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.

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.

An adaptive pulmonary nodule detection algorithm.

Recently, lung cancer has been paid more and more attention. People have reached a consensus that early detection and early treatment can improve the survival rate of patients. Among them, pulmonary nodules are the important reference for doctors to determine the lung health. With the continuous improvement of CT image resolution, more suspected pulmonary nodule information appears from the impact of chest CT. How to relatively and accurately locate the suspected nodule location from a large number of CT images has brought challenges to the doctor's daily diagnosis. To solve the problem that the original DBSCAN clustering algorithm needs manual setting of the threshold, this paper proposes a region growing algorithm and an adaptive DBSCAN clustering algorithm to improve the accuracy of pulmonary nodule detection. The image is roughly processed and ROI (Regions of Interest) region is roughly extracted by CLAHE transform. The region growing algorithm is used to roughly process the adjacent region's expansibility and the suspected region in ROI, and mark the center point in the region and the boundary point of its point set. The mean value of region range is taken as the threshold value of DBSCAN clustering algorithm. The center of the point domain is used as the starting point of clustering, and the rough set of points is used as the MinPts threshold. Finally, the clustering results are labeled in the initial CT image. Experiments show that the pulmonary nodule detection method proposed in this paper effectively improves the accuracy of the detection results.

A similarity measure method fusing deep feature for mammogram retrieval.

BACKGROUND: Breast cancer is one of the most important malignant tumors among women causing a serious impact on women's lives and mammography is one the most important methods for breast examination. When diagnosing the breast disease, radiologists sometimes may consult some previous diagnosis cases as a reference. But there are many previous cases and it is important to find which cases are the similar cases, which is a big project costing lots of time. Medical image retrieval can provide objective reference information for doctors to diagnose disease. The method of fusing deep features can improve the retrieval accuracy, which solves the "semantic gap" problem caused by only using content features and location features. METHODS: A similarity measure method combining deep feature for mammogram retrieval is proposed in this paper. First, the images are pre-processed to extract the low-level features, including content features and location features. Before extracting location features, registration with the standard image is performed. Then, the Convolutional Neural Network, the Stacked Auto-encoder Network, and the Deep Belief Network are built to extract the deep features, which are regarded as high-level features. Next, content similarity and deep similarity are calculated separately using the Euclidean distance between the query image and the dataset images. The location similarity is obtained by calculating the ratio of intersection to union of the mass regions. Finally, content similarity, location similarity, and deep similarity are fused to form the image fusion similarity. According to the similarity, the specified number of the most similar images can be returned. RESULTS: In the experiment, 740 MLO mammograms are used, which are from women in Northeast China. The content similarity, location similarity, and deep similarity are fused by different weight coefficients. When only considering low-level features, the results are better with fusing 60% content feature similarity and 40% lesion location feature similarity. On this basis, CNN deep similarity, DBN deep similarity, and SAE deep similarity are fused separately. The experiments show that when fusing 60% DBN deep feature similarity and 40% low-level feature similarity, the results have obvious advantages. At this time, the precision is 0.745, recall is 0.850, comprehensive evaluation index is 0.794. CONCLUSIONS: We propose a similarity measure method fusing deep feature, content feature, and location feature. The retrieval results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval and location-based image retrieval.

Breast microcalcifications detection based on fusing features with DTCWT.

BACKGROUND: Breast cancer is a common disease in women. Early detection and early treatment can reduce breast cancer mortality. Studies have shown that breast cancer microcalcifications is one of the important clinical manifestations of early breast cancer, and sometimes even the only manifestation. When the mammography image shows typical malignant microcalcification, it can be diagnosed as breast cancer without any other signs of malignancy. In the aided diagnosis of microcalcifications, it is a crucial step to automatically find and locate regions of interest containing microcalcifications. However, the existing feature extraction method for microcalcifications only extracts features in the time domain or wavelet domain, and does not completely represent all the information of the region of interest. An extraction method based on the combination of Dual-Tree Complex Wavelet Transform (DTCWT) and texture features is proposed in the paper. METHODS: First, the processing operations including denoising, enhancement, and edge detection were performed on mammograms. Sub-image segmentation is then performed. DTCWT features and texture features are extracted for each sub-image.DTCWT features are combined with texture features, and then genetic algorithm is used for feature optimization. The features are classified by the Extreme Learning Machine (ELM) to achieve rapid detection and automatic extraction of ROI with microcalcifications. The experimental results verify that the feature model proposed in this paper has the highest detection rate for ROI regions. The ROI region extracted by the proposed feature model was used as subsequent experimental data. Three different methods were used to detect the microcalcifications, including Top-hat, wavelet transform, and methods combining Top-Hat and wavelet transform. RESULTS: The method was applied to 100 mammograms from the mammograms database of women in Northeast China. In the automatic extraction of ROI, the accuracy, sensitivity, specificity, positive accuracy and negative accuracy of the proposed model combined with DTCWT were 95.92%, 96.71%, 92.20%, 93.65%, 96.33%, respectively. When the Top-hat algorithm was used for microcalcifications detection, the sensitivity reached 89.6%, and the false positive detection rate was 2.6. When the wavelet transform algorithm was used for microcalcifications detection, the sensitivity was 91.1%, and the false positive detection rate was 3.28. When the combined algorithm was used for microcalcifications detection, the sensitivity was 86.7%, and the false positive detection rate decreased to 1.35. CONCLUSIONS: The proposed model combined with DTCWT features achieves better result in the automatic extraction of ROI. Moreover, in the subsequent detection of microcalcifications based on three methods, the three methods achieved better results in sensitivity and false positive detection rate, respectively.

Breast mass detection and diagnosis using fused features with density.

BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to its morphology and texture features, but also closely related to the density of the tumor. However, in the current research on breast masses detection and diagnosis, people usually use the fusion feature of morphology and texture but neglect density, or only the density feature is considered. Therefore, this paper proposes a method to detect and diagnose the breast mass using fused features with density. METHODS: In this paper, we first propose a method based on sub-region clustering to detect the breast mass. The breast region is divided into sub-regions of equal size, and each sub-region is extracted based on local density feature, after that, an Unsupervised ELM (US-ELM) is used for clustering to complete the mass detection. Second, the feature model is constructed based on the mass. This model is composed of the mass region density feature, morphology feature and texture feature. And Genetic Algorithm is used for feature selection, and the optimized feature model is formed. Finally, ELM is used to diagnose benign or malignant mass. RESULTS: An experiment on the real dataset of 480 mammograms in Northeast China shows that our proposed method can effectively improve the detection and diagnosis accuracy of breast masses, where we obtained 0.9184 precision in detection of breast masses and 0.911 accuracy in diagnosis of breast masses. CONCLUSIONS: We have proposed a mass detection system, which achieves better detection accuracy performance than the existing state-of-art algorithm. We also propose a mass diagnosis system based on the fused features with density, which is more efficient than other feature model and classifier on the same dataset.

Improvements to the gastric cancer tumor-node-metastasis staging system based on computer-aided unsupervised clustering.

BACKGROUND: The Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) classification is a key gastric cancer prognosis system. This study aimed to create a new TNM system to provide a reference for the clinical diagnosis and treatment of gastric cancer. METHODS: A review of gastric cancer patients' records was conducted in The First Hospital of China Medical University and the Liaoning Cancer Hospital and Institute. Based on patients' prognoses data, computer-aided unsupervised clustering was performed for all possible TNM staging situations to create a new staging division system. RESULTS: The primary outcome measure was 5-year survival, analyzed according to TNM classifications. Computer-aided unsupervised clustering for all TNM staging situations was used to create TNM division criteria that were more consistent with clinical situations. Furthermore, unsupervised clustering for the number of lymph node metastasis in the N stage led to the formulation of a classification method that differs from the existing N stage criteria, and unsupervised clustering for tumor size provided an additional reference for prognosis estimates. CONCLUSIONS: Finally, we developed a TNM staging system based on the computer-aided unsupervised clustering method; this system was more in line with clinical prognosis data when compared with the 7th edition of UICC gastric cancer TNM classification.

Long non-coding RNA CASC2 inhibits tumorigenesis via the miR-181a/PLXNC1 axis in melanoma.

Melanoma is the most malignant and aggressive form of skin carcinoma originating in the pigment-producing melanocytes. In this study, to further investigate the molecular mechanisms of the development and progression of melanoma, we explored the impacts of long non-coding RNA (lncRNA) CASC2 on melanoma cell functions. Microarray analysis was carried out to identify the expression of lncRNA CASC2 in melanoma cells. MiR-181a was predicted as a sponging target of CASC2 by miRcode, while the 3'-UTR of Plexin C1 (PLXNC1) was a potential target of miR-181a according to the TargetScan database. The correlation among CASC2, miR-181a, and PLXNC1 was verified by dual luciferase reporter assay and qRT-PCR. After manipulation of CASC2, miR-181a and PLXNC1 expression with transfection in A375 and M14 cells, cell viability, apoptosis, and invasive ability were evaluated using CCK-8, flow cytometry and Transwell assays, respectively. A low expression of CASC2 was detected in melanoma tissues and cells. Dual luciferase reporting assay confirmed that miR-181a targeted the 3'-UTR of PLXNC1. Furthermore, CASC2 could efficiently sponge miR-181a, thereby facilitating the expression of PLXNC1. Up-regulation of CASC2 suppressed the cell proliferation and invasion, but induced the apoptosis of melanoma cells. Our results demonstrated that lncRNA CASC2 can promote PLXNC1 expression by sponging miR-181a, thereby inhibiting the proliferation and invasion of melanoma cells, indicating that lncRNA CASC2 functions via the miR-181a/PLXNC1 axis in melanoma.

A similarity measure method combining location feature for mammogram retrieval.

BACKGROUND: Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. METHODS: This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. RESULTS: In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. CONCLUSIONS: The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

5-Aminolaevulinic Acid-Based Photodynamic Therapy Restrains Pathological Hyperplasia of Fibroblasts.

BACKGROUND This study aimed to explore whether 5-aminolaevulinic acid-based photodynamic therapy (ALA-PDT) restrains pathological hyperplasia of fibroblasts from hyperplastic scar tissues, and to investigate the potential mechanism. MATERIAL AND METHODS We used MTT assay, flow cytometry, and terminal-deoxynucleotidyl transferase mediated nick-end labeling (TUNEL) to examine the effects of ALA-PDT on proliferation, cell cycle, and apoptosis of fibroblasts isolated from hyperplastic scar tissues. The growth-promoting effect of fibroblasts on vascular endothelial cells was measured by cell co-culture. Real-time PCR and Western blot analysis were performed to detect the expression levels of transforming growth factor-ß1 (TGF-ß1), α-smooth muscle actin (a-SMA), Collagen I, Collagen III, vascular endothelial growth factor-A (VEGFA), and basic fibroblast growth factor (bFGF). RESULTS ALA-PDT inhibited proliferation delayed cell cycle progress, promoted apoptosis of fibroblasts, and suppressed its growth-promoting effect on vascular endothelial cells, and decreased expression of TGF-ß1, α-SMA, Collagen I, Collagen III, VEGFA, and bFGF. CONCLUSIONS ALA-PDT effectively restrained pathological hyperplasia of fibroblasts from hyperplastic scar tissues, which may provide a research basis for clinical therapy of hyperplastic scars.

Silencing of long noncoding RNA MALAT1 by miR-101 and miR-217 inhibits proliferation, migration, and invasion of esophageal squamous cell carcinoma cells.

MALAT1, a highly conserved long noncoding RNA, is deregulated in several types of cancers. However, its role in esophageal squamous cell carcinoma (ESCC) and its posttranscriptional regulation remain poorly understood. In this study we provide first evidences that a posttranscriptional regulation mechanism of MALAT1 by miR-101 and miR-217 exists in ESCC cells. This posttranscriptional silencing of MALAT1 could significantly suppress the proliferation of ESCC cells through the arrest of G2/M cell cycle, which may be due to MALAT1-mediated up-regulation of p21 and p27 expression and the inhibition of B-MYB expression. Moreover, we also found the abilities of migration and invasion of ESCC cells were inhibited after overexpression of miR-101, miR-217, or MALAT1 siRNA. This might be attributed to the deregulation of downstream genes of MALAT1, such as MIA2, HNF4G, ROBO1, CCT4, and CTHRC1. A significant negative correlation exists between miR-101 or miR-217 and MALAT1 in 42 pairs of ESCC tissue samples and adjacent normal tissues. Mice xenograft data also support the tumor suppressor role of both miRNAs in ESCCs.

Establishment of a Novel Myelodysplastic Syndrome (MDS) Xenotransplantation Model.

BACKGROUND: Myelodysplastic syndrome (MDS) is a clonal disease of the elderly characterized by chronic cytopenia, dysplasia, and a high risk of progression to acute myeloid leukemia (AML). Up until now, few animal models that fully recapitulate clinical features of this disease have been available. METHODS: This study aimed to establish a new MDS xenograft model utilizing a human MDS-derived cell line with heterozygous Y641C mutation of EZH2 (SKM-1). 1 x 107 SKM-1 cells were inoculated into anti-mouse CD122 monoantibody conditioned nonobese diabetic severe combined immunodeficiency (NOD/SCID) mice by intravenous injection. Decitabine was injected intraperitoneally for evaluation of epigenetic drugs in vivo. RESULTS: It is shown that the heterozygous Y641C mutation in the EZH2 gene mutation, which may destabilize the protein (ᇞᇞG = 1.46 kcal/mol), can be found in SKM-1 cells. Most mice presented anemia and leukopenia at three to four weeks after inoculation. The peripheral blood and bone marrow smear showed prominent dysplasia on erythrocytes and granulocytes as well as monocytes. CONCLUSIONS: These findings suggest that intravenous inoculation of cells from the human MDS-derived cell line prior to targeted depletion of CD122+ cells could provide a novel MDS-like xenotransplant mouse model. It is a useful tool for evaluating potential existing and novel therapeutics for MDS.

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.

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.

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.

[Construction of a recombinant plasmid of BC022687 and identification of its expression and localization in CHO cells].

OBJECTIVE: To construct a mammalian expression plasmid of the BC022687 gene and investigate the expression and localization of the fusion protein in Chinese hamster ovary (CHO) cells. METHODS: The BC022687 coding sequence was amplified by polymerase chain reaction (PCR) and subcloned into the pEGFP-C1 vector carrying the gene of green fluorescence protein (GFP). After the target region was sequenced, the recombinant plasmid was transfected into CHO cells, and its expression in the CHO cells was determined by Western blot. The localization of GFP-tagged BC022687 in the CHO cells was observed by laser scanning confocal microscopy. RESULTS: BC022687 was successfully cloned into the mammalian expression vector pEGFP-C1, with the restriction fragment length of 950 bp. The expression of the fusion protein was confirmed, with the relative molecular weight of 64 000. The GFP-tagged BC022687 protein was mainly localized in the cytoplasm, and also presented in the centrioles in the transfected CHO cells. CONCLUSION: The successful construction of the plasmid expressing BC022687 in CHO cells has laid a foundation for further studies on the role of this protein in ciliogenesis.

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.

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.

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.