Mesenchymal stem cells and their derived exosomes for the treatment of COVID-19

Coronavirus disease 2019(COVID-19)is an acute respiratory infection caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).SARS-CoV-2 infection typically presents with fever and respiratory symptoms,which can progress to severe respiratory distress syndrome and multiple organ failure.In severe cases,these complications may even lead to death.One of the causes of COVID-19 deaths is the cytokine storm caused by an overactive immune response.Therefore,suppressing the overactive immune response may be an effective strategy for treating COVID-19.Mesenchymal stem cells(MSCs)and their derived exosomes(MSCs-Exo)have potent homing abilities,immunomodulatory functions,regenerative repair,and antifibrotic effects,promising an effective tool in treating COVID-19.In this paper,we review the main mechanisms and potential roles of MSCs and MSCs-Exo in treating COVID-19.We also summarize relevant recent clinical trials,including the source of cells,the dosage and the efficacy,and the clinical value and problems in this field,providing more theoretical references for the clinical use of MSCs and MSCs-Exo in the treatment of COVID-19.

The Relationship Between Ultraviolet B and DNA Methylation in Skin Cancers

Ultraviolet B is regarded as an important factor in many skin diseases,especially skin cancers.Increasingly more evidence is showing that changes in DNA methylation occur in patients with skin cancers.Changes in DNA methylation have also been observed in ultraviolet B-irradiated cells and mouse models.DNA methylation modifier enzymes are simultaneously affected.We herein review the evidence to date showing that Ultraviolet B affects changes in DNA methylation modifier enzymes in skin cancers.However,the mechanism of how ultraviolet B regulates the changes in DNA methylation modifier enzymes remains to be further elucidated.Understanding the mechanism by which ultraviolet B modulates DNA methylation modifier enzymes can help to identify potential therapeutic markers or targets and develop novel strategies for preventing or treating ultraviolet B-induced skin damage.

Fabrication and evaluation of molecularly imprinted regenerated cellulose composite membranes via atom transfer radical polymerization

A simple and effective method for surface molecularly imprinted composite membranes （MICMs） for artemisinin （Ars） based on regenerated cellulose membranes was first prepared through surface- initiated atom transfer radical polymerization （ATRP）, and the as-prepared MICMs were then evaluated as adsorbents for selective recognition and separation of Ars molecules. Batch rebinding studies were conducted to determine the specific adsorption equilibrium, kinetics and selective permeation performance. The adsorption capacity of MICMs toward Ars by the Langmuir isotherm model was 2.008 mgg-1, which was nearly 5.0 times higher than non-molecularly imprinted composite membranes （NICMs）. The kinetic property of MICMs was well-fitted by the pseudo-second-order rate equation. The selective permeation experiments were successfully investigated to prove the excellent selective permeation performance for Ars than the competitive analog （artemether）.

A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.