Objective Pulmonary oxygen poisoning resulting from hyperbaric oxygen,frequently occurs in specialized operations,without any current effective prevention or treatment measures.To elucidate the impact and mechanism of neostigmine(NEO)in combination with anisodamine(ANI)(neoscopolamine)on pulmonary oxygen toxicity.Methods The animal model of pulmonary oxygen poisoning was established.C57BL/6 mice were exposed to 2.5 ATA 99.9%oxygen for 6 h.The control group mice were injected with normal saline ip,while the treatment group mice received injections of ANI(25 mg/kg,ip)and NEO(50 μg/kg,ip).Lung tissues were collected and stained with HE to observe any pathological injuries after exposure.Evans blue stain was utilized to identify lung permeability,wet/dry lung ratio,and protein concentration in the bronchoalveolar lavage fluid(BALF)to assess the lung injury's severity.The modifications in inflammatory factors,oxidative stress indicators,and iron content in lung tissue were assessed.Results The results showed that the 2.5 ATA 99.9%oxygen-exposed group experienced a significant worsening of lung injury,as well as increased lung permeability,lung wet/dry ratio,and protein content in alveolar lavage fluid when compared to the control group.Moreover,mRNA levels of pro-inflammatory cytokines IL-1β,IL-6,TNF-α,and IFN-γ in the lung tissue of the model group were significantly elevated,while the levels of anti-inflammatory cytokines IL-4 and TGF-β were significantly reduced.The oxidative index MDA also significantly increased,while the antioxidant index GSH significantly decreased.Additionally,the expression of GPX4,a marker of ferroptosis,increased with an increase in iron content.Neoscopolamine treatment successfully reversed those effects.Conclusion The combined use of ANI and NEO had a protective effect on pulmonary oxygen poisoning.Neoscopolamine may inhibit inflammation and oxidative stress by activating the cholinergic anti-inflammatory pathway,thereby reducing the content of free iron in lung tissue and finally inhibiting cell ferroptosis.

Objective To explore the risk factors for platelet transfusion refractoriness(PTR)in patients with hemato-logical disorders,construct a prediction model and validate the model efficacy.Methods Patients with hematological disor-ders who received platelet transfusion therapy in the Chengdu Second People's Hospital from December 2021 to December 2022 were retrospectively included to judge the effectiveness of platelet transfusion and screened for risk factors by univariate and multivariate logistic regression.A prediction model for PTR was constructed using receiver operating characteristic(ROC)curve,calibration curve and decision curve(DCA)to assess the differentiation,calibration and clinical value of the model,respectively.Results A total of 334 hematological patients were included,including 168 males and 176 females,with a PTR incidence of 40.4%.Univariate and multivariate logistic regression analysis showed that platelet transfusion vol-ume,erythrocyte transfusion volume,and neutrophil ratio were risk factors for PTR(P<0.05).A prediction model for PTR in hematological patients was established based on these risk factors.The area under the model's curve was 0.8377(95%CI:0.723-0.772),the sensitivity was 58.52%,and the specificity was 89.95%.The calibration curve showed that the S∶P was 0.964,the maximum absolute difference Emax was 0.032,and the average absolute difference Eavg was 0.009.The DCA a-nalysis showed that the model had clinical application value when the risk threshold ranged from 0.2 to 0.9.Conclusion The PTR prediction model based on platelet transfusion volume,erythrocyte transfusion volume and neutrophil ratio can pro-vide a basis for effective platelet transfusion in hematological patients.

Objective: To establish HPLC fingerprints of different parts of chicory stems, leaves, roots, flowers and seeds, and compare the similarities and differences of chemical components in different parts, so as to provide a scientific basis for the comprehensive utilization of chicory. Methods: To establish the HPLC fingerprint of chicory, the chromatographic column was chosen with Agilent ZORBAX Eclipse XDB-C

OBJECTIVE： To establish HPLC fingerprints of Paeonia tactilora decoction pieces， and to conduct its cluster analysis and principal component analysis. METHODS： HPLC method was adopted. The determination was performed on SunFire® C18 column with mobile phase consisted of acetonitril-0.05％ phosphoric acid solution （gradient elution） at the flow rate of 1.0 mL/min. The detection wavelength was set at 230 nm， the column temperature was 30 ℃， the collection time was 70 min，and sample size was 15 μL. Using paeoniflorin as reference， HPLC fingerprints of 26 batches P. tactilora decoction pieces from different habitats and 30 batches by different processed methods were established. The similarity of samples was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition） to confirm common peak. Cluster analysis and principal component analysis were performed by using SPSS 20.0 software. RESULTS： There were 9 common peaks in HPLC fingerprints of 26 batches of sample from different habitats， the similarity of which was higher than 0.880. Six peaks were identified， including gallic acid， catechin， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 26 batches of samples were clustered into 2 categories when cosine distance was 15. S1-S21 were clustered into one category； S22-S26 were clustered into the other category. By principal component analysis， the accumulative contribution rate of two main components was 81.124％. There were 10 common peaks in HPLC fingerprints of 30 batches of sample by different processed methods， the simi- larity of which was higher than 0.970. Seven peaks were identified， including gallic acid， catechin， aplopaeonoside， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 30 batches of samples were clustered into 2 categories when cosine distance was 25. B1-B10 were clustered into one category； C1-C10 and J1-J10 were clustered into the other category. By principal component analysis， the accumulative contribution rate of four main components was 86.887％. CONCLUSIONS： Established HPLC fingerprint， the results of cluster analysis and principal component analysis can provide reference for quality control of decoction pieces of P. tactilora.

Objective:This study analyzed the expression of CD24 antigen on bone marrow plasma cells (BMPC) of patients with multiple myeloma (MM) and the predictive value of induction therapy.Methods:This clinical observational study utilized 258 MM patients samples treated at the Hematology Department of Beijing Jishuitan Hospital who met the inclusion criteria in the Department of Hematology, Capital Medical University, from August 12th, 2022 to February 1st, 2024. According to the different stages of the disease, patients were divided into three groups: 78 cases of Newly Diagnosed Multiple Myeloma(NDMM) (42 males and 36 females, aged 62±11), 56 cases of the relapse refractory group (34 males and 22 females, aged 64±9), and 124 cases of the disease remission group (68 males and 56 females, aged 62±10). Multiparameter flow cytometry (MFC) was used to detect the expression level of CD24 antigen on BMPC and the relationship between CD24 and MM disease status. The clinical data and test results of 78 NDMM patients at initial diagnosis were retrospectively analyzed, including gender, age, MFC detection of the positive expression rate of antigens (CD19, CD20, CD24, CD27, CD56), the results of efficacy evaluation after induction therapy, ISS staging, R-ISS staging, blood hemoglobin, β2-microglobulin, human serum albumin, serum creatinine, lactate dehydrogenas, correction of calcium, BMPC ratio, and the results of FISH. The patients were divided into a deep remission group [including complete remission (CR) and very good partial remission (VGPR)] with 43 cases and a non-deep remission group (non CR and VGPR) with 17 cases according to the difference of antigen positive expression rate after induction therapy. The differences of antigen expression on BMPC between the two groups were compared. Binary logistic regression was used to analyze the relationship between the expression of each antigen and the efficacy after induction therapy in patients, and the results showed that CD24 was more correlated with the achievement of deep remission after induction therapy than other antigens. Therefore, taking the positive expression rate of CD24 in NDMM patients at the initial diagnosis and deep remission after induction therapy as the research objects, the predictive value of CD24 for NDMM patients reaching deep remission after induction therapy was analyzed by using receiver operating characteristic curve (ROC), and the optimal cutoff value was obtained. NDMM was divided into two groups according to the cut-off value, and the differences between the two groups in clinical baseline data and prognostic indicators were compared.Results:The positive rates of plasma cell CD24 expression in the NDMM group, the relapse refractory group and the disease remission group were 2.18 (95% CI 0.08-81.85)%, 3.81 (95% CI 0.10-64.56)%, 8.74 (95% CI 0.79-95.55)% respectively. Compared with the disease remission group, the NDMM and relapse refractory group was lower ( Z=-7.889, -5.282, respectively, P<0.001). Univariate analysis showed that there was a significant difference in the positive expression rate of CD24 at initial diagnosis between the deep remission group and the non-deep remission group ( Z=-3.265, P<0.001), while there was no significant difference in CD19 ( Z=-0.271, P=0.787), CD20 ( Z=-0.205, P=0.837), CD27 ( Z=-0.582, P=0.560), and CD56 ( Z=-0.328, P=0.743) between the two groups. Binary logistic regression analysis showed that compared with other antigens [CD19 ( OR=1.045, 95% CI 0.975-1.120, P=0.217), CD20 ( OR=1.000, 95% CI 0.971-1.030, P=0.976), CD27 ( OR=0.997, 95% CI 0.977-1.016, P=0.734), CD56 ( OR=1.006, 95% CI 0.990-1.006, P=0.449)], the expression of CD24 ( OR=0.423, 95% CI 0.990-1.006, P=0.449) on BMPC in NDMM patients was most closely related to the achievement of deep remission was achieved after induction therapy. The lower the proportion of CD24 at the initial diagnosis was, the lower the probability of achieving deep remission after induction therapy was. The area under the curve (AUC) of CD24 in predicting deep remission after induction therapy was 0.772 (95% CI 0.655-0.889, P=0.001), with a sensitivity of 60.50%, a specificity of 85.00%, and the optimal critical value was 2.21%. Compared with the group with plasma CD24 positive rate>2.21%, the group with plasma CD24 positive rate<2.21% had a higher proportion of male (39.47%vs 65.00%, χ2=5.092, P=0.024), ISS stagingⅢ (41.67% vs 58.33%, χ2=6.175, P=0.046), β2 microglobulin (3.19 mg/L vs 4.14 mg/L, Z=-2.257, P=0.024), and BMPC [(8.672±1.827)% vs (19.530±3.188)%, t=-2.963, P=0.004] detected by MFC, and the differences were statistically significant. Conclusions:The low positive rate of plasma cell CD24 is closely related to the higher tumor burden and the worse disease status of MM patients. In addition, the positive expression rate of CD24 is at initial diagnosis can predict the efficacy achieved after induction therapy, and the lower positive rate of CD24 is, the worse the efficacy achieved after induction therapy. At the same time, MFC detection of CD24 is convenient and efficient in the evaluation and prediction of MM.