Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia.

Background: Metagenomic next-generation sequencing (mNGS) is a novel non-invasive and comprehensive technique for etiological diagnosis of infectious diseases. However, its practical significance has been seldom reported in the context of hematological patients with high-risk febrile neutropenia, a unique patient group characterized by neutropenia and compromised immune responses. Methods: This retrospective study evaluated the results of plasma cfDNA sequencing in 164 hematological patients with high-risk febrile neutropenia. We assessed the diagnostic efficacy and clinical impact of mNGS, comparing it with conventional microbiological tests. Results: mNGS identified 68 different pathogens in 111 patients, whereas conventional methods detected only 17 pathogen types in 36 patients. mNGS exhibited a significantly higher positive detection rate than conventional methods (67.7% vs. 22.0%, P < 0.001). This improvement was consistent across bacterial (30.5% vs. 9.1%), fungal (19.5% vs. 4.3%), and viral (37.2% vs. 9.1%) infections (P < 0.001 for all comparisons). The anti-infective treatment strategies were adjusted for 51.2% (84/164) of the patients based on the mNGS results. Conclusions: mNGS of plasma cfDNA offers substantial promise for the early detection of pathogens and the timely optimization of anti-infective therapies in hematological patients with high-risk febrile neutropenia.

Case report: Donor-derived CLL-1 chimeric antigen receptor T-cell therapy for relapsed/refractory acute myeloid leukemia bridging to allogeneic hematopoietic stem cell transplantation after remission.

Background: Explore the efficacy and safety of donor-derived CLL-1 chimeric antigen receptor T-cell therapy (CAR-T) for relapsed/refractory acute myeloid leukemia (R/R AML) bridging to allogeneic hematopoietic stem cell transplantation (allo-HSCT) after remission. Case presentation: An adult R/R AML patient received an infusion of donor-derived CLL-1 CAR-T cells, and the conditioning regimen bridging to allo-HSCT was started immediately after remission on day 11 after CAR-T therapy upon transplantation. Then, routine post-HSCT monitoring of blood counts, bone marrow (BM) morphology, flow cytometry, graft-versus-host disease (GVHD) manifestations, and chimerism status were performed. Result: After CAR-T therapy, cytokine release syndrome was grade 1. On day 11 after CAR-T therapy, the BM morphology reached complete remission (CR), and the conditioning regimen bridging to allo-HSCT started. Leukocyte engraftment, complete donor chimerism, and platelet engraftment were observed on days +18, +23, and +26 post-allo-HSCT, respectively. The BM morphology showed CR and flow cytometry turned negative on day +23. The patient is currently at 4 months post-allo-HSCT with BM morphology CR, negative flow cytometry, complete donor chimerism, and no extramedullary relapse/GVHD. Conclusion: Donor-derived CLL-1 CAR-T is an effective and safe therapy for R/R AML, and immediate bridging to allo-HSCT after remission may better improve the long-term prognosis of R/R AML.

FAT1 inhibits the proliferation of DLBCL cells via increasing the m6A modification of YAP1 mRNA.

Emerging evidence shows that FAT atypical cadherin 1 (FAT1) mutations occur in lymphoma and are associated with poorer overall survival. Considering that diffuse large B cell lymphoma (DLBCL) is the category of lymphoma with the highest incidence rate, this study aims to explore the role of FAT1 in DLBCL. The findings demonstrate that FAT1 inhibits the proliferation of DLBCL cell lines by downregulating the expression of YAP1 rather than by altering its cellular localization. Mechanistic analysis via meRIP-qPCR/luciferase reporter assays showed that FAT1 increases the m6A modification of YAP1 mRNA 3'UTR and the subsequent binding of heterogeneous nuclear ribonucleoprotein D (HNRNPD) to the m6A modified YAP1 mRNA, thus decreasing the stability of YAP1 mRNA. Furthermore, FAT1 increases YAP1 mRNA 3'UTR m6A modification by decreasing the activity of the TGFß-Smad2/3 pathway and the subsequent expression of ALKBH5, which is regulated at the transcriptional level by Smad2/3. Collectively, these results reveal that FAT1 inhibits the proliferation of DLBCL cells by increasing the m6A modification of the YAP1 mRNA 3'UTR via the TGFß-Smad2/3-ALKBH5 pathway. The findings of this study therefore indicate that FAT1 exerts anti-tumor effects in DLBCL and may represent a novel target in the treatment of this form of lymphoma.

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Family Aggregation of Hematological Malignancies Discovered from an Acute Myeloid Leukemia Patient with STK11 and THBD Gene Mutation.

Acute myeloid leukemia (AML) is a large class of heterogeneous hematological malignancies with the highest incidence rate in acute leukemia. Its pathogenesis is still unclear, which may be related to genetics. According to the latest AML NCCN guidelines, genes involved in AML family genetic changes include RUNX1, ANKRD26, CEBPA. Finding new genes related to AML genetics is of great significance for predicting the prognosis of patients, developing targeted drugs, and selecting transplant donors. Here, we report a case of adult female AML patient whose three relatives suffered from hematological malignancies, including Waldenstrom macroglobulinemia, NK/T-cell lymphoma, and angioimmunoblastic T-cell lymphoma. The screen for genetic susceptibility genes related to blood and immune system diseases was carried out, and the result showed that the patient herself, her son, her daughter, and her two cousins all had STK11 p.F354L and/or THBD p.D486Y mutations. At present, there is no research or case report on the relationship between STK11/THBD and family aggregation of hematological malignancies. We report for the first time that an AML patient with STK11 and THBD mutations has a family aggregation of hematological malignancies, and consider that STK11 and THBD may be related to family genetic changes which ultimately cause the family aggregation of hematological malignancies.

An acquired BMF with FANCL gene heterozygous mutation: Case report.

RATIONALE: Bone marrow failure (BMF) includes inherited and acquired BMFs. Acquired BMF can be secondary to various factors, such as autoimmune dysfunction, benzene, drugs, radiation, viral infection and so on. Fanconi anemia (FA) complementation group L (FANCL) is an E3 ubiquitin ligase that participates in the repair of DNA damage. Homozygous or compound heterozygous mutations of FANCL can lead to the onset of FA, which is one of the most common inherited BMFs. PATIENT CONCERNS AND DIAGNOSES: Here, we report a case of acquired BMF. This patient had a history of benzene exposure for half a year before the onset of the disease, and presented with progressive pancytopenia, especially the reduction of erythrocytes and megakaryocyte, without malformation. Interestingly, this patient and his brother/father had a heterozygous (non-homozygous/compound heterozygous) mutation (Exon9, c.745C > T, p.H249Y) in the FANCL gene. INTERVENTIONS AND OUTCOMES: The patient successfully underwent unrelated and fully compatible umbilical cord blood hematopoietic stem cell transplantation. LESSONS SUBSECTIONS: We report for the first time an acquired BMF case with FANCL gene heterozygous mutation, and the mutation site (Exon9, c.745C > T, p.H249Y) has never been reported. This case suggests that heterozygous mutations in FANCL gene may be associated with increased susceptibility to acquired BMF. Based on current reports and this case, we speculate that heterozygous mutations in the FA complementation gene may exist in a certain proportion of tumor and acquired BMF patients, but have not been detected. We recommend routine screening for FA complementation gene mutations in tumor and acquired BMF patients in clinical practice. If positive results are found, further screening can be conducted on their families.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.

OBJECTIVE: To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML). METHODS: In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude. RESULTS: The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1-96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively. CONCLUSION: It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs. Model performance for both validation and test sets was assessed using mean absolute error (MAE), mean squared error (MSE), root MSE (RMSE), Spearman rank correlation coefficients (Sr), and Determination coefficient (R2) between the measured and predicted GPR values. Results: In the test set, the MAE of the prediction model were 0.402, 0.511, 1.724, and 2.530, the MSE were 0.640, 0.986, 6.654, and 9.508, the RMSE were 0.800, 0.993, 2.580, and 3.083, the Sr were 0.643, 0.684, 0.821, and 0.824 (P < .001) and the R2 were 0.4110, 0.4666, 0.6677, and 0.6769 for 3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm, respectively. The MAE and RMSE of the prediction model decreased with stricter gamma criteria while the Sr and R2 between measured and predicted GPR values increased. Conclusions: The CNN prediction model based on delivery fluence informed by log files could accurately predict IMRT QA passing rates for different gamma criteria. It could reduce QA workload and improve efficiency in pretreatment QA. Our results suggest that the CNN prediction model based on delivery fluence informed by log files may be a promising tool for the gamma evaluation of IMRT QA.

Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction.

The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered "failed" while the GPR higher than 85% is considered "pass"), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as the input for UNet++, with QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as the output. In the test set, the accuracy of the classification model was 95.79%. The mean absolute error (MAE) were 0.82, 0.88, 2.11, 2.52, and the root mean squared error (RMSE) were 1.38, 1.57, 3.33, 3.72 for 3%/3mm, 3%/2 mm, 2%/3 mm, 2%/2 mm, respectively. The trend and position of the predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on UNet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that UNet++ based Virtual QA is promising in quality assurance for radiotherapy.

Cytotoxic T lymphocytes promote cytarabine-induced acute myeloid leukemia cell apoptosis via inhibiting Bcl-2 expression.

Acute myeloid leukemia (AML) remains difficult to cure due to its drug tolerance and refractoriness. Immunotherapy is a growing area of cancer research, which has been applied for the treatment of numerous types of cancer, including leukemia. The present study generated AML cell-specific cytotoxic T lymphocytes (CTLs) in vitro and investigated the effect of combining CTL treatment with one of the most commonly used drugs for the treatment of hematological malignancies, cytarabine, on AML cell apoptosis. Firstly, it was observed that monocyte-depleted peripheral blood lymphocytes from healthy donors could be used to generate large numbers of CD3+CD8+ CTLs through immune stimulation. These CD3+CD8+ CTLs could effectively recognize and induce the apoptosis of human Kasumi-3 AML cells. In addition, cytarabine-induced AML cell apoptosis was enhanced by CTL treatment. Western blotting revealed that Bcl-2 expression was downregulated in AML cells following cytarabine and CTL treatment, indicating that the synergistic effect of this treatment on AML cell apoptosis is due to the downregulation of Bcl-2. These results highlight the potential application of CTL immunotherapy for the treatment of AML. Further studies optimizing the specificity and potency of CTLs, and identifying favorable combinations with other chemotherapeutic drug are required.

Infusion of basic fibroblast growth factor ameliorates acute graft versus host disease in mice after haploidentical hematopoietic stem cell transplantation.

The aim of this study was to investigate the effect of basic fibroblast growth factor (bFGF) on acute graft versus host disease (aGVHD) in mice after haploidentical hematopoietic stem cell transplantation (haplo-HSCT). Haplo-HSCT mice model was established followed by dividing into three groups with 12 mice in each group, group 1 with infusion of 100g/kg bFGF, group 2 with infusion of 20 g/kg bFGF and control group without infusion. Clinical manifestation and survival time of mice after haplo-HSCT were monitored. On day 14 post transplantation, mice were sacrificed for pathology analysis of liver and the changes of mesenchymal stem cells (MSC). Compared to haplo-HSCT group, clinical manifestations of aGVHD in bFGF infusion group were significantly ameliorated. Furthermore, bFGF infusion also significantly prolonged the survival time of mice after transplantation (P<0.05) as demonstrated by Kaplan-Meier survival analysis with more infusion of bFGF, the longer survival of mice. Pathology analysis showed the severity of aGVHD in bFGF infusion group (1 and 2) was less severe than haplo-HSCT group with higher proliferation of bone marrow MSC in group 1. In conclusion, these studies demonstrated that infusion of bFGF ameliorated aGVHD in mice after haplo-HSCT.

Enhanced antitumor effect of combining chemotherapy with Epstein-Barr virus (EBV)-specific cytotoxic T lymphocytes in mice with EBV-related non-Hodgkin's lymphoma.

Epstein-Barr virus (EBV)-related non-Hodgkin's lymphoma (NHL) represents a major problem in hematological clinical studies due to its drug tolerance and refractoriness. EBV infection is a key factor driving the process of tumor growth. Immune therapy is an important biotherapeutic method of treating cancer, which is attracting increasing attention. We hypothesized that combining conventional chemotherapy with immune therapy in the treatment of EBV-related NHL may achieve better outcomes. First, we successfully cloned large numbers of EBV-specific T cells by immune stimulation ex vivo. Subsequently, the combined therapy was applied in a murine model of human EBV-related NHL. As expected, combined therapy inhibited tumor growth more effectively compared with monotherapy. In addition, we continuously tested the tumor-associated immune microenvironment and observed that the numbers of tumor-infiltrating cytotoxic T lymphocytes (CTLs) and macrophages were elevated following combined therapy. These effects suggest that EBV-specific CTLs may indirectly promote an innate immune reaction in lymphoma by activating tumor-infiltrating macrophage proliferation. Our findings may provide a guide for the prospective treatment of EBV-related NHL.

Preparation and characteristics of bioflocculants from excess biological sludge.

In this study the feasibility of preparing bioflocculant from excess biological sludge was investigated. Hydrochloric acid was used to disintegrate sludge to prepare bioflocculant. The effects of acid dosage and flocculating conditions were studied. The optimized disintegration conditions was that acid dosage was 10 mL for 50 mL sludge suspension. Factors such as bioflocculant dosage, pH and temperature of the flocculant system were also tested. The optimal conditions were flocculant concentration 3.0% (v/v) and pH10.5 of flocculating suspension. Under these conditions, 99.5% of flocculating rate for 4 g/L kaolin clay was achieved. Ethanol and sodium hydroxide were applied to purify the crude sludge bioflocculant together or separately. Results showed that sodium hydroxide could separate the bioflocculant from aqueous solution more effectively than ethanol. Analysis of the purified bioflocculant by Fourier-transform infrared spectrophotometer (FT-IR) and chemical methods indicated that the main component was polysaccharide. Performance test showed that the sludge bioflocculant had moderate thermostability.