Objective To introduce and enhance an experimental technique for intraventricular drug delivery via an implantable osmotic pump.Methods Eight-week-old male SD rats were selected and the requisite equipment and reagents were prepared.The osmotic pump was assembled and brought to operational status before conducting the implantation surgery.Following anesthesia,the rats underwent skin preparation and the upper surface of the skull was surgically exposed.A point directly above the ventricle was located using a brain stereotaxic apparatus,and a small hole was drilled at that location with a high-speed cranial drill.The pump body was then implanted subcutaneously in the neck and the needle was inserted into the drilled hole,and secured with dental cement.Once solidified,the needle base was removed,the subcutaneous soft tissue and scalp were sutured in layers,and the animal was returned to its cage for rearing in isolation.Results The osmotic pump was successfully implanted subcutaneously in the rat's neck,the needle was securely fixed to the skull,and the catheter interface remained intact.The rats were sacrificed and the brain tissue was removed.Examination of the extracted brain tissue revealed no significant hematoma around the puncture site or needle tract,and the presence of blue dye in the ventricular and adjacent tissues indicated successful drug delivery to the ventricle.Conclusions The introduction of a brain stereotaxic apparatus to aid localization,coupled with enhancements to the operational procedure,may improve the accuracy and safety of the implantation process resulting in a high success rate for intraventricular drug administration.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.