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This paper studies the transport of monoclonal antibodies through skin tissue and initial lymphatics, which impacts the pharmacokinetics of monoclonal antibodies. Our model integrates a macroscale representation of the entire skin tissue with a mesoscale model that focuses on the papillary dermis layer. Our results indicate that it takes hours for the drugs to disperse from the injection site to the papillary dermis before entering the initial lymphatics. Additionally, we observe an inhomogeneous drug distribution in the interstitial space of the papillary dermis, with higher drug concentrations near initial lymphatics and lower concentrations near blood capillaries. To validate our model, we compare our numerical simulation results with experimental data, finding a good alignment. Our parametric studies on the drug molecule properties and injection parameters suggest that a higher diffusion coefficient increases the transport and uptake rate while binding slows down these processes. Furthermore, shallower injection depths lead to faster lymphatic uptake, whereas the size of the injection plume has a minor effect on the uptake rate. These findings advance our understanding of drug transport and lymphatic absorption after subcutaneous injection, offering valuable insights for optimizing drug delivery strategies and the design of biotherapeutics. [Display omitted] [ABSTRACT FROM AUTHOR]

SUMMARY: In eukaryotes, alternative splicing (AS) greatly expands the diversity of transcripts. However, it is challenging to accurately determine full‐length splicing isoforms. Recently, more studies have taken advantage of Pacific Bioscience (PacBio) long‐read sequencing to identify full‐length transcripts. Nevertheless, the high error rate of PacBio reads seriously offsets the advantages of long reads, especially for accurately identifying splicing junctions. To best capitalize on the features of long reads, we used Illumina RNA‐seq reads to improve PacBio circular consensus sequence (CCS) quality and to validate splicing patterns in the rice transcriptome. We evaluated the impact of CCS accuracy on the number and the validation rate of splicing isoforms, and integrated a comprehensive pipeline of splicing transcripts analysis by Iso‐Seq and RNA‐seq (STAIR) to identify the full‐length multi‐exon isoforms in rice seedling transcriptome (Oryza sativa L. ssp. japonica). STAIR discovered 11 733 full‐length multi‐exon isoforms, 6599 more than the SMRT Portal RS_IsoSeq pipeline did. Of these splicing isoforms identified, 4453 (37.9%) were missed in assembled transcripts from RNA‐seq reads, and 5204 (44.4%), including 268 multi‐exon long non‐coding RNAs (lncRNAs), were not reported in the MSU_osa1r7 annotation. Some randomly selected unreported splicing junctions were verified by polymerase chain reaction (PCR) amplification. In addition, we investigated alternative polyadenylation (APA) events in transcripts and identified 829 major polyadenylation [poly(A)] site clusters (PACs). The analysis of splicing isoforms and APA events will facilitate the annotation of the rice genome and studies on the expression and polyadenylation of AS genes in different developmental stages or growth conditions of rice. Significance statement: In this study, we integrated PacBio full‐length cDNA sequencing and RNA‐seq into a STAIR pipeline to improve the discovery of splicing isoforms in rice, and further investigated alternative polyadenylation events in transcripts. The analysis of splicing isoforms and alternative polyadenylation events will facilitate the annotation of the rice genome and the understanding of the expressions of alternative splicing genes in rice. [ABSTRACT FROM AUTHOR]

Robotic surgery represents a major breakthrough in the evolution of medical technology. Accordingly, efficient skill training and assessment methods should be developed to meet the surgeon's need of acquiring such robotic skills over a relatively short learning curve in a safe manner. Different from conventional training and assessment methods, we aim to explore the surface electromyography (sEMG) signal during the training process in order to obtain semantic and interpretable information to help the trainee better understand and improve his/her training performance. As a preliminary study, motion primitive recognition based on sEMG signal is studied in this work. Using machine learning (ML) technique, it is shown that the sEMG-based motion recognition method is feasible and promising for hand motions along 3 Cartesian axes in the virtual reality (VR) environment of a commercial robotic surgery training platform, which will hence serve as the basis for new robotic surgical skill assessment criterion and training guidance based on muscle activity information. Considering certain motion patterns were less accurately recognized than others, more data collection and deep learning-based analysis will be carried out to further improve the recognition accuracy in future research.

American ginseng (Panax quinquefolius L.) has been used for a wide range of therapeutic purposes in China. The major bioactive phytochemicals responsible for this plant's pharmacological features are ginsenosides. Thus far, little is known regarding the genes involved in ginsenosides biosynthesis in this species. As a non-model plant, information about its genomes is generally not available. In this study, we generated 6678 expressed sequence tags (ESTs) from the flower, leaf and root cDNA libraries of American ginseng. Assembly of ESTs resulted in 3349 unigenes including 534 contigs (with ESTs number ranging from 2 to 52) and 2815 singletons. By analyzing the predominant transcripts within specific tissues, a gene expression pattern was obtained in a tissue-specific manner. They were assigned according to the functional classification of unigenes to broad ranges of Gene Ontology categories which include biological processes, cellular components and molecular functions. Based on blastx search results, 24 unigenes representing candidates related to ginsenosides biosynthesis were identified. Cloning and characterization of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR, EC: 1.1.1.34), the rate-limiting enzyme in mevalonic acid pathway, demonstrated that it belonged to the plant HMGR family and was highly expressed in leaves. Putative transcription factors were detected in 63 unigenes, including zinc finger, WRKY, homeobox and MADS-box family proteins. Five hundred and eighty-eight simple sequence repeat motifs were identified, of which, dimer was the most abundant motif. These data will provide useful information on transcript profiles, gene discovery, transcriptional regulation, flower biogenesis and marker-assisted selections. The analysis and information from this study will greatly contribute to the improvement of this medicinal plant as well as of other species in the Araliaceae family, for the purpose of ensuring adequate drug resources.