Predictive model of the efficiency of hematopoietic stem cell collection in patients with multiple myeloma and lymphoma based on multiple peripheral blood markers.

INTRODUCTION: Autologous hematopoietic stem cell transplantation (ASCT) has gained extensive application in the treatment of lymphoma and multiple myeloma (MM). Plenty of studies demonstrate that peripheral blood indicators could be considered potential predictive biomarkers for hematopoietic stem cells (HSCs) collection efficiency, including white blood cell count (WBC), monocyte count (Mono), platelet count (PLT), hematocrit, and hemoglobin levels. Currently, clinically practical predictive models based on these peripheral detection indicators to quickly, conveniently, and accurately predict collection efficiency are lacking. METHODS: In total, 139 patients with MM and lymphoma undergoing mobilization and collection of ASCT were retrospectively studied. The study endpoint was successful collection of autologous HSCs. We analyzed the effects of clinical characteristics and peripheral blood markers on collection success, and screened variables to establish a prediction model. We determined the optimal cutoff value of peripheral blood markers for predicting successful stem cell collection and the clinical value of a multi-marker prediction approach. We also established a prediction model for collection efficacy. RESULTS: Univariate and multivariate logistic regression analyses showed that the mobilization regimen, Mono, PLT, mononuclear cell count (MNC), and peripheral blood CD34+ cell count (PB CD34+ counts) were significant predictors of successful collection of peripheral blood stem cells (PBSC). Two predictive models were constructed based on the results of multivariate logistic analyses. Model 1 included the mobilization regimen, Mono, PLT, and MNC, whereas Model 2 included the mobilization regimen, Mono, PLT, MNC, and PB CD34+ counts. Receiver operating characteristic (ROC) curve analysis showed that the PB CD34+ counts, Model 1, and Model 2 could predict successful HSCs collection, with cutoff values of 26.92 × 106/L, 0.548, and 0.355, respectively. Model 1 could predict successful HSCs collection with a sensitivity of 84.62%, specificity of 75.73%, and area under the curve (AUC) of 0.863. Model 2 could predict successful HSCs collection with a sensitivity of 83.52%, specificity of 94.17%, and AUC of 0.946; thus, it was superior to the PB CD34+ counts alone. CONCLUSION: Our findings suggest that the combination of the mobilization regimen, Mono, PLT, MNC, and PB CD34+ counts before collection has predictive value for the efficacy of autologous HSCs collection in patients with MM and lymphoma. Using models based on these predictive markers may help to avoid over-collection and improve patient outcomes.

Antimicrobial and molecular interaction studies on derivatives of curcumin against Streptococcus pneumoniae which caused pneumonia

Background: The antimicrobial properties and molecular interaction analysis of curcumin and its derivatives against three different strains of Streptococcus pneumoniae (Penicillin-susceptible, Penicillin-intermediate and Penicillin-resistant) are studied. Results: These properties were analyzed based on the measurement of the inhibition zone, minimum inhibitory concentration (MIC), and rate of kill revealed that curcumin monoglucoside, curcumin diglucoside and curcumin possessed strong antimicrobial properties even on the Penicillin-resistant strains. Additionally, the molecular docking simulation analyses against Penicillin Binding Protein of S. pneumoniae also confirm that these compounds docked at the active site of the enzyme. Further, the molecular dynamics simulation validates the conformational stability of the docked ligand-protein complexes in the dynamic environment. Conclusion: curcumin monoglucoside, curcumin diglucoside and curcumin can be prescribed for treatment against Penicillin-resistant S. pneumoniae.