Lonza (Switzerland)

Abstract Interoperability in health information systems is crucial for accurate data exchange across environments such as electronic health records, clinical notes, and medical research. The main challenge arises from the wide variation in biomedical concepts, their representation across different systems and languages, and the limited context, complicating data integration and standardization. Inspired by recent advances in large language models (LLMs), this study explores their potential role as biomedical knowledge engineers to (semi-)automate multilingual biomedical concept normalization, a key task for semantic interoperability of medical concepts. We developed a novel multilingual dataset comprising 59’104 unique terms mapped to 27’280 distinct biomedical concepts, designed to assess language model performance across this task within five European languages: English, French, German, Spanish, and Turkish. We then proposed a multi-stage pipeline based on a retrieve-then-rerank approach using sparse and dense retrievers, rerankers, and fusion approaches, leveraging discriminative and generative LLMs, with a predefined primary knowledge organization system. Our experiments show that the best discriminative model, e5, achieves an accuracy of 71%, surpassing the best generative model, Mistral, by 2% (p-value < 0.001). For semi-automated workflows, e5 maintained superior performance with 82% recall@10 versus Mistral’s 78%. Our findings demonstrate a pathway to how LLM-based approaches can advance the normalization of multilingual biomedical terms as well as the limitations of LLMs in encoding biomedical concepts.

Flo8 is a main transcriptional regulator of flocculation and pseudohyphal growth in yeast. Disruption of FLO8 in the popular recombinant protein production host Komagataella phaffii (Pichia pastoris) prevents pseudohyphal growth and reduces cell-to-surface adherence, making the mutant an interesting platform for research and industry. However, knowledge of the physiological impact of the mutation remained scarce. In-depth analysis of transcriptome data from FLO8-deficient K. phaffii revealed that Flo8 affects genes involved in cell cycle, mating, respiration, and catabolite repression additionally to flocculation targets. One gene with considerably increased expression in flo8 was GTH1, encoding a high-affinity glucose transporter in K. phaffii. Its promoter (PG1) was previously established as a strong, glucose-regulatable alternative to methanol-induced promoters. PG1 and its improved derivatives PG1-3, D-PGS4 and D-PGS5, proved to be promising candidates for controlling recombinant protein production in the FLO8-deficient background. In small-scale screenings, PG13-controlled intracellular EGFP levels were 2.8-fold higher, and yields of different secreted recombinant proteins were up to 4.8-fold increased. The enhanced productivity of the flo8 mutant in combination with the PG1 variants was transferrable to glucose-limited fed-batch processes and could largely be attributed to higher transcriptional activity of the promoter, leading to a much higher productivity per chromosomally integrated gene copy. K. phaffii flo8 has many advantageous characteristics, such as reduced surface growth and increased transcriptional strength of glucose-regulatable promoters. These features turn the flo8 strain into a valuable new base strain for various experimental designs and establish flo8 as an excellent strain background for methanol-free recombinant protein production processes.

本試験は若齢雄豚の血漿中L-カルニチン濃度と精子性状との関係を評価することを目的とした。血液と精液は53頭の189〜394日齢（平均約300日齢）のデュロック種豚から採取した。総精子数，精子の総運動率，直進運動率と血漿中L-カルニチン濃度との相関分析を行った。そのうち精子の直進運動精子率と遊離型L-カルニチン濃度との間に弱い正の相関（r＝0.253， p＝0.068）を認め，L-カルニチンの血中濃度測定により精子性状を推定し得る可能性が示唆された。今後より多くの雄豚を対象とした試験によるL-カルニチン血中濃度と精子性状の因果関係の究明が待たれる。

p=0,54) entre grupos de letramento em Sade.A varivel estudada cor(etnia) referida pelo entrevistado no apresentou significncia estatstica entre os diferentes grupos de letramento em Sade.A varivel analisada grau de escolaridade apresentou significncia estatstica (p = 0,0000051) entre os diferentes grupos de letramento em Sade.Concluso: Mais da metade dos trabalhadores administrativos operacionais entrevistados do hospital de Mangabeira apresentam letramento em

ABSTRACT Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody’s sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.

Circular RNAs (circRNAs) are formed in all domains of life and via different mechanisms. There has been an explosion in the number of circRNA papers in recent years; however, as a relatively young field, circRNA biology has an urgent need for common experimental standards for isolating, analyzing, expressing and depleting circRNAs. Here we propose a set of guidelines for circRNA studies based on the authors' experience. This Perspective will specifically address the major class of circRNAs in Eukarya that are generated by a spliceosome-catalyzed back-splicing event. We hope that the implementation of best practice principles for circRNA research will help move the field forward and allow a better functional understanding of this fascinating group of RNAs.

Abstract Controlled primary cell wall remodeling adapts plant growth under stressful conditions, but how these changes are conveyed to adjust cellulose synthesis is unknown. Here, we identify the Tetratricopeptide Thioredoxin-Like (TTL) proteins as new members of the cellulose synthase complex (CSC) and describe their unique and hitherto unknown dynamic association with the CSC under cellulose-deficient conditions. We find that TTLs are essential for maintaining cellulose synthesis under salinity stress, establishing a stress-resilient cortical microtubule array, and stabilizing CSCs at the plasma membrane. To fulfill these functions, TTLs interact with Cellulose Synthase1 (CESA1) and engage with cortical microtubules to promote their polymerization. We propose that TTLs function as bridges connecting stress perception with dynamic regulation of cellulose biosynthesis at the plasma membrane.One Sentence SummaryTTLs are peripheral membrane proteins that maintain the integrity of the cellulose synthase complex upon adverse conditions.