Home-based physiology labs in the time of COVID-19 prove popular with medical students.

The COVID-19 pandemic and the resulting "lockdown" have forced many medical schools to shift from traditional "face-to-face" teaching methodologies and embrace full online delivery. Although lectures and tutorials are readily communicated by this approach, the execution of laboratory exercises is much more difficult. To overcome these challenges, face-to-face laboratory sessions were replaced by a blended learning approach in which students were provided instructional material online and then required to conduct the laboratory exercises at home. These laboratory exercises made use of easily accessible household materials and mobile applications. A self-report survey was designed to assess students' perception of their learning experience and attitudes to the home-based laboratory exercises. The survey consisted of 16 questions that students had to respond to using a 5-point Likert scale. Students were also allowed to provide open responses to select questions. Overall, the 80% of students that completed the survey expressed strong satisfaction with their learning experience and were enthusiastic toward home-based laboratory exercises. However, concerns about not being able to complete particular face-to-face exercises that required specialized equipment were expressed. Several students proposed a combined approach going forward. Our results show that home-based laboratory exercises offer a multimodal option that enriches the learning curriculum by engaging students in "hands-on" bespoke practicals using inexpensive household materials.

Optimisation of cancer classification by machine learning generates an enriched list of candidate drug targets and biomarkers.

The Cancer Genome Atlas has provided expression values of 18 015 genes for different cancer types. Studies on the classification of cancers by machine learning algorithms have used different data and methods, which makes it difficult to compare their performance. It is unclear, which algorithm performs best and if maximum levels of accuracy have been obtained. In this study, we aimed to optimise the diagnosis of cancer by comparing the performance of five algorithms using the same data, and by identifying the smallest possible number of differentiator genes. Classification accuracies of five algorithms of cancer type and primary site were determined using a gene expression dataset of 5629 samples and a dataset of 9144 samples, respectively. When trained with sample sets ranging from 16 718 to 40 genes, Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN) consistently achieved 100% or near 100% accuracy in the classification of both cancer type and primary site. Reduction of training sets to the 40 highest-ranked genes resulted in 78-fold and 45-fold faster processing times for RF and GBM, respectively. The olfactory receptor family, keratin associated proteins, and defensin beta family were among the highest ranked genes. The ensemble and NN algorithms were the most accurate at distinguishing between cancer types and primary sites, whereas KNN was the fastest. Training sets can be reduced to the 40 highest-ranked differentiator genes without any significant loss of accuracy, amongst which there are potential drug targets and biomarkers.

Cancer driver genes: a guilty by resemblance doctrine.

A major benefit of expansive cancer genome projects is the discovery of new targets for drug treatment and development. To date, cancer driver genes have been primarily identified by methods based on gene mutation frequency. This approach fails to identify culpable genes that are not mutated, rarely mutated, or contribute to the development of rare forms of cancer. Due to the complexity of the disease and the sheer volume of data, computational methods may encounter a NP-complete problem. We have developed a novel pathway and reach (PAR) method that employs a guilty by resemblance approach to identify cancer driver genes that avoids the above problems. Essentially PAR sifts through a list of genes of biological pathways to find those that are common to the same pathways and possess a similar 2-reach topology metric as a reference set of recognized driver genes. This approach leads to faster processing times and eliminates any dependency on gene mutation frequency. Out of the three pathways, signal transduction, immune system, and gene expression, a set of 50 candidate driver genes were identified, 30 of which were new. The top five were HGF, E2F1, C6, MIF, and CDK2.

The use of gene interaction networks to improve the identification of cancer driver genes.

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

Exome Sequencing in Classic Hairy Cell Leukaemia Reveals Widespread Variation in Acquired Somatic Mutations between Individual Tumours Apart from the Signature BRAF V(600)E Lesion.

In classic Hairy cell leukaemia (HCLc), a single case has thus far been interrogated by whole exome sequencing (WES) in a treatment naive patient, in which BRAF V(600)E was identified as an acquired somatic mutation and confirmed as occurring near-universally in this form of disease by conventional PCR-based cohort screens. It left open however the question whether other genome-wide mutations may also commonly occur at high frequency in presentation HCLc disease. To address this, we have carried out WES of 5 such typical HCLc cases, using highly purified splenic tumour cells paired with autologous T cells for germline. Apart from BRAF V(600)E, no other recurrent somatic mutation was identified in these HCLc exomes, thereby excluding additional acquired mutations as also prevalent at a near-universal frequency in this form of the disease. These data then place mutant BRAF at the centre of the neoplastic drive in HCLc. A comparison of our exome data with emerging genetic findings in HCL indicates that additional somatic mutations may however occur recurrently in smaller subsets of disease. As mutant BRAF alone is insufficient to drive malignant transformation in other histological cancers, it suggests that individual tumours utilise largely differing patterns of genetic somatic mutations to coalesce with BRAF V(600)E to drive pathogenesis of malignant HCLc disease.

PDBe: Protein Data Bank in Europe.

The Protein Data Bank in Europe (PDBe; pdbe.org) is actively involved in managing the international archive of biomacromolecular structure data as one of the partners in the Worldwide Protein Data Bank (wwPDB; wwpdb.org). PDBe also develops new tools to make structural data more widely and more easily available to the biomedical community. PDBe has developed a browser to access and analyze the structural archive using classification systems that are familiar to chemists and biologists. The PDBe web pages that describe individual PDB entries have been enhanced through the introduction of plain-English summary pages and iconic representations of the contents of an entry (PDBprints). In addition, the information available for structures determined by means of NMR spectroscopy has been expanded. Finally, the entire web site has been redesigned to make it substantially easier to use for expert and novice users alike. PDBe works closely with other teams at the European Bioinformatics Institute (EBI) and in the international scientific community to develop new resources with value-added information. The SIFTS initiative is an example of such a collaboration--it provides extensive mapping data between proteins whose structures are available from the PDB and a host of other biomedical databases. SIFTS is widely used by major bioinformatics resources.