Investigation of Levan-derived Nanoparticles of Dolutegravir: A Promising Approach for the Delivery of anti-HIV Drug as Milk Admixture.

Nanoparticles composed of Levan and Dolutegravir (DTG) have been successfully synthesized using a spray drying procedure specifically designed for milk/food admixture applications. Levan, obtained from the microorganism Bacillus subtilis, was thoroughly characterized using MALDI-TOF and solid-state NMR technique to confirm its properties. In the present study, this isolated Levan was utilized as a carrier for drug delivery applications. The optimized spray-dried nanoparticles exhibited a smooth surface morphology with particle sizes ranging from 195 to 329 nm. In the in-vitro drug release experiments conducted in water media, the spray-dried nanoparticles showed 100% release, whereas the unprocessed drug exhibited only 50% release at the end of 24 hours. Notably, the drug release in milk was comparable to that in plain media, indicating the compatibility. The improved dissolution rate observed for the nanoparticles could be attributed to the solid-state conversion (confirmed by XRD analysis) of DTG from its crystalline to amorphous state. The stability of the drug was verified using Fourier Transform Infra-Red Spectroscopy and Thermogravimetry-Differential Scanning Calorimetry analysis. To evaluate the in-vitro cellular toxicity, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was conducted, which revealed the CC50 value of 88.88 ± 5.10 µg/mL for unprocessed DTG and 101.08 ± 37.37 µg/mL for DTG nanoparticles. These results indicated that the toxicity of the nanoparticles was comparable to the unprocessed drug. Furthermore, the anti-HIV activity of the nanoparticles in human cell lines was found to be similar to that of the pure drug, emphasizing the therapeutic efficacy of DTG in combating HIV.

Identification of novel mammalian viruses in tree shrews ( Tupaia belangeri chinensis).

The Chinese tree shrew ( Tupaia belangeri chinensis), a member of the mammalian order Scandentia, exhibits considerable similarities with primates, including humans, in aspects of its nervous, immune, and metabolic systems. These similarities have established the tree shrew as a promising experimental model for biomedical research on cancer, infectious diseases, metabolic disorders, and mental health conditions. Herein, we used meta-transcriptomic sequencing to analyze plasma, as well as oral and anal swab samples, from 105 healthy asymptomatic tree shrews to identify the presence of potential zoonotic viruses. In total, eight mammalian viruses with complete genomes were identified, belonging to six viral families, including Flaviviridae, Hepeviridae, Parvovirinae, Picornaviridae, Sedoreoviridae, and Spinareoviridae. Notably, the presence of rotavirus was recorded in tree shrews for the first time. Three viruses - hepacivirus 1, parvovirus, and picornavirus - exhibited low genetic similarity (<70%) with previously reported viruses at the whole-genome scale, indicating novelty. Conversely, three other viruses - hepacivirus 2, hepatovirus A and hepevirus - exhibited high similarity (>94%) to known viral strains. Phylogenetic analyses also revealed that the rotavirus and mammalian orthoreovirus identified in this study may be novel reassortants. These findings provide insights into the diverse viral spectrum present in captive Chinese tree shrews, highlighting the necessity for further research into their potential for cross-species transmission.

Conversion of monoclonal IgG to dimeric and secretory IgA restores neutralizing ability and prevents infection of Omicron lineages.

The emergence of Omicron lineages and descendent subvariants continues to present a severe threat to the effectiveness of vaccines and therapeutic antibodies. We have previously suggested that an insufficient mucosal immunoglobulin A (IgA) response induced by the mRNA vaccines is associated with a surge in breakthrough infections. Here, we further show that the intramuscular mRNA and/or inactivated vaccines cannot sufficiently boost the mucosal secretory IgA response in uninfected individuals, particularly against the Omicron variant. We thus engineered and characterized recombinant monomeric, dimeric, and secretory IgA1 antibodies derived from four neutralizing IgG monoclonal antibodies (mAbs 01A05, rmAb23, DXP-604, and XG014) targeting the receptor-binding domain of the spike protein. Compared to their parental IgG antibodies, dimeric and secretory IgA1 antibodies showed a higher neutralizing activity against different variants of concern (VOCs), in part due to an increased avidity. Importantly, the dimeric or secretory IgA1 form of the DXP-604 antibody significantly outperformed its parental IgG antibody, and neutralized the Omicron lineages BA.1, BA.2, and BA.4/5 with a 25- to 75-fold increase in potency. In human angiotensin converting enzyme 2 (ACE2) transgenic mice, a single intranasal dose of the dimeric IgA DXP-604 conferred prophylactic and therapeutic protection against Omicron BA.5. Thus, dimeric or secretory IgA delivered by nasal administration may potentially be exploited for the treatment and prevention of Omicron infection, thereby providing an alternative tool for combating immune evasion by the current circulating subvariants and, potentially, future VOCs.

Betacoronaviruses SARS-CoV-2 and HCoV-OC43 infections in IGROV-1 cell line require aryl hydrocarbon receptor.

The emergence of novel betacoronaviruses has posed significant financial and human health burdens, necessitating the development of appropriate tools to combat future outbreaks. In this study, we have characterized a human cell line, IGROV-1, as a robust tool to detect, propagate, and titrate betacoronaviruses SARS-CoV-2 and HCoV-OC43. IGROV-1 cells can be used for serological assays, antiviral drug testing, and isolating SARS-CoV-2 variants from patient samples. Using time-course transcriptomics, we confirmed that IGROV-1 cells exhibit a robust innate immune response upon SARS-CoV-2 infection, recapitulating the response previously observed in primary human nasal epithelial cells. We performed genome-wide CRISPR knockout genetic screens in IGROV-1 cells and identified Aryl hydrocarbon receptor (AHR) as a critical host dependency factor for both SARS-CoV-2 and HCoV-OC43. Using DiMNF, a small molecule inhibitor of AHR, we observed that the drug selectively inhibits HCoV-OC43 infection but not SARS-CoV-2. Transcriptomic analysis in primary normal human bronchial epithelial cells revealed that DiMNF blocks HCoV-OC43 infection via basal activation of innate immune responses. Our findings highlight the potential of IGROV-1 cells as a valuable diagnostic and research tool to combat betacoronavirus diseases.

Entangling of Peptide Nanofibers Reduces the Invasiveness of SARS-CoV-2.

The viral spike (S) protein on the surface of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to angiotensin-converting enzyme 2 (ACE2) receptors on the host cells, facilitating its entry and infection. Here, functionalized nanofibers targeting the S protein with peptide sequences of IRQFFKK, WVHFYHK and NSGGSVH, which are screened from a high-throughput one-bead one-compound screening strategy, are designed and prepared. The flexible nanofibers support multiple binding sites and efficiently entangle SARS-CoV-2, forming a nanofibrous network that blocks the interaction between the S protein of SARS-CoV-2 and the ACE2 on host cells, and efficiently reduce the invasiveness of SARS-CoV-2. In summary, nanofibers entangling represents a smart nanomedicine for the prevention of SARS-CoV-2.

Synthesis and anti-HIV activity of non-nucleoside reverse-transcriptase inhibitor DB02 phosphate derivatives based on water-soluble optimization.

To improve the water solubility of anti-human immunodeficiency virus (HIV) agent DB02, an excellent non-nucleoside reverse-transcriptase inhibitor (NNRTI) obtained in our previous efforts, we designed and synthesized four phosphate derivatives of DB02 based on the molecular model of DB02 with RT. Here, the antiviral activity of these four derivatives was detected, leading to the discovery of compound P-2, which possessed a superior potency to the lead compound DB02 against wild-type HIV-1 and a variety of HIV-resistant mutant viruses significantly. Furthermore, the water solubility of P-2 was nearly 17 times higher than that of DB02, and the pharmacokinetic test in rats showed that P-2 demonstrate significantly improved oral bioavailablity of 14.6%. Our study showed that the introduction of a phosphate ester group at the end of the C-2 side chain of DB02 was beneficial to the improvement of its antiviral activity and pharmacokinetic properties, which provided a promising lead for the further development of S-DACOs type of NNRTIs.

A pair of new oxindole alkaloids isolated from Uncaria macrophylla.

A pair of new oxindole alkaloids, named macrophyllines C (1) and D (2), together with two known oxindole alkaloids isorhynchophylline (3) and corynoxine (4) were isolated from Uncaria macrophylla. Their structures were elucidated based on detailed spectroscopic analysis and by comparison with literature data. In addition, all the isolates were tested for their anti-HIV activities and cytotoxicities in C8166 cells and compounds 2-4 showed weak anti-HIV activities with EC50 values of 11.31 ± 3.29 µM, 18.77 ± 6.14 µM and 30.02 ± 3.73 µM, respectively.

Structure-Based Discovery and Characterization of a Preclinical Drug Candidate for the Treatment of HIV-1 Infection.

HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) area key component of the current HIV-1 combination drug regimens. Although they exhibit potent anti-HIV-1 activity and modest toxicity, the emergence of mutant strains limits their application in clinical. Our previous research efforts contributed to the identification of compound K-5a2, which exhibits nanomolar activity in HIV-1-infected MT-4 cells. In this study, K-5a2 was shown to have a high level of anti-HIV-1 activity against various lab-adapted strains and clinical isolate strains, being comparable to ETR. Moreover, we showed the feasibility of K-5a2 as a preclinical anti-HIV-1 candidate by establishing its synergistic or additive anti-HIV-1 activity in combination with other representative anti-HIV-1 drugs and candidates. In addition, K-5a2 exhibited no inhibitory activity to the primary CYP isoforms and favorable pharmacokinetics. Taken together, its robust anti-HIV-1 potency, synergistic or additive effects with other anti-HIV drugs, and favorable pharmacokinetic and safety profiles make K-5a2 a potent alternative drug for HIV/AIDS treatment.

ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects.

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), much research on these topics is available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers which lack standardization, integration and curation. To address these challenges, we built a pilot database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specializes in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 31 712 VMs entries, 105 624 VISs, 16 310 viral target genes and 1 110 015 virus sequences of eight viruses in 77 human diseases obtained from the public domain. Furthermore, in ViMIC users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1358 transcription regulators and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

[Artificial intelligence based Chinese clinical trials eligibility criteria classification].

Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro F1 score of 0.81 by using a pre-trained language model, i.e. bidirectional encoder representations from transformers (BERT) and ensemble modeling. With the error analysis we found out that from the point of data processing steps the data pre-processing and post-processing were very important for classification, while from the point of data volume these categories with less data volume showed lower classification performance. Finally, we hope that this study could provide a valuable dataset and state-of-the-art result for the research of Chinese medical short text classification.

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods.

BACKGROUND: Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria. METHODS: We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE). RESULTS: We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484. CONCLUSION: As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms.

A SIRD epidemic model with community structure.

The study of epidemics spreading with community structure has become a hot topic. The classic SIR epidemic model does not distinguish between dead and recovered individuals. It is inappropriate to classify dead individuals as recovered individuals because the real-world epidemic spread processes show different recovery rates and death rates in different communities. In the present work, a SIRD epidemic model with different recovery rates is proposed. We pay more attention to the changes in the number of dead individuals. The basic reproductive number is obtained. The stationary solutions of a disease-free state and an endemic state are given. We show that quarantining communities can decrease the basic reproductive number, and the total number of dead individuals decreases in a disease-free steady state with an increase in the number of quarantined communities. The most effective quarantining strategy is to preferentially quarantine some communities/cities with a greater population size and a fraction of initially infected individuals. Furthermore, we show that the population flows from a low recovery rate and high population density community/city/country to some high recovery rate and low population density communities/cities/countries, which helps to reduce the total number of dead individuals and prevent the prevalence of epidemics. The numerical simulations on the real-world network and the synthetic network further support our conclusions.

pH-sensitive chitosan nanoparticles loaded with dolutegravir as milk and food admixture for paediatric anti-HIV therapy.

The present study aims to develop Chitosan-based polymeric nanoparticles of anti-HIV drug Dolutegravir, to aid appropriate dose adjustment and ease of oral administration as milk and food admixture for children. The isolated Chitosan from the crab shell species Portunus Sanguinolentus has been characterized for their physicochemical properties. Nanoparticles were developed with varying ratio of drug: Chitosan and assessed for particle size (140-548 nm), zeta potential (+26.1 mV) with a maximum of 75 % drug content. Nanoparticles exhibited improved stability and drug release in the 0.1 N HCl medium compared to pure drug. The MTT assay and the Syncytia inhibition assay in C8166 (T-lymphatic cell line) infected with HIVIIIB viral strain, which showed better therapeutic efficiency and lesser cytotoxicity compared to the pure drug. In consonance with the data obtained, the use of chitosan from a novel source for drug delivery carrier has opened exceptional prospects for delivering drugs efficiently to paediatrics.

Epidemic spreading on multilayer homogeneous evolving networks.

Multilayer networks are widely used to characterize the dynamic behavior of complex systems. The study of epidemic spreading dynamics on multilayer networks has become a hot topic in network science. Although many models have been proposed to explore epidemic spreading across different networks, there is still a lack of models to study the spreading of diseases in the process of evolution on multilayer homogeneous networks. In the present work, we propose an epidemic spreading dynamic model of homogeneous evolving networks that can be used to analyze and simulate the spreading of epidemics on such networks. We determine the global epidemic threshold. We make the interesting discovery that increasing the epidemic threshold of a single network layer is conducive to mitigating the spreading of an epidemic. We find that the initial average degree of a network and the evolutionary parameters determine the changes in the epidemic threshold and the spreading process. An approach for calculating the falling and rising threshold zones is presented. Our work provides a good strategy to control epidemic spreading. Generally, controlling or changing the threshold in a single network layer is easier than trying to directly change the threshold in all network layers. Numerical simulations of small-world and random networks further support and enrich our conclusions.

GIDB: a knowledge database for the automated curation and multidimensional analysis of molecular signatures in gastrointestinal cancer.

Gastrointestinal (GI) cancer is common, characterized by high mortality, and includes oesophagus, gastric, liver, bile duct, pancreas, rectal and colon cancers. The insufficient specificity and sensitivity of biomarkers is still a key clinical hindrance for GI cancer diagnosis and successful treatment. The emergence of `precision medicine', `basket trial' and `field cancerization' concepts calls for an urgent need and importance for the understanding of how organ system cancers occur at the molecular levels. Knowledge from both the literature and data available in public databases is informative in elucidating the molecular alterations underlying GI cancer. Currently, most available cancer databases have not offered a comprehensive discovery of gene-disease associations, molecular alterations and clinical information by integrated text mining and data mining in GI cancer. We develop GIDB, a panoptic knowledge database that attempts to automate the curation of molecular signatures using natural language processing approaches and multidimensional analyses. GIDB covers information on 8730 genes with both literature and data supporting evidence, 248 miRNAs, 58 lncRNAs, 320 copy number variations, 49 fusion genes and 2381 semantic networks. It presents a comprehensive database, not only in parallelizing supporting evidence and data integration for signatures associated with GI cancer but also in providing the timeline feature of major molecular discoveries. It highlights the most comprehensive overview, research hotspots and the development of historical knowledge of genes in GI cancer. Furthermore, GIDB characterizes genomic abnormalities in multilevel analysis, including simple somatic mutations, gene expression, DNA methylation and prognosis. GIDB offers a user-friendly interface and two customizable online tools (Heatmap and Network) for experimental researchers and clinicians to explore data and help them shorten the learning curve and broaden the scope of knowledge. More importantly, GIDB is an ongoing research project that will continue to be updated and improve the automated method for reducing manual work.

Predicting missing links in complex networks based on common neighbors and distance.

The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity.