Anaemia in patients undergoing major bowel surgery – Prevalence and current practice: A public and private institution experience

Aim: The study aimed to compare the frequency and alignment of preoperative anaemia screening and treatment with Australian guidelines in elective bowel surgery and determine the impact on clinical outcomes. Methods: We performed a retrospective observational study, with an audit of 559 adult patients who underwent major elective bowel surgery in an Australian metropolitan hospital, January 2016–December 2018. Outcome measures included rate of anaemia, guideline compliance, hospital length of stay, and transfusion rate. Results: Preoperative anaemia assessment occurred in 82.6% of patients. However, only 5.2% received recommended biochemical tests at least one week before surgery. Only 25.2% of anaemic patients received preoperative treatment; they experienced a longer hospital length of stay (9.93 days versus 7.88 days, p < 0.001) and an increased rate of transfusion (OR: 3.186, p < 0.05). Conclusion: The gaps between current preoperative anaemia screening, management and national guidelines may place patients at higher risk of poor surgical outcome

Nuclear Localization of Cyclin B1 Controls Mitotic Entry After DNA Damage

Mitosis in human cells is initiated by the protein kinase Cdc2-cyclin B1, which is activated at the end of G2 by dephosphorylation of two inhibitory residues, Thr14 and Tyr15. The G2 arrest that occurs after DNA damage is due in part to stabilization of phosphorylation at these sites. We explored the possibility that entry into mitosis is also regulated by the subcellular location of Cdc2-cyclin B1, which is suddenly imported into the nucleus at the end of G2. We measured the timing of mitosis in HeLa cells expressing a constitutively nuclear cyclin B1 mutant. Parallel studies were performed with cells expressing Cdc2AF, a Cdc2 mutant that cannot be phosphorylated at inhibitory sites. Whereas nuclear cyclin B1 and Cdc2AF each had little effect under normal growth conditions, together they induced a striking premature mitotic phenotype. Nuclear targeting of cyclin B1 was particularly effective in cells arrested in G2 by DNA damage, where it greatly reduced the damage-induced G2 arrest. Expression of nuclear cyclin B1 and Cdc2AF also resulted in significant defects in the exit from mitosis. Thus, nuclear targeting of cyclin B1 and dephosphorylation of Cdc2 both contribute to the control of mitotic entry and exit in human cells

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot match specialist capabilities of fine-tuned models. For example, most explorations to date on medical competency benchmarks have leveraged domain-specific training, as exemplified by efforts on BioGPT and Med-PaLM. We build on a prior study of GPT-4's capabilities on medical challenge benchmarks in the absence of special training. Rather than using simple prompting to highlight the model's out-of-the-box capabilities, we perform a systematic exploration of prompt engineering. We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks. The prompting methods we explore are general purpose, and make no specific use of domain expertise, removing the need for expert-curated content. Our experimental design carefully controls for overfitting during the prompt engineering process. We introduce Medprompt, based on a composition of several prompting strategies. With Medprompt, GPT-4 achieves state-of-the-art results on all nine of the benchmark datasets in the MultiMedQA suite. The method outperforms leading specialist models such as Med-PaLM 2 by a significant margin with an order of magnitude fewer calls to the model. Steering GPT-4 with Medprompt achieves a 27% reduction in error rate on the MedQA dataset over the best methods to date achieved with specialist models and surpasses a score of 90% for the first time. Beyond medical problems, we show the power of Medprompt to generalize to other domains and provide evidence for the broad applicability of the approach via studies of the strategy on exams in electrical engineering, machine learning, philosophy, accounting, law, nursing, and clinical psychology.Comment: 21 pages, 7 figure

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.Comment: 28 pages, 15 figures. arXiv admin note: text overlap with arXiv:1103.434

A large effective population size for established within-host influenza virus infection

Strains of the influenza virus form coherent global populations, yet exist at the level of single infections in individual hosts. The relationship between these scales is a critical topic for understanding viral evolution. Here we investigate the within-host relationship between selection and the stochastic effects of genetic drift, estimating an effective population size of infection N-e for influenza infection. Examining whole-genome sequence data describing a chronic case of influenza B in a severely immunocompromised child we infer an N-e of 2.5 x 10(7) (95% confidence range 1.0 x 10(7) to 9.0 x 10(7)) suggesting that genetic drift is of minimal importance during an established influenza infection. Our result, supported by data from influenza A infection, suggests that positive selection during within-host infection is primarily limited by the typically short period of infection. Atypically long infections may have a disproportionate influence upon global patterns of viral evolution.Peer reviewe

Classification of HIV-1 Sequences Using Profile Hidden Markov Models

Accurate classification of HIV-1 subtypes is essential for studying the dynamic spatial distribution pattern of HIV-1 subtypes and also for developing effective methods of treatment that can be targeted to attack specific subtypes. We propose a classification method based on profile Hidden Markov Model that can accurately identify an unknown strain. We show that a standard method that relies on the construction of a positive training set only, to capture unique features associated with a particular subtype, can accurately classify sequences belonging to all subtypes except B and D. We point out the drawbacks of the standard method; namely, an arbitrary choice of threshold to distinguish between true positives and true negatives, and the inability to discriminate between closely related subtypes. We then propose an improved classification method based on construction of a positive as well as a negative training set to improve discriminating ability between closely related subtypes like B and D. Finally, we show how the improved method can be used to accurately determine the subtype composition of Common Recombinant Forms of the virus that are made up of two or more subtypes. Our method provides a simple and highly accurate alternative to other classification methods and will be useful in accurately annotating newly sequenced HIV-1 strains

Structural and Functional Diversity of the Microbial Kinome

The eukaryotic protein kinase (ePK) domain mediates the majority of signaling and coordination of complex events in eukaryotes. By contrast, most bacterial signaling is thought to occur through structurally unrelated histidine kinases, though some ePK-like kinases (ELKs) and small molecule kinases are known in bacteria. Our analysis of the Global Ocean Sampling (GOS) dataset reveals that ELKs are as prevalent as histidine kinases and may play an equally important role in prokaryotic behavior. By combining GOS and public databases, we show that the ePK is just one subset of a diverse superfamily of enzymes built on a common protein kinase–like (PKL) fold. We explored this huge phylogenetic and functional space to cast light on the ancient evolution of this superfamily, its mechanistic core, and the structural basis for its observed diversity. We cataloged 27,677 ePKs and 18,699 ELKs, and classified them into 20 highly distinct families whose known members suggest regulatory functions. GOS data more than tripled the count of ELK sequences and enabled the discovery of novel families and classification and analysis of all ELKs. Comparison between and within families revealed ten key residues that are highly conserved across families. However, all but one of the ten residues has been eliminated in one family or another, indicating great functional plasticity. We show that loss of a catalytic lysine in two families is compensated by distinct mechanisms both involving other key motifs. This diverse superfamily serves as a model for further structural and functional analysis of enzyme evolution

Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files

Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II

Background: Next generation sequencing provides detailed insight into the variation present within viral populations, introducing the possibility of treatment strategies that are both reactive and predictive. Current software tools, however, need to be scaled up to accommodate for high-depth viral data sets, which are often temporally or spatially linked. In addition, due to the development of novel sequencing platforms and chemistries, each with implicit strengths and weaknesses, it will be helpful for researchers to be able to routinely compare and combine data sets from different platforms/chemistries. In particular, error associated with a specific sequencing process must be quantified so that true biological variation may be identified. Results: Segminator II was developed to allow for the efficient comparison of data sets derived from different sources. We demonstrate its usage by comparing large data sets from 12 influenza H1N1 samples sequenced on both the 454 Life Sciences and Illumina platforms, permitting quantification of platform error. For mismatches median error rates at 0.10 and 0.12%, respectively, suggested that both platforms performed similarly. For insertions and deletions median error rates within the 454 data (at 0.3 and 0.2%, respectively) were significantly higher than those within the Illumina data (0.004 and 0.006%, respectively). In agreement with previous observations these higher rates were strongly associated with homopolymeric stretches on the 454 platform. Outside of such regions both platforms had similar indel error profiles. Additionally, we apply our software to the identification of low frequency variants. Conclusion: We have demonstrated, using Segminator II, that it is possible to distinguish platform specific error from biological variation using data derived from two different platforms. We have used this approach to quantify the amount of error present within the 454 and Illumina platforms in relation to genomic location as well as location on the read. Given that next generation data is increasingly important in the analysis of drug-resistance and vaccine trials, this software will be useful to the pathogen research community. A zip file containing the source code and jar file is freely available for download from http://www.bioinf.manchester.ac.uk/segminator/