Consensus statement: support for supervisors of surgical training in Australia and Aotearoa New Zealand.

The Supervisor Support Consensus Statement has been developed after consultation with supervisors of surgical training for the Royal Australasian College of Surgeons (RACS) programmes in Australia and Aotearoa New Zealand and other key stakeholders. Six key areas have been recognized with specific recommendations crafted to improve the support and recognition of Supervisors: 1. Clarity of role, 2. Education and Training, 3. Local support, 4. RACS support, 5. Recognition and valuing of the Supervisor role, 6. Risk Management. The purpose of this consensus statement is to clearly articulate supervisor opinions on the support they require to undertake this important role. It has been produced by an independent writing group of experienced surgical supervisors and educators, with support from RACS education department. The consensus statement is a response to a needs assessment of supervisors of surgical training. The statements in this consensus document have been generated from comments and feedback from supervisors that have been refined through process of extensive consultation using a Delphi methodology. We advise specialty training Committees consider these statements and mandate them as part of their accreditation of terms. The role of the supervisor of training requires greater recognition, and incorporation in the Enterprise Bargaining Agreement (EBA) in Australia and the ASMS Te Whatu Ora SECA in Aotearoa New Zealand would ensure the provisions in this document are enacted. The six areas identified have transferability to other specialities and countries and are valuable when considering how to support supervisors involved in training our next generation of specialist doctors.

Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance.

Harsh environments in poorly perfused tumor regions may select for traits driving cancer aggressiveness. Here, we investigated whether tumor acidosis interacts with driver mutations to exacerbate cancer hallmarks. We adapted mouse organoids from normal pancreatic duct (mN10) and early pancreatic cancer (mP4, KRAS-G12D mutation, ± p53 knockout) from extracellular pH 7.4 to 6.7, representing acidic niches. Viability was increased by acid adaptation, a pattern most apparent in wild-type (WT) p53 organoids, and exacerbated upon return to pH 7.4. This led to increased survival of acid-adapted organoids treated with gemcitabine and/or erlotinib, and, in WT p53 organoids, acid-induced attenuation of drug effects. New genetic variants became dominant during adaptation, yet they were unlikely to be its main drivers. Transcriptional changes induced by acid and drug adaptation differed overall, but acid adaptation increased the expression of gemcitabine resistance genes. Thus, adaptation to acidosis increases cancer cell viability after chemotherapy.

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead.

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

High N6-methyladenosine-activated TCEAL8 mRNA is a novel pancreatic cancer marker.

N6-methyladenosine (m6A) is an RNA modification involved in RNA processing and widely found in transcripts. In cancer cells, m6A is upregulated, contributing to their malignant transformation. In this study, we analyzed gene expression and m6A modification in cancer tissues, ducts, and acinar cells derived from pancreatic cancer patients using MeRIP-seq. We found that dozens of RNAs highly modified by m6A were detected in cancer tissues compared with ducts and acinar cells. Among them, the m6A-activated mRNA TCEAL8 was observed, for the first time, as a potential marker gene in pancreatic cancer. Spatially resolved transcriptomic analysis showed that TCEAL8 was highly expressed in specific cells, and activation of cancer-related signaling pathways was observed relative to TCEAL8-negative cells. Furthermore, among TCEAL8-positive cells, the cells expressing the m6A-modifying enzyme gene METTL3 showed co-activation of Notch and mTOR signaling, also known to be involved in cancer metastasis. Overall, these results suggest that m6A-activated TCEAL8 is a novel marker gene involved in the malignant transformation of pancreatic cancer.