Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.008
Tejal R. Waykar, Satish K. Mandlik, Deepa S. Mandlik
<div><h3>Objective</h3><div>To investigate the protective effects of naringenin (NRG) against dexamethasone (DEX)-induced osteoporosis (OP) in rats.</div></div><div><h3>Methods</h3><div>Molecular docking of NRG was done with AutoDock Vina 1.2.0 software. Forty-eight female Wistar rats were randomly divided into six groups (<em>n</em> = 8 each): normal control (NC), DEX (7 mg/kg, i.m.), NRG-low (NRG-L; 25 mg/kg, i.g.), NRG-medium (NRG-M; 50 mg/kg, i.g.), NRG-high (NRG-H; 100 mg/kg, i.g.), and alendronate (ALN; 0.25 mg/d, i.g.) groups. OP was induced by administering DEX once a week for five weeks in all groups except NC group. Begining in the third week after the initial DEX administration, the rats in NRG-L, NRG-M, NRG-H, and ALN groups received the corresponding treatments daily for three weeks, while NC and DEX groups received no additional treatment. Serum samples were collected at the end of the experiment for biochemical, bone turnover, antioxidant, lipid profile, and inflammatory cytokine analyses. Femur bones underwent physical parameter testing and histopathological examination.</div></div><div><h3>Results</h3><div>The molecular docking results illustrated that NRG docked with calcitonin (CT), low-density lipoprotein (LDL), bone morphogenetic protein (BMP), vascular endothelial growth factor (VEGF) receptor, forkhead transcription factors, and osteoprogenitor cells showed good binding energy. In rats administered with 25, 50, and 100 mg/kg NRG, there was a significant enhancement in serum biochemical indices, characterized by a reduction in tartrate-resistant acid phosphatase (TRAP), parathyroid hormone (PTH), and an elevation in osteocalcin (OC) and CT levels (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Despite no significant changes in thickness, weight, and length (<em>P</em> > 0.05), there was a marked increase in bone mineral density (BMD) (<em>P</em> < 0.01, <em>P</em> < 0.001, and <em>P</em> < 0.001, respectively). Antioxidant enzyme markers showed significant upregulation, with higher glutathione, superoxide dismutase, and catalase, and a concurrent decrease in malondialdehyde (MDA) (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). The lipid profile also improved significantly, with lower cholesterol (CH), triglycerides (TG), and low-density lipoprotein (LDL) levels, and an increase in high-density lipoprotein (HDL) level (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Inflammatory cytokine levels were reduced, as evidenced by decreases in tumor necrosis factor (TNF), interleukin (IL)-6, and IL-1<em>β</em> (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Furthermore, histological alterations revealed obvious improvements, and the body weight of rats treated with NRG showed an increase compared with DEX group.</div></div><div><h3>Conclusion</h3><div>These findings im
目的 研究柚皮苷（NRG）对地塞米松（DEX）诱导的大鼠骨质疏松症（OP）的保护作用。 方法 使用 AutoDock Vina 1.2.0 软件对 NRG 进行分子对接。将48只雌性Wistar大鼠随机分为6组（每组8只）：正常对照组（NC）、DEX组（7 mg/kg，静注）、NRG-低（NRG-L；25 mg/kg，静注）、NRG-中（NRG-M；50 mg/kg，静注）、NRG-高（NRG-H；100 mg/kg，静注）和阿仑膦酸钠组（ALN；0.25 mg/d，静注）。除NC组外，其他各组均通过每周一次的DEX诱导OP，为期五周。从首次给予 DEX 后的第三周开始，NRG-L 组、NRG-M 组、NRG-H 组和 ALN 组的大鼠每天接受相应的治疗，持续三周，而 NC 组和 DEX 组不接受额外的治疗。实验结束时收集血清样本，进行生化、骨转换、抗氧化、血脂和炎症细胞因子分析。结果分子对接结果表明，NRG 与降钙素（CT）、低密度脂蛋白（LDL）、骨形态发生蛋白（BMP）、血管内皮生长因子（VEGF）受体、叉头转录因子和骨生成细胞的对接显示出良好的结合能。大鼠服用 25、50 和 100 毫克/千克 NRG 后，血清生化指标显著提高，其特征是抗酒石酸磷酸酶（TRAP）和甲状旁腺激素（PTH）降低，骨钙素（OC）和 CT 水平升高（分别为 P <0.05、P <0.01 和 P <0.001）。尽管厚度、重量和长度没有明显变化（P> 0.05），但骨矿物质密度（BMD）明显增加（分别为 P < 0.01、P < 0.001 和 P < 0.001）。抗氧化酶标记物显示出明显的上调，谷胱甘肽、超氧化物歧化酶和过氧化氢酶升高，丙二醛（MDA）同时下降（分别为 P <0.05、P <0.01 和 P <0.001）。血脂状况也有明显改善，胆固醇（CH）、甘油三酯（TG）和低密度脂蛋白（LDL）水平降低，高密度脂蛋白（HDL）水平升高（分别为 P <0.05、P <0.01 和 P <0.001）。炎症细胞因子水平降低，表现为肿瘤坏死因子（TNF）、白细胞介素（IL）-6 和 IL-1β 的下降（分别为 P < 0.05、P < 0.01 和 P < 0.001）。结论这些研究结果表明，NRG 对 DEX 诱导的大鼠 OP 具有保护作用，因为它能通过增加骨转换标志物（包括 OC 和 CT）的数量、恢复抗氧化状态、脂质代谢和炎症标志物来促进骨形成过程。
{"title":"Unveiling osteoprotective potential of biologically active naringenin in rats with dexamethasone-induced osteoporosis","authors":"Tejal R. Waykar, Satish K. Mandlik, Deepa S. Mandlik","doi":"10.1016/j.dcmed.2024.09.008","DOIUrl":"10.1016/j.dcmed.2024.09.008","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the protective effects of naringenin (NRG) against dexamethasone (DEX)-induced osteoporosis (OP) in rats.</div></div><div><h3>Methods</h3><div>Molecular docking of NRG was done with AutoDock Vina 1.2.0 software. Forty-eight female Wistar rats were randomly divided into six groups (<em>n</em> = 8 each): normal control (NC), DEX (7 mg/kg, i.m.), NRG-low (NRG-L; 25 mg/kg, i.g.), NRG-medium (NRG-M; 50 mg/kg, i.g.), NRG-high (NRG-H; 100 mg/kg, i.g.), and alendronate (ALN; 0.25 mg/d, i.g.) groups. OP was induced by administering DEX once a week for five weeks in all groups except NC group. Begining in the third week after the initial DEX administration, the rats in NRG-L, NRG-M, NRG-H, and ALN groups received the corresponding treatments daily for three weeks, while NC and DEX groups received no additional treatment. Serum samples were collected at the end of the experiment for biochemical, bone turnover, antioxidant, lipid profile, and inflammatory cytokine analyses. Femur bones underwent physical parameter testing and histopathological examination.</div></div><div><h3>Results</h3><div>The molecular docking results illustrated that NRG docked with calcitonin (CT), low-density lipoprotein (LDL), bone morphogenetic protein (BMP), vascular endothelial growth factor (VEGF) receptor, forkhead transcription factors, and osteoprogenitor cells showed good binding energy. In rats administered with 25, 50, and 100 mg/kg NRG, there was a significant enhancement in serum biochemical indices, characterized by a reduction in tartrate-resistant acid phosphatase (TRAP), parathyroid hormone (PTH), and an elevation in osteocalcin (OC) and CT levels (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Despite no significant changes in thickness, weight, and length (<em>P</em> > 0.05), there was a marked increase in bone mineral density (BMD) (<em>P</em> < 0.01, <em>P</em> < 0.001, and <em>P</em> < 0.001, respectively). Antioxidant enzyme markers showed significant upregulation, with higher glutathione, superoxide dismutase, and catalase, and a concurrent decrease in malondialdehyde (MDA) (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). The lipid profile also improved significantly, with lower cholesterol (CH), triglycerides (TG), and low-density lipoprotein (LDL) levels, and an increase in high-density lipoprotein (HDL) level (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Inflammatory cytokine levels were reduced, as evidenced by decreases in tumor necrosis factor (TNF), interleukin (IL)-6, and IL-1<em>β</em> (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.001, respectively). Furthermore, histological alterations revealed obvious improvements, and the body weight of rats treated with NRG showed an increase compared with DEX group.</div></div><div><h3>Conclusion</h3><div>These findings im","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 171-183"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.005
Zhou Zhan , Peng Qinghua , Xiao Xiaoxia , Zou Beiji , Liu Bin , Guo Shuixia
<div><h3>Objective</h3><div>Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure (HBV-ACLF) generally have small sample sizes and a class imbalance. However, most machine learning models are designed based on balanced data and lack interpretability. This study aimed to propose a traditional Chinese medicine (TCM) diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory, which is clinically interpretable and highly accurate.</div></div><div><h3>Methods</h3><div>We collected medical records from 261 patients diagnosed with HBV-ACLF, including three syndromes: Yang jaundice (214 cases), Yang-Yin jaundice (41 cases), and Yin jaundice (6 cases). To avoid overfitting of the machine learning model, we excluded the cases of Yin jaundice. After data standardization and cleaning, we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice. To address the class imbalance issue, we employed the oversampling method and five machine learning methods, including logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) to construct the syndrome diagnosis models. This study used precision, F1 score, the area under the receiver operating characteristic (ROC) curve (AUC), and accuracy as model evaluation metrics. The model with the best classification performance was selected to extract the diagnostic rule, and its clinical significance was thoroughly analyzed. Furthermore, we proposed a novel multiple-round stable rule extraction (MRSRE) method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.</div></div><div><h3>Results</h3><div>The precision of the five machine learning models built using oversampled balanced data exceeded 0.90. Among these models, the accuracy of RF classification of syndrome types was 0.92, and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94, respectively. Additionally, the AUC was 0.98. The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse, yellowing of the urine, skin, and eyes, normal tongue body, healthy sublingual vessel, nausea, oil loathing, and poor appetite. The main features of Yang jaundice were a red tongue body and thickened sublingual vessels, whereas those of Yang-Yin jaundice were a dark tongue body, pale white tongue body, white tongue coating, lack of strength, slippery pulse, light red tongue body, slimy tongue coating, and abdominal distension. This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.</div></div><div><h3>Conclusion</h3><div>Our model can be utilized for differentiating HBV-ACLF syndromes, which has the potential to be applied to generate other clinically i
{"title":"An interpretability model for syndrome differentiation of HBV-ACLF in traditional Chinese medicine using small-sample imbalanced data","authors":"Zhou Zhan , Peng Qinghua , Xiao Xiaoxia , Zou Beiji , Liu Bin , Guo Shuixia","doi":"10.1016/j.dcmed.2024.09.005","DOIUrl":"10.1016/j.dcmed.2024.09.005","url":null,"abstract":"<div><h3>Objective</h3><div>Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure (HBV-ACLF) generally have small sample sizes and a class imbalance. However, most machine learning models are designed based on balanced data and lack interpretability. This study aimed to propose a traditional Chinese medicine (TCM) diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory, which is clinically interpretable and highly accurate.</div></div><div><h3>Methods</h3><div>We collected medical records from 261 patients diagnosed with HBV-ACLF, including three syndromes: Yang jaundice (214 cases), Yang-Yin jaundice (41 cases), and Yin jaundice (6 cases). To avoid overfitting of the machine learning model, we excluded the cases of Yin jaundice. After data standardization and cleaning, we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice. To address the class imbalance issue, we employed the oversampling method and five machine learning methods, including logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) to construct the syndrome diagnosis models. This study used precision, F1 score, the area under the receiver operating characteristic (ROC) curve (AUC), and accuracy as model evaluation metrics. The model with the best classification performance was selected to extract the diagnostic rule, and its clinical significance was thoroughly analyzed. Furthermore, we proposed a novel multiple-round stable rule extraction (MRSRE) method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.</div></div><div><h3>Results</h3><div>The precision of the five machine learning models built using oversampled balanced data exceeded 0.90. Among these models, the accuracy of RF classification of syndrome types was 0.92, and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94, respectively. Additionally, the AUC was 0.98. The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse, yellowing of the urine, skin, and eyes, normal tongue body, healthy sublingual vessel, nausea, oil loathing, and poor appetite. The main features of Yang jaundice were a red tongue body and thickened sublingual vessels, whereas those of Yang-Yin jaundice were a dark tongue body, pale white tongue body, white tongue coating, lack of strength, slippery pulse, light red tongue body, slimy tongue coating, and abdominal distension. This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.</div></div><div><h3>Conclusion</h3><div>Our model can be utilized for differentiating HBV-ACLF syndromes, which has the potential to be applied to generate other clinically i","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 137-147"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.010
Zhao Hongqing , Mou Qingrui , Jiang Jiaqi , Zhu Xuan , Liu Zhuo , Wang Yuhong
<div><h3>Objective</h3><div>To investigate the effect of Zuogui Jiangtang Jieyu Formula (左归降糖解郁方, ZJJF) on hippocampal neuron apoptosis in diabetic rats with depression and to ascertain whether its mechanism involves the regulation of JNK signaling pathway.</div></div><div><h3>Methods</h3><div>(i) A total of 72 specific pathogen-free (SPF) grade male Sprague Dawley (SD) rats were randomly divided into six groups, with 12 rats in each group: control, model, metformin (Met, 0.18 g/kg) + fluoxetine (Flu, 1.8 mg/kg), and the high-, medium-, and low-ZJJF dosages (ZJJF-H, 20.52 g/kg; ZJJF-M, 10.26 g/kg; ZJJF-L, 5.13 g/kg) groups. All groups except control group were injected once via the tail vein with streptozotocin (STZ, 38 mg/kg) combined with 28 d of chronic unpredictable mild stress (CUMS) to establish diabetic rat models with depression. During the CUMS modeling period, treatments were administered via gavage, with control and model groups receiving an equivalent volume of distilled water for 28 d. The efficacy of ZJJF in reducing blood sugar and alleviating depression was evaluated by measuring fasting blood glucose, insulin, and glycated hemoglobin levels, along with behavioral assessments, including the open field test (OFT), forced swim test (FST), and sucrose preference test (SPT). Hippocampal tissue damage and neuronal apoptosis were evaluated using hematoxylin-eosin (HE) staining and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining. Apoptosis-related proteins Bax, Bcl-2, caspase-3, and the expression levels of JNK/Elk-1/c-fos signaling pathway were detected using Western blot and real-time quantitative polymerase chain reaction (RT-qPCR). (ii) To further elucidate the role of JNK signaling pathway in hippocampal neuronal apoptosis and the pharmacological effects of ZJJF, an additional 50 SPF grade male SD rats were randomly divided into five groups, with 10 rats in each group: control, model, SP600125 (SP6, a JNK antagonist, 10 mg/kg), ZJJF (20.52 g/kg), and ZJJF (20.52 g/kg) + Anisomycin (Aniso, a JNK agonist, 15 mg/kg) groups. Except for control group, all groups were established as diabetic rat models with depression, and treatments were administered via gavage for ZJJF and intraperitoneal injection for SP6 and Aniso for 28 d during the CUMS modeling period. Behavioral changes in rats were evaluated through the OFT, FST, and SPT, and hippocampal neuron damage and apoptosis were observed using HE staining, Nissl staining, TUNEL staining, and transmission electron microscopy (TEM). Changes in apoptosis-related proteins and JNK signaling pathway in the hippocampal tissues of rats were also analyzed.</div></div><div><h3>Results</h3><div>(i) ZJJF significantly reduced the high blood glucose, insulin, and glycated hemoglobin levels in model rats (<em>P</em> < 0.01). It increased autonomous activity and decreased despair-like behaviors (<em>P</em> < 0.01), improved the pathological damage of hippocam
{"title":"Zuogui Jiangtang Jieyu Formula ameliorating hippocampal neuronal apoptosis in diabetic rats with depression by inhibiting JNK signaling pathway","authors":"Zhao Hongqing , Mou Qingrui , Jiang Jiaqi , Zhu Xuan , Liu Zhuo , Wang Yuhong","doi":"10.1016/j.dcmed.2024.09.010","DOIUrl":"10.1016/j.dcmed.2024.09.010","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the effect of Zuogui Jiangtang Jieyu Formula (左归降糖解郁方, ZJJF) on hippocampal neuron apoptosis in diabetic rats with depression and to ascertain whether its mechanism involves the regulation of JNK signaling pathway.</div></div><div><h3>Methods</h3><div>(i) A total of 72 specific pathogen-free (SPF) grade male Sprague Dawley (SD) rats were randomly divided into six groups, with 12 rats in each group: control, model, metformin (Met, 0.18 g/kg) + fluoxetine (Flu, 1.8 mg/kg), and the high-, medium-, and low-ZJJF dosages (ZJJF-H, 20.52 g/kg; ZJJF-M, 10.26 g/kg; ZJJF-L, 5.13 g/kg) groups. All groups except control group were injected once via the tail vein with streptozotocin (STZ, 38 mg/kg) combined with 28 d of chronic unpredictable mild stress (CUMS) to establish diabetic rat models with depression. During the CUMS modeling period, treatments were administered via gavage, with control and model groups receiving an equivalent volume of distilled water for 28 d. The efficacy of ZJJF in reducing blood sugar and alleviating depression was evaluated by measuring fasting blood glucose, insulin, and glycated hemoglobin levels, along with behavioral assessments, including the open field test (OFT), forced swim test (FST), and sucrose preference test (SPT). Hippocampal tissue damage and neuronal apoptosis were evaluated using hematoxylin-eosin (HE) staining and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining. Apoptosis-related proteins Bax, Bcl-2, caspase-3, and the expression levels of JNK/Elk-1/c-fos signaling pathway were detected using Western blot and real-time quantitative polymerase chain reaction (RT-qPCR). (ii) To further elucidate the role of JNK signaling pathway in hippocampal neuronal apoptosis and the pharmacological effects of ZJJF, an additional 50 SPF grade male SD rats were randomly divided into five groups, with 10 rats in each group: control, model, SP600125 (SP6, a JNK antagonist, 10 mg/kg), ZJJF (20.52 g/kg), and ZJJF (20.52 g/kg) + Anisomycin (Aniso, a JNK agonist, 15 mg/kg) groups. Except for control group, all groups were established as diabetic rat models with depression, and treatments were administered via gavage for ZJJF and intraperitoneal injection for SP6 and Aniso for 28 d during the CUMS modeling period. Behavioral changes in rats were evaluated through the OFT, FST, and SPT, and hippocampal neuron damage and apoptosis were observed using HE staining, Nissl staining, TUNEL staining, and transmission electron microscopy (TEM). Changes in apoptosis-related proteins and JNK signaling pathway in the hippocampal tissues of rats were also analyzed.</div></div><div><h3>Results</h3><div>(i) ZJJF significantly reduced the high blood glucose, insulin, and glycated hemoglobin levels in model rats (<em>P</em> < 0.01). It increased autonomous activity and decreased despair-like behaviors (<em>P</em> < 0.01), improved the pathological damage of hippocam","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 195-208"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.001
Yuan Gao , Zixuan Wu , Boyang Sheng , Fu Zhang , Yong Cheng , Junfeng Yan , Qinghua Peng
Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
{"title":"The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine","authors":"Yuan Gao , Zixuan Wu , Boyang Sheng , Fu Zhang , Yong Cheng , Junfeng Yan , Qinghua Peng","doi":"10.1016/j.dcmed.2024.09.001","DOIUrl":"10.1016/j.dcmed.2024.09.001","url":null,"abstract":"<div><div>Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 101-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.002
Anying Xu , Tianshu Wang , Tao Yang , Han Xiao , Xiaoyu Zhang , Ziyan Wang , Qi Zhang , Xiao Li , Hongcai Shang , Kongfa Hu
Objective
To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes.
Methods
First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model.
Results
It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively.
Conclusion
The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.
{"title":"Constitution identification model in traditional Chinese medicine based on multiple features","authors":"Anying Xu , Tianshu Wang , Tao Yang , Han Xiao , Xiaoyu Zhang , Ziyan Wang , Qi Zhang , Xiao Li , Hongcai Shang , Kongfa Hu","doi":"10.1016/j.dcmed.2024.09.002","DOIUrl":"10.1016/j.dcmed.2024.09.002","url":null,"abstract":"<div><h3>Objective</h3><div>To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes.</div></div><div><h3>Methods</h3><div>First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model.</div></div><div><h3>Results</h3><div>It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively.</div></div><div><h3>Conclusion</h3><div>The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 108-119"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.004
Liqin Zhong , Guojiang Xin , Qinghua Peng , Ji Cui , Lei Zhu , Hao Liang
Objective
To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.
Methods
A total of 1 001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1 007 images of pathological (non-stained) tongue coating from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine with lung cancer, diabetes, and hypertension were collected. The tongue images were randomized into the training, validation, and testing datasets in a 7 : 2 : 1 ratio. A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets. The training period was 90 epochs. The model’s performance was evaluated by its accuracy, loss curve, recall, F1 score, confusion matrix, receiver operating characteristic (ROC) curve, and precision-recall (PR) curve in the tasks of predicting stained tongue coating images in the testing dataset. The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine (TCM).
Results
The training results showed that after 90 epochs, the model presented an excellent classification performance. The loss curve and accuracy were stable, showing no signs of overfitting. The model achieved an accuracy, recall, and F1 score of 92%, 91%, and 92%, respectively. The confusion matrix revealed an accuracy of 92% for the model and 69% for TCM practitioners. The areas under the ROC and PR curves were 0.97 and 0.95, respectively. Conclusion: The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners. This model has the potential to assist doctors in identifying false tongue coating and preventing misdiagnosis.
{"title":"Deep learning-based recognition of stained tongue coating images","authors":"Liqin Zhong , Guojiang Xin , Qinghua Peng , Ji Cui , Lei Zhu , Hao Liang","doi":"10.1016/j.dcmed.2024.09.004","DOIUrl":"10.1016/j.dcmed.2024.09.004","url":null,"abstract":"<div><h3>Objective</h3><div>To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.</div></div><div><h3>Methods</h3><div>A total of 1 001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1 007 images of pathological (non-stained) tongue coating from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine with lung cancer, diabetes, and hypertension were collected. The tongue images were randomized into the training, validation, and testing datasets in a 7 : 2 : 1 ratio. A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets. The training period was 90 epochs. The model’s performance was evaluated by its accuracy, loss curve, recall, F1 score, confusion matrix, receiver operating characteristic (ROC) curve, and precision-recall (PR) curve in the tasks of predicting stained tongue coating images in the testing dataset. The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine (TCM).</div></div><div><h3>Results</h3><div>The training results showed that after 90 epochs, the model presented an excellent classification performance. The loss curve and accuracy were stable, showing no signs of overfitting. The model achieved an accuracy, recall, and F1 score of 92%, 91%, and 92%, respectively. The confusion matrix revealed an accuracy of 92% for the model and 69% for TCM practitioners. The areas under the ROC and PR curves were 0.97 and 0.95, respectively. <strong>Conclusion</strong>: The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners. This model has the potential to assist doctors in identifying false tongue coating and preventing misdiagnosis.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 129-136"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.007
Kai Wang , Ping Dong , Hongzhang Guo
<div><h3>Objective</h3><div>To explore the differential expression and mechanisms of bone formation-related genes in osteoporosis (OP) leveraging bioinformatics and machine learning methodologies, and to predict the active ingredients of targeted traditional Chinese medicine (TCM) herbs.</div></div><div><h3>Methods</h3><div>The Gene Expression Omnibus (GEO) and GeneCards databases were employed to conduct a comprehensive screening of genes and disease-associated loci pertinent to the pathogenesis of OP. The R package was utilized as the analytical tool for the identification of differentially expressed genes. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis and support vector machine-recursive feature elimination (SVM-RFE) algorithm were employed in defining the genetic signature specific to OP. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the selected pivotal genes were conducted. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was leveraged to examine the infiltration patterns of immune cells, with Spearman’s rank correlation analysis utilized to assess the relationship between the expression levels of the genes and the presence of immune cells. Coremine Medical Database was used to screen out potential TCM herbs for the treatment of OP. Comparative Toxicogenomics Database (CTD) was employed for forecasting the TCM active ingredients targeting the key genes. AutoDock Vina 1.2.2 and GROMACS 2020 softwares were employed to conclude analysis results, facilitating the exploration of binding affinity and conformational dynamics between the TCM active ingredients and their biological targets.</div></div><div><h3>Results</h3><div>Ten genes were identified by intersecting the results from the GEO and GeneCards databases. Through the application of LASSO regression and SVM-RFE algorithm, four pivotal genes were selected: coat protein (<em>CP</em>), kallikrein 3 (<em>KLK3</em>), polymerase <em>γ</em> (<em>POLG</em>), and transient receptor potential vanilloid 4 (<em>TRPV4</em>). GO and KEGG pathway enrichment analyses revealed that these trait genes were predominantly engaged in the regulation of defense response activation, maintenance of cellular metal ion balance, and the production of chemokine ligand 5. These genes were notably associated with signaling pathways such as ferroptosis, porphyrin metabolism, and base excision repair. Immune infiltration analysis showed that key genes were highly correlated with immune cells. Macrophage M0, M1, M2, and resting dendritic cell were significantly different between groups, and there were significant differences between different groups (<em>P</em> < 0.05). The interaction counts of resveratrol, curcumin, and quercetin with <em>KLK3</em> were 7, 3, and 2, respectively. It shows that the interactions of resveratrol, curcumin, and quercetin with <em>KLK3</em> were
目的利用生物信息学和机器学习方法探索骨质疏松症（OP）中骨形成相关基因的差异表达及其机制，并预测靶向中药（TCM）的有效成分。方法利用基因表达总库（GEO）和基因卡片数据库对与OP发病机制相关的基因和疾病相关位点进行全面筛选。利用 R 软件包作为分析工具来鉴定差异表达基因。采用最小绝对收缩和选择算子（LASSO）逻辑回归分析和支持向量机-递归特征消除（SVM-RFE）算法来定义 OP 的特异性遗传特征。对选定的关键基因进行了基因本体（GO）和京都基因组百科全书（KEGG）通路富集分析。通过估算 RNA 转录本的相对子集（CIBERSORT）算法进行细胞类型鉴定，以检查免疫细胞的浸润模式，并利用斯皮尔曼秩相关分析评估基因表达水平与免疫细胞存在之间的关系。利用 Coremine 医学数据库筛选出治疗 OP 的潜在中草药。比较毒物基因组学数据库（CTD）用于预测针对关键基因的中药活性成分。利用AutoDock Vina 1.2.2和GROMACS 2020软件总结分析结果，有助于探索中药活性成分与其生物靶标之间的结合亲和力和构象动力学。通过应用LASSO回归和SVM-RFE算法，选出了四个关键基因：衣壳蛋白（CP）、allikrein 3（KLK3）、聚合酶γ（POLG）和瞬时受体位点类香草素4（TRPV4）。GO 和 KEGG 通路富集分析表明，这些性状基因主要参与调控防御反应激活、维持细胞金属离子平衡和产生趋化因子配体 5。这些基因明显与信号通路有关，如铁蛋白沉积、卟啉代谢和碱基切除修复。免疫浸润分析表明，关键基因与免疫细胞高度相关。巨噬细胞M0、M1、M2和静息树突状细胞在组间有显著差异，不同组间差异显著（P <0.05）。白藜芦醇、姜黄素和槲皮素与 KLK3 的相互作用次数分别为 7、3 和 2。这表明白藜芦醇、姜黄素和槲皮素与 KLK3 的相互作用是实质性的。结论包括 CP、KLK3、POLG 和 TRPV4 在内的关键基因具有显著的预后价值，在 OP 的诊断评估中发挥着重要作用。中药中的天然化合物白藜芦醇、姜黄素和槲皮素显示出有效调节骨形成基因 KLK3 的潜力。这项研究为解释 OP 的发病机制和开发临床药物提供了科学依据。
{"title":"Integrative analysis of bone-formation associated genes and immune cell infiltration in osteoporosis, and the prediction of active ingredients in targeted traditional Chinese medicine","authors":"Kai Wang , Ping Dong , Hongzhang Guo","doi":"10.1016/j.dcmed.2024.09.007","DOIUrl":"10.1016/j.dcmed.2024.09.007","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the differential expression and mechanisms of bone formation-related genes in osteoporosis (OP) leveraging bioinformatics and machine learning methodologies, and to predict the active ingredients of targeted traditional Chinese medicine (TCM) herbs.</div></div><div><h3>Methods</h3><div>The Gene Expression Omnibus (GEO) and GeneCards databases were employed to conduct a comprehensive screening of genes and disease-associated loci pertinent to the pathogenesis of OP. The R package was utilized as the analytical tool for the identification of differentially expressed genes. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis and support vector machine-recursive feature elimination (SVM-RFE) algorithm were employed in defining the genetic signature specific to OP. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the selected pivotal genes were conducted. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was leveraged to examine the infiltration patterns of immune cells, with Spearman’s rank correlation analysis utilized to assess the relationship between the expression levels of the genes and the presence of immune cells. Coremine Medical Database was used to screen out potential TCM herbs for the treatment of OP. Comparative Toxicogenomics Database (CTD) was employed for forecasting the TCM active ingredients targeting the key genes. AutoDock Vina 1.2.2 and GROMACS 2020 softwares were employed to conclude analysis results, facilitating the exploration of binding affinity and conformational dynamics between the TCM active ingredients and their biological targets.</div></div><div><h3>Results</h3><div>Ten genes were identified by intersecting the results from the GEO and GeneCards databases. Through the application of LASSO regression and SVM-RFE algorithm, four pivotal genes were selected: coat protein (<em>CP</em>), kallikrein 3 (<em>KLK3</em>), polymerase <em>γ</em> (<em>POLG</em>), and transient receptor potential vanilloid 4 (<em>TRPV4</em>). GO and KEGG pathway enrichment analyses revealed that these trait genes were predominantly engaged in the regulation of defense response activation, maintenance of cellular metal ion balance, and the production of chemokine ligand 5. These genes were notably associated with signaling pathways such as ferroptosis, porphyrin metabolism, and base excision repair. Immune infiltration analysis showed that key genes were highly correlated with immune cells. Macrophage M0, M1, M2, and resting dendritic cell were significantly different between groups, and there were significant differences between different groups (<em>P</em> < 0.05). The interaction counts of resveratrol, curcumin, and quercetin with <em>KLK3</em> were 7, 3, and 2, respectively. It shows that the interactions of resveratrol, curcumin, and quercetin with <em>KLK3</em> were ","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 160-170"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.009
Yuzhen Chen , Yongzhi Zhao , Yiwen Zhang , Fang Chen , Muhammad Iqbal Choudhary , Xinmin Liu , Ning Jiang
<div><h3>Objective</h3><div>To investigate the antidepressant effects of Yuanzhi (Polygalae Radix, PR) aqueous extract on chronic unpredictable mild stress (CUMS)-induced depression rat models and the underlying mechanisms.</div></div><div><h3>Methods</h3><div>A total of 40 male Sprague Dawley (SD) rats were randomly divided into control, model, low dose of PR (PR-L, 0.5 g/kg), high dose of PR (PR-H, 1 g/kg), and fluoxetine (10 mg/kg) groups, with 8 rats in each group. Except for the rats in control group, those in the other four groups underwent CUMS-induced depression modeling. PR and fluoxetine were administered intragastrically once daily, 30 min prior to the CUMS procedure, for 14 consecutive days until the behavioral tests were performed. After CUMS modeling, the sucrose preference test (SPT), open field test (OFT), novelty-suppressed feeding test (NSFT), forced swim test (FST), and tail suspension test (TST) were employed to assess the pharmacological effects of PR on the mitigation of depressive-like behaviors in rat models. Additionally, the enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the serum levels of tumor necrosis factor (TNF)-<em>α</em>, interleukin (IL)-6, and IL-1<em>β</em> in the rats. Western blot analysis was also conducted to evaluate the protein expression levels of nuclear factor kappa-B (NF-<em>κ</em>B), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), and caspase-1 in the hippocampal tissues of the rats. Immunofluorescence staining was performed to observe the morphological changes in ionized calcium-binding adapter molecule 1 positive (Iba-1<sup>+</sup>) cells in the dentate gyrus (DG) of rats with CUMS-induced depression.</div></div><div><h3>Results</h3><div>(i) Treatment with PR-H and fluoxetine resulted in significant enhancements in both the total distance and time the rats moved during tests (<em>P</em> < 0.01 and <em>P</em> < 0.05, respectively). Post-administration of PR-H and fluoxetine also led to statistically significant increase in sucrose preference among rats (<em>P</em> < 0.05). Besides, PR-L, PR-H, and fluoxetine treatment markedly decreased the latency of ingestion (<em>P</em> < 0.05, <em>P</em> < 0.05, and <em>P</em> < 0.01, respectively). As observed from the FST, PR-L, PR-H, and fluoxetine presented antidepressant effects on rats with CUMS-induced depression, leading to the reduction in time of their immobility (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.01, respectively). The results of TST indicated reduced immobility time in rats receiving PR-H and fluoxetine treatment as well (<em>P</em> < 0.01). (ii) Rats in model group showed an increase in the levels of Iba-1<sup>+</sup> microglia in their left and right brains in comparison w
{"title":"Antidepressant effects of Yuanzhi (Polygalae Radix) extract on chronic unpredictable mild stress-induced depression in rats: modulation of the NLRP3 inflammasome and NF-κB pathway","authors":"Yuzhen Chen , Yongzhi Zhao , Yiwen Zhang , Fang Chen , Muhammad Iqbal Choudhary , Xinmin Liu , Ning Jiang","doi":"10.1016/j.dcmed.2024.09.009","DOIUrl":"10.1016/j.dcmed.2024.09.009","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the antidepressant effects of Yuanzhi (Polygalae Radix, PR) aqueous extract on chronic unpredictable mild stress (CUMS)-induced depression rat models and the underlying mechanisms.</div></div><div><h3>Methods</h3><div>A total of 40 male Sprague Dawley (SD) rats were randomly divided into control, model, low dose of PR (PR-L, 0.5 g/kg), high dose of PR (PR-H, 1 g/kg), and fluoxetine (10 mg/kg) groups, with 8 rats in each group. Except for the rats in control group, those in the other four groups underwent CUMS-induced depression modeling. PR and fluoxetine were administered intragastrically once daily, 30 min prior to the CUMS procedure, for 14 consecutive days until the behavioral tests were performed. After CUMS modeling, the sucrose preference test (SPT), open field test (OFT), novelty-suppressed feeding test (NSFT), forced swim test (FST), and tail suspension test (TST) were employed to assess the pharmacological effects of PR on the mitigation of depressive-like behaviors in rat models. Additionally, the enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the serum levels of tumor necrosis factor (TNF)-<em>α</em>, interleukin (IL)-6, and IL-1<em>β</em> in the rats. Western blot analysis was also conducted to evaluate the protein expression levels of nuclear factor kappa-B (NF-<em>κ</em>B), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), and caspase-1 in the hippocampal tissues of the rats. Immunofluorescence staining was performed to observe the morphological changes in ionized calcium-binding adapter molecule 1 positive (Iba-1<sup>+</sup>) cells in the dentate gyrus (DG) of rats with CUMS-induced depression.</div></div><div><h3>Results</h3><div>(i) Treatment with PR-H and fluoxetine resulted in significant enhancements in both the total distance and time the rats moved during tests (<em>P</em> < 0.01 and <em>P</em> < 0.05, respectively). Post-administration of PR-H and fluoxetine also led to statistically significant increase in sucrose preference among rats (<em>P</em> < 0.05). Besides, PR-L, PR-H, and fluoxetine treatment markedly decreased the latency of ingestion (<em>P</em> < 0.05, <em>P</em> < 0.05, and <em>P</em> < 0.01, respectively). As observed from the FST, PR-L, PR-H, and fluoxetine presented antidepressant effects on rats with CUMS-induced depression, leading to the reduction in time of their immobility (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.01, respectively). The results of TST indicated reduced immobility time in rats receiving PR-H and fluoxetine treatment as well (<em>P</em> < 0.01). (ii) Rats in model group showed an increase in the levels of Iba-1<sup>+</sup> microglia in their left and right brains in comparison w","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 184-194"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.003
Yulin Shi , Jiayi Liu , Yi Chun , Lingshuang Liu , Jiatuo Xu
Objective
To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.
Methods
From July 1, 2020 to March 31, 2022, clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, respectively. We obtained tongue images from patients with benign lung nodules and lung cancer using the TFDA-1 digital tongue diagnosis instrument, and analyzed these images with the TDAS V2.0 software. The extracted indicators included color space parameters in the Lab system for both the tongue body (TB) and tongue coating (TC) (TB/TC-L, TB/TC-a, and TB/TC-b), textural parameters [TB/TC-contrast (CON), TB/TC-angular second moment (ASM), TB/TC-entropy (ENT), and TB/TC-MEAN], as well as TC parameters (perAll and perPart). The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis, and the overall correlation was analyzed using canonical correlation analysis (CCA).
Results
Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values. Simple correlation analysis indicated that the correlation of TB-L with TC-L, TB-b with TC-b, and TB-b with perAll in lung cancer group was higher than that in benign nodules group. Moreover, the correlation of TB-a with TC-a, TB-a with perAll, and the texture parameters of the TB (TB-CON, TB-ASM, TB-ENT, and TB-MEAN) with the texture parameters of the TC (TC-CON, TC-ASM, TC-ENT, and TC-MEAN) in benign nodules group was higher than lung cancer group. CCA further demonstrated a strong correlation between the TB and TC parameters in lung cancer group, with the first and second pairs of typical variables in benign nodules and lung cancer groups indicating correlation coefficients of 0.918 and 0.817 (P < 0.05), and 0.940 and 0.822 (P < 0.05), respectively.
Conclusion
Benign lung nodules and lung cancer patients exhibited differences in correlation in the L, a, and b values of the TB and TC, as well as the perAll value of the TC, and the texture parameters (TB/TC-CON, TB/TC-ASM, TB/TC-ENT, and TB/TC-MEAN) between the TB and TC. Additionally, there were differences in the overall correlation of the TB and TC between the two groups. Objective tongue diagnosis indicators can effectively assist in the diagnosis of benign lung nodules and lung cancer, thereby providing a scientific basis for the early detection, diagnosis, and treatment of lung cancer.
{"title":"Tongue image feature correlation analysis in benign lung nodules and lung cancer","authors":"Yulin Shi , Jiayi Liu , Yi Chun , Lingshuang Liu , Jiatuo Xu","doi":"10.1016/j.dcmed.2024.09.003","DOIUrl":"10.1016/j.dcmed.2024.09.003","url":null,"abstract":"<div><h3>Objective</h3><div>To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.</div></div><div><h3>Methods</h3><div>From July 1, 2020 to March 31, 2022, clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, respectively. We obtained tongue images from patients with benign lung nodules and lung cancer using the TFDA-1 digital tongue diagnosis instrument, and analyzed these images with the TDAS V2.0 software. The extracted indicators included color space parameters in the Lab system for both the tongue body (TB) and tongue coating (TC) (TB/TC-L, TB/TC-a, and TB/TC-b), textural parameters [TB/TC-contrast (CON), TB/TC-angular second moment (ASM), TB/TC-entropy (ENT), and TB/TC-MEAN], as well as TC parameters (perAll and perPart). The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis, and the overall correlation was analyzed using canonical correlation analysis (CCA).</div></div><div><h3>Results</h3><div>Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values. Simple correlation analysis indicated that the correlation of TB-L with TC-L, TB-b with TC-b, and TB-b with perAll in lung cancer group was higher than that in benign nodules group. Moreover, the correlation of TB-a with TC-a, TB-a with perAll, and the texture parameters of the TB (TB-CON, TB-ASM, TB-ENT, and TB-MEAN) with the texture parameters of the TC (TC-CON, TC-ASM, TC-ENT, and TC-MEAN) in benign nodules group was higher than lung cancer group. CCA further demonstrated a strong correlation between the TB and TC parameters in lung cancer group, with the first and second pairs of typical variables in benign nodules and lung cancer groups indicating correlation coefficients of 0.918 and 0.817 (<em>P</em> < 0.05), and 0.940 and 0.822 (<em>P</em> < 0.05), respectively.</div></div><div><h3>Conclusion</h3><div>Benign lung nodules and lung cancer patients exhibited differences in correlation in the L, a, and b values of the TB and TC, as well as the perAll value of the TC, and the texture parameters (TB/TC-CON, TB/TC-ASM, TB/TC-ENT, and TB/TC-MEAN) between the TB and TC. Additionally, there were differences in the overall correlation of the TB and TC between the two groups. Objective tongue diagnosis indicators can effectively assist in the diagnosis of benign lung nodules and lung cancer, thereby providing a scientific basis for the early detection, diagnosis, and treatment of lung cancer.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 120-128"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/j.dcmed.2024.04.006
Ran Li , Gao Ren , Junfeng Yan , Beiji Zou , Qingping Liu
Objective
To construct a traditional Chinese medicine (TCM) knowledge base using knowledge graph based on deep learning methods, and to explore the application of joint models in intelligent question answering systems for TCM.
Methods
Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medica were applied to construct a comprehensive knowledge graph serving as the foundation for the intelligent question answering system. In the study, a BERT+Slot-Gated (BSG) deep learning model was applied for the identification of TCM entities and question intentions presented by users in their questions. Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user. The Flask framework and BSG model were utilized to develop the intelligent question answering system of TCM.
Results
A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn. In the question answering test assisted by a question corpus, the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9, and the accuracy rate for identifying intentions was 99.75%. This indicates that the system is both feasible and practical. Users can interact with the system through the WeChat Official Account platform.
Conclusion
The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension, indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering systems in TCM.
{"title":"Intelligent question answering system for traditional Chinese medicine based on BSG deep learning model: taking prescription and Chinese materia medica as examples","authors":"Ran Li , Gao Ren , Junfeng Yan , Beiji Zou , Qingping Liu","doi":"10.1016/j.dcmed.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.dcmed.2024.04.006","url":null,"abstract":"<div><h3>Objective</h3><p>To construct a traditional Chinese medicine (TCM) knowledge base using knowledge graph based on deep learning methods, and to explore the application of joint models in intelligent question answering systems for TCM.</p></div><div><h3>Methods</h3><p>Textbooks <em>Prescriptions of Chinese Materia Medica</em> and <em>Chinese Materia Medica</em> were applied to construct a comprehensive knowledge graph serving as the foundation for the intelligent question answering system. In the study, a BERT+Slot-Gated (BSG) deep learning model was applied for the identification of TCM entities and question intentions presented by users in their questions. Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user. The Flask framework and BSG model were utilized to develop the intelligent question answering system of TCM.</p></div><div><h3>Results</h3><p>A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn. In the question answering test assisted by a question corpus, the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9, and the accuracy rate for identifying intentions was 99.75%. This indicates that the system is both feasible and practical. Users can interact with the system through the WeChat Official Account platform.</p></div><div><h3>Conclusion</h3><p>The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension, indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering systems in TCM.</p></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 1","pages":"Pages 47-55"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377724000247/pdfft?md5=a22431df2b6bfa13b9ef1cab13e2e4d5&pid=1-s2.0-S2589377724000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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