Efficacy of acupuncture for depression: a systematic review and meta-analysis.

Introduction: Depression is a pervasive mental health challenge with substantial global ramifications. Contemporary therapeutic strategies predominantly consist of psychological interventions and pharmacological treatments. Acupuncture, deeply rooted in ancient traditions and bolstered by a plethora of clinical trials, demonstrates considerable efficacy in depression. This study aims to elucidate the efficacy and safety of acupuncture as a standalone therapy for depression patients by reviewing randomized controlled trials that compare acupuncture treatment with conventional drug therapy. Methods: Comprehensive searches were conducted across six databases in both Chinese and English: CNKI, Wanfang, VIP, Embase, Medline, and CENTRAL. The literature search spanned from 1 July 2013, to 1 July 2023. Two researchers independently carried out literature screening and data extraction. Review Manager 5.4 was utilized for data analysis and bias risk assessment. A total of 20 randomized controlled trials were included in the qualitative synthesis, involving 1,376 participants and 43 relevant acupoints. Results: The Meta-analysis results, based on the HAMD scale scores, revealed that acupuncture regimens (RR: -1.63; 95% CI: -2.49 to -0.76; P = 0.0002; I2 = 86%; n = 1,668) were significantly more effective compared to standalone medication. Moreover, efficacy metrics from the HAMD highlighted a substantial advantage of acupuncture (RR: 2.6; 95% CI: 1.6 to 4.23; P = 0.0001; I2 = 0; n = 614). Further assessments utilizing SERS and TESS demonstrated a lower incidence of side effects and adverse outcomes in the acupuncture group. Conclusions: Acupuncture, when compared to conventional pharmacotherapy, exhibits significant efficacy as a standalone treatment after four weeks of intervention, with fewer side effects and adverse reactions. However, further investigation is needed to determine the most effective acupoints and appropriate types of acupuncture techniques for treating depression. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023443711.

Development of Home Beauty Devices for Facial Rejuvenation: Establishment of Efficacy Evaluation System.

Background: Home beauty devices for facial rejuvenation utilizing technologies such as radiofrequency, microcurrent, and light emitting diode have gained widespread attention due to their claimed ability to improve skin tightness and elasticity, making them popular among consumers. However, there is controversy within the industry regarding the effectiveness and safety of these devices. Objective: This study aims to verify the safety and effectiveness of these home beauty devices in treating skin aging based on relevant efficacy evaluation indicators. Methods: A systematic search of PubMed and web of science was conducted to include original research literature on the efficacy of home beauty devices for facial rejuvenation over the past two decades. The selected literature was processed and analyzed based on efficacy evaluation indicators such as sample size, follow-up period, experimental results, adverse reactions, and others. Results: After screening, a total of 18 clinical studies were included. A comprehensive analysis of the experimental results and adverse reaction indicators from existing literature revealed that home beauty devices for facial rejuvenation can improve skin aging to a certain extent. Apart from transient redness and swelling, no other adverse reactions were observed. Conclusion: Despite the variety of home beauty devices for facial rejuvenation available in the market, corresponding research reports are limited. Existing studies suffer from issues such as small sample sizes and short follow-up periods, highlighting the need for a more comprehensive efficacy evaluation system. Furthermore, the physical stimulation of meridian acupoints by home beauty devices for facial rejuvenation may meet the multi-dimensional anti-aging needs of patients, suggesting a potential direction for future research.

Luteolin, a flavone ingredient: Anticancer mechanisms, combined medication strategy, pharmacokinetics, clinical trials, and pharmaceutical researches.

Current pharmaceutical research is energetically excavating the pharmacotherapeutic role of herb-derived ingredients in multiple malignancies' targeting. Luteolin is one of the major phytochemical components that exist in various traditional Chinese medicine or medical herbs. Mounting evidence reveals that this phytoconstituent endows prominent therapeutic actions on diverse malignancies, with the underlying mechanisms, combined medication strategy, and pharmacokinetics elusive. Additionally, the clinical trial and pharmaceutical investigation of luteolin remain to be systematically delineated. The present review aimed to comprehensively summarize the updated information with regard to the anticancer mechanism, combined medication strategies, pharmacokinetics, clinical trials, and pharmaceutical researches of luteolin. The survey corroborates that luteolin executes multiple anticancer effects mainly by dampening proliferation and invasion, spurring apoptosis, intercepting cell cycle, regulating autophagy and immune, inhibiting inflammatory response, inducing ferroptosis, and pyroptosis, as well as epigenetic modification, and so on. Luteolin can be applied in combination with numerous clinical anticarcinogens and natural ingredients to synergistically enhance the therapeutic efficacy of malignancies while reducing adverse reactions. For pharmacokinetics, luteolin has an unfavorable oral bioavailability, it mainly persists in plasma as glucuronides and sulfate-conjugates after being metabolized, and is regarded as potent inhibitors of OATP1B1 and OATP2B1, which may be messed with the pharmacokinetic interactions of miscellaneous bioactive substances in vivo. Besides, pharmaceutical innovation of luteolin with leading-edge drug delivery systems such as host-guest complexes, nanoparticles, liposomes, nanoemulsion, microspheres, and hydrogels are beneficial to the exploitation of luteolin-based products. Moreover, some registered clinical trials on luteolin are being carried out, yet clinical research on anticancer effects should be continuously promoted.

A real-world data analysis-based study of Chinese medicine treatment patterns after breast cancer surgery.

Based on the real clinical data of Hospital Information System to explore the common clinical syndromes of traditional Chinese medicine after breast cancer surgery, analysis of traditional Chinese medicine in the treatment of breast cancer after the compatibility law. The real medical records of breast cancer patients after surgery in a tertiary hospital in Sichuan Province were collected and screened to build a medical record database. Python language was used for data preprocessing to remove outliers and fill in missing values. Using International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Modeler software, Apriori association rules algorithm for data analysis, mining Chinese medicine treatment of breast cancer after common syndromes and the corresponding medication rules. A total of 472 cases of clinical real medical record data were included. Data analysis showed that there were 42 TCM syndromes after breast cancer surgery, of which the highest frequency was liver depression and spleen deficiency, qi deficiency and blood stasis, qi stagnation and blood stasis, qi and blood deficiency, qi and yin deficiency, phlegm and blood stasis. A total of 416 kinds of traditional Chinese medicine were involved. High-frequency drugs included angelica sinensis, coix seed, bupleurum, ginger magnolia bark, keel, oyster, astragalus, platycodon grandiflorum, antler frost, vinegar tortoise shell, poria cocos, lily, Jianqu, Ophiopogon japonicus (Maidong), Shancigu, etc. A total of 18 pairs of commonly used drug combinations were excavated, such as Fushen-Gancao-Chaihu-Angelica, Huangqi-Baishao-Jianghoupu, Chaihu-Huanhua-Maidong-Lily, Baizhu-Huangqi-Maidong, Fuling-Baishao, etc. The clinical syndrome type of traditional Chinese medicine after breast cancer surgery is mainly liver depression and spleen deficiency syndrome. The clinical treatment is mainly soothing liver and relieving depression, and harmonizing liver and spleen. Analyze the syndrome type and the corresponding drug compatibility law, and provide decision support for the clinical dialectical prescription of traditional Chinese medicine after breast cancer surgery.

Glucose trend prediction model based on improved wavelet transform and gated recurrent unit.

Glucose trend prediction based on continuous glucose monitoring (CGM) data is a crucial step in the implementation of an artificial pancreas (AP). A glucose trend prediction model with high accuracy in real-time can greatly improve the glycemic control effect of the artificial pancreas and effectively prevent the occurrence of hyperglycemia and hypoglycemia. In this paper, we propose an improved wavelet transform threshold denoising algorithm for the non-linearity and non-smoothness of the original CGM data. By quantitatively comparing the mean square error (MSE) and signal-to-noise ratio (SNR) before and after the improvement, we prove that the improved wavelet transform threshold denoising algorithm can reduce the degree of distortion after the smoothing of CGM data and improve the extraction effect of CGM data features at the same time. Based on this finding, we propose a glucose trend prediction model (IWT-GRU) based on the improved wavelet transform threshold denoising algorithm and gated recurrent unit. We compared the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ($ {\mathrm{R}}^{2} $) of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support vector regression (SVR), Gated Recurrent Unit (GRU) and IWT-GRU on the original CGM monitoring data of 80 patients for 7 consecutive days with different prediction horizon (PH). The results showed that the IWT-GRU model outperformed the other four models. At PH = 45 min, the RMSE was 0.5537 mmol/L, MAPE was 2.2147%, $ {\mathrm{R}}^{2} $ was 0.989 and the average runtime was only 37.2 seconds. Finally, we analyze the limitations of this study and provide an outlook on the future direction of blood glucose trend prediction.

Revisiting Supersaturation of a Biopharmaceutical Classification System IIB Drug: Evaluation via a Multi-Cup Dissolution Approach and Molecular Dynamic Simulation.

As a subclass of the biopharmaceutical classification system (BCS) class II, basic drugs (BCS IIB) exhibit pH-dependent solubility and tend to generate supersaturation in the gastrointestinal tract, leading to less qualified in vitro-in vivo correlation (IVIVC). This study aims to develop a physiologically based multi-cup dissolution approach to improve the evaluation of the supersaturation for a higher quality of IVIVC and preliminarily explores the molecular mechanism of supersaturation and precipitation of ketoconazole affected by Polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA) and hydroxypropyl methyl-cellulose (HPMC). The concentration of ketoconazole in each cup of the dynamic gastrointestinal model (DGIM) was measured using fiber optical probes. Molecular interactions between ketoconazole and PVPVA or HPMC were simulated by Materials Studio. The results demonstrated that PVPVA and HPMC improved and maintained the supersaturation of ketoconazole. PVPVA exhibited superior precipitation inhibitory effect on ketoconazole molecule aggregation due to slightly stronger van der Waals forces as well as unique electrostatic forces, thereby further enhancing in vitro drug absorption, which correlated well with in vivo drug absorption. Compared with a conventional dissolution apparatus paddle method, the DGIM improved the mean prediction error through the IVIVC from 19.30% to 9.96%, reaching the qualification criteria. In conclusion, the physiologically based multi-cup dissolution approach enables improved evaluation of supersaturation in gastrointestinal transportation of BCS IIB drug ketoconazole, enabling screening screen precipitation inhibitors and achieving qualified IVIVC for drug formulation studies.

Medical prediction from missing data with max-minus negative regularized dropout.

Missing data is a naturally common problem faced in medical research. Imputation is a widely used technique to alleviate this problem. Unfortunately, the inherent uncertainty of imputation would make the model overfit the observed data distribution, which has a negative impact on the model generalization performance. R-Drop is a powerful technique to regularize the training of deep neural networks. However, it fails to differentiate the positive and negative samples, which prevents the model from learning robust representations. To handle this problem, we propose a novel negative regularization enhanced R-Drop scheme to boost performance and generalization ability, particularly in the context of missing data. The negative regularization enhanced R-Drop additionally forces the output distributions of positive and negative samples to be inconsistent with each other. Especially, we design a new max-minus negative sampling technique that uses the maximum in-batch values to minus the mini-batch to yield the negative samples to provide sufficient diversity for the model. We test the resulting max-minus negative regularized dropout method on three real-world medical prediction datasets, including both missing and complete cases, to show the effectiveness of the proposed method.

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network.

When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. OBJECTIVE: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. METHODS: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. RESULTS: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. CONCLUSION: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability.

Herb-symptom analysis of Erchen decoction combined with Xiebai powder formula and its mechanism in the treatment of chronic obstructive pulmonary disease.

Background: In recent years, the incidence and mortality rates of chronic obstructive pulmonary disease (COPD) have increased significantly. Erchen Decoction combined with Xiebai Powder (ECXB) formula is mainly used to treat lung diseases in traditional Chinese medicine (TCM). However, the active ingredients of ECXB formula, COPD treatment-related molecular targets, and the mechanisms are still unclear. To reveal its underlying action of mechanism, network pharmacology, molecular docking, and molecular dynamic (MD) simulation approaches were used to predict the active ingredients and potential targets of ECXB formula in treating COPD. As a result, Herb-Symptom analysis showed that the symptoms treated by both TCM and modern medicine of ECXB formula were similar to the symptoms of COPD. Network pharmacology identified 170 active ingredients with 137 targets, and 7,002 COPD targets was obtained. 120 targets were obtained by intersection mapping, among which the core targets include MAPK8, ESR1, TP53, MAPK3, JUN, RELA, MAPK1, and AKT1. Functional enrichment analysis suggested that ECXB formula might exert its treat COPD pharmacological effects in multiple biological processes, such as cell proliferation, apoptosis, inflammatory response, and synaptic connections, and ECXB formula treated COPD of the KEGG potential pathways might be associated with the TNF signaling pathway, cAMP signaling pathway, and VEGF signaling pathway. Molecular docking showed that ECXB formula treatment COPD core active ingredients can bind well to core targets. MD simulations showed that the RELA-beta-sitosterol complex and ESR1-stigmasterol complex exhibited higher conformational stability and lower interaction energy, further confirming the role of ECXB formula in the treatment of COPD through these core components and core targets. Our study analyzed the medication rule of ECXB formula in the treatment of COPD from a new perspective and found that the symptoms treated by both TCM and modern medicine of ECXB formula were similar to the symptoms of COPD. ECXB formula could treat COPD through multi-component, multi-target, and multi-pathway synergistic effects, providing a scientific basis for further study on the mechanism of ECXB formula treatment of COPD. It also provides new ideas for drug development.

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application.

Tongue diagnosis is an essential technique of traditional Chinese medicine (TCM) diagnosis, and the need for objectifying tongue images through image processing technology is growing. The present study provides an overview of the progress made in tongue objectification over the past decade and compares segmentation models. Various deep learning models are constructed to verify and compare algorithms using real tongue image sets. The strengths and weaknesses of each model are analyzed. The findings indicate that the U-Net algorithm outperforms other models regarding precision accuracy (PA), recall, and mean intersection over union (MIoU) metrics. However, despite the significant progress in tongue image acquisition and processing, a uniform standard for objectifying tongue diagnosis has yet to be established. To facilitate the widespread application of tongue images captured using mobile devices in tongue diagnosis objectification, further research could address the challenges posed by tongue images captured in complex environments.

Supersaturation and Precipitation Applicated in Drug Delivery Systems: Development Strategies and Evaluation Approaches.

Supersaturation is a promising strategy to improve gastrointestinal absorption of poorly water-soluble drugs. Supersaturation is a metastable state and therefore dissolved drugs often quickly precipitate again. Precipitation inhibitors can prolong the metastable state. Supersaturating drug delivery systems (SDDS) are commonly formulated with precipitation inhibitors, hence the supersaturation is effectively prolonged for absorption, leading to improved bioavailability. This review summarizes the theory of and systemic insight into supersaturation, with the emphasis on biopharmaceutical aspects. Supersaturation research has developed from the generation of supersaturation (pH-shift, prodrug and SDDS) and the inhibition of precipitation (the mechanism of precipitation, the character of precipitation inhibitors and screening precipitation inhibitors). Then, the evaluation approaches to SDDS are discussed, including in vitro, in vivo and in silico studies and in vitro-in vivo correlations. In vitro aspects involve biorelevant medium, biomimetic apparatus and characterization instruments; in vivo aspects involve oral absorption, intestinal perfusion and intestinal content aspiration and in silico aspects involve molecular dynamics simulation and pharmacokinetic simulation. More physiological data of in vitro studies should be taken into account to simulate the in vivo environment. The supersaturation theory should be further completed, especially with regard to physiological conditions.

Grey correlation analysis on influencing factors of Yang deficiency constitution / 数字中医药（英文）

@#【Objective 】 To explore the influencing factors of Yang deficiency constitution in traditional Chinese medicine (TCM) from the perspective of mathematics with the use of calculation formulas, so as to protect patients from getting diseases caused by Yang deficiency constitution and provide suggestions for TCM disease prevention. 【Methods】  Based on the classification and determination criteria of TCM constitution implemented by China Association of Chinese Medicine, data for 24 solar terms from May 5, 2020(Start of Summer) to April 20, 2021 (Grain Rain) for the identification of Yang deficiency were collected by mobile constitution identification system. The grey correlation analysis method was used to determine the grey correlation degree of the factors influencing Yang deficiency constitution. In addition, a random forest model was constructed for the verification of the results from the grey correlation analysis, and for the evaluation of correlation degree between Yang deficiency constitution and its influencing factors. 【Results】  A total of 16 259 sets of data were collected from healthy or sub-healthy individuals aged from 18 to 60 years living in the central and northeastern parts of Sichuan Province(China) for the identification of TCM constitutions. After screening and preprocessing, a total of 544 sets of data for the identification of Yang deficiency constitution, involving 18 aspects of factors influencing Yang deficiency constitution. The results of the grey correlation analysis showed that there were 12 influencing factors whose grey correlation degree with Yang deficiency constitution was greater than 0.6. The accuracy of these 12 influencing factors with the training set and validation set of the Yang deficiency constitution random forest model were 98.39% and 93.12%, respectively. 【Conclusion】  In the sample data selected in this paper, grey correlation analysis is the appropriate technology to analyze the influencing factors of Yang deficiency constitution. It provides a new idea and a new methodological reference for the research and analysis of the influencing factors of TCM constitution.

A bibliometric study of global trends in diabetes and gut flora research from 2011 to 2021.

Background and objectives: Diabetes mellitus is a serious metabolic disease that causes a serious economic burden worldwide. Gut flora is a major component of diabetes research, and the aim of this study was to understand the trends and major components of research related to diabetes and gut flora in the last 11 years. Methods: We searched the Web of Science Core Collection database for articles on diabetes and gut flora related research from 2011-2021 on July 2, 2022. The literature data were analyzed for country, institution, author, steward, journal, and highly cited literature using Citespace.5.8.R3 and Vosviewer1.6.17. Results: Finally 4834 articles that met the requirements were included. The overall trend of articles published in the last 11 years is increasing, and the trend of articles published after 2019 is increasing significantly. In total, 109 countries, 4820 institutions, and 23365 authors were involved in the field of research. The highest number of publications was 1262 articles from the United States, the institution with the most publications was the University of Copenhagen with 134 articles, and the author with the most publications was PATRICE D CANI with 52 articles. Conclusion: The number of studies related to diabetes and intestinal flora is increasing and more and more researchers are involved in this field. Intestinal flora provides a key research direction for the treatment of diabetes. In the future, gut flora will remain the focus of the diabetes field.

RAFF-Net: An improved tongue segmentation algorithm based on residual attention network and multiscale feature fusion.

Objective: Due to the complexity of face images, tongue segmentation is susceptible to interference from uneven tongue texture, lips and face, resulting in traditional methods failing to segment the tongue accurately. To address this problem, RAFF-Net, an automatic tongue region segmentation network based on residual attention network and multiscale feature fusion, was proposed. It aims to improve tongue segmentation accuracy and achieve end-to-end automated segmentation. Methods: Based on the UNet backbone network, different numbers of ResBlocks combined with the Squeeze-and-Excitation (SE) block was used as an encoder to extract image layered features. The decoder structure of UNet was simplified and the number of parameters of the network model was reduced. Meanwhile, the multiscale feature fusion module was designed to optimize the network parameters by combining a custom loss function instead of the common cross-entropy loss function to further improve the detection accuracy. Results: The RAFF-Net network structure achieved Mean Intersection over Union (MIoU) and F1-score of 97.85% and 97.73%, respectively, which improved 0.56% and 0.46%, respectively, compared with the original UNet; ablation experiments demonstrated that the improved algorithm could contribute to the enhancement of tongue segmentation effect. Conclusion: This study combined the residual attention network with multiscale feature fusion to effectively improve the segmentation accuracy of the tongue region, and optimized the input and output of the UNet network using different numbers of ResBlocks, SE block, multiscale feature fusion and weighted loss function, increased the stability of the network and improved the overall effect of the network.

Global trends in intestinal flora and ulcerative colitis research during the past 10 years: A bibliometric analysis.

Background and aim: Ulcerative colitis is a chronic inflammatory bowel disease, and intestinal flora plays an important role in ulcerative colitis. In this study, we conducted a bibliometric analysis of publications in the field of intestinal flora and ulcerative colitis research in the past 10 years to summarize the current status of the field and analyze the trends in the field. Methods: On July 15, 2022, we chose the Web of Science Core Collection database as the study's data source. CiteSpace.5.8.R3 and VOSviewer 1.6.17 were used to examine publications of research on intestinal flora and ulcerative colitis that were published between 2012 and 2021. We looked through the papers for journals, organizations, nations and regions, authors, and key terms. Results: This analysis covered a total of 2,763 papers on studies into intestinal flora and ulcerative colitis. There were 13,913 authors, 93 nations, 3,069 organizations, and 759 journals in all of the articles. In the USA, 767 publications were the most. The university with the most publications was Harvard Medical School. The author with the most articles was Antonio Gasbarrini. Conclusion: This study summarizes the global research trends in intestinal flora and ulcerative colitis. Publications in this field have increased year by year in the last decade and the field of research on intestinal flora and ulcerative colitis has good prospects for growth.

Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021.

Background: The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years. Methods: We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results: We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions: Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.

Publication Trends in Rehabilitative Effects of Acupuncture: A Visual Analysis of the Literature.

Objectives: To conduct a comprehensive analysis of scientific outputs in 2011-2021 regarding the rehabilitative effects of acupuncture on diseases. Methods: The study was conducted in the form of knowledge graph and data visualization, with data being drawn from the Web of Science Core Collection database. Results: Articles and reviews were the dominant types; China, Guangzhou University of Chinese Medicine and Medicine ranked was the active country, institution, and journal, respectively, in terms of issued articles. Systematic reviews and the meta-analyses of stroke and pain were extensively carried out in the past decade, whose principal interventions were manual acupuncture, electroacupuncture, scalp acupuncture, and dry needling correspondingly at Baihui (DU20) and Zusanli (ST36). And most frequently utilized rehabilitation assessment criteria were the Fugl-Meyer Assessment Scale and the Barthel Index. More recently, motor function and chronic obstructive pulmonary disease have captured researchers' attention, which might be the futuristic frontier. Conclusions: This article provided a relatively panoramic picture of the scientific outputs in acupuncture for disease rehabilitation, which may help readers embrace the heated topic and grasp the recent research focus on this field.

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

BACKGROUND: Nowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient's symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients' symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients' diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy. OBJECTIVE: This study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases. METHODS: The relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data. RESULTS: The data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%. CONCLUSIONS: The main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

A coordinate-based meta-analysis of acupuncture for chronic pain: Evidence from fMRI studies.

Background: Chronic pain (CP) patients tend to represent aberrant functional brain activity. Acupuncture is an effective clinical treatment for CP, and some fMRI studies were conducted to discover the alternation of brain regions after acupuncture therapy for CP. However, the heterogeneity of neuroimaging studies has prevented researchers from systematically generalizing the central mechanisms of acupuncture in the treatment of CP. Methods: We searched bibliographic databases, including PubMed, EMBASE, PsycINFO, Web of Science Core Collection, ScienceDirect, China Academic Journal Network Publishing Database, etc., and trials registration platforms (From inception to September 1st, 2022). Two independent researchers assessed the study's bias and quality. Furthermore, activation likelihood estimation (ALE) analysis was applied to explore aberrant brain functional activity and acupuncture's central mechanism for CP. Results: Totally 14 studies with 524 CP patients were included in the study. ALE analysis showed that CP patients presented with decreased ALFF/ReHo in the precuneus, posterior cingulate cortex, right inferior parietal lobule, right superior temporal gyrus, cingulate gyrus, superior frontal gyrus, left medial frontal gyrus including medial prefrontal gurus, left middle frontal gyrus. Conclusion: This ALE meta-analysis pointed out that acupuncture could modulate the default mode network, the frontoparietal network to treat CP. This provided a systematic summary of the neuroimage biomarker of acupuncture for the treatment of CP. Systematic review registration: PROSPERO, identifier: CRD42021239633.

Visualization analysis of the international standard ISO/TC 249 for traditional Chinese medicine / 数字中医药（英文）

@#Objective This study proposes to visually review the current situation and progress of standards sets by the International Organization for Standardization/Technical Committee on Traditional Chinese Medicine (ISO/TC 249). The review aims to explore the development strategies of the standards, which will exhibit the considerable impact on the economy, trade and exchanges, and cooperation in the area of traditional Chinese medicine (TCM). Methods ISO/TC 249 standards were searched on the ISO website, and their title, proposed time, current stage, scope, and classification were obtained for further summarization. Gephi was utilized to portray the co-occurrence network graph of the ISO/TC 249 standards subject. Results In ISO/TC 249, there were 116 standards, including 81 published standards and 35 developing standards by April 30, 2022. Two withdrawal standards were published after revision, which were not counted in the total standards. The number of published standards has been increasing since the first standard was published in 2014, whose title was “Sterile acupuncture needles for single use”. Among these standards, 17.24% (20/116) standards were in review, 56.03% (65/116) in publication, 3.45% (4/116) in approval, 5.17% (6/116) in enquiry, 3.45% (4/116) in committee, and 14.66% (17/116) in preparation, respectively. With 116 standards, most of the research focused on the medicament, as its classification of the International Classification for Standards (ICS) showed the proportion reaching 49.54%. The network analysis data revealed that the top five most frequent words were “materials” “root” “requirements” “products” and “system”, after removing the noise data, such as prepositions, conjunctions, and pronouns. Additionally, the word “system” co-exists with the terms “computerized” “coding” “image” “tongue” and “analysis”; the word “requirement” co-exists with “manufacturing” “decoction” “process” and “materials”; whereas the word “devices” co-exists with “pulse” “electric” “skin” and “measurement”. Conclusion With the increased diversification and complexity of problems, the development of standards is also oriented to multidisciplinary fields to cultivate the interdisciplinary talents, and especially the international standardization talents of compound TCM. Multi-angle analysis, formulation, and demonstration of standards, in line with industry needs in different disciplines, enhance the availability of standards and the ability to serve the industry.