Glofitamab for diffuse large B cell lymphoma / 中国药房

Diffuse large B cell lymphoma （DLBCL） is a malignant tumor derived from mature B cells. Currently， chemotherapy is still the main clinical treatment. However， some patients experience recurrence or refractory conditions after treatment. On June 15， 2023， the FDA approved the marketing of glofitamab， a CD3/CD20 bispecific monoclonal antibody， to provide the new treatment plan for patients with recurrent or refractory DLBCL after receiving 2-line or above systemic treatment. This article reviews pharmacological effects， clinical studies， safety， usage and dosage of glofitamab. Glofitamab mainly plays a therapeutic role in DLBCL by promoting the activation and proliferation of T cells，activating T cells to release tumor cell-killing proteins， and mediating the lysis of B cells. Clinical studies have shown that glofitamab has a better complete and objective response rate for recurrent or refractory DLBCL. Common adverse reactions caused by glofitamab include mild/moderate cytokine release syndrome， musculoskeletal pain， rash， fatigue， and so on，without significant drug interactions.

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes.

The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.

An ultra-high affinity synthetic nanobody blocks SARS-CoV-2 infection by locking Spike into an inactive conformation

Without an effective prophylactic solution, infections from SARS-CoV-2 continue to rise worldwide with devastating health and economic costs. SARS-CoV-2 gains entry into host cells via an interaction between its Spike protein and the host cell receptor angiotensin converting enzyme 2 (ACE2). Disruption of this interaction confers potent neutralization of viral entry, providing an avenue for vaccine design and for therapeutic antibodies. Here, we develop single-domain antibodies (nanobodies) that potently disrupt the interaction between the SARS-CoV-2 Spike and ACE2. By screening a yeast surface-displayed library of synthetic nanobody sequences, we identified a panel of nanobodies that bind to multiple epitopes on Spike and block ACE2 interaction via two distinct mechanisms. Cryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2. Affinity maturation and structure-guided design of multivalency yielded a trivalent nanobody, mNb6-tri, with femtomolar affinity for SARS-CoV-2 Spike and picomolar neutralization of SARS-CoV-2 infection. mNb6-tri retains stability and function after aerosolization, lyophilization, and heat treatment. These properties may enable aerosol-mediated delivery of this potent neutralizer directly to the airway epithelia, promising to yield a widely deployable, patient-friendly prophylactic and/or early infection therapeutic agent to stem the worst pandemic in a century.

From teachers and residents evaluation about the standardized training of traditional Chinese medicine explore and improve traditional Chinese medicine clinical training mode / 国际中医中药杂志

The views from teachers and residents were analyzed to discusses the problems existing in the standardized training of TCM residents,and try to find the solution.A total of 98 standardized training residents and 111 teachers from Dongfang Hospital affiliated to Beijing University of Chinese Medicine (BUCM) were enrolled.The questionnaire sceened by star APP on the phone was used to investigate them with six practical questions,each of which had three options.Both groups agreed that undergraduates' rotate period for 3 years,masters' period for 2 years,and doctors' period for 1 year.As to the item of TCM hospital selection,both groups chose traditional Chinese medicine hospital for resident standardization training.As to the item of majors,59 teachers (53.2％) and 67 (68.4％) residents chose to learn their majors.The teachers suggested that student independent management can improve sense of presence in the department.But 36.7％ of residents considered increasing income can improve sense of presence.There are some problems in TCM residents training system,and we suggest that it should be proceeding from reality in residents and constantly improved in the future.

Low-Rank Latent Pattern Approximation With Applications to Robust Image Classification.

This paper develops a novel method to address the structural noise in samples for image classification. Recently, regression-related classification methods have shown promising results when facing the pixelwise noise. However, they become weak in coping with the structural noise due to ignoring of relationships between pixels of noise image. Meanwhile, most of them need to implement the iterative process for computing representation coefficients, which leads to the high time consumption. To overcome these problems, we exploit a latent pattern model called low-rank latent pattern approximation (LLPA) to reconstruct the test image having structural noise. The rank function is applied to characterize the structure of the reconstruction residual between test image and the corresponding latent pattern. Simultaneously, the error between the latent pattern and the reference image is constrained by Frobenius norm to prevent overfitting. LLPA involves a closed-form solution by the virtue of a singular value thresholding operator. The provided theoretic analysis demonstrates that LLPA indeed removes the structural noise during classification task. Additionally, LLPA is further extended to the form of matrix regression by connecting multiple training samples, and alternating direction of multipliers method with Gaussian back substitution algorithm is used to solve the extended LLPA. Experimental results on several popular data sets validate that the proposed methods are more robust to image classification with occlusion and illumination changes, as compared to some existing state-of-the-art reconstruction-based methods and one deep neural network-based method.

Robust Visual Tracking via Convolutional Networks Without Training.

Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.

Bone marrow mesenchymal stem cells inhibit inflammatory response of microglial cells / 中国实验动物学报

Objective To investigate the inhibitory effect of bone marrow mesenchymal stem cells ( BMMSC) on the inflammatory response of microglial cells .Methods The samples were divided into four groups .Group I:microglial (BV-2) cells were cultured in DMEM (high glucose).Group II: BV-2 cells were cultured in DMEM containing lipopo-lysaccharide (LPS).Group III:BV-2cells and BMMSCs were co-cultured in DMEM.Group IV: BMMSCs were cultured in DMEM containing LPS .The growth state and ultrastructure of BV-2 cells were observed and the changes of TNF-αex-pression were detected .Results Different cell densities of BV-2 cells were observed under the optical microscope in an or-der from high level to low level:group I >group III >group II.The expressions of TNF-αwere:group Ⅱ >groupⅢ>group Ⅰ.Ultrastructural observation of BV-2 cells showed that there were a large number of swollen mitochondria and endoplasmic reticulum , some of them showed vacuolization .No BV-2 cells with multiple hucleoli were found in the group II indicating the absence of active cell growth .At the same time, cytolysis was observed only in the group II .The growth of BV-2 cells in the group III was better than that in the group II .Conclusions BMMSCs can inhibit inflammatory response of microglial cells, therefore, play a neuroprotective role.