To summarize Professor WANG Xixing's clinical experience in treating advanced breast cancer with anxiety and depression from the perspective of shaoyang pivot. It is believed that the core pathogenesis of advanced breast cancer with anxiety and depression lies in the dysfunction of shaoyang pivot （referring to the imbalanced regulatory function of the shaoyang meridian system that governs the transportation and transformation of qi， blood， and body fluids）. This dysfunction can lead to abnormal circulation of qi， blood， and body fluids， as well as the intermingling of phlegm and blood stasis， which further promotes the spread and diffusion of cancer toxin. Meanwhile， it disturbs mental activity， resulting in a condition characterized by stagnation of cancer toxin and concurrent disorders of both the physical body and the spirit. Based on this pathogenesis， the basic therapeutic principles of harmonizing shaoyang， regulating the pivot to calm the spirit， and dissipating masses and resolving toxins are proposed. Clinically， the disease is classified into three syndromes for differentiation and treatment. For shaoyang pivot dysfunction syndrome， treatment should use self-prescribed Chaiqin Hengshu Ningxin Decoction （柴芩衡枢宁神汤）； for sanjiao pivot dysfunction syndrome， treatment should prescribe Chaigui Tongshu Dashen Decoction （柴归通枢达神饮）； for gallbladder function disorder syndrome， treatment should apply Wendan Qishu Shoushen Decoction （温胆启枢守神汤）. Throughout the treatment process， the concept of "simultaneous treatment of cancer and depression" is implemented to smooth the shaoyang pivot， block the vicious cycle where cancer toxin and emotional abnormalities mutually reinforce each other.

OBJECTIVE To investigate the effect and mechanism of picroside Ⅱ on the malignant progression of non-small cell lung cancer （NSCLC）. METHODS A549 cells were divided into the control group， picroside Ⅱ low-， medium- and high- concentration groups， K6PC-5 [sphingosine kinase 1 （SPHK1） activator] group， and picroside Ⅱ high-dose+K6PC-5 group. Cell proliferation， migration and invasion were detected. Besides， the expression of proliferating cell nuclear antigen （PCNA）， matrix metalloproteinase-2 （MMP-2）， MMP-9， SPHK1， sphingosine-1-phosphate receptor 3 （S1PR3） and extracellular signal-regulated kinase 1/2 （ERK1/2） protein in the cells were also observed. BALB/c nude mice were subcutaneously inoculated with A549 cell suspension to establish NSCLC xenograft models. Then they were assigned to the nude mouse-control group， nude mouse-picroside Ⅱ low-， medium- and high-dose groups， nude mouse-K6PC-5 group， and nude mouse-picroside Ⅱ high-dose+K6PC-5 group （with 5 mice in each group） to investigate the effect of picroside Ⅱ on their tumor mass and volume. RESULTS Compared with the control group， the OD450 values， EdU-positive cell rates， scratch healing rates， cell invasion number， and the relative expression levels of PCNA， MMP-2， MMP-9， SPHK1， S1PR3 and ERK1/2 protein in the low-， medium- and high-concentration groups of picroside Ⅱ were significantly decreased. Compared with the nude mouse-control group， the tumor mass and volume in the nude mouse-low-， medium- and high-dose groups of picroside Ⅱ were significantly decreased or shrunk. The changes of above indicators were concentration/dose-dependent （P＜0.05）. The changing trend of the corresponding indicators in the K6PC-5 ZYTS181） group and the nude mouse-K6PC-5 group was opposite （P＜0.05）. Compared with the picroside Ⅱ high-concentration group or the nude mice-picroside Ⅱ high-dose group， the above quantitative indicators in the picroside Ⅱ high- concentration+K6PC-5 group cells and the nude mouse-picroside Ⅱ high-dose+K6PC-5 group nude mice were significantly increased or enlarged （P＜0.05）. CONCLUSIONS Picroside Ⅱ may inhibit the malignant progression of NSCLC by inhibiting SPHK1/sphingosine-1-phosphate/S1PR3 signaling pathway.

Objective:To develop a prognostic prediction model for patients with colorectal cancer based on a peripheral blood cell composite score (PBCS) system.Methods:This retrospective observational study included patients who had primary colorectal cancer without distant metastasis, who did not undergo radiotherapy or chemotherapy before surgery, who did not receive leukocyte or platelet-raising therapy within 1 month before surgery, and whose postoperative pathology confirmed colorectal adenocarcinoma with complete tumor resection. Patients with severe anemia, infection, or hematologic diseases before surgery, as well as those with severe heart, lung, or other important organ diseases or concurrent malignant tumors, were excluded. In total, 1021 patients with colorectal cancer who underwent surgical treatment in the Department of Gastrointestinal Surgery of the Fourth Hospital of Hebei Medical University from April 2018 to April 2020 were retrospectively included as the training set (766 patients) and the internal validation set (255 patients). Additionally, using the same criteria, 215 patients with colorectal cancer who underwent surgical treatment in another treatment group from March 2015 to December 2020 were selected as the external validation set. The "surv_cutpoint" function in R software was used to analyze the optimal cut-off values of neutrophils, lymphocytes, and platelets, and a PBCS system was established based on the optimal cut-off values. The scoring rules of the PBCS system were as follows: Neutrophils and platelets below the optimal cut-off value = 1 point, otherwise 0 points; Lymphocytes above the optimal cut-off value = 1 point, otherwise 0 points. The scores of the three cell types were added together to obtain the PBCS. Univariate and multivariate Cox regression analyses were performed to explore the correlation between patients' clinicopathological features and prognosis, and a nomogram was constructed based on the Cox regression analysis to predict patients' prognosis. The accuracy of the nomogram prediction model was validated using the C-index, calibration curve, and decision curve analysis.Results:The optimal cut-off values for neutrophils, lymphocytes, and platelets were 4.40×10 9/L, 1.41×10 9/L, and 355×10 9/L, respectively. The patients were divided into high and low groups according to the optimal cut-off values of these cells. Survival curve analysis showed that a high lymphocyte count (training set: P=0.042, internal validation: P=0.010, external validation: P=0.029), low neutrophil count (training set: P=0.035, internal validation: P=0.001, external validation: P=0.024), and low platelet count (training set: P=0.041, internal validation: P=0.030, external validation: P=0.024) were associated with prolonged overall survival (OS), with statistically significant differences in all cases. Survival analysis of different PBCS groups showed that patients with a high PBCS had longer OS than those with a low PBCS ( P<0.05). Univariate and multivariate Cox regression analysis results showed that aspirin use history, vascular thrombus, neural invasion, CA19-9, N stage, operation time, M stage, and PBCS were independent factors affecting OS (all P<0.05). The PBCS was also an independent factor affecting disease-specific survival ( P<0.05), but not progression-free survival ( P>0.05). The above independent risk or protective factors were included in R software to construct a nomogram for predicting OS. The C-index (0.873), calibration curve, and decision curve analysis (threshold probability: 0.0%–75.2%) all indicated that the nomogram prediction model had good predictive performance for OS. Conclusion:This study demonstrates that the PBCS constructed based on preoperative peripheral blood levels of neutrophils, lymphocytes, and platelets is an independent factor associated with the prognosis of patients with colorectal cancer. The nomogram model constructed based on this score system exhibits good predictive efficacy for the prognosis of these patients.

Objective:To develop a prognostic prediction model for patients with colorectal cancer based on a peripheral blood cell composite score (PBCS) system.Methods:This retrospective observational study included patients who had primary colorectal cancer without distant metastasis, who did not undergo radiotherapy or chemotherapy before surgery, who did not receive leukocyte or platelet-raising therapy within 1 month before surgery, and whose postoperative pathology confirmed colorectal adenocarcinoma with complete tumor resection. Patients with severe anemia, infection, or hematologic diseases before surgery, as well as those with severe heart, lung, or other important organ diseases or concurrent malignant tumors, were excluded. In total, 1021 patients with colorectal cancer who underwent surgical treatment in the Department of Gastrointestinal Surgery of the Fourth Hospital of Hebei Medical University from April 2018 to April 2020 were retrospectively included as the training set (766 patients) and the internal validation set (255 patients). Additionally, using the same criteria, 215 patients with colorectal cancer who underwent surgical treatment in another treatment group from March 2015 to December 2020 were selected as the external validation set. The "surv_cutpoint" function in R software was used to analyze the optimal cut-off values of neutrophils, lymphocytes, and platelets, and a PBCS system was established based on the optimal cut-off values. The scoring rules of the PBCS system were as follows: Neutrophils and platelets below the optimal cut-off value = 1 point, otherwise 0 points; Lymphocytes above the optimal cut-off value = 1 point, otherwise 0 points. The scores of the three cell types were added together to obtain the PBCS. Univariate and multivariate Cox regression analyses were performed to explore the correlation between patients' clinicopathological features and prognosis, and a nomogram was constructed based on the Cox regression analysis to predict patients' prognosis. The accuracy of the nomogram prediction model was validated using the C-index, calibration curve, and decision curve analysis.Results:The optimal cut-off values for neutrophils, lymphocytes, and platelets were 4.40×10 9/L, 1.41×10 9/L, and 355×10 9/L, respectively. The patients were divided into high and low groups according to the optimal cut-off values of these cells. Survival curve analysis showed that a high lymphocyte count (training set: P=0.042, internal validation: P=0.010, external validation: P=0.029), low neutrophil count (training set: P=0.035, internal validation: P=0.001, external validation: P=0.024), and low platelet count (training set: P=0.041, internal validation: P=0.030, external validation: P=0.024) were associated with prolonged overall survival (OS), with statistically significant differences in all cases. Survival analysis of different PBCS groups showed that patients with a high PBCS had longer OS than those with a low PBCS ( P<0.05). Univariate and multivariate Cox regression analysis results showed that aspirin use history, vascular thrombus, neural invasion, CA19-9, N stage, operation time, M stage, and PBCS were independent factors affecting OS (all P<0.05). The PBCS was also an independent factor affecting disease-specific survival ( P<0.05), but not progression-free survival ( P>0.05). The above independent risk or protective factors were included in R software to construct a nomogram for predicting OS. The C-index (0.873), calibration curve, and decision curve analysis (threshold probability: 0.0%–75.2%) all indicated that the nomogram prediction model had good predictive performance for OS. Conclusion:This study demonstrates that the PBCS constructed based on preoperative peripheral blood levels of neutrophils, lymphocytes, and platelets is an independent factor associated with the prognosis of patients with colorectal cancer. The nomogram model constructed based on this score system exhibits good predictive efficacy for the prognosis of these patients.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Non-small cell lung cancer(NSCLC)is one of the malignant tumors with high morbidity and mortality worldwide.Ferroptosis is a new type of programmed cell death caused by abnormal accumulation of iron-dependent reactive oxygen species(ROS)leading to lipid peroxidation.It involves the balance between iron metabolism,lipid metabolism,oxy-gen free radical reaction and lipid peroxidation.Recent studies have found that ferroptosis is closely related to the occurrence and development of NSCLC.Due to the emergence of chemotherapy resistance and radiotherapy resistance in the treatment of NSCLC,there is an urgent need to develop new effective drugs and treatment strategies.Traditional Chinese medicine has unique advantages in the prevention and treatment of NSCLC due to its multi-targets and minimal side effects.In this review,we summarize the mechanism of ferroptosis in NSCLC,and discuss the research status of active ingredients of traditional Chinese medicine,single-herb traditional Chinese medicine and Chinese herbal compounds in the intervention of NSCLC through ferroptosis,in order to provide a new theoretical basis for the research of ferroptosis pathway and the prevention and treatment of NSCLC by targeted ferroptosis of traditional Chinese medicine.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.