Long-term morbidity and follow-up after choledochal malformation surgery; A plea for a quality of life study

Much about the aetiology, pathophysiology, natural course and optimal treatment of choledochal malformation remains under debate. Surgeons continuously strive to optimize their roles in the management of choledochal malformation. Nowadays the standard treatment is complete cyst excision followed by Rouxen-Y hepaticojejunostomy, be it via a laparotomy, laparoscopy or robot-assisted procedure. Whatever surgical endeavor is undertaken, it will be a major operation, with significant morbidity. It is important to realize that especially in asymptomatic cases, this is considered prophylactic surgery, aimed at preventing symptoms but even more important the development of malignancy later in life. A clear overview of long-term outcomes is therefore necessary. This paper aims to review the long-term outcomes after surgery for choledochal malformation. We will focus on biliary complications such as cholangitis, the development of malignancy and quality of life. We will try and identify factors related to a worse outcome. Finally, we make a plea for a large scale study into quality and course of life after resection of a choledochal malformation, to help patients, parents and their treating physicians to come to a well-balanced decision regarding the treatment of a choledochal malformation. (C) 2020 The Authors. Published by Elsevier Inc

Meta-analysis of risk of developing malignancy in congenital choledochal malformation

BackgroundCholedochal malformations comprise various congenital cystic dilatations of the extrahepatic and/or intrahepatic biliary tree. Choledochal malformation is generally considered a premalignant condition, but reliable data on the risk of malignancy and optimal surgical treatment are lacking. The objective of this systematic review was to assess the prevalence of malignancy in patients with choledochal malformation and to differentiate between subtypes. In addition, the risk of malignancy following cystic drainage versus complete cyst excision was assessed. MethodsA systematic review of PubMed and Embase databases was performed in accordance with the PRISMA statement. A meta-analysis of the risk of malignancy following cystic drainage versus complete cyst excision was undertaken in line with MOOSE guidelines. Prevalence of malignancy was defined as the rate of biliary cancer before resection, and malignant transformation as new-onset biliary cancer after surgery. ResultsEighteen observational studies were included, reporting a total of 2904 patients with a median age of 36 years. Of these, 312 in total developed a malignancy (107 per cent); the prevalence of malignancy was 73 per cent and the rate of malignant transformation was 34 per cent. Patients with types I and IV choledochal malformation had an increased risk of malignancy (P = 0016). Patients who underwent cystic drainage had an increased risk of developing biliary malignancy compared with those who had complete cyst excision, with an odds ratio of 397 (95 per cent c.i. 240 to 655). ConclusionThe risk of developing malignancy among patients with choledochal malformation was almost 11 per cent. The malignancy risk following cystic drainage surgery was four times higher than that after complete cyst excision. Complete surgical resection is recommended in patients with choledochal malformation. Choledochal cysts should be resecte

Structure elucidation of sildenafil analogues in herbal products.

The structure of unknown compounds present in herbal products was elucidated using liquid chromatography-electrospray ionization-mass spectrometry, direct-infusion electrospray ionization-mass spectrometry, and nuclear magnetic resonance. Compounds 1-3 were identified as sildenafil analogues, 1 bearing an N-ethylpiperazine moiety instead of an N-methylpiperazine, and an acetyl group instead of the sulfonyl group, named acetildenafil, 2 bearing an N-ethylpiperazine moiety instead of an N-methylpiperazine (homosildenafil), and 3 bearing an N-hydroxylethylpiperazine moiety instead of an N-methylpiperazine, named hydroxyhomosildenafil. When analysing products marketed for penile erectile dysfunction or marketed as aphrodisiacs, attention should be given to the possible presence of these components

Root System Architecture from Coupling Cell Shape to Auxin Transport

Lateral organ position along roots and shoots largely determines plant architecture, and depends on auxin distribution patterns. Determination of the underlying patterning mechanisms has hitherto been complicated because they operate during growth and division. Here, we show by experiments and computational modeling that curvature of the Arabidopsis root influences cell sizes, which, together with tissue properties that determine auxin transport, induces higher auxin levels in the pericycle cells on the outside of the curve. The abundance and position of the auxin transporters restricts this response to the zone competent for lateral root formation. The auxin import facilitator, AUX1, is up-regulated by auxin, resulting in additional local auxin import, thus creating a new auxin maximum that triggers organ formation. Longitudinal spacing of lateral roots is modulated by PIN proteins that promote auxin efflux, and pin2,3,7 triple mutants show impaired lateral inhibition. Thus, lateral root patterning combines a trigger, such as cell size difference due to bending, with a self-organizing system that mediates alterations in auxin transport

Identifying Priorities for Physiotherapy Research in the UK: the James Lind Alliance Physiotherapy Priority Setting Partnership

Objectives To identify unanswered questions for physiotherapy research and help set and prioritise the top 10 generic research priorities for the UK physiotherapy profession; updating previous clinical condition- specific priorities to include patient and carer perspectives, and reflect changes in physiotherapy practice, service provision and new technologies. Design The James Lind Alliance (JLA) Priority Setting Partnership (PSP) methodology was adopted, utilising evidence review, survey and consensus methods. Participants Anyone with experience and/or an interest in UK physiotherapy: patients, carers, members of the public, physiotherapists, student physiotherapists, other healthcare professionals, researchers, educators, service providers, commissioners and policy makers. Results Five hundred and ten respondents (50% patients, carers or members of the public) identified 2152 questions (termed “uncertainties”). Sixty-five indicative questions were developed from the uncertainties using peer reviewed thematic analysis. These were ranked in a second national survey (1,020 responses (62% were complete)). The top 25 questions were reviewed in a final prioritisation workshop using an adapted nominal group technique. The top 10 research priorities focused on optimisation (top priority); access; effectiveness; patient and carer knowledge, experiences, needs and expectations; supporting patient engagement and self-management; diagnosis and prediction. Conclusions This study is currently the UK's most inclusive consultation exercise to identify patients‘and healthcare professionals‘priorities for physiotherapy research. The exercise deliberately sought to capture generic issues relevant to all specialisms within physiotherapy. The research priorities identified a range of gaps in existing evidence to inform physiotherapy policy and practice. The results will assist research commissioning bodies and inform funding decisions and strategy

Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis - a proof of concept study

Introduction Prosthetic valve endocarditis (PVE) is a serious complication of prosthetic valve implantation, with an estimated yearly incidence of at least 0.4-1.0%. The Duke criteria and subsequent modifications have been developed as a diagnostic framework for infective endocarditis (IE) in clinical studies. However, their sensitivity and specificity are limited, especially for PVE. Furthermore, their most recent versions (ESC2015 and ESC2023) include advanced imaging modalities, e.g., cardiac CTA and [18F]FDG PET/CT as major criteria. However, despite these significant changes, the weighing system using major and minor criteria has remained unchanged. This may have introduced bias to the diagnostic set of criteria. Here, we aimed to evaluate and improve the predictive value of the modified Duke/ESC 2015 (MDE2015) criteria by using machine learning algorithms.Methods In this proof-of-concept study, we used data of a well-defined retrospective multicentre cohort of 160 patients evaluated for suspected PVE. Four machine learning algorithms were compared to the prediction of the diagnosis according to the MDE2015 criteria: Lasso logistic regression, decision tree with gradient boosting (XGBoost), decision tree without gradient boosting, and a model combining predictions of these (ensemble learning). All models used the same features that also constitute the MDE2015 criteria. The final diagnosis of PVE, based on endocarditis team consensus using all available clinical information, including surgical findings whenever performed, and with at least 1 year follow up, was used as the composite gold standard.Results The diagnostic performance of the MDE2015 criteria varied depending on how the category of 'possible' PVE cases were handled. Considering these cases as positive for PVE, sensitivity and specificity were 0.96 and 0.60, respectively. Whereas treating these cases as negative, sensitivity and specificity were 0.74 and 0.98, respectively. Combining the approaches of considering possible endocarditis as positive and as negative for ROC-analysis resulted in an excellent AUC of 0.917. For the machine learning models, the sensitivity and specificity were as follows: logistic regression, 0.92 and 0.85; XGBoost, 0.90 and 0.85; decision trees, 0.88 and 0.86; and ensemble learning, 0.91 and 0.85, respectively. The resulting AUCs were, in the same order: 0.938, 0.937, 0.930, and 0.941, respectively.Discussion In this proof-of-concept study, machine learning algorithms achieved improved diagnostic performance compared to the major/minor weighing system as used in the MDE2015 criteria. Moreover, these models provide quantifiable certainty levels of the diagnosis, potentially enhancing interpretability for clinicians. Additionally, they allow for easy incorporation of new and/or refined criteria, such as the individual weight of advanced imaging modalities such as CTA or [18F]FDG PET/CT. These promising preliminary findings warrant further studies for validation, ideally in a prospective cohort encompassing the full spectrum of patients with suspected IE

Imaging and Modeling of Myocardial Metabolism

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed

Quantitative Detection of Schistosoma japonicum Cercariae in Water by Real-Time PCR

In China alone, an estimated 30 million people are at risk of schistosomiasis, caused by the Schistosoma japonicum parasite. Disease has re-emerged in several regions that had previously attained transmission control, reinforcing the need for active surveillance. The environmental stage of the parasite is known to exhibit high spatial and temporal variability, and current detection techniques rely on a sentinel mouse method which has serious limitations in obtaining data in both time and space. Here we describe a real-time PCR assay to quantitatively detect S. japonicum cercariae in laboratory samples and in natural water that has been spiked with known numbers of S. japonicum. Multiple primers were designed and assessed, and the best performing set, along with a TaqMan probe, was used to quantify S. japonicum. The resulting assay was selective, with no amplification detected for Schistosoma mansoni, Schistosoma haematobium, avian schistosomes nor organisms present in non-endemic surface water samples. Repeated samples containing various concentrations of S. japonicum cercariae showed that the real-time PCR method had a strong linear correlation (R2 = 0.921) with light microscopy counts, and the detection limit was below the DNA equivalent of half of one cercaria. Various cercarial concentrations spiked in 1 liter of natural water followed by a filtration process produced positive detection from 93% of samples analyzed. The real-time PCR method performed well quantifying the relative concentrations of various spiked samples, although the absolute concentration estimates exhibited high variance across replicated samples. Overall, the method has the potential to be applied to environmental water samples to produce a rapid, reliable assay for cercarial location in endemic areas