FOTF-CPI: A compound-protein interaction prediction transformer based on the fusion of optimal transport fragments.

Compound-protein interaction (CPI) affinity prediction plays an important role in reducing the cost and time of drug discovery. However, the interpretability of how fragments function in CPI is impacted by the fact that current methods ignore the affinity relationships between fragments of compounds and fragments of proteins in CPI modeling. This article introduces an improved Transformer called FOTF-CPI (a Fusion of Optimal Transport Fragments compound-protein interaction prediction model). We use an optimal transport-based fragmentation approach to improve the model's understanding of compound and protein sequences. Additionally, a fused attention mechanism is employed, which combines the features of fragments to capture full affinity information. This fused attention redistributes higher attention scores to fragments with higher affinity. Experimental results show FOTF-CPI achieves an average 2% higher performance than other models on all three datasets. Furthermore, the visualization confirms the potential of FOTF-CPI for drug discovery applications.

SG-ATT: A Sequence Graph Cross-Attention Representation Architecture for Molecular Property Prediction.

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently use domain knowledge to enhance molecular graph feature encoding and combine the features of molecular SMILES sequences. The SG-ATT fuses the two-dimensional molecular features so that the current model input molecular information contains molecular structure information and semantic information. The SG-ATT was tested on nine molecular property prediction tasks. Among them, the biggest SG-ATT model performance improvement was 4.5% on the BACE dataset, and the average model performance improvement was 1.83% on the full dataset. Additionally, specific model interpretability studies were conducted to showcase the performance of the SG-ATT model on different datasets. In-depth analysis was provided through case studies of in vitro validation. Finally, network tools for molecular property prediction were developed for the use of researchers.

Novel approach by natural language processing for COVID-19 knowledge discovery.

BACKGROUND: The impact of COVID-19 on public health has mandated an 'all hands on deck' scientific response. The current clinical study and basic research on COVID-19 are mainly based on existing publications or our knowledge of coronavirus. However, efficiently retrieval of accurate, relevant knowledge on COVID-19 can pose significant challenges for researchers. METHODS: To improve quality in accessing important literature findings, we developed a novel natural language processing (NLP) method to automatically recognize the associations among potential targeted host organ systems, associated clinical manifestations, and pathways. We further validated these associations through clinician experts' evaluations and prioritize candidate drug targets through bioinformatics network analysis. RESULTS: We found that the angiotensin-converting enzyme 2 (ACE2), a receptor that SARS-CoV-2 required for cell entry, is associated with cardiovascular and endocrine organ system and diseases. Furthermore, we found SARS-CoV-2 is associated with some important pathways such as IL-6, TNF-alpha, and IL-1 beta-induced dyslipidemia, which are related to inflammation, lipogenesis, and oxidative stress mechanisms, suggesting potential drug candidates. CONCLUSION: We prioritized the list of therapeutic targets involved in antiviral and immune modulating drugs for experimental validation, rendering it valuable during public health crises marked by stresses on clinical and research capacity. Our automatic intelligence pipeline also contributes to other novel and emerging disease management and treatments in the future.

S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules.

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.

Erythrocyte-mimicking subcutaneous platform with a laser-controlled treatment against diabetes.

Exogenous insulin (INS) is critical for managing diabetes. However, owing to its short in vivo half-life, frequent injection of INS is un-avoidable, which is both painful and inconvenient, compromising the quality of life. Herein, we developed a laser-regulated INS release system (INS-ICG@ER hydrogel) that allowed an on-demand release of INS from the subcutaneous INS reservoir by remote laser control without the frequent injection of INS. The amino acid hydrogel functions as a hydrogel 3D scaffold material, which offers increased subcutaneous stability of drug loaded erythrocytes (ER). This INS-ICG@ER hydrogel would release INS due to the elevated content of reactive oxygen species (ROS), generated by ICG under laser irritation. Conversely, the ROS would be scavenged without the laser irradiation and stopped the release of INS from INS-ICG@ER hydrogel. Furthermore, the release of INS from INS-ICG@ER hydrogel could be regulated by laser irradiation. The INS-ICG@ER hydrogels could control the hyperglycemia within 2 h in diabetic mice and maintained their normal blood glucose level (BGL) for up to 6 days with laser irradiation 30 min prior to meals avoiding the frequent injection of free INS. This delivery system is an effective method that offers a spatiotemporally controlled release of INS to control the glucose level in vivo.

SARS-CoV-2: minireview and review of first case in Foshan, China.

The first case of #COVID19 in Foshan provides a reference for the treatment of severe #SARSCoV2 pneumonia https://bit.ly/3eD81qj.

Overcoming Hypoxia-Restrained Radiotherapy Using an Erythrocyte-Inspired and Glucose-Activatable Platform.

Radiotherapy (RT) as one of the most powerful cancer treatment strategies has been greatly restricted by tumor hypoxia. A mounting effort has been devoted to develop oxygen delivery systems for boosting the RT effect. Unluckily, those systems only supplied modest oxygen, which could not afford more than once and long-time RT. Herein, we describe the development of a glucose-regulated drug release platform, allowing for a long-term tumor normoxic microenvironment and repeated RT for a long time. The repeated cycles resulted in sustained high Endostar plasma levels, which dramatically normalized the tumor vasculature and chronically reversed tumor hypoxia. Taking advantage of the inexhaustible supply of oxygen, Endo@GOx-ER enabled RT achieved an impressive cancer treatment output. To the best of our knowledge, our strategy is the initial attempt to overcome tumor-hypoxia-limited RT through the normalization of tumor vasculature by using an erythrocyte-inspired and glucose-activatable platform and it visually casts a light on the clinical development.

[Acute myocardial infarction complicated with cardiac arrest successfully managed by extracorporeal membrane oxygenation combined with intra-aortic balloon counterpulsation: a case report].

OBJECTIVE: Patients in acute myocardial infarction (AMI) with serious complications such as malignant ventricular arrhythmia and cardiogenic shock couldn't receive emergency percutaneous coronary intervention (PCI) procedures and had high perioperative mortality. A case of AMI patients who suffered cardiac arrest and long-term cardiopulmonary resuscitation (CPR) was admitted to the department of critical care medicine of the First People's Hospital of Foshan. With the assistance of extracorporeal membrane oxygenation (ECMO), PCI and intra-aortic balloon counterpulsation (IABP) were performed. ECMO and ventilator were successfully weaned after anti-shock, mechanical ventilation, organ support treatment. The patient was successfully treated and discharged. After 6 months of follow-up, the patient recovered well in heart function.

Cadmium Disrupts the Balance between Hydrogen Peroxide and Superoxide Radical by Regulating Endogenous Hydrogen Sulfide in the Root Tip of Brassica rapa.

Cd (cadmium) stress always alters the homeostasis of ROS (reactive oxygen species) including H2O2 (hydrogen sulfide) and [Formula: see text] (superoxide radical), leading to the oxidative injury and growth inhibition in plants. In addition to triggering oxidative injury, ROS has been suggested as important regulators modulating root elongation. However, whether and how Cd stress induces the inhibition of root elongation by differentially regulating endogenous H2O2 and [Formula: see text], rather than by inducing oxidative injury, remains elusive. To address these gaps, histochemical, physiological, and biochemical approaches were applied to investigate the mechanism for Cd to fine-tune the balance between H2O2 and [Formula: see text] in the root tip of Brassica rapa. Treatment with Cd at 4 and 16 µM significantly inhibited root elongation, while only 16 µM but not 4 µM of Cd induced oxidative injury and cell death in root tip. Fluorescent and pharmaceutical tests suggested that H2O2 and [Formula: see text] played negative and positive roles, respectively, in the regulation of root elongation in the presence of Cd (4 µM) or not. Treatment with Cd at 4 µM led to the increase in H2O2 and the decrease in [Formula: see text] in root tip, which may be attributed to the up-regulation of Br_UPB1s and the down-regulation of their predicted targets (four peroxidase genes). Cd at 4 µM resulted in the increase in endogenous H2S in root tip by inducing the up-regulation of LCDs and DCDs. Treatment with H2S biosynthesis inhibitor or H2S scavenger significantly blocked Cd (4 µM)-induced increase in endogenous H2S level, coinciding with the recovery of root elongation, the altered balance between H2O2 and [Formula: see text], and the expression of Br_UPB1s and two peroxidase genes. Taken together, it can be proposed that endogenous H2S mediated the phytotoxicity of Cd at low concentration by regulating Br_UPB1s-modulated balance between H2O2 and [Formula: see text] in root tip. Such findings shed new light on the regulatory role of endogenous H2S in plant adaptions to Cd stress.