Objective To explore the potential mechanism of Babao Dan on primary liver cancer based on network pharmacology. Methods First, the diethylnitrosamine-induced hepatocellular carcinoma rat（HCC）model was used to observe the effects of Babao Dan. Then, the effective components in Babao Dan were detected by UPLC-MS, and the potential target sites of these effective components were predicted in the Swiss Target Prediction databases, etc. The corresponding target sites for HCC were screened using GeneCards, OMIM and Therapeutic Target Database, and the common target sites between Babao Dan and HCC were obtained after getting the intersection. The protein-protein interaction network was drawn by Cytoscape software and the STRING database, and the key molecules regulating HCC by Babao Dan were screened out. The effective target sites were subjected to GO analysis in the DAVID database and enrichment analysis in the Pathway’s KEGG. Finally, the clinical relevance of key molecules to liver cancer patients was verified by the TCGA database. Results Babao Dan could slow down the tumor development. 851 chemical components were detected in BaBao Dan by UPLC-MS , 9 major active components and 285 target sites were identified. 637 hepatocellular carcinoma-related targets were screened out, and 16 targets of Babao Dan regulating HCC were identified. GO enrichment analysis showed 802 biological processes, 11 cell compositions, and 43 molecular functions, while KEGG pathway enrichment analysis identified a total of 90 pathways. Correlation analysis of TCGA identified three key molecules associated with the survival of liver cancer patients. Conclusion In the primary rat liver cancer model, Babao Dan was found to significantly prolong the survival of cancer-induced rats and reduce tumor burden. The initial prediction of the mechanism by which Babao Dan regulating liver cancer was made through UPLC-MS analysis and network pharmacology methods, indicating that Babao Dan has the characteristics of multi-component, multi-pathway, and multi-target regulation of primary liver cancer, which could provide a reference for further relevant experimental research.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective To research the clinical value of ultrasonic grey scale intensity in identifying the properties of pleural effusion.Methods A prospective study approach was used to collect data from patients who underwent puncture drainage due to clinical needs.We enrolled 120 patients for whom we performed routine ultrasonography and measured the ultrasonic grey scale intensity(echo intensity.EI)value.According to the clinical test results,pleural effusion was divided into exudate fluid,leakage fluid,malignant pleural effusion and benign pleural effusion.The subject operating characteristic curve(ROC curve)of the ultrasound intensity EI value was drawn,and the area under the curve and the optimal cut-off value was calculated.Results Of the 120 pleural effusions,there were 90 exudates[mean intensity EI value:(-46.55±6.35)],30 leaks[mean intensity EI value:(-55.51±5.30)],62 malignant pleural effusions[mean intensity EI value:(-47.24±5.37)],and 58 benign pleural effusions[mean intensity EI value:(-50.46±7.92)].The mean EI value of the exudate group was significantly higher than that of the leakage group(P＜0.05),and that of the benign pleural effusion group was lower than that of the malignant pleural effusion group(P＜0.05).ROC curve analysis revealed that the EI value had high accuracy in distinguishing exudate from lea-king fluid(the area under the curve is 0.856),and the optimal cut-off value was-50.95,with a sensitivity of 80.0％and speci-ficity of 83.3％.The area under the curve of the EI+TP+LDH value for distinguishing benign and malignant pleural effusion was 0.736,and the optimal cut-off was 89.10,with a sensitivity of 98.4％and a specificity of 46.6％.Conclusion The quanti-tative EI value of ultrasonic grey scale intensity has high diagnostic value in distinguishing pleural effusion from exudate or leak-age.The EI+TP+LDH value has a moderate ability to identify benign and malignant pleural effusion.

A three-dimensional feature construction method based on spectral information is presented,in which the power values of each channel are arranged into two-dimensional feature vectors based on electrode positions.The different frequency band features are arranged into a three-dimensional integral feature tensor to extract the information in frequency domain.Meanwhile,in order to reduce the influence of volume conductor effect,functional connectivity is utilized to map the temporal electroencephalogram data to the spatial brain functional network for extracting the spatial information.By analyzing the relationship between features and target classes,a 3D-CNN-Attention network model is proposed to incorporate an Attention mechanism in 3D-CNN network to enhance the electroencephalogram feature learning capability.A series of comparative experiments on publicly available datasets show that 3D-CNN-Attention network framework outperforms other methods in depression detection,obtaining an accuracy rate of up to 96.32%.The proposed method provides an effective solution for depression detection.

Protein self-assemblies at the micro- and nano-scale are of great interest because of their morphological diversity and good biocompatibility. High-throughput screening of protein self-assembly at different scales and morphologies using protein crystallization screening conditions is an emerging method. When using this method to screen protein self-assembly conditions, some apparently transparent droplets are often observed, in which it is not clear whether self-assembly occurs. We explored the interaction between β-lactoglobulin and the protein crystallization kit Index™ C10 and observed the presence of micro- and nano-scale protein self-assemblies in the transparent droplets. The diverse morphology of the micro- and nano-scale self-assemblies in the transparent droplets formed by mixing different initial concentrations of β-lactoglobulin and Index™ C10 was further investigated by scanning electron microscope. Self-assembly process of fluorescence-labelled β-lactoglobulin was monitored continuously by laser confocal microscope, allowing real-time observation of the liquid-liquid phase separation phenomenon and the morphology of the final self-assemblies. The internal structure of the self-assemblies was gradually ordered over time by in-situ X-ray diffraction. This indicates that the self-assembly phenomenon within transparent droplets, observed in protein self-assembly condition screening experiments, is worthy of further in-depth exploration.
Crystallization ; Lactoglobulins

Clinical studies have validated low-level viremia is associated with a variety of adverse outcomes in patients with chronic hepatitis B during the course of receiving nucleos(t)ide analogue antiviral therapy. With the advancement of PCR technology, the high sensitivity PCR detection of HBV DNA can reach the lower limit of detection of < 5-10 IU/mL. The standard criterion for judging among patients who have achieved complete virological response is HBV DNA levels < 20 IU/ml. The use of highly sensitive PCR tests can detect very low-level viremia (HBV DNA < 20 IU/ml, but > 5-10 IU/mL) in some patients. However, there are currently fewer relevant studies, and more research data needs to be accumulated to answer this clinical question of whether long-term very low-level viremia affects the clinical outcome of patients with chronic hepatitis B.

[Abstract] Objective: To investigate the expression of chemokine-like factor-like MARVEL transmembrane domain-containing family member 6 (CMTM6) in breast cancer tissues and its correlation with clinicopathological features and prognosis of patients. Methods:Atotal of 136 breast cancer tissue chips (purchased from Superchip Company), including 42 pairs of matched cancer and paracancerous tissues, were used for this study. The expression level of CMTM6 in cancer and paracancerous tissues was detected by immunohistochemistry. The comparison of CMTM6 expression between breast cancer and paracancerous tissues was conducted by paired χ2 test. The relationship between CMTM6 expression in breast cancer tissues and the clinicopathological characteristics of patients was analyzed by χ2 test. Kaplan-Meier and Log rank test analyses were used to analyze the relationship between CMTM6 expression and the survival of patients, and Cox model was used to evaluate the effect of different indicators on the prognosis of patients. Results: The expression of CMTM6 in breast cancer tissues was significantly higher than that in paracancerous tissues (P<0.01). The expression of CMTM6 was correlated with pathological type of breast cancer and HER2 positivity (P<0.05). The survival time of patients in CMTM6 high expression group was significantly shorter than that of patients in CMTM6 low expression group (P<0.05). Pathological type (HR=10.374, 95%CI: 3.529-30.497, P<0.01), TNM stage (HR=4.599, 95%CI: 1.784-11.856, P<0.01), triple-negative breast cancer (HR=3.370, 95%CI: 1.055-10.761, P<0.05) and high expression of CMTM6 (HR=0.195, 95%CI: 0.073-0.518, P<0.01) were independent risk factors for prognosis of breast cancer patients. Conclusion: CMTM6 is highly expressed in breast cancer tissues, which can be used as a risk factor for prognosis evaluation of breast cancer patients.

Objective: To explore the effect of enteral nutrition on tumor cell proliferation activity in rectal cancer patients with nutritional risk treated with preoperative neoadjuvant therapy. Methods: Sixty-six rectal cancer patients with nutritional risk treated with preoperative neoadjuvant therapy from January 2016 to January 2018 in the Yongchuan Hospital Affiliated to Chongqing Medical University were selected. The patients were divided into experimental group (enteral nutrition combined with neoadjuvant therapy) and control group (simple adjuvant therapy) according to the random digits table method, with 33 cases in each group. The expressions of proliferating cell nuclear antigen (PCNA) and Ki-67 antigen before and after treatment were detected by immunohistochemical method; the albumin and prealbumin before and after treatment were observed, and the nutrition risk screening 2002 (NRS2002) was evaluated. Results: There were no statistical differences in the expressions of PCNA and Ki-67 antigen before treatment between 2 groups (P>0.05); the expressions of PCNA and Ki-67 antigen after treatment in experimental group were significantly lower than those in control group, and there were statistical differences (P<0.05). There were no statistical differences in the NRS2002 score, albumin and prealbumin before treatment between 2 groups (P>0.05); the NRS2002 score after treatment in experimental group was significantly lower than that in control group: (1.58 ± 0.50) scores vs. (3.65 ± 0.72) scores, the albumin and prealbumin after treatment were significantly higher than those in control group: (35.92 ± 2.77) g/L vs. (31.12 ± 1.76) g/L and (204.58 ± 23.86) mg/L vs. (157.46 ± 18.99) mg/L, and there were statistical differences (P<0.01). Conclusions Enteral nutrition can reduce the proliferation activity of tumor cell in rectal cancer patients with nutritional risk treated with preoperative neoadjuvant therapy, and it can improve the nutritional status of patients.

The main work and achievements of clinical pathway work in China since 2009 were systematically reviewed in the paper. It analyzed the problems existing in the implementation of clinical pathway management in China, and suggested on such management in the future. The suggestions include:deeper understanding, convergence with the payment system reform, strengthened quality control, further informatization,and better performance appraisal system.