Aromatherapy refers to the method of using the aromatic components of plants in appropriate forms to act on the entire body or a specific area to prevent and treat diseases. Essential oils used in aromatherapy are hydrophobic liquids containing volatile aromatic molecules， such as limonene， linalool， linalool acetate， geraniol， and citronellol. These chemicals have been extensively studied and shown to have a variety of functions， including reducing anxiety， relieving depression， promoting sleep， and providing pain relief. Terpenoids are a class of organic molecules with relatively low lipid solubility. After being inhaled， they can pass through the nasal mucosa for transfer or penetrate the skin and enter the bloodstream upon local application. Some of these substances also have the ability to cross the blood-brain barrier， thereby exerting effects on the central nervous system. Currently， the academic community generally agrees that products such as essential oils and aromatherapy from aromatic plants have certain health benefits. However， the process of extracting a single component from it and successfully developing it into a drug still faces many challenges. Its safety and efficacy still need to be further verified through more rigorous and systematic experiments. This article systematically elaborated on the efficacy of aromatic substances， including plant extracts and natural small molecule compounds， in antibacterial and antiviral fields and the regulation of nervous system activity. As a result， a deeper understanding of aromatherapy was achieved. At the same time， the potential of these aromatic substances for drug development was thoroughly explored， providing important references and insights for possible future drug research and application.

Periodontitis is a common oral disease characterized by progressive alveolar bone resorption and inflammation of the periodontal tissues. Dimethyl fumarate (DMF) has been used in the treatment of various immune-inflammatory diseases due to its excellent anti-inflammatory and antioxidant functions. Here, we investigated for the first time the therapeutic effect of DMF on periodontitis. In vivo studies showed that DMF significantly inhibited periodontal destruction, enhanced mitophagy, and decreased the M1/M2 macrophage ratio. In vitro studies showed that DMF inhibited macrophage polarization toward M1 macrophages and promoted polarization toward M2 macrophages, with improved mitochondrial function, inhibited oxidative stress, and increased mitophagy in RAW 264.7 cells. Furthermore, DMF increased intracellular mitochondrial Tu translation elongation factor (TUFM) levels to maintain mitochondrial homeostasis, promoted mitophagy, and modulated macrophage polarization, whereas TUFM knockdown decreased the protective effect of DMF. Finally, mechanistic studies showed that DMF increased intracellular TUFM levels by protecting TUFM from degradation via the ubiquitin-proteasomal degradation pathway. Our results demonstrate for the first time that DMF protects mitochondrial function and inhibits oxidative stress through TUFM-mediated mitophagy in macrophages, resulting in a shift in the balance of macrophage polarization, thereby attenuating periodontitis. Importantly, this study provides new insights into the prevention of periodontitis.
Dimethyl Fumarate/pharmacology* ; Mitophagy/drug effects* ; Animals ; Mice ; Macrophages/metabolism* ; Periodontitis/prevention & control* ; RAW 264.7 Cells ; Oxidative Stress/drug effects* ; Peptide Elongation Factor Tu/metabolism* ; Mice, Inbred C57BL ; Male ; Mitochondria/drug effects*

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7-T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7-T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7-T8 layer from chest CT images was 95.06％,84.83％,92.27％and 95.78％,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.

This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers. A novel gesture recognition approach was proposed, which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers. The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach. Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input. Subsequently, a multi-view convolutional neural network model was constructed, utilizing asymmetric convolutional kernels of sizes 1 × n and 3 × n within the same layer to enhance the representation capability of surface electromyography signals. Experimental results showed that the proposed method achieved recognition accuracies of 98.11% for 13 gestures and 98.75% for 12 multi-finger movements, significantly outperforming existing machine learning approaches. The proposed gesture recognition method, based on surface electromyography grayscale images and multi-view convolutional neural networks, demonstrates simplicity and efficiency, substantially improving recognition accuracy and exhibiting strong potential for practical applications.
Electromyography/methods* ; Neural Networks, Computer ; Humans ; Gestures ; Signal Processing, Computer-Assisted ; Machine Learning ; Pattern Recognition, Automated/methods* ; Algorithms ; Convolutional Neural Networks

Objective: Based on physical activity (PA) and sedentary behavior (SB) variables on weekdays and weekends, the study aims to cluster the physical activities inside and outside kindergartens and to explore the cluster characteristics of different children using physical fitness indicators, so as to provide new strategies and methods for early childhood education and health. Methods: From March to June 2019, 291 children aged 3-6 years from 6 kindergartens in Nanchang were recruited by a stratified cluster random sampling method. The ActiGraph GT3X-BT triaxial accelerometer was used to measure and analyze the PA and SB levels inside and outside the kindergarten. A twostep clustering algorithm model was employed for cluster analysis. Physical fitness were measured and evaluated according to the "National Physical Fitness Measurement Standard Manual (Preschool Section)". Differences in physical fitness among different clusters of children were compared, and the cluster characteristics of different children were analyzed. Results: The clustering algorithm model indicated that based on six indicators, including PA and SB inside the kindergarten on weekdays, and PA and SB outside the kindergarten on both weekdays and weekends, children could be divided into three categories:active inside (high PA, low SB inside), active outside (high PA outside), and inactive (low PA, high SB both inside and outside). The average silhouette coefficient of the model was 0.3, indicating good clustering results. Both the active inside and active outside children showed significantly higher PA inside on weekdays, PA outside on weekdays and weekends, daily low intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) than the inactive children ( F=157.91, 80.79 , 95.86, 95.52, 124.74, P <0.05). After adjusting for gender and age, the physical fitness scores of both active outside ( 19.03 ±0.47) and active inside (19.11±0.40) were significantly higher than those of the inactive children (17.94±0.31). Additionally, active inside children (3.91±0.14) also showed significantly better performance in continuous double-leg jumps, compared to inactive children (3.45±0.11) ( P <0.05). Conclusion Children active inside and those active outside perform well in PA. Future research should focus on the proportion of structured and unstructured PA time to enhance the overall physical fitness of children.

At present, the passive simulated lung including the splint lung is an important device for hospitals and manufacturers in testing the functions of a respirator. However, the human respiration simulated by this passive simulated lung is quite different from the actual respiration. And it is not able to simulate the spontaneous breathing. Therefore, including" the device simulating respiratory muscle work "," the simulated thorax" and" the simulated airway", an active mechanical lung to simulate human pulmonary ventilation was designed:3D printed human respiratory tract was developed and connected the left and right air bags at the end of the respiratory tract to simulate the left and right lungs of the human body. By controlling a motor running to drive the crank and rod to move a piston back and forth, and to deliver an alternating pressure in the simulated pleural, and so as to generate an active respiratory airflow in airway. The experimental respiratory airflow and pressure from the active mechanical lung developed in this study are consistent with the target airflow and pressure which collected from the normal adult. The developed active mechanical lung function will be conducive to improve the quality of the respirator.
Adult ; Humans ; Lung/physiology* ; Respiration ; Pulmonary Ventilation ; Respiration, Artificial ; Ventilators, Mechanical

Objective To explore the influence of WeChat platform rehabilitation guidance mode on refracture of osteoporotic fracture patients.Methods Totally 100 osteoporotic fracture patients admitted to the Department of Orthopedics of the hospital from 2018 to 2019 were selected and randomly divided into control group and experimental group with 50 patients in each group.The control group adopted the conventional discharge rehabilitation guidance mode,and the experimental group implemented WeChat platform rehabilitation guidance mode on this basis,including WeChat platform construction and detailed intervention mode.The changes of bone mineral density and the recurrence rate of osteoporotic fracture were compared between the two groups at admission and after 3 years of follow-up.Results There were 7 cases of lost follow-up in the experimental group,and there were 6 cases of lost follow-up in the control group.After 3 years,the bone mineral density of the experimental group was significantly higher than that of the control group,and the recurrence fracture rate was significantly lower than that of the control group(P<0.05).Conclusion The application of WeChat platform rehabilitation guidance mode can effectively improve bone density and reduce the incidence of refracture in osteoporotic fracture patients.

Objective To compare the effect of fluoroscopic triggering method and empirical delay method on image quality in the liver Gd-EOB-DTPA dynamic enhanced MRI,and to investigate the value of fluoroscopic triggering method in Gd-EOB-DTPA dynamic enhanced MRI.Methods The patients underwent Gd-EOB-DTPA dynamic enhanced MRI were randomly divided into two groups according to the starting modes in the artery phase.Group A used fluoroscopic triggering method and group B used empirical delay method.Eliminating the images with severe respiratory motion artifacts,the quality of the remaining images in 78 cases of group A and 85 cases of group B were assessed in scores (excellent=5 scores;good=4 scores).Data was statistically analyzed with Mann-whitney tests,and P<0.05 was considered statistically significant.Results The excellent rate of the images in group A was 96.15% (75/78).The excellent rate of the images in group B was 67.06% (57/85).There were significant differences between the two groups in the excellent rate (χ2=27.889, P<0.001)and the image quality scores (Z=-4.747,P<0.001).Conclusion For the liver Gd-EOB-DTPA dynamic enhanced MRI, fluoroscopic triggering method is more likely to get better image quality and higher success rate in artery phase than empirical delay method,which indicates that fluoroscopic triggering method have obviously advantages in clinical applications.

Objective To investigate the clinical efficacy of Kirschner wire,reconstruction plate and locking compression plate internal fixation in the treatment of Robinson type 2A and 2B midshaft clavicle fracture.Methods One hundred and fifty patients with midshaft clavicle fracture in the hospital from August 2006 to August 2015 were selected and divided into the group A,B and C.The group A adopted Kirschner wire,group B adopted the reconstruction plate and group C adopted the locking compression plate internal fixation.Then the incision length,operative time,intraoperative blood loss,fracture healing time,postoperative Constant-Murley scores and DASH scores and postoperative complications incidence rate were compared among the three groups.Results The incision length,operative time and intraoperative blood loss in the group A and C were significantly better than those in the group B(P＜0.05).The fracture healing time,postoperative activity,muscle strength and daily life score,postoperative complications incidence rate,postoperative DASH score in the group C were significantly better than those in the group A and B(P＜0.05).Conclusion The locking compression plate in the treatment of Robinson type 2A and 2B midshaft clavicle fracture can effectively reduce the surgical trauma,shortens the fracture healing time and decreases the postoperative complications.