Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

Objective:To further improve the understanding of paroxysmal nocturnal hemoglobinuria (PNH), we retrospectively analyzed and summarized the clinical characteristics, treatment status, and survival status of patients with PNH in Zhejiang Province.Methods:This study included 289 patients with PNH who visited 20 hospitals in Zhejiang Province. Their clinical characteristics, comorbidity, laboratory test results, and medications were analyzed and summarized.Results:Among the 289 patients with PNH, 148 males and 141 females, with a median onset age of 45 (16-87) years and a peak onset age of 20-49 years (57.8% ). The median lactic dehydrogenase (LDH) level was 1 142 (604-1 925) U/L. Classified by type, 70.9% (166/234) were classical, 24.4% (57/234) were PNH/bone marrow failure (BMF), and 4.7% (11/234) were subclinical. The main clinical manifestations included fatigue or weakness (80.8%, 235/289), dizziness (73.4%, 212/289), darkened urine color (66.2%, 179/272), and jaundice (46.2%, 126/270). Common comorbidities were hemoglobinuria (58.7% ), renal dysfunction (17.6% ), and thrombosis (15.0% ). Moreover, 82.3% of the patients received glucocorticoid therapy, 70.9% required blood transfusion, 30.7% used immunosuppressive agents, 13.8% received anticoagulant therapy, and 6.3% received allogeneic hematopoietic stem cell transplantation. The 10-year overall survival (OS) rate was 84.4% (95% CI 78.0% -91.3% ) . Conclusion:Patients with PNH are more common in young and middle-aged people, with a similar incidence rate between men and women. Common clinical manifestations include fatigue, hemoglobinuria, jaundice, renal dysfunction, and recurrent thrombosis. The 10-year OS of this group is similar to reports from other centers in China.

Medium spiny neurons (MSNs) in the striatum, which can be divided into D1 and D2 MSNs, originate from the lateral ganglionic eminence (LGE). Previously, we reported that Six3 is a downstream target of Sp8/Sp9 in the transcriptional regulatory cascade of D2 MSN development and that conditionally knocking out Six3 leads to a severe loss of D2 MSNs. Here, we showed that Six3 mainly functions in D2 MSN precursor cells and gradually loses its function as D2 MSNs mature. Conditional deletion of Six3 had little effect on cell proliferation but blocked the differentiation of D2 MSN precursor cells. In addition, conditional overexpression of Six3 promoted the differentiation of precursor cells in the LGE. We measured an increase of apoptosis in the postnatal striatum of conditional Six3-knockout mice. This suggests that, in the absence of Six3, abnormally differentiated D2 MSNs are eliminated by programmed cell death. These results further identify Six3 as an important regulatory element during D2 MSN differentiation.

Mouse cortical radial glial cells (RGCs) are primary neural stem cells that give rise to cortical oligodendrocytes, astrocytes, and olfactory bulb (OB) GABAergic interneurons in late embryogenesis. There are fundamental gaps in understanding how these diverse cell subtypes are generated. Here, by combining single-cell RNA-Seq with intersectional lineage analyses, we show that beginning at around E16.5, neocortical RGCs start to generate ASCL1

Objective To observe the change of intima/media thickness ratio and expression of inflammatory factors in renal small artery of diabetic rats, and to explore the correlations of intim/media ratio with inflammatory factors and vascular lesions of diabetic nephropathy (DN) rats. Methods Seventy healthy SD rats were randomly divided into diabetic nephropathy group (DN, n=40) and normal control group (N, n=30). DN rat model was induced by intraperitoneal injection of streptozotocin (STZ). Thirty-five DN rats were successfully established. N group received same dose of citrate buffer. Rats were sacrificed after 4, 12, 24 weeks respectively.The intima/media thickness ratio in renal small artery was detected by immunofluorescence. The monocyte chemoattractant protein-1 (MCP-1) protein and mRNA expression of renal small artery were detected by immunohistochemistry and in-situ hybridization at each time point. Results Blood glucose and urine protein excretion (24 h) at different time points in DN group were significantly higher than those of N group (P＜0.05). From the 12th week, Scr, BUN, serum phosphorus were significantly higher than those of N group (P＜0.05). At the 4th week, renal small artery had the expression of MCP-1 protein and mRNA. The expression increased gradually with time, reached the highest at the 24th week, and was significantly higher than that of N group at each time point (P＜0.05). Immunofluorescence results showed that as compared to N group, in the first 4 weeks, intima/media thickness ratio in DN group was not different, at the 12th week the ratio was higher but without significant difference, at the 24th week the ratio was significantly higher (P＜0.05). Small artery intima/media thickness ratio of DN group was positively correlated with MCP-1, cholesterol, triglyceride, serum phosphate (r=0.742, P＜0.01; r=0.740, P＜0.01; r=0.829, P＜0.01; r=0.580, P＜0.01). Conclusions The arterioles intima/media thickness ratio of early DN is significantly correlated with MCP-1, lipids and phosphorus. MCP-1 may be involved in the DN vascular disease.

OBJECTIVE: To investigate the effects of PUW (a fraction containing 60% corilagin from a Chinese herbal plant Phyllanthus urinaria) on thrombosis and coagulation system. METHODS: Myers's method was used to evaluate the protection of intravenously administered PUW against the male mouse sudden death caused by injection of 75 mg/kg arachidonic acid in the tail vein; Charlton's method was modified to observe antithrombotic effect of PUW in rat electrically stimulated carotid artery thrombosis model; and the model of venous thrombosis was produced to investigate the antithrombotic effect of PUW. Rosette assay was used to observe the effect of PUW on platelet-neutrophil adhesion. The effects of PUW were monitored on euglobulin lysis time (ELT), prothrombin time (PT), kaolin partial thromboplastin time (KPTT), and bleeding time (BT) in rats, according to the methods of Kowalski, HUANG Zheng-Liang, and GU Yue-Fang, et al, respectively. RESULTS: The results showed that PUW administered intravenously significantly decreased the mouse mortality, prolonged the occlusion time of rat carotid arteries, and reduced the wet and dry thrombus weight of the inferior vena cava, respectively. PUW markedly inhibited the binding of activated platelets to neutrophils, obtaining 39.7 mg/L of the medium inhibitory concentration. Intravenously administered PUW significantly shortened ELT, prolonged KPTT while had no influence on PT; PUW increased BT in rat tail tips but the BT caused by PUW was much shorter than that by aspirin or urokinase. CONCLUSION: It is suggested that PUW has the potential of antithrombosis due to its inhibition of platelet-neutrophil adhesion. PUW shows the tendency to bleeding, however, it could not cause serious bleeding side effect as compared with aspirin or urokinase.