Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

BACKGROUND:With the continuous progress of science and technology,the introduction of new technologies and methods will bring more possibilities and new breakthroughs for the application of shape memory alloys in the fields of assistive and rehabilitation. OBJECTIVE:To review the application status of shape memory alloys in assistive and rehabilitation equipment,discuss their main methods,techniques and results,summarize and put forward suggestions,hoping that shape memory alloys can be continuously optimized and bring more new changes for the development of assistive and rehabilitation equipment. METHODS:WanFang,PubMed,and Web of Science databases were searched by computer."Shape memory alloys,application progress,orthodontics,orthopedic,prosthesis,rehabilitation,properties,implantation,mechanical properties,nickel-titanium memory alloys,actuation"were used as Chinese search terms."Shape memory alloys,application,orthodontics,orthopedic,prosthetics,rehabilitation,properties,implant,drive,progress,prostheses"were used as English search terms.Finally,91 articles were included for review. RESULTS AND CONCLUSION:(1)Shape memory alloy has the characteristics of corrosion resistance,wear resistance,biocompatibility,fatigue resistance,kink resistance and other properties.Compared with other traditional materials(stainless steel,titanium alloy,cobalt-chromium alloy,etc.),shape memory alloy has lower elastic modulus and no biological toxicity,which is suitable for long-term implantation as an implant prosthesis.Due to its shape memory effect and excellent mechanical properties,it is mainly used as a driving element or as a bridge connecting the device and the human body in artificial limbs,orthoses and rehabilitation equipment.(2)The use of shape memory alloy drive elements can reduce the weight of the device,eliminate noise,easy to operate,easy to carry,better assist joint movement;compared with the use of pneumatic,hydraulic,and electrical drive methods of the device,it has obvious advantages.(3)In addition,shape memory alloy can produce permanent and stable stress during deformation.Compared with stainless steel,titanium alloy and aluminum alloy,shape memory alloy has a higher material recovery rate and does not need to be replaced and adjusted frequently,so it is more practical in the correction of deformity.(4)At present,shape memory alloy is most commonly used in orthosis,and the best clinical application effect is in stapes prosthesis.However,due to the limitations of technology and cost,shape memory alloys are rarely used in artificial limbs and rehabilitation equipment,and there is a lack of large sample size studies on the application effect.(5)Although shape memory alloys have been developed in the field of auxiliary and rehabilitation,there are still many problems:it is difficult to accurately control the shape memory alloys;the cooling speed of shape memory alloy is slow;the deformation speed of shape memory alloy cannot be controlled;there is a lack of comparative research and expert consensus on shape memory alloys with different properties;shape memory alloys are costly and expensive.(6)In the future,attention should be paid to the development of new shape memory alloys,increase comparative research,and use new technologies and methods(such as 4D printing)to solve the existing problems,so as to develop high-performance assistive devices and rehabilitation equipment.

Messenger ribonucleic acid (mRNA) is a promising therapeutic drug with great potential in the fields of immunology, oncology, vaccines and inborn metabolic diseases. However, due to its instability and susceptibility to nuclease degradation, efficient delivery vectors are required. Lipid nanoparticles (LNPs) are recognized as the most mature delivery vectors due to their advantages of easy formulation, high stability, efficient cell uptake and endosomal escape. However, the accumulation of LNPs in the liver severely limits the targeting and treatment of mRNA-LNP technology beyond the liver. To overcome this obstacle, researchers have been focusing on various means to achieve precise delivery of extrahepatic tissues and organs. This article mainly expounds the research progress of LNP-specific delivery mRNA from three aspects: endogenous targeting, active targeting and selection of administration route, in order to provide ideas and directions for the design of new mRNA-LNP delivery systems in the future.

Due to patient compliance and convenience, oral medication is likely the most common and acceptable method of drug administration. However, traditional dosage forms such as tablets or capsules may lead to low drug bioavailability and poor therapeutic efficiency. Therefore, with advancements in material science and micro/nano manufacturing technology, various carriers have been developed to enhance drug absorption in the gastrointestinal tract. In this context, we initially discuss the key biological factors that hinder drug transport and absorption (including anatomical, physical, and biological factors). Building on this foundation, recent progress in both conventional and innovative oral drug delivery routes aimed at improving drug bioavailability and targeting is reviewed. Finally, we explore future prospects for oral drug delivery systems as well as potential challenges in clinical translation.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Traditional Chinese medicine (TCM) has historically played a pivotal role in the prevention and treatment of warm diseases, establishing a comprehensive theoretical framework that underpins its practices. The distinctive and indispensable contributions of aromatic Chinese herbs in dispelling harmful influences and mitigating the spread of these diseases are well recognized; however, further investigation is warranted to elucidate their systematic properties and regularities, and the theory of aromatic Chinese herbs in preventing and treating warm diseases still needs to be comprehensively summarized. This study employs the principles rooted in TCM, with particular emphasis on the framework for warm diseases. An analysis of the disease mechanisms, transmission dynamics, and preventive strategies is conducted during the early stage of infection, throughout the course of the disease, and in the post-illness phase. Furthermore, the characteristics and applications of aromatic Chinese herbs are integrated with insights drawn from modern pharmacological research to explore their specific roles in the prevention and management of warm diseases. The utilization of aromatic Chinese herbs manifests in a variety of therapeutic effects: aromatic medicinals purging filth and dispelling pathogens for preventing epidemic disease, aromatic medicinals regulation for relieving superficies syndrome and dispersing evils, aromatic medicinals ventilation the lung to relieve cough and asthma, aromatic medicinals resolving the dampness to awaken the spleen and stomach, aromatic medicinals opening the orifices to restore consciousness, aromatic and pungent medicinals to regulate qi, aromatic medicinals dredging the vessels to activate blood circulation and dissipate blood stasis, and aromatic medicinals clearing latent heat from the yin level. These properties facilitate tailored approaches to address the diverse manifestations of warm diseases and their associated symptoms, providing clear guidance for clinical application to achieve pre-disease prevention, active disease treatment, complication prevention, and post-recovery relapse avoidance. The use of aromatic Chinese herbs in preventing and treating warm diseases demonstrates theoretical, practical, systematic, and regular characteristics. The theory of the properties of aromatic Chinese herbs has been expanded and sublimated in clinical practice, and its scientific connotation has been expounded in modern research. Under the guidance of the theory of treatment based on syndrome differentiation, and by taking into account the distinct stages and pathologies of warm diseases, the rational selection of aromatic Chinese herbs can improve the clinical efficacy.