Development of a nomogram to predict the risk of secondary failure of platelet recovery in patients with ß-thalassemia major after hematopoietic stem cell transplantation: a retrospective study.

Background: Secondary failure of platelet recovery (SFPR) is a common complication that influences survival and quality of life of patients with ß-thalassemia major (ß-TM) after hematopoietic stem cell transplantation (HSCT). Objectives: A model to predict the risk of SFPR in ß-TM patients after HSCT was developed. Design: A retrospective study was used to develop the prediction model. Methods: The clinical data for 218 ß-TM patients who received HSCT comprised the training set, and those for another 89 patients represented the validation set. The least absolute shrinkage and selection operator regression algorithm was used to identify the critical clinical factors with nonzero coefficients for constructing the nomogram. Calibration curve, C-index, and receiver operating characteristic curve assessments and decision curve analysis (DCA) were used to evaluate the calibration, discrimination, accuracy, and clinical usefulness of the nomogram. Internal and external validation were used to test and verify the predictive model. Results: The nomogram based on pretransplant serum ferritin, hepatomegaly, mycophenolate mofetil use, and posttransplant serum albumin could be conveniently used to predict the SFPR risk of thalassemia patients after HSCT. The calibration curve of the nomogram revealed good concordance between the training and validation sets. The nomogram showed good discrimination with a C-index of 0.780 (95% CI: 70.3-85.7) and 0.868 (95% CI: 78.5-95.1) and AUCs of 0.780 and 0.868 in the training and validation sets, respectively. A high C-index value of 0.766 was reached in the interval validation assessment. DCA confirmed that the nomogram was clinically useful when intervention was decided at the possibility threshold ranging from 3% to 83%. Conclusion: We constructed a nomogram model to predict the risk of SFPR in patients with ß-TM after HSCT. The nomogram has a good predictive ability and may be used by clinicians to identify SFPR patients early and recommend effective preventive measures.

Predicting the risk of acute kidney injury after hematopoietic stem cell transplantation: development of a new predictive nomogram.

The purpose was to predict the risk of acute kidney injury (AKI) within 100 days after hematopoietic stem cell transplantation (HSCT) in patients with hematologic disease by using a new predictive nomogram. Collect clinical data of patients with hematologic disease undergoing HSCT in our hospital from August 2012 to March 2018. Parameters with non-zero coefficients were selected by the Least Absolute Selection Operator (LASSO). Then these parameters were selected to build a new predictive nomogram model. Receiver operating characteristic (ROC) curve, calibration curve, C-index, and decision curve analysis (DCA) were used for the validation of the evaluation model. Finally, the nomogram was further evaluated by internal verification. According to 2012 Kidney Disease Improving Global Guidelines (KDIGO) diagnostic criteria, among 144 patients, the occurrence of AKI within 100 days after HSCT The rate was 29.2% (42/144). The C-index of the nomogram was 0.842. The C-value calculated by the internal verification was 0.809. The AUC was 0.842, and The DCA range of the predicted nomogram was from 0.01 to 0.71. This article established a high-precision nomogram for the first time for predicting the risk of AKI within 100 days after HSCT in patients with hematologic diseases. The nomogram had good clinical validity and reliability. For clinicians, it was very important to prevent AKI after HSCT.

Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation.

Purpose: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB). Methods: Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI). Results: The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes. Conclusion: Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.

Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis.

Purpose: The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis. Methods: A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR). The statistical parameters analyzed by the Least Absolute Shrinkage and Selection Operator (LASSO) and receiver-operating characteristic (ROC) curves were used to construct the nomogram. The nomogram was assessed by C-index, calibration curve, ROC curve, and decision curve analysis (DCA) curve. Results: The C-index of the nomogram in the training set and external validation set was 0.801 and 0.861, respectively. Similarly, AUC was 0.801 in the former and 0.861 in the latter. The net benefit of the former nomogram ranged from 0.1 to 0.95 and 0.02 to 0.99 in the latter nomogram. Furthermore, there was a correlation between MLR and the severity of spinal tuberculosis. Conclusion: MLR was an independent factor in the diagnosis of spinal tuberculosis and was associated with the severity of spinal tuberculosis. Additionally, MLR may be a predictor of active spinal tuberculosis.

Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram.

OBJECTIVE: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. METHODS: The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. RESULTS: The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%-.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01-0.79. CONCLUSION: A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

Traditional Uses, Chemical Constituents, Biological Properties, Clinical Settings, and Toxicities of Abelmoschus manihot L.: A Comprehensive Review.

Abelmoschus manihot, an annual herbal flowering plant, is widely distributed throughout eastern Europe and in temperate and subtropical regions of Asia. Its flowers have been traditionally used for the treatment of chronic kidney disease in China. Currently, more than 128 phytochemical ingredients have been obtained and identified from the flowers, seeds, stems, and leaves of A. manihot. The primary components are flavonoids, amino acids, nucleosides, polysaccharides, organic acids, steroids, and volatile oils. A. manihot and its bioactive constituents possess a plethora of biological properties, including antidiabetic nephropathy, antioxidant, antiadipogenic, anti-inflammatory, analgesic, anticonvulsant, antidepressant, antiviral, antitumor, cardioprotective, antiplatelet, neuroprotective, immunomodulatory, and hepatoprotective activities, and have effects on cerebral infarction, bone loss, etc. However, insufficient utilization and excessive waste have already led to a rapid reduction of resources, meaning that a study on the sustainable use of A. manihot is urgent and necessary. Moreover, the major biologically active constituents and the mechanisms of action of the flowers have yet to be elucidated. The present paper provides an early and comprehensive review of the traditional uses, chemical constituents, pharmacological activities, and pharmaceutical, quality control, toxicological, and clinical settings to emphasize the benefits of this plant and lays a solid foundation for further development of A. manihot.

Construction of a prognosis­associated long noncoding RNA­mRNA network for multiple myeloma based on microarray and bioinformatics analysis.

At present, the association between prognosis­associated long noncoding RNAs (lncRNAs) and mRNAs is yet to be reported in multiple myeloma (MM). The aim of the present study was to construct prognostic models with lncRNAs and mRNAs, and to map the interactions between these lncRNAs and mRNAs in MM. LncRNA and mRNA data from 559 patients with MM were acquired from the Genome Expression Omnibus (dataset GSE24080), and their prognostic values were calculated using the survival package in R. Multivariate Cox analysis was used on the top 20 most significant prognosis­associated mRNAs and lncRNAs to develop prognostic signatures. The performances of these prognostic signatures were tested using the survivalROC package in R, which allows for time­dependent receiver operator characteristic (ROC) curve estimation. Weighted correlation network analysis (WGCNA) was conducted to investigate the associations between lncRNAs and mRNAs, and a lncRNA­mRNA network was constructed using Cytoscape software. Univariate Cox regression analysis identified 39 lncRNAs and 1,445 mRNAs that were significantly associated with event­free survival of MM patients. The top 20 most significant survival­associated lncRNAs and mRNAs were selected as candidates for analyzing independent MM prognostic factors. Both signatures could be used to separate patients into two groups with distinct outcomes. The areas under the ROC curves were 0.739 for the lncRNA signature and 0.732 for the mRNA signature. In the lncRNA­mRNA network, a total of 143 mRNAs were positively or negatively associated with 23 prognosis­associated lncRNAs. NCRNA00201, LOC115110 and RP5­968J1.1 were the most dominant drivers. The present study constructed a model that predicted prognosis in MM and formed a network with the corresponding prognosis­associated mRNAs, providing a novel perspective for the clinical diagnosis and treatment of MM, and suggesting novel directions for interpreting the mechanisms underlying the development of MM.