Biology subjects

Azizi, S.

Publications and source records attributed to Azizi, S..

3 recordsLinked to original sources

bioRxiv2026

Multi-site phenotypic characterisation of Kisumu colonies for comparison of insecticide susceptibility between testing centres

Testing of insecticides and insecticide-based vector control tools relies on the use of bioassays whereby insects are exposed and mortality, or another relevant entomological endpoint, is measured. To be able to compare results between products and across time and space standard laboratory strains are used. Insecticide-susceptible strains are often used as comparators in studies involving insecticide-resistant populations. Kisumu is a long-established, widely-used insecticide-susceptible strain of Anopheles gambiae. In this study, 16 facilities across different countries exposed their Kisumu colonies aged 3-5 days to four insecticides (permethrin, alpha-cypermethrin, DDT and pirimiphos-methyl) at a range of concentrations in WHO tube assays to assess susceptibility. Insecticide-treated filter papers were made at the Liverpool School of Tropical Medicine and sent to testing centres, along with a standard protocol and testing conditions requirements, to standardise testing as much as possible. A first observation was that DDT results were affected by the use of Risella oil, the WHO-recommended carrier oil but immiscible in acetone, leading to uneven coating of papers and increased variability in bioassay results (40x variance between colonies). Bioassay data generated by the testing facilities suggest a wide range of susceptibility to different insecticides between colonies of Kisumu: LC50 values between colonies differed, though not significantly, by up to 4x for permethrin, 6x for pirimiphos-methyl, and 15x for alpha-cypermethrin. Some colonies are classified as insecticide resistant based on the WHO definition of <98% mortality on exposure to a discriminating concentration. The assumption that Kisumu is a standard susceptible strain, consistent between facilities is challenged, which has implications when comparing results between studies. We propose genotypic analysis of the colonies to identify signs of resistance markers indicative of contamination from resistant strains or wild mosquitoes, and generation of guidance to support the maintenance and monitoring of susceptible mosquito strains.

zoology↗

bioRxiv2025

Scaling Large Language Models for Next-Generation Single-Cell Analysis

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing has transformed our understanding of cellular diversity, yet current single-cell foundation models (scFMs) remain limited in their scalability, flexibility across diverse tasks, and ability to natively integrate textual information. In this work, we build upon the Cell2Sentence (C2S) framework, which represents scRNA-seq profiles as textual "cell sentences," to train Large Language Models (LLMs) on a corpus comprising over one billion tokens of transcriptomic data, biological text, and metadata. Scaling the model to 27 billion parameters yields consistent improvements in predictive and generative capabilities and supports advanced downstream tasks that require synthesis of information across multi-cellular contexts. Targeted fine-tuning with modern reinforcement learning techniques produces strong performance in perturbation response prediction, natural language interpretation, and complex biological reasoning. This predictive strength enabled a dual-context virtual screen that nominated the kinase inhibitor silmitasertib (CX-4945) as a candidate for context-selective upregulation of antigen presentation. Experimental assessment in human cell models unseen during training supported this prediction, demonstrating that C2S-Scale can effectively guide the discovery of context-conditioned biology. C2S-Scale unifies transcriptomic and textual data at unprecedented scales, surpassing both specialized single-cell models and general-purpose LLMs to provide a platform for next-generation single-cell analysis and the development of "virtual cells."

bioinformatics↗

bioRxiv2024

The Negative Effects of Smoking Cigarettes on Morphological and Histological Damages to 18 and 21 Days Mouse Embryo Liver

Smoking cigarettes can lead to morphological and histological changes in several human and animal tissues. This paper describes the effects of CS exposure on the liver, weight, and length of mouse embryos. 42 NMRI adult mice were used for mating. Pregnant female mice were categorized into 3 groups: F (filtered), NF (non-filtered), and C (controlled). Each group included 14 female mice exposed to 12 cigarettes daily; 7 mice for 18 days and 7 mice for 21 days. In addition, only the group C were exposed to ambient air. Seven mouse embryos from each group were euthanized. Their livers were fixed for histological processing. The Livers of groups F & NF as compared with group C revealed changes in cellular architecture, centrilobular veins, inflammatory cells, kupffer cells, cytoplasmic vacuolation, hepatocytes necrosis, and decreased parenchymal cellularity. The average weight of embryo, liver, and CR in F & NF significantly reduced (p<0.05 and p<0.01).

pathology↗