Platelet-Drug Conjugates Engineered via One-step Fusion Approach for Metastatic and Postoperative Cancer Treatment.

The exploration of cell-based drug delivery systems for cancer therapy has gained growing attention. Approaches to engineering therapeutic cells with multidrug loading in an effective, safe, and precise manner while preserving their inherent biological properties remain of great interest. Here, we report a strategy to simultaneously load multiple drugs in platelets in a one-step fusion process. We demonstrate doxorubicin (DOX)-encapsulated liposomes conjugated with interleukin-15 (IL-15) could fuse with platelets to achieve both cytoplasmic drug loading and surface cytokine modification with a loading efficiency of over 70% within minutes. Due to their inherent targeting ability to metastatic cancers and postoperative bleeding sites, the engineered platelets demonstrated a synergistic therapeutic effect to suppress lung metastasis and postoperative recurrence in mouse B16F10 melanoma tumor models.

Malicious Traffic Identification with Self-Supervised Contrastive Learning.

As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is able to overcome the shortcomings of traditional methods that rely on labeled samples and is able to learn data feature representations carrying semantic information from unlabeled data, thus improving the model accuracy. In this paper, a new malicious traffic feature extraction model based on a Transformer is proposed. Employing a self-attention mechanism, the proposed feature extraction model can extract the bytes features of malicious traffic by performing calculations on the malicious traffic, thereby realizing the efficient identification of malicious traffic. In addition, a bidirectional GLSTM is introduced to extract the timing features of malicious traffic. The experimental results show that the proposed method is superior to the latest published methods in terms of accuracy and F1 score.

A combined monthly precipitation prediction method based on CEEMD and improved LSTM.

With the continuous decline of water resources due to population growth and rapid economic development, precipitation prediction plays an important role in the rational allocation of global water resources. To address the non-linearity and non-stationarity of monthly precipitation, a combined prediction method based on complementary ensemble empirical mode decomposition (CEEMD) and a modified long short-term memory (LSTM) neural network was proposed. Firstly, the CEEMD method was used to decompose the monthly precipitation series into a set of relatively stationary sub-sequence components, which can better reflect the local characteristics of the sequence and further understand the nonlinear dynamic characteristics of the sequence. Then, improved LSTM neural networks were employed to predict each sub-sequence. The proposed improvement method optimized the hyper-parameters of LSTM neural networks using particle swarm optimization algorithm, which avoided the randomness of artificial parameter selection. Finally, the predicted results of each component were superimposed to obtain the final prediction result. The proposed method was validated by taking the monthly precipitation data from 1961 to 2020 in Changde City, Hunan Province as an example. The results of the case study show that, compared with other traditional prediction methods, the proposed method can better reflect the trend of precipitation changes and has higher prediction accuracy.

A dual-targeting near-infrared biomimetic drug delivery system for HBV treatment.

Hepatitis B virus (HBV) infection is a serious global public health threat. It remains elusive to achieve a functional HBV cure with currently available antivirals. Herein, a photo-responsive delivery vehicle composed of Nd3+ -sensitized core-shell upconversion nanoparticle (UCNP), mesoporous silica nanoparticle (MSN), antisense oligonucleotides (ASOs), and capsid-binding inhibitor C39 was established, which was named UMAC according to the initials of its components. Subsequently, the as-synthesized delivery vehicle was encapsulated by ß- D-galactopyranoside (Gal) modified red blood cell (RBC) membrane vesicles, which enabled precise targeting of the liver cells (UMAC-M-Gal). Both in vitro and in vivo experiments demonstrated that this biomimetic system could successfully achieve controlled drug release under light conditions at 808 nm, leading to effective suppression of HBV replication in this dual-targeted therapeutic approach. Together, these results substantiate the system has huge prospects for application to achieve functional HBV cure, and provides a promising novel strategy for drug delivery.

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of human behavior. However, the implementation of real-time human pose estimation remains a challenge. On the one hand, the performance of semantic keypoint tracking in live video footage requires high computational resources and large parameters, which limiting the accuracy of pose estimation. On the other hand, some transformer-based models were proposed recently with outstanding performance and much fewer parameters and FLOPs. However, the self-attention module in the transformer is not computationally friendly, which makes it difficult to apply these excellent models to real-time jobs. To overcome the above problems, we propose a transformer-like model, named ShiftPose, which is regression-based approach. The ShiftPose does not contain any self-attention module. Instead, we replace the self-attention module with a non-parameter operation called the shift operator. Meanwhile, we adapt the bridge-branch connection, instead of a fully-branched connection, such as HRNet, as our multi-resolution integration scheme. Specifically, the bottom half of our model adds the previous output, as well as the output from the top half of our model, corresponding to its resolution. Finally, the simple, yet promising, disentangled representation (SimDR) was used in our study to make the training process more stable. The experimental results on the MPII datasets were 86.4 PCKH, 29.1PCKH@0.1. On the COCO dataset, the results were 72.2 mAP and 91.5 AP50, 255 fps on GPU, with 10.2M parameters, and 1.6 GFLOPs. In addition, we tested our model for single-stage 3D human pose estimation and draw several useful and exploratory conclusions. The above results show good performance, and this paper provides a new method for high-performance, real-time attitude detection and tracking.

SARS-CoV-2-related pangolin coronavirus exhibits similar infection characteristics to SARS-CoV-2 and direct contact transmissibility in hamsters.

To date, intermediate hosts of SARS-CoV-2 remain obscure and controversial. Several studies have shown that SARS-CoV-2-related pangolin coronavirus (Pangolin-CoV) has a high sequence similarity to SARS-CoV-2 and might be the initial source of SARS-CoV-2; however, the biological characteristics of Pangolin-CoV are still largely unknown. In this study, we evaluated the pathogenicity and transmissibility of Pangolin-CoV in Syrian golden hamsters Mesocricetus auratus (Linnaeus, 1758) and compared it with SARS-CoV-2. Pangolin-CoV could effectively infect hamsters, showed similar tissue tropism to SARS-CoV-2 and replicated efficiently in the respiratory system and brain. The infected hamsters had no weight loss but had obvious viral shedding and lung pathological injury. Notably, Pangolin-CoV could transmit between hamsters by direct contact but not via aerosols, and the infected hamsters could exhale infectious viral aerosols (>1 µm). These results highlight the importance of continuous monitoring of coronaviruses in pangolins owing to the potential threat of Pangolin-CoV to human health.

Aerosol Transmission of the Pandemic SARS-CoV-2 and Influenza A Virus Was Blocked by Negative Ions.

The pandemic of respiratory diseases, such as coronavirus disease 2019 (COVID-19) and influenza, has imposed significant public health and economic burdens on the world. Wearing masks is an effective way to cut off the spread of the respiratory virus. However, due to cultural differences and uncomfortable wearing experiences, not everyone is willing to wear masks; there is an urgent need to find alternatives to masks. In this study, we tested the disinfection effect of a portable ionizer on pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (strain V34) and influenza A virus (strain CA04). Negative ions significantly reduced the concentration of particulate matter in the air above and effectively disinfected viruses stuck to the solid plate at the level of both nucleic acid and virus titer. The disinfection efficiency was >99.8% after 1-h exposure. Moreover, negative ions effectively disinfected aerosolized viruses; the disinfection efficiency was more than 87.77% after purification for 10 min. Furthermore, negative ions had a significant protective effect on susceptible animals exposed to viral aerosols. When the negative ionizer was switched from off to on, the inhalation 50% infective dose (ID50) for golden hamsters challenged with SARS-CoV-2 rose from 9.878 median tissue culture infective dose (TCID50) [95% confidence interval (CI), 6.727-14.013 TCID50] to 43.891 TCID50 (95% CI, 29.31-76.983 TCID50), and the inhalation ID50 for guinea pigs challenged with influenza A virus rose from 6.696 TCID50 (95% CI, 3.251-9.601 TCID50) to 28.284 TCID50 (95% CI, 19.705-40.599 TCID50). In the experiment of transmission between susceptible animals, negative ions 100% inhibited the aerosol transmission of SARS-CoV-2 and influenza A virus. Finally, we tested the safety of negative ion exposure. Balb/c mice exposed to negative ions for 4 weeks showed no abnormalities in body weight, blood routine analysis, and lung pathology. Our study demonstrates that air ions can be used as a safe and effective means of blocking respiratory virus transmission and contribute to pandemic prevention and control.

Cloning and expression of Mycobacterium bovis secreted protein MPB83 in Escherichia coli.

The gene encoding MPB83 from Mycobacterium bovis Vallee111 chromosomal DNA was amplified by using polymerase chain reaction (PCR) technique, and the PCR product was approximately 600bp DNA segment. Using TA cloning technique, the PCR product was cloned into pGEM-T vector and the cloning plasmid pGEM-T-83 was constructed successfully. pGEM-T-83 and pET28a(+) were digested by BamHI and EcoRI double enzymes. The purified MPB83 gene was subcloned into the expression vector pET28a(+), and the prokaryotic expression vector pET28a-83 was constructed. Plasmid containing pET28a-83 was transformed into competence Escherichia coli BL21 (DE3). The bacterium was induced by isopropyl-Beta-D-thiogalactopyranoside (IPTG) and its lysates were loaded directly onto sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE), approximately 26 kDa exogenous protein was observed on the SDS-PAGE. The protein was analyzed using Western-blotting. The results indicated that the protein was of antigenic activity of M.bovis. The results were expected to lay foundation for further studies on the subunit vaccine and DNA vaccine of MPB83 gene in their prevention against bovine tuberculosis.