Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research.

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist.

Fragile X-associated tremor/ataxia syndrome treated with multitarget deep brain stimulation.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a progressive hereditary neurodegenerative disorder which causes intention tremor and cerebellar ataxia. It typically affects the ageing population. Deep brain stimulation (DBS) is widely accepted in the treatment of common movement disorders and has been trialled in treating rare and complex neurodegenerative disorders. We report a case of a man in his 40s with a long history of tremor affecting his hands. MRI brain revealed high T2 signal in the middle cerebellar peduncles. Genetic testing revealed FMR1 premutation confirming the diagnosis of FXTAS. Subsequently, he was treated with multitarget DBS of the ventralis intermediate nucleus and ventralis oralis posterior nuclei bilaterally, with excellent neurological function at 9 years follow-up. This case suggests multitarget DBS for FXTAS with neurophysiology-guided DBS programming can provide excellent long-term tremor suppression in selected patients.

Gene of the month: GATA3.

GATA binding protein 3 (GATA3) is a zinc-finger pioneer transcription factor involved in diverse processes. GATA3 regulates gene expression through binding nucleosomal DNA and facilitating chromatin remodelling. Post-translational modifications modulate its activity. During development, GATA3 plays a key role in cell differentiation. Mutations in GATA3 are linked to breast and bladder cancer. GATA3 expression is a feature of the luminal subtype of bladder cancer and has implications for immune status and therapeutic response. It also has clinical relevance in squamous cell carcinomas and soft tissue sarcomas. This paper reviews the structure and function of GATA3, its role in cancer and its use and pitfalls as an immunohistochemical marker.