Is it time for integration of surgical skills simulation into the United Kingdom undergraduate medical curriculum? A perspective from King’s College London School of Medicine

PURPOSE: Changes in undergraduate medical curricula, combined with reforms in postgraduate education, have training implications for surgical skills acquisition in a climate of reduced clinical exposure. Confidence and prior experience influences the educational impact of learning. Currently there is no basic surgical skills (BSS) programme integrated into undergraduate curricula in the United Kingdom. We explored the role of a dedicated BSS programme for undergraduates in improving confidence and influencing careers in King's College London School of Medicine, and the programme was evaluated. METHODS: A programme was designed in-line with the established Royal College of Surgeons course. Undergraduates were taught four key skills over four weeks: knot-tying, basic-suturing, tying-at-depth and chest-drain insertion, using low-fidelity bench-top models. A Likert-style questionnaire was designed to determine educational value and influence on career choice. Qualitative data was collected. RESULTS: Only 29% and 42% of students had undertaken previous practice in knot-tying and basic suturing, respectively. 96% agreed that skills exposure prior to starting surgical rotations was essential and felt a dedicated course would augment undergraduate training. There was a significant increase in confidence in the practice and knowledge of all skills taught (p<0.01), with a greater motivation to be actively involved in the surgical firm and theatres. CONCLUSION: A simple, structured BSS programme can increase the confidence and motivation of students. Early surgical skills targeting is valuable for students entering surgical, related allied, and even traditionally non-surgical specialties such as general practice. Such experience can increase the confidence of future junior doctors and trainees. We advocate the introduction of a BSS programme into United Kingdom undergraduate curricula

Candida carriage in the alimentary tract of liver transplant candidates

Thirty randomly selected patients with advanced chronic liver disease, which had been evaluated for possible liver transplantation, were sampled endoscopically at 7 alimentary tract locations to assess the frequency and amount of Candida carriage. Eightyone percent (127/156) of the samples obtained contained Candida and 53% (82/156) yielded high counts (> 300 CFU/ml). The most predominant Candida species isolated at each site was Candida albicana, which accounted for 103 (64%) of the 160 fungal isolates. The other Candida species isolated included C tropicalis 30 (19%), C krusei 16 (10%), and C glabrata 11 (7%), Although the number of sites at which yeast was present and the quantities of yeast at each site varied widely among the patients studied, 100% of the patients had Candida in at least one site of the gastrointestinal tract. Eighty-six percent (24/28) of the duodenal aspirates contained Candida and 50% (14/28) of the duodenal samples contained greater than 300 CFU/ml. A positive culture from the stomach was a reliable predictor of the presence of Candida in the duodenum (P=0.0001), but a positive culture at no other site readily predicted the presence of Candida at yet another site. Importantly, there was no correlation between the presence or absence of Candida in either oral or rectal swabs and colonization at other anatomic sites within the gastrointestinal tract, These findings are important in liver transplantation, particularly in those cases in which the bowel has been opened to create a choledochojejunostomy anastomosis. The operative attempts to reduce gastrointestinal fungal carriage using oral antifungal agents may be justified before liver transplantation in an effort to lower the risk of posttransplantation fungal infections, particularly in those patients expected to have a Roux-en-Y choledochojejunostomy biliary reconstruction. © 1994 by Williams and Wilkins

Advanced machine-learning techniques in drug discovery

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery

Beyond Node Degree: Evaluating AS Topology Models

This is the accepted version of 'Beyond Node Degree: Evaluating AS Topology Models', archived originally at arXiv:0807.2023v1 [cs.NI] 13 July 2008.Many models have been proposed to generate Internet Autonomous System (AS) topologies, most of which make structural assumptions about the AS graph. In this paper we compare AS topology generation models with several observed AS topologies. In contrast to most previous works, we avoid making assumptions about which topological properties are important to characterize the AS topology. Our analysis shows that, although matching degree-based properties, the existing AS topology generation models fail to capture the complexity of the local interconnection structure between ASs. Furthermore, we use BGP data from multiple vantage points to show that additional measurement locations significantly affect local structure properties, such as clustering and node centrality. Degree-based properties, however, are not notably affected by additional measurements locations. These observations are particularly valid in the core. The shortcomings of AS topology generation models stems from an underestimation of the complexity of the connectivity in the core caused by inappropriate use of BGP data

Genetic diversity in pigeonpea [Cajanus cajan (L.) Millsp.] Landraces as revealed by simple sequence repeat markers

Genetic relationships among 88 pigeonpea accessions from a presumed centre of origin and diversity, India and a presumed secondary centre of diversity in East Africa were evaluated using six microsatellite markers. Forty-seven (47) alleles were detected in the populations studied, with a mean of eight alleles per locus. Populations were defined by region (India and East Africa) and sub-populations by country in the case of East Africa and State in the case of India. Substantial differentiation among regions was evident from Roger&#8217;s modified distance and Wright&#8217;s F statistic. Greatest genetic diversity in terms of number of alleles, number of rare alleles and Nei&#8217;s unbiased estimate of gene diversity (H) was found in India as opposed to East Africa. This supports the hypothesis that India is the centre of diversity and East Africa is a secondary centre of diversity. Within East Africa, germplasm from Tanzania had the highest diversity according to Nei&#8217;s unbiased estimate of gene diversity, followed byKenya and Uganda. Germplasm from Kenya and Tanzania were more closely related than that of Uganda according to Roger&#8217;s modified distance. Within India, results did not indicate a clear centre of diversity. Values of genetic distance indicated that genetic relationships followed geographicalproximity

Quadratic Word Equations with Length Constraints, Counter Systems, and Presburger Arithmetic with Divisibility

Word equations are a crucial element in the theoretical foundation of constraint solving over strings, which have received a lot of attention in recent years. A word equation relates two words over string variables and constants. Its solution amounts to a function mapping variables to constant strings that equate the left and right hand sides of the equation. While the problem of solving word equations is decidable, the decidability of the problem of solving a word equation with a length constraint (i.e., a constraint relating the lengths of words in the word equation) has remained a long-standing open problem. In this paper, we focus on the subclass of quadratic word equations, i.e., in which each variable occurs at most twice. We first show that the length abstractions of solutions to quadratic word equations are in general not Presburger-definable. We then describe a class of counter systems with Presburger transition relations which capture the length abstraction of a quadratic word equation with regular constraints. We provide an encoding of the effect of a simple loop of the counter systems in the theory of existential Presburger Arithmetic with divisibility (PAD). Since PAD is decidable, we get a decision procedure for quadratic words equations with length constraints for which the associated counter system is \emph{flat} (i.e., all nodes belong to at most one cycle). We show a decidability result (in fact, also an NP algorithm with a PAD oracle) for a recently proposed NP-complete fragment of word equations called regular-oriented word equations, together with length constraints. Decidability holds when the constraints are additionally extended with regular constraints with a 1-weak control structure.Comment: 18 page

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care

Diffuse Gamma Rays: Galactic and Extragalactic Diffuse Emission

"Diffuse" gamma rays consist of several components: truly diffuse emission from the interstellar medium, the extragalactic background, whose origin is not firmly established yet, and the contribution from unresolved and faint Galactic point sources. One approach to unravel these components is to study the diffuse emission from the interstellar medium, which traces the interactions of high energy particles with interstellar gas and radiation fields. Because of its origin such emission is potentially able to reveal much about the sources and propagation of cosmic rays. The extragalactic background, if reliably determined, can be used in cosmological and blazar studies. Studying the derived "average" spectrum of faint Galactic sources may be able to give a clue to the nature of the emitting objects.Comment: 32 pages, 28 figures, kapproc.cls. Chapter to the book "Cosmic Gamma-Ray Sources," to be published by Kluwer ASSL Series, Edited by K. S. Cheng and G. E. Romero. More details can be found at http://www.gamma.mpe-garching.mpg.de/~aws/aws.htm