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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 .

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.

Background: Secondary failure of platelet recovery (SFPR) is a common complication that influences survival and quality of life of patients with β-thalassemia major (β-TM) after hematopoietic stem cell transplantation (HSCT). Objectives: A model to predict the risk of SFPR in β-TM patients after HSCT was developed. Design: A retrospective study was used to develop the prediction model. Methods: The clinical data for 218 β-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 β-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.

Hematopoietic stem cell transplantation (HSCT) is the only definitive cure for transfusion-dependent α-thalassemia, though comprehensive studies on its effectiveness are limited. In this retrospective study, we analyzed the clinical characteristics of 21 pediatric patients with transfusion-dependent α-thalassemia who underwent HSCT, all of whom received a standardized conditioning regimen consisting of busulfan, cyclophosphamide, fludarabine, and anti-thymocyte globulin. After a median follow-up of 25 months (range: 7–92 months), the two-year overall survival (OS) and event-free survival (EFS) rates were both 90.2% (95% CI: 66.2–97.4%), and the two-year graft-versus-host disease-free, relapse-free survival (GRFS) rate was 82.3% (95% CI: 52.6–94.3%). The transplant-related mortality rate at two years was 5.0% (95% CI: 0.7–30.0%), with no cases of graft failure observed. Among the 19 surviving patients, hemoglobin levels significantly increased compared to pre-transplant levels ( p  < 0.05), and all became transfusion-independent. Hematopoietic stem cell transplantation is a curative treatment for α-thalassemia. For patients with transfusion-dependent α-thalassemia, HSCT should be performed as early as possible at an experienced transplant center when a suitable donor is available.

Background: Cytomegalovirus infection is a common complication following hematopoietic stem cell transplantation that significantly influences clinical outcomes. Objectives: To develop and validate a predictive model for cytomegalovirus infection risk in patients with β-thalassemia major undergoing hematopoietic stem cell transplantation. Design: Retrospective cohort study. Methods: Clinical data from 291 β-thalassemia major patients undergoing hematopoietic stem cell transplantation were retrospectively analyzed. Independent risk factors identified via univariate and multivariate logistic regression analyses formed the basis of a predictive nomogram. The model’s performance was evaluated by the concordance index (C-index), receiver operating characteristic curves, calibration plots, and decision curve analysis. Internal validation was performed using bootstrap resampling, and external validation was conducted with an independent cohort of 84 patients from another center. Results: Three independent predictors of cytomegalovirus infection were identified: serum albumin levels, donor type, and grade III–IV acute graft-versus-host disease. A nomogram incorporating these predictors was established, demonstrating good discriminative ability (C-index: 0.745; 95% CI: 0.684–0.807). Internal and external validations yielded C-indices of 0.746 and 0.649, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.745 in the training cohort and 0.649 in the validation cohort. Conclusion: We developed and validated a reliable predictive model for assessing cytomegalovirus infection risk after hematopoietic stem cell transplantation in β-thalassemia major patients. This scoring system offers clinicians a practical tool for early risk stratification and intervention.

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.

Background: Patients with transfusion-dependent thalassemia frequently experience significant psychological distress, with high rates of anxiety and depression reported. Despite this, the psychological status of thalassemia patients after hematopoietic stem cell transplantation (HSCT) remains less explored. Gaining insights into the symptoms of depression and anxiety in this population is crucial, as it can inform tailored support and improve quality of life. Objectives: This study aimed to assess the prevalence of anxiety and depression in post-HSCT thalassemia patients and to identify potential risk factors. Design: This was a cross-sectional study. Methods: A cross-sectional study involved 170 transfusion-dependent thalassemia patients who underwent HSCT from January 2017 to November 2022. The Hospital Anxiety and Depression Scale (HADS), a self-report questionnaire, was used to evaluate the levels of anxiety and depression. Results: The prevalence of anxiety and depression was 11.2% and 8.8%, respectively. Patients with a history of splenectomy and those with chronic graft-versus-host disease (cGVHD) exhibited significantly higher HADS scores (p < 0.01). Correlation analysis revealed positive associations between these variables and HADS scores, with correlation coefficients of 0.256 for splenectomy with HADS-Anxiety (HADS-A) and 0.227 with HADS-Depression (HADS-D) scores. For cGVHD, the correlation coefficients were 0.290 with HADS-A and 0.388 with HADS-D scores. Conclusion: The study revealed the psychological burden faced by post-transplant thalassemia patients, particularly those with a history of splenectomy and cGVHD. These findings underscore the need for targeted psychological support and interventions for this vulnerable patient group.

Background: Acute graft-versus-host disease (aGvHD) is the primary cause of mortality following allogeneic hematopoietic cell transplantation (HCT). Objectives: This study aimed to predict the risk of aGvHD after HCT in patients with thalassemia major using a novel predictive nomogram. Design: A retrospective study was used to develop the prediction model. Methods: We performed retrospective analyses on 402 consecutive thalassemia patients who underwent HCT. Risk factors for aGvHD were analyzed using Cox proportional regression models. T-lymphocyte subsets were collected from 240 patients at the time of neutrophil engraftment. Least Absolute Shrinkage and Selection Operator regression was utilized to screen the indices, with cut-off values established through restricted cubic spline (RCS) regression. The predictive model was developed by integrating these T-lymphocyte subsets with clinical features, aiming to enhance the accuracy of aGvHD risk prediction. Results: Among 402 thalassemia patients analyzed post-transplantation, significant independent risk factors for aGvHD included matched unrelated donors, haploid-related donors, peripheral blood stem cell infusions, and donor age older than 40 years. Our RCS analysis indicated a marked increase in aGvHD risk when CD4+ T-cell counts exceeded 36 cells/μL and CD8+ T-cell counts exceeded 43 cells/μL during neutrophil engraftment. The integration of T-lymphocyte subsets with clinical risk factors into a Cox regression model demonstrated good predictive performance for assessing aGvHD risk. Conclusion: This study presents a novel model designed to predict aGvHD in thalassemia patients post-transplantation by utilizing T-lymphocyte data at the time of engraftment. The model facilitates the creation of personalized treatment plans, aiming to minimize the incidence of aGvHD and improve patient outcomes.

PurposeThe purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).MethodsClinical 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).ResultsThe 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.ConclusionLymphocytes 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.