Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.

Refractory skin defects such as pressure ulcers, diabetic ulcers, and vascular ulcers represent a challenge for clinicians and researchers in many aspects. The treatment strategies for wound healing have high cost and limited efficacy. To ease the financial and psychological burden on patients, a more effective therapeutic approach is needed to address the chronic wound. MSC-derived exosomes (MSC-exosomes), the main bioactive extracellular vesicles of the paracrine effect of MSCs, have been proposed as a new potential cell-free approach for wound healing and skin regeneration. The benefits of MSC-exosomes include their ability to promote angiogenesis and cell proliferation, increase collagen production, regulate inflammation, and finally improve tissue regenerative capacity. However, poor targeting and easy removability of MSC-exosomes from the wound are major obstacles to their use in clinical therapy. Thus, the concept of bioengineering technology has been introduced to modify exosomes, enabling higher concentrations and construction of particles of greater stability with specific therapeutic capability. The use of biomaterials to load MSC-exosomes may be a promising strategy to concentrate dose, create the desired therapeutic efficacy, and maintain a sustained release effect. The beneficial role of MSC-exosomes in wound healing is been widely accepted; however, the potential of bioengineering-modified MSC-exosomes remains unclear. In this review, we attempt to summarize the therapeutic applications of modified MSC-exosomes in wound healing and skin regeneration. The challenges and prospects of bioengineered MSC-exosomes are also discussed.

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Intelligent lung nodules classification is a meaningful and challenging research topic for early precaution of lung cancers, which aims to diagnose the malignancy of candidate nodules from the pulmonary computed tomography images. Nowadays, deep learning methods have made significant achievements in the medical field and promoted developments of lung nodules classification. Nevertheless, mainstream CNNs-based networks typically excel in learning coarse-grained local feature representations via stacked local-aware and weight-shared convolutions, and cannot practically model the long-range context interaction and the spatial dependencies. To tackle the above difficulties, we innovatively propose an effective Multi-Granularity Dilated Transformer to learn the long-range context relations, and explore fine-grained local details via the proposed Local Focus Scheme. Specifically, we delicately design a novel Deformable Dilated Transformer to incorporate diverse contextual information with self-attention for learning long-range global spatial dependencies. Moreover, numerous investigations indicate that local details are extremely crucial to classify indistinguishable lung nodules. Thus, we propose the Local Focus Scheme to focus on the more discriminative local features by modeling channel-wise grouped topology. Consequently, the Multi-Granularity Dilated Transformer is constructed by leveraging the Local Focus Scheme to guide the Deformable Dilated Transformer for learning fine-grained local cues. Experimental results on the mainstream benchmark LIDC-IDRI demonstrate the superiority of our model compared with the state-of-the-art methods.