Capsaicin combined with stem cells improved mitochondrial dysfunction in PIG3V cells, an immortalized human vitiligo melanocyte cell line, by inhibiting the HSP70/TLR4/mTOR/FAK signaling axis.

BACKGROUND: Vitiligo is a common autoimmune skin disease. Capsaicin has been found to exert a positive effect on vitiligo treatment, and mesenchymal stem cells (MSCs) are also confirmed to be an ideal cell type. This study aimed to explore the influence of capsaicin combined with stem cells on the treatment of vitiligo and to confirm the molecular mechanism of capsaicin combined with stem cells in treating vitiligo. METHODS AND RESULTS: PIG3V cell proliferation and apoptosis were detected using CCK-8 and TUNEL assays, MitoSOX Red fluorescence staining was used to measure the mitochondrial ROS level, and JC-1 staining was used to detect the mitochondrial membrane potential. The expression of related genes and proteins was detected using RT‒qPCR and Western blotting. Coimmunoprecipitation was used to analyze the protein interactions between HSP70 and TLR4 or between TLR4 and mTOR. The results showed higher expression of HSP70 in PIG3V cells than in PIG1 cells. The overexpression of HSP70 reduced the proliferation of PIG3V cells, promoted apoptosis, and aggravated mitochondrial dysfunction and autophagy abnormalities. The expression of HSP70 could be inhibited by capsaicin combined with MSCs, which increased the levels of Tyr, Tyrp1 and DCT, promoted the proliferation of PIG3V cells, inhibited apoptosis, activated autophagy, and improved mitochondrial dysfunction. In addition, capsaicin combined with MSCs regulated the expression of TLR4 through HSP70 and subsequently affected the mTOR/FAK signaling pathway CONCLUSIONS: Capsaicin combined with MSCs inhibits TLR4 through HSP70, and the mTOR/FAK signaling pathway is inhibited to alleviate mitochondrial dysfunction and autophagy abnormalities in PIG3V cells.

Spatiotemporal Distributions of Acoustic Propagation in Skull During Ultrasound Neuromodulation.

There is widespread interest and concern about the evidence and hypothesis that the auditory system is involved in ultrasound neuromodulation. We have addressed this problem by performing acoustic shear wave simulations in mouse skull and behavioral experiments in deaf mice. The simulation results showed that shear waves propagating along the skull did not reach sufficient acoustic pressure in the auditory cortex to modulate neurons. Behavioral experiments were subsequently performed to awaken anesthetized mice with ultrasound targeting the motor cortex or ventral tegmental area (VTA). The experimental results showed that ultrasound stimulation (US) of the target areas significantly increased arousal scores even in deaf mice, whereas the loss of ultrasound gel abolished the effect. Immunofluorescence staining also showed that ultrasound can modulate neurons in the target area, whereas neurons in the auditory cortex required the involvement of the normal auditory system for activation. In summary, the shear waves propagating along the skull cannot reach the auditory cortex and induce neuronal activation. Ultrasound neuromodulation-induced arousal behavior needs direct action on functionally relevant stimulation targets in the absence of auditory system participation.

Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction.

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Peripheral Blood Smears Distinguish Infective Fever after CAR-T Therapy.

BACKGROUND: Chimeric antigen receptor (CAR) T-cell therapy carries the risk of inducing severe and life-threatening toxicities such as cytokine release syndrome (CRS), neurotoxicity, and infection. Although CRS and infections have similar symptoms, their treatment strategies differ, and early diagnosis is very important. For CRS and infections, the fastest detection time currently takes more than 24 h, so a quick and simple method to identify a fever after CAR T-cell infusion is urgently needed. METHODS: We enrolled 27 patients with recurrent fever treated with different types of CAR T-cells, including cluster of differentiation (CD) 7, CD19, CD22, and CD19-CD22 bicistronic CAR T-cells, and evaluated the infection events occurring in these patients. We detailed the morphology of CAR T-cells in peripheral blood smears (PBS) and reported the infection events, CAR transgene copy number, and inflammatory indicators within the first month after treatment. RESULTS: Similar morphological characteristics were observed in the PBS of different CAR T-cells, namely, enlarged cell bodies, deep outside and shallow inside basophilic blue cytoplasm, and natural killer (NK) cell-like purplish red granules. There were ten infections in nine of the twenty-seven patients (33%). The percentage of atypical lymphocytes in PBS was significantly associated with CAR transgene copy number and absolute lymphocyte count in all patients. The atypical lymphocyte percentage was significantly higher in the non-infection group. CONCLUSIONS: In conclusion, the unique morphology of CAR T-cells in PBS can be used to evaluate CAR T-cell kinetics and provide reliable evidence for the rapid early identification of fever after CAR T-cell infusion. CLINICAL TRIAL REGISTRATIONS: ChiCTR-OPN-16008526; ChiCTR-OPN-16009847; ChiCTR2000038641; NCT05618041; NCT05388695.

MAG-Res2Net: a novel deep learning network for human activity recognition.

Objective.Human activity recognition (HAR) has become increasingly important in healthcare, sports, and fitness domains due to its wide range of applications. However, existing deep learning based HAR methods often overlook the challenges posed by the diversity of human activities and data quality, which can make feature extraction difficult. To address these issues, we propose a new neural network model called MAG-Res2Net, which incorporates the Borderline-SMOTE data upsampling algorithm, a loss function combination algorithm based on metric learning, and the Lion optimization algorithm.Approach.We evaluated the proposed method on two commonly utilized public datasets, UCI-HAR and WISDM, and leveraged the CSL-SHARE multimodal human activity recognition dataset for comparison with state-of-the-art models.Main results.On the UCI-HAR dataset, our model achieved accuracy, F1-macro, and F1-weighted scores of 94.44%, 94.38%, and 94.26%, respectively. On the WISDM dataset, the corresponding scores were 98.32%, 97.26%, and 98.42%, respectively.Significance.The proposed MAG-Res2Net model demonstrates robust multimodal performance, with each module successfully enhancing model capabilities. Additionally, our model surpasses current human activity recognition neural networks on both evaluation metrics and training efficiency. Source code of this work is available at:https://github.com/LHY1007/MAG-Res2Net.

Recovery-model: A model for CAR T-cell-related thrombocytopenia in relapsed/refractory multiple myeloma.

BACKGROUND: Patients with multiple myeloma (MM) treated with anti-B cell maturation antigen (BCMA) and chimeric antigen receptor (CAR) T-cell therapy tend to show delayed platelet recovery. PATIENTS AND METHODS: This single-center retrospective observational study included a cohort of patients with MM treated with anti-BCMA CAR-T cells in ChiCTR-OPC-16009113, ChiCTR1800018137, and ChiCTR1900021153. RESULTS: Fifty-eight patients with MM treated with anti-BCMA CAR-T cells were included. Delayed platelet recovery (platelet count not recovering to 50 × 109/L within 28 days) was observed in 36 % of patients. Regression analysis identified several factors that influenced platelet recovery, and accordingly, a Recovery-Model was developed. A high Recovery-Model score indicates a greater risk of delayed platelet recovery after CAR-T cell infusion and reflects the risk of hematologic toxicity. The model's predictive biomarkers included baseline platelet count, baseline hemoglobin level, logarithm of baseline Ferritin level, and cytokine release syndrome grade. Finally, survival analysis showed a significant relationship between overall survival, delayed platelet recovery (p = 0.0457), and a high Recovery-Model score (p = 0.0011). CONCLUSIONS: Inflammation-related factors and bone marrow reserves are associated with delayed platelet recovery. Therefore, we developed a model to predict the risk of delayed platelet recovery and hematological toxicity in relapsed/refractory patients with MM after anti-BCMA CAR-T cell treatment.

MSM-ViT: A multi-scale MobileViT for pulmonary nodule classification using CT images.

BACKGROUND: Accurate classification of benign and malignant pulmonary nodules using chest computed tomography (CT) images is important for early diagnosis and treatment of lung cancer. In terms of natural image classification, the ViT-based model has greater advantages in extracting global features than the traditional CNN model. However, due to the small image dataset and low image resolution, it is difficult to directly apply the ViT-based model to pulmonary nodule classification. OBJECTIVE: To propose and test a new ViT-based MSM-ViT model aiming to achieve good performance in classifying pulmonary nodules. METHODS: In this study, CNN structure was used in the task of classifying pulmonary nodules to compensate for the poor generalization of ViT structure and the difficulty in extracting multi-scale features. First, sub-pixel fusion was designed to improve the ability of the model to extract tiny features. Second, multi-scale local features were extracted by combining dilated convolution with ordinary convolution. Finally, MobileViT module was used to extract global features and predict them at the spatial level. RESULTS: CT images involving 442 benign nodules and 406 malignant nodules were extracted from LIDC-IDRI data set to verify model performance, which yielded the best accuracy of 94.04% and AUC value of 0.9636 after 10 cross-validations. CONCLUSION: The proposed new model can effectively extract multi-scale local and global features. The new model performance is also comparable to the most advanced models that use 3D volume data training, but its occupation of video memory (training resources) is less than 1/10 of the conventional 3D models.

Coexistence of primary mediastinal MALT lymphoma and multiple myeloma like POEMS syndrome: A case report and literature review.

RATIONALE: The coexistence of the extranidal marginal zone lymphoma (MZL) of mucosa-associated lymphoid tissue (MALT) and multiple myeloma (MM) is an exceedingly rare situation. The rare situation precludes any evidence-based guidelines for MZL or MM. PATIENT CONCERNS AND DIAGNOSES: We presented a unique case of the coexistence of primary mediastinal MALT lymphoma and MM like polyneuropathy, organomegaly, endocrinopathy, M-protein, skin syndrome. INTERVENTIONS AND OUTCOMES: The patient was first diagnosed with polyneuropathy, organomegaly, endocrinopathy, M-protein, skin syndrome in the department of neurology, then MM in the department of hematology, and the mediastinal MALT simultaneously coexisting with MM was found by biopsy in the department of thoracic surgery. The patient received combination therapy with rituximab and bortezomib followed by lenalidomide maintenance. To understand MZL lymphoma with plasmacytic differentiation better, we analyzed cases of MZL lymphomas with plasma cell neoplasms. Most of these cases were MZL lymphomas with light chain-restricted plasmacytic differentiation. The lymphomas relapsed with plasma cell neoplasms or transformed into plasma cell neoplasms after anti-lymphoma therapy. LESSONS: The case demonstrated clinical complexity and the importance of the detailed assessment. The case and literature review demonstrated the value of detecting light chain-restricted plasmacytic differentiation for the treatment of MZL lymphoma with rituximab plus lenalidomide or bortezomib.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer.

BACKGROUND: The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. METHODS: In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. RESULTS: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. CONCLUSIONS: These genes could provide a valuable reference in clinical treatment among breast cancer patients.

The diagnostic role of complete MICM-P in metastatic carcinoma of bone marrow (MCBM) presented with atypical symptoms: A 7-year retrospective study of 45 cases in a single center.

Metastatic carcinoma of bone marrow (MCBM) tends to present with atypical symptoms and can be easily misdiagnosed or miss diagnosed. This study was conducted to investigate the clinical-pathological and hematological characteristics of MCBM patients in order to develop strategies for early detection, staging, treatment selection and prognosis predicting. We retrospectively analyzed 45 patients with MCBM diagnosed by bone marrow biopsy in our hospital during the past 7 years. The clinical symptoms, hemogram and myelogram features, Hematoxylin and eosin staining and immunohistochemistry staining of bone marrow biopsies, location of primary carcinoma and corresponding treatment of the 45 MCBM patients were analyzed in this study. In total, 35 (77.9%) of all patients presented pains including bone pain (73.3%) as the main manifestation, and 37 (82.2%) patients had anemia. Metastatic cancer cells were found in only 22 patients (48.9%) upon bone marrow smear examination, but in all 45 patients by bone marrow biopsy. The bone marrow of 18 (40.0%) patients was dry extraction. Distribution of metastatic carcinoma was diffuse in 20 (44.4%) patients and multi-focal in 25 (55.6%) patients, complicated with myelofibrosis in 34 (75.6%) patients. For bone marrow biopsy immunohistochemistry, 97.8% of the patients were CD45-negative, while 75.6% of the patients were Cytokeratin-positive. There were 30 patients (66.7%) identified with primary malignancies. The overall survival (OS) of 1 year for MCBM patients was 6.7%. There was a trend that patients with cancer of known primary obtained better prognosis according to the survival curve, but the finding was not statistically significant with Log-rank P = .160. Complete MICM-P plays a significant role in early diagnosis of MCBM. Bone marrow biopsy combined with immunohistochemistry is an underappreciated method for the diagnosis of MCBM, which should be taken as part of regular tests as well as bone marrow smear. Understanding the clinical-pathological and hematological characteristics of MCBM and conducting bone marrow biopsy in time are of great significance for early detection and treatment selection.

Study on the Mechanism of miR-125b-5p Affecting Melanocyte Biological Behavior and Melanogenesis in Vitiligo through Regulation of MITF.

Objective: The goal was to confirm the mechanism by which miR-125b-5p influences melanocyte biological behavior and melanogenesis in vitiligo by regulating MITF. Methods: oe-MITF, sh-MITF, miR-125b-5p mimic, NC-mimic, NC-inhibitor, and miR-125b-5p inhibitor were transfected into cells by cell transfection. Western blotting was used to detect the related protein expression, qRT-PCR was used to detect miR-125b-5p and MITF expression, immunohistochemistry was used to detect the MITF-positive cells in vitiligo patients tissues, and a dual-luciferase reporter system was used to detect the target of miR-125b-5p and MITF. PIG1 and PIG3V cell proliferation by the CCK-8 method, cell cycle progression and apoptosis by flow cytometry, apoptosis was detected by TUNEL, Tyr activity and melanin content were measured using Tyr and melanin content assay kits. Results: Compared with the healthy control group, the expression of miR-125b-5p in the tissues and serum of vitiligo patients was upregulated, and the expression of MITF was downregulated; compared with PIG1 cells, the expression of miR-125b-5p and MITF in the PIG3V group was consistent with the above. Compared with the NC-minic group, the cell proliferation activity of the miR-125b-5p mimic group decreased, apoptosis increased, and the expression levels of melanogenesis-related proteins Tyr, Tyrp1, Tyrp2, and DCT were downregulated. Compared with the NC-inhibitor group, the above indices in the miR-125b-5p inhibitor group were all opposite to those in the miR-125b-5p mimic group. Transfection of oe-MITF into the miR-125b-5p mimic group reversed the effect of the miR-125b-5p mimic, while transfection of sh-MITF enhanced the effect of the miR-125b-5p mimic. Conclusion: miR-125b-5p affects vitiligo melanocyte biological behavior and melanogenesis by downregulating MITF expression.

An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer's Disease.

In the diagnosis of Alzheimer's Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion.

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject's brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model.

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain's connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.

Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation.

Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model.

Pulmonary Nodule Detection Based on Multiscale Feature Fusion.

As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis.

Treatment Outcomes and Prognostic Factors in 66 Patients with Chronic Myelomonocytic Leukemia (CMML) in a Single Center.

Introduction: Chronic myelomonocytic leukemia (CMML) is a rare hematological malignancy bearing of both myelodysplastic syndrome and myeloproliferative neoplasm characteristics. Despite the low incidence, the clinical diagnosis of CMML was difficult and the survival was poor. The optimal first-line therapy for CMML still remains a matter of debate. Methods: We retrospectively analyzed the clinical characteristics of 66 CMML patients in a single center during the past 10 years and studied the survival status of CMML patients in the real world and the influence of treatment methods on the prognosis of patients. Results: For the 66 CMML patients, the median age was 60 years old (IQR 47.0-67.0), and an approximately 1.6:1.0 male-to-female ratio was found. CMML-0, CMML-1 and CMML-2 accounted for 13.7% (9/66), 43.9% (29/66) and 42.4% (28/66), respectively. The chromosome abnormality rate was 27.2% (18/66). Gene mutation was detected in 60 patients by sequenced in first-generation with 51.1% (22/43) gene mutations and in NGS with 82.3% (14/17) gene mutations. The top three mutation genes were ASXL1MT (11/60, 18.3%), TET2MT (10/60, 16.7%), and SRSF2 MT (9/60, 15.0%). There were 27 patients in supportive therapy group, and 39 patients in chemotherapy group including patients undergoing HSCT. Patients in chemotherapy group showed better OS than those in the supportive group before and after PSM analysis with p < 0.05. Patients with blast cell in bone marrow ≥10% or WHO CMML-2 benefited more from chemotherapy treatment achieving better OS. Multivariate analysis showed that supportive therapy and intermediate-2/high in CPSS were independent risk factors for OS after PSM. Discussion: Chemotherapy including hypomethylating agents prolonged overall survival of CMML patients, especially in patients with blast cell ≥10% in bone marrow or WHO CMML-2 comparing with supportive therapy. Sequencing may provide direct insight into the molecular mechanism by detection of gene mutation, enabling personalized treatment in the future.

Longitudinal Monitoring of Plasma Circulating Tumour DNA Enables the Prediction of Early Relapse in Patients with Non-Hodgkin Lymphoma: A Case Series.

Growing evidence now suggests that circulating tumour DNA (ctDNA) has great potential as a non-invasive biomarker for disease monitoring, since ctDNA carries tumour-specific modifications. In particular, monitoring ctDNA has important implications for identifying patients with haematological malignancies at clinical risk of disease progression. We hereby describe three patients with B-cell non-Hodgkin lymphoma and investigate the clinical value of sequential ctDNA profiling for the early detection of tumour relapse. Somatic mutations in diagnostic tumour biopsy samples of these three patients were identified by applying high-throughput next-generation sequencing. Droplet digital PCR probes and primers were designed and tested for each hotspot mutation. Serial ctDNA analysis was subsequently conducted among these three patients. We found that the longitudinal monitoring of plasma ctDNA could predict for at least one month in advance compared with flow cytometry, cytology and conventional imaging modalities. Therefore, our results support liquid biopsy based on ctDNA as a non-invasive complementary modality to other detection methods for detecting early relapse and contribute to more precise management for non-Hodgkin lymphoma patients.