Neuropathic pain and cancer pain are two difficult types of chronic pain to be controlled by currently available analgesics.Animal models can provide pivotal systems for preclinical study of pain.This article reviews some new models of chronic pain,such as those of cancer pain,central nerve pain,and the pain caused by peripheral nerve injury and disease-induced peripheral neuropathy.

OBJECTIVE: To apply single cell sequencing based on multiple annealing and looping amplification cycles (MALBAC) for the determination of the rate and type of mosaicisms of high-quality embryos at cleavage stage. METHODS: After thawing and removing of zona pellucida by enzymatic digestion, blastomeres were collected the high-quality embryos donated by couples whom had given birth to healthy offspring by intracytoplasmic sperm injection and embryo transfer. The whole genome of single cell was amplified and subjected to next generation sequencing. RESULTS: From a total of 23 embryos, 184 blastomeres were collected. 175 (95.1%) of the blastomeres were successfully sequenced, of which 100 (57.1%) were found to harbor chromosomal aneuploidies. Among the 23 embryos, 3 (13.0%) were diploid, 20 (87.0%) were mosaicisms, which included 5 (21.7%) aneuploid mosaicisms, 7 (30.4%) diploid-aneuploid mosaicisms, 5 (21.7%) abnormal mosaicisms, and 3 (13.0%) irregular segregations. CONCLUSION There is a high rate of chromosomal mosaicisms in high-quality cleavage embryos. Mosaicisms of complex chromosomal abnormality or with high proportion of abnormal cells may be an important factor affecting the potential of embryonic development.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.

ObjectiveTo observe the clinical efficacy and safety of treating mediate-risk pure ground glass pulmonary nodules （pGGNs） based on the state theory. MethodsA prospective clinical randomized controlled trial was used. Totally 141 cases of mediate-risk pGGNs were divided into treatment group （92 cases） and control group （49 cases） according to the random table method. The treatment group was given the basic Sanjie Formula （基础散结方） orally with modification according to the identification of traditional Chinese medicine （TCM） state， 1 dose per day， 3 months as a course of treatment.Three months after the treatment patients were checked by CT. Patients who were clinically judged as cure， moderate to low risk， and turned to surgical resection do not carry out a second course of treatment，and the rest of the patients continued to complete the second courses. Patients in the control group did not receive any treatment and were only followed up periodically. Patients in both groups received a CT review 3 months and 6 months after enrolled. Comprehensive curative effect was evaluated according to the reduction rate of the area of pulmonary nodules shown in chest CT， to further explore the clinical effective difference for patients at different TCM state； the risk of malignancy index （Mayo score） was calculated by Mayo model at enrollment and 3 months and 6 months after enrolled. Adverse events were monitored continuously during the study. ResultsDuring the follow-up， 8 cases in the treatment group and 7 cases in the control group were lost. A total of 126 cases completed the whole process， including 84 cases in the treatment group and 42 cases in the control group. The total effective rates at 3 months and 6 months of the treatment group were 46.15% （30/65） and 45.71% （32/70） in the treatment group， while the total effective rates at 3 months and 6 months in the control group were 12.5% （4/32） and 10.00% （4/40）. Compared with the control group， the comprehensive curative effect of 3 months and 6 months of enrollment in treatment group was significantly better than that in corresponding control group （P＜0.01）. The pulmonary nodule area and Mayo score in the treatment group decreased after 3 and 6 months of enrollment （P＜0.01）. In contrast， there was no statistically significant difference in nodule area between pre- and post-enrollment time points in the control group （P＞0.05）， and probability of Mayo risk increased in the control group after 6 months of enrollment compared to pre-enrollment （P＜0.05）. Among the 84 patients in the treatment group， there were 15 cases of qi deficiency state， 7 cases of yin deficiency state， 5 cases of yang deficiency state， 20 cases of qi depression state， 32 cases of damp-heat state， and 5 cases of harmonious state； the difference in the distribution of the total clinical effective rate of the patients with different TCM states after treatment was statistically significant （P＜0.05）， and the total effective rate of two-by-two comparison of qi depression state was higher （13/20，65.00%） than that of the total effective rate of damp-heat state （8/32，25.00%， P＜0.00833）. There were no significant changes in blood routine， urine routine， liver function and kidney function in both groups， and no adverse events occurred. ConclusionTreating mediate-risk pGGNs based on the state theory can effectively reduce the area of pulmonary nodules and inhibit the growth of malignant risk of pulmonary nodules.