With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

AIM: To evaluate the surgical outcomes and changes in the ocular surface microenvironment following radiofrequency minimally invasive treatment for conjunctivochalasis-induced epiphora.METHODS: Patients with epiphora primarily caused by conjunctivochalasis were enrolled. All patients had conjunctivochalasis of ≥grade II, and their symptoms showed no significant improvement after previous pharmacological treatment. All patients underwent radiofrequency minimally invasive correction of conjunctivochalasis, supplemented with artificial tears, anti-inflammatory therapy, and ocular surface repair treatment postoperatively. At 8 wk post-surgery, the ocular surface disease index(OSDI), eye redness, tear secretion, non-invasive tear break-up time, lipid layer thickness, tear ferning test, and conjunctival impression cytology were assessed to compare treatment efficacy and observe changes in the ocular surface microenvironment.RESULTS: A total of 43 cases(43 eyes)of conjunctivochalasis and with a main complaint of epiphora were included, including 23 males and 20 males, with a mean age of 64.69±3.36 years. The total effective rate of surgery was 91% at 8 wk postoperatively. Compared with preoperative values, the OSDI scores significantly decreased and the non-invasive tear break-up time was prolonged at 8 wk post-surgery(all P<0.05). No statistically significant differences were observed in lipid layer thickness or tear secretion at 8 wk postoperatively(all P>0.05). The normal rate of chloramphenicol taste test increased from 21% preoperatively to 63% postoperatively; the normal rate of eye redness increased from 40% to 70%; normal rate of tear ferning grading improved from 30% to 63%; and normal conjunctival impression cytology grading increased from 21% to 74%.CONCLUSION: Radiofrequency minimally invasive treatment is effective for conjunctivochalasis and is straightforward to perform. Patients with conjunctivochalasis often present with other ocular surface issues beyond conjunctivochalasis itself, such as insufficient tear secretion, reduced lipid layer thickness, and other dry eye-related problems. Therefore, a comprehensive approach emphasizing tear dynamics should be adopted during treatment.

The abnormal accumulation of neutrophil extracellular traps （NETs） can promote the initiation and progression of pancreatic cancer， which is considered a potential therapeutic target for this disease. The Miraculous Pivot·Inquiry About Statement （《灵枢·口问》） have recorded the concept of "defensive qi stagnation". Based on the recognition that the function of defensive qi is similar to the immune function of neutrophils， and combining traditional Chinese medicine theory with clinical practice， it is proposed that the abnormal accumulation of NETs may be a pathological product of "defensive qi stagnation"， with the spleen being the critical site of pathology. Further exploring the application strategy of cleaning spleen and restoring defensive qi method in pancreatic cancer treatment， it is proposed to employ three approaches such as dredging method to eliminate spleen stagnation and inhibit pancreatic cancer proliferation， cleaning method to remove spleen dampness and suppress the inflammatory micro-environment， and tonifying method to strengthen Weiqi and to improve the immune microenvironment， which aims to provide new insights for the clinical treatment of pancreatic cancer with traditional Chinese medicine.

This article combined the pathogenic characteristics of "latent wind" with the theory of collateral diseases to clarify the pathological features of tumor blood vessels， including their active proliferation， high permeabi-lity， and promotion of metastasis. The theory framework of "latent wind in collaterals" as the tumor mechanism was proposed， which suggests that at the site of tumor lesions， the collaterals inherit the nature of latent wind to grow excessively， adopt an open and discharge nature to leak essence， and tumor toxins， characterized by their rapid movement and frequent changes， spread and metastasize， driving the progression of malignant tumors. Focusing on the fundamental pathogenesis of "latent wind in collaterals"， specific clinical treatment principles and methods centered on treating wind are proposed， including regulating qi and dispelling wind， clearing heat and extinguishing wind， unblocking collaterals and expelling wind， and reinforcing healthy qi to calm wind， so as to provide references for enhancing the precision of traditional Chinese medicine in treating malignant tumors.

OBJECTIVE To explore the effect and mechanism of Zhiqiao gancao decoction （ZQGCD） on pathological angiogenesis of degenerative intervertebral disc. METHODS The rats were randomly divided into sham operation group （normal saline）， model group （normal saline）， hypoxia inducible factor-1α （HIF-1α） inhibitor （YC-1） group [2 mg/（kg·d）， tail vein injection]， and ZQGCD low-dose， medium-dose and high-dose groups [3.06， 6.12， 12.24 g/（kg·d）]， with 8 rats in each group. Except for sham operation group， lumbar disc degeneration model of rat was constructed in all other groups. After modeling， they were given relevant medicine once a day， for consecutive 3 weeks. After the last medication， pathological changes and angiogenesis of the intervertebral disc tissue in rats were observed； the levels of inflammatory factors [interleukin-1β （IL-1β）， IL-6， tumor necrosis factor-α （TNF-α）] and the expressions of angiogenesis-related proteins [HIF-1α， vascular endothelial growth factor （VEGF）， VEGF receptor 2 （VEGFR2）， angiotensin 1（Ang 1）， Ang 2] in the com intervertebral disc tissue in rats were all determined. In cell experiment， the primary nucleus pulposus cells were isolated and cultured from rats， and cellular degeneration was induced using 50 ng/mL TNF-α. The cells were divided into blank control group （10% blank control serum）， TNF-α group （10% blank control serum）， YC-1 group （10% blank control serum+0.2 mmol/L YC-1）， and 5%， 10%， 15% drug-containing serum group （5%， 10%， 15% drug-containing serum）. After 24 hours of intervention， the nucleus pulposus cells were co-cultured with HUVEC. The expressions of Collagen Ⅱ， matrix metalloproteinase-3 （MMP-3） in nucleus pulposus cells were detected. HUVEC proliferation， migration and tube forming ability were detected， and the expression levels of the HIF-1α/VEGF/Ang signal axis and angiogenesis- related proteins （add MMP-2， MMP-9） in HUVEC were detected. RESULTS Animal experiments had shown that compared with model group， the positive expression of CD31 in the intervertebral disc tissues of rats in each drug group was down-regulated （P＜ 0.05）， the levels of inflammatory factors and angiogenesis-related proteins were decreased significantly （P＜0.05）， and the pathological changes in the intervertebral disc were alleviated. Cell experiments had shown that compared with TNF-α group， the expression of Collagen Ⅱ in nucleus pulposus cells of all drug groups was significantly up-regulated （P＜0.05）， and the expression of MMP-3 was significantly down-regulated （P＜0.05）； the proliferation， migration and tubulogenesis of HUVEC were significantly weakened （P＜0.05）. The mRNA and protein expressions of HIF-1α， VEGF， Ang 2 as well as the expression of angiogenesis-related proteins （except for the expression of Ang 2 mRNA and HIF-1α， VEGFR2， Ang 2 protein in 5% drug- containing serum group） were significantly down-regulated （P＜0.05）. CONCLUSIONS ZQGCD may inhibit the HIF-1α/VEGF/ Ang signal axis to weaken the angiogenic ability of vascular endothelial cells， improve pathological angiogenesis in the intervertebral disc， and delay the degeneration of the intervertebral disc.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.