, we made a point that PNAS-4 might regulate functions of some target protein related to apoptosis by deSumoylation as a new deSumoylating isopeptidase, and consequently promote apoptosis.
ObjectiveTo investigate the neural mechanisms of subjects with anisometropic amblyopia using event-related potential (ERP) technology.MethodsNineteen subjects diagnosed with anisometropic amblyopia were consecutively recruited from the outpatients of the West China Hospital of Sichuan University from June 2013 to February 2014, and twenty normal subjects were recruited as control subjects. All subjects were given different orientation Gabor stimuli with three spatial frequencies (1, 2, 8 cpd) and their ERP was recorded. The differences of P3b, including amplitude and latency, were compared between amblyopic eye and fellow or control eye.ResultsAmong the three groups of amblyopic eye and fellow or control eye, there was no statistically significant difference in accuracy or response time at three spatial frequencies (P>0.05), in latency or amplitude of P3b at low spatial frequencies (1, 2 cpd) ((P>0.05), or in amplitude of P3b at high spatial frequency (8 cpd) (P>0.05). P3b latency of amblyopic eye extended at high spatial frequency (8 cpd), compared to fellow or control eye. Amblyopic eye was most significantly weakened in Brodmann area 17 at the P3b wave of each spatial frequency (1, 2, 8 cpd), and there was most significantly weakened in high spatial frequency (8 cpd).ConclusionThe P3b latency of amblyopic eye extended at high spatial frequency suggests that the cognitive function of amblyopia is impaired, at an extent, in the late visual processing stage.
Endogenous adult neural stem cells are closely related to the normal physiological functions of the brain and many neurodegenerative diseases. Neurons are affected by factors such as extracellular microenvironment and intracellular signaling. In recent years, some specific signaling pathways have been found that affect the occurrence of neural stem cells in adult neural networks, including proliferation, differentiation, maturation, migration, and integration with host functions. In this paper, we summarize the signals and their molecular mechanisms, including the related signaling pathways, neurotrophic factors, neurotransmitters, intracellular transcription factors and epigenetic regulation of neuronal differentiation from both the extracellular and intracellular aspects, providing basic theoretical support for the treatment of central nervous system diseases through neural stem cells approach.
Currently, all the conventional antibiotics have developed corresponding drug-resistant pathogenic strains, which have increasingly become a serious threat to people's health. Development of completely new types of antibiotics is one of effective ways to solve the drug resistance issue. Antimicrobial peptides with broad-spectrum antibacterial and antimicrobial activity and wild variety become the ideal alternative to traditional antibiotics. Antimicrobial peptides are derived from wide range of sources, such as plants, animals, and microorganisms. Mechanism of function of the antimicrobial peptides and the investigation approaches of different antimicrobial peptides also vary dramatically. In this paper, we give an overview of preparation, antibacterial mechanisms, and research methodology of antimicrobial peptides.
ObjectiveAlthough evidence links idiopathic pulmonary fibrosis (IPF) and diabetes mellitus (DM), the exact underlying common mechanism of its occurrence is unclear. This study aims to explore further the molecular mechanism between these two diseases. MethodsThe microarray data of idiopathic pulmonary fibrosis and diabetes mellitus in the Gene Expression Omnibus (GEO) database were downloaded. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify co-expression genes related to idiopathic pulmonary fibrosis and diabetes mellitus. Subsequently, differentially expressed genes (DEGs) analysis and three public databases were employed to analyze and screen the gene targets related to idiopathic pulmonary fibrosis and diabetes mellitus. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape. In addition, common microRNAs (miRNAs), common in idiopathic pulmonary fibrosis and diabetes mellitus, were obtained from the Human microRNA Disease Database (HMDD), and their target genes were predicted by miRTarbase. Finally, we constructed a common miRNAs-mRNAs network by using the overlapping genes of the target gene and the shared gene. ResultsThe results of common gene analysis suggested that remodeling of the extracellular matrix might be a key factor in the interconnection of DM and IPF. Finally, hub genes (MMP1, IL1R1, SPP1) were further screened. miRNA-gene network suggested that has-let-19a-3p may play a key role in the common molecular mechanism between IPF and DM. ConclusionsThis study provides new insights into the potential pathogenic mechanisms between idiopathic pulmonary fibrosis and diabetes mellitus. These common pathways and hub genes may provide new ideas for further experimental studies.
Objective To investigate the load distribution on the more painful and less painful limbs in patients with mild-to-moderate and severe bilateral knee osteoarthritis (KOA) and explore the compensatory mechanisms in both limbs among bilateral KOA patients with different severity levels. Methods A total of 113 participants were enrolled between July 2022 and September 2023. This cohort comprised 43 patients with mild-to-moderate bilateral KOA (Kellgren-Lawrence grade 2-3), 43 patients with severe bilateral KOA (Kellgren-Lawrence grade 4), and 27 healthy volunteers (healthy control group). The visual analogue scale (VAS) score for pain, the Hospital for Special Surgery (HSS) score, passive knee range of motion (ROM), and hip-knee-ankle angle (HKA) were used to assess walking pain intensity, joint function, and lower limb alignment in KOA patients, respectively. Motion trajectories of reflective markers and ground reaction force data during walking were captured using a gait analysis system. Musculoskeletal modeling was then employed to calculate biomechanical parameters, including the peak knee adduction moment (KAM), KAM impulse, peak joint contact force (JCF), and peak medial/lateral contact forces (MCF/LCF). Statistical analyses were performed to compare differences in clinical and gait parameters between bilateral limbs. Additionally, one-dimensional statistical parametric mapping was utilized to analyze temporal gait data. Results Mild-to-moderate KOA patients showed the significantly higher HSS score (67.7±7.9) than severe KOA patients (51.9±8.9; t=8.747, P<0.001). The more painful limb in all KOA patients exhibited significantly greater HKA and higher VAS scores compared to the less painful limb (P<0.05). While bilateral knee ROM did not differ significantly in mild-to-moderate KOA patients (P>0.05), the severe KOA patients had significantly reduced ROM in the more painful limb versus the less painful limb (P<0.05). Healthy controls showed no significant bilateral difference in any biomechanical parameters (P>0.05). All KOA patients demonstrated longer stance time on the less painful limb (P<0.05). Critically, severe KOA patients exhibited significantly higher peak KAM, KAM impulse, and peak MCF in the more painful limb (P<0.05), while mild-to-moderate KOA patients showed the opposite pattern with lower peak KAM and KAM impulse in the more painful limb (P<0.05) and a similar trend for peak MCF. Conclusion Patients with mild-to-moderate KOA effectively reduce load on the more painful limb through compensatory mechanisms in the less painful limb. Conversely, severe bilateral varus deformities in advanced KOA patients nullify compensatory capacity in the less painful limb, paradoxically increasing load on the more painful limb. This dichotomy necessitates personalized management strategies tailored to disease severity.
Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.
Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.
Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.