Asarum forbesii is a perennial herb born in a shaded and humid environment, which is warm in nature. With the efficacy of warming lung, resolving fluid retention, and relieving coughs, it can be used to treat the syndrome of cold fluid accumulating in lung. To investigate the effects of different shading conditions on the composition and efficacy of A. forbesii, this study planted A. forbesii under 20% natural light(NL20), 40% natural light(NL40), 60% natural light(NL60), and 80% natural light(NL80) and utilized ultra performance liquid chromatography(UPLC) and micro broth 2-fold dilution method to detect the volatile chemical compounds and the minimum inhibitory concentration. At the same time, the study investigated the effects of A. forbesii grown under different shading conditions on the signs, pathological changes of lung tissues, serum cytokine levels, activities of mitochondrial respiratory chain complexes Ⅰ-Ⅴ in lung tissues, and relative expression of related genes of mice with syndrome of cold fluid accumulating in lung. The results indicated that with the increase of shading, the content of kakuol, methyl eugenol, and asarinin in A. forbesii and the antibacterial effect showed a tendency of increasing first and then decreasing, and the NL40 group was significantly better than the other groups. Under the conditions of NL20 and NL40, A. forbesii significantly alleviated the pathological damage to lung tissues, restored the homeostasis of the lung, and enhanced the energy metabolism level of mice with syndrome of cold fluid accumulating in lung. In addition, A. forbesii planted under the two conditions reduced the content of interleukin-8(IL-8), interleukin-13(IL-13), tumor necrosis factor-α(TNF-α), and mucin 5AC(MUC5AC), increased the levels of interleukin-10(IL-10) and aquaporin 1(AQP1), lowered the expression of MMP9, VEGF, TGF-β, and MAPK3. In conclusion, the therapeutic effect of A. forbesii on the syndrome of cold fluid accumulating in lung was positively correlated with the degree of shading, and the chemical composition and efficacy of warming lung and resolving fluid retention were optimal under the conditions of NL20-NL40. This study can provide reference for the pharmacological research and cultivation of A. forbesii.
Animals ; Mice ; Lung/pathology* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Light ; Cytokines/genetics* ; Humans

Dendrobium officinale(DO) is a traditional Chinese medicinal and edible plant, while it is critically endangered worldwide. This article, primarily based on the original research findings of the author's team and available articles, provides a comprehensive overview of the factors contributing to the endangerment of DO and the key technologies for the conservation, efficient cultivation, and value-added utilization of this plant. The scarcity of wild populations, low seed-setting rates, lack of endosperm in seeds, and the need for symbiosis with endophytic fungi for seed germination under natural conditions are identified as the primary causes for the rarity and endangerment of DO. Artificial seed production and tissue culture are highlighted as key technologies for alleviating the endangered status. The physiological and ecological mechanisms underlying the adaptation of DO to epiphytic growth are explored, and it is proposed that breaking the coupling of high temperature and high humidity is essential for preventing southern blight, a devastating affliction of DO. The roles of endophytic fungi in promoting the growth, improving the quality, and enhancing the stress resistance of DO are discussed. Furthermore, the integration of variety breeding, environment selection, and co-culture with endophytic fungi is emphasized as a crucial approach for efficient cultivation. The value-added applications of DO in pharmaceuticals, health foods, food products, and daily chemicals-particularly in the food and daily chemical industries-are presented as key drivers for a 10-billion-RMB-level industry. This systematic review offers valuable insights for the further development, utilization, and industrialization of DO resources, as well as for the broader application of conservation strategies for other rare and endangered plant species.
Dendrobium/microbiology* ; Endangered Species ; Seeds/microbiology* ; Fungi/physiology*

Recent data suggest that vascular endothelial growth factor receptor inhibitor (VEGFRi) can enhance the anti-tumor activity of the anti-programmed cell death-1 (anti-PD-1) antibody in colorectal cancer (CRC) with microsatellite stability (MSS). However, the comparison between this combination and standard third-line VEGFRi treatment is not performed, and reliable biomarkers are still lacking. We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi (combination group, n=54) or VEGFRi alone (VEGFRi group, n=32), and their efficacy and safety were evaluated. We additionally examined the immune characteristics of the MSS CRC tumor microenvironment (TME) through single-cell and spatial transcriptomic data, and an MSS CRC immune cell-related signature (MCICRS) that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort. Compared with VEGFRi alone, the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit (median progression-free survival: 4.4 vs. 2.0 months, P=0.0024; median overall survival: 10.2 vs. 5.2 months, P=0.0038) and a similar adverse event incidence. Through single-cell and spatial transcriptomic analysis, we determined ten MSS CRC-enriched immune cell types and their spatial distribution, including naive CD4⁺ T, regulatory CD4⁺ T, CD4⁺ Th17, exhausted CD8⁺ T, cytotoxic CD8⁺ T, proliferated CD8⁺ T, natural killer (NK) cells, plasma, and classical and intermediate monocytes. Based on a systemic meta-analysis and ten machine learning algorithms, we obtained MCICRS, an independent risk factor for the prognosis of MSS CRC patients. Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation, and hence a significant relation with the superior efficacy of pan-cancer immunotherapy. More importantly, the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort. Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity. MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.
Humans ; Colorectal Neoplasms/drug therapy* ; Male ; Female ; Immunotherapy ; Middle Aged ; Aged ; Tumor Microenvironment/immunology* ; Retrospective Studies ; Microsatellite Instability ; Transcriptome ; Single-Cell Analysis ; Programmed Cell Death 1 Receptor/immunology* ; Gene Expression Profiling ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; Receptors, Vascular Endothelial Growth Factor/antagonists & inhibitors*

Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.

The evaluation, diagnosis, real-time monitoring, and guided operation conducted by ultrasound specialist nurses in acute and critical care can provide an objective basis for nursing decision-making, which is of great significance for optimizing nursing procedures and improving the survival rate and rehabilitation rate of patients. This paper summarizes the concept, origin, training status, and practice scope of ultrasound specialist nurses in the field of acute and critical care and puts forward the prospect of development in order to provide a reference for the training of ultrasound specialist nurses in the field of acute and critical care in China.

Carpal tunnel syndrome (CTS) is a condition caused by compression of the median nerve in carpal canal. In recent years, due to popularity of electronic devices such as computers, the incidence of CTS has shown a rapid rising trend. Its treatment methods include surgical treatment and conservative treatment. For mild to and moderate CTS, conservative treatment is preferred. Acupotomy, as an innovative and unique treatment method, could relieve pressure in carpal canal by releasing transverse ligament of wrist and promote local blood circulation to treat CTS, and has characteristics of less trauma, short course of treatment and low cost, which is more acceptable to patients. In addition, the combination of needle-knife and other therapies also has a good effect. However, traditional needle-knife therapy has certain limitations in operation, and its safety problems can be effectively solved with the help of ultrasound technology. Therefore, ultrasus-guided needle-knife therapy for CTS has become a current research hotspot, but its long-term therapeutic effect still needs to be further verified.
Humans ; Carpal Tunnel Syndrome/therapy* ; Acupuncture Therapy/methods*

AIM: To evaluate the clinical efficacy of femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation.METHODS:The retrospective study enrolled 22 cases(26 eyes)of cataract patients who underwent femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation from August 2020 to August 2021. Follow-up to 3mo after surgery, the changes of far, intermediate and near visual acuity, aberration, Strehl ratio(SR)and modulation transfer function cutoff(MTF-cutoff)frequency were compared. Defocus curve at 1mo postoperatively was made, and the visual quality and satisfaction were evaluated after 3mo of surgery.RESULTS: The visual acuity of all patients was better than 0.1(LogMAR)at the far, intermediate and near distance at 1d, 1wk, 1 and 3mo postoperatively, and it was significantly improved compared with those before surgery(all P<0.01). The defocus curve transitioned smoothly between +0.5 and -3.0D at 1mo after surgery, and visual acuity was better than 0.63. The total aberration and spherical aberration in the whole eye were significantly lower after surgery than before, and the SR and MTF-cutoff were significantly improved at 1d and 1wk after surgery(all P<0.05). With high satisfaction and good visual quality, patients could watch at far, intermediate and near distance without wearing glasses at 3mo after surgery.CONCLUSION: Femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation gave patients a comfortable and satisfactory full-course vision.

Objective: To explore the influencing factors of pregnancy-induced hypertensive disorders in pregnancy (HDP) with organ or system impairment in pregnant women, and to analyze and compare the differences of HDP subtypes in different regions of China. Methods: A total of 27 680 pregnant women with HDP with complete data from 161 hospitals in 24 provinces, autonomous regions and municipalities were retrospectively collected from January 1, 2018 to December 31, 2018. According to their clinical manifestations, they were divided into hypertension group [a total of 10 308 cases, including 8 250 cases of gestational hypertension (GH), 2 058 cases of chronic hypertension during pregnancy] and hypertension with organ or system impairment group [17 372 cases, including 14 590 cases of pre-eclampsia (PE), 137 cases of eclampsia, 2 645 cases of chronic hypertension with PE]. The subtype distribution of HDP in East China (6 136 cases), North China (4 821 cases), Central China (3 502 cases), South China (8 371 cases), Northeast China (1 456 cases), Southwest China (2 158 cases) and Northwest China (1 236 cases) were analyzed. By comparing the differences of HDP subtypes and related risk factors in different regions, regional analysis of the risk factors of HDP pregnant women with organ or system impairment was conducted. Results: (1) The proportions of HDP pregnant women with organ or system impairment in Northeast China (79.05%, 1 151/1 456), Central China (68.42%, 2 396/3 502) and Northwest China (69.34%, 857/1 236) were higher than the national average (62.76%, 17 372/27 680); the proportions in North China (59.18%, 2 853/4 821), East China (60.85%, 3 734/6 136) and South China (59.56%, 4 986/8 371) were lower than the national average, and the differences were statistically significant (all P<0.05). (2) Univariate analysis showed that the proportions of primiparas, non-Han, non-urban household registration, irregular prenatal examination and PE history in the hypertension with organ or system impairment group were higher than those in the hypertension group, and the differences were statistically significant (all P<0.05). Multivariate logistic regression analysis showed that primiparas, non-Han, non-urban household registration, irregular prenatal examination and PE history were independent risk factors for HDP pregnant women with organ or system impairment (all P<0.05). (3) Primipara: the rates of primipara in Northeast China, North China and Southwest China were higher than the national average level, while those in South China, Central China and Northwest China were lower than the national average level. Non-Han nationality: the rates of non-Han nationality in Northeast China, North China and Northwest China were higher than the national average, while those in East China, South China and Central China were lower than the national average. Non-urban household registration: the rates of non-urban household registration in Northeast China, North China, and Southwest China were lower than the national average, while those in East China, Central China were higher than the national average. Irregular prenatal examination: the rates of irregular prenatal examination in North China, South China and Southwest regions were lower than the national average level, while those in Northeast China, Central China and Northwest China were higher than the national average level. History of PE: the incidence rates of PE in Northeast China, North China, South China and Southwest China were lower than the national average level, while those in Central China and Northwest China were higher than the national average level. Conclusions: Primiparas, non-Han, non-urban household registration, irregular prenatal examination, and PE history are risk factors for HDP pregnant women with organ or system impairment. Patients in Northeast, Central and Northwest China have more risk factors, and are more likely to be accompanied by organ or system function damage. It is important to strengthen the management of pregnant women and reduce the occurrence of HDP.
Humans ; Pregnancy ; Female ; Hypertension, Pregnancy-Induced/diagnosis* ; Retrospective Studies ; Pre-Eclampsia/epidemiology* ; Risk Factors ; Incidence

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography