Case report: targeted whole exome sequencing enables the first prenatal diagnosis of the lethal skeletal dysplasia Osteocraniostenosis.

BACKGROUND: Osteocraniostenosis (OCS) is a rare genetic disorder characterised by premature closure of cranial sutures, gracile bones and perinatal lethality. Previously, diagnosis has only been possible postnatally on clinical and radiological features. This study describes the first prenatal diagnosis of OCS. CASE PRESENTATION: In this case prenatal ultrasound images were suggestive of a serious but non-lethal skeletal dysplasia. Due to the uncertain prognosis the parents were offered Whole Exome Sequencing (WES), which identified a specific gene mutation in the FAMIIIa gene. This mutation had previously been detected in two cases and was lethal in both perinatally. This established the diagnosis, a clear prognosis and allowed informed parental choice regarding ongoing pregnancy management. CONCLUSIONS: This case report supports the use of targeted WES prenatally to confirm the underlying cause and prognosis of sonographically suspected abnormalities

Youth Correction: Principles of Diagnosis, Treatment, and Prognosis

With the increasing share of variable renewable generation, balancing electric powersystems could become a major concern for system operators because of their variableand hardly predictable nature. However, gas technologies appear as a solutionto provide this flexibility, but the impacts on the gas power system have hardly beeninvestigated. In this thesis, consulting reports on the subject matter, regulator suggestions andgas-electricity interaction models in scientific literature are studied and four sourcesare identified to be used for balancing: linepack, storage facilities, liquefied natural gasand intraday gas supply from adjacent areas. Then, a gas-electricity model for flexibility supply is designed and three case studies are simulated in order to analyze bothgas and electric power systems’ behaviors. In these case studies, electricity generation,contribution of gas sources and costs are analysed. The study concludes that critical situations on gas market that can occur, e.g. incases of large variation in the net electricity demand and limited availability of linepackand storage facilities, the need of intraday modulation can exceed the possibilities toprovide for it. Then, gas cannot be supplied to power plants during peak periods, andmore gas than necessary is used during off-peak periods. The case studies also showthat day-ahead forecast errors in variable renewable generation can be handled mucheasier than variations by the gas system but leads to higher costs

The great simulator

Cardiac myxoma often simulates multisystem disease and initially defies diagnosis. In this paper, we present three cases of cardiac myxoma in adults. Their clinical features and differential diagnosis will be reviewed, with special emphasis on their insidiousness and suspicion necessary for correct and prompt diagnosis. The role of echocardiography in diagnosis and treatment will be discussed, and finally, the surgical management and prognosis is briefly outlined.peer-reviewe

Handwriting analysis for diagnosis and prognosis of Parkinson’s disease

At present, there are no quantitative, objective methods for diagnosing the Parkinson disease. Existing methods of quantitative analysis by myograms suffer by inaccuracy and patient strain; electronic tablet analysis is limited to the visible drawing, not including the writing forces and hand movements. In our paper we show how handwriting analysis can be obtained by a new electronic pen and new features of the recorded signals. This gives good results for diagnostics. Keywords: Parkinson diagnosis, electronic pen, automatic handwriting analysi

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction: Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics. Objectives: Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs. Methods: A literature search of the current relevant primary research was performed. Results: Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance. Conclusion: Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner

Chronic Healthcare Spending Disease: A Macro Diagnosis and Prognosis

The amount Canadians spend on healthcare is set to rise rapidly over the next two decades and Canadians need to face up to tough choices to deal with this “spending disease.” The study examines the trajectory of total healthcare spending – public and private – in Canada and the policy choices Canadians must make in response. The authors estimate the extent to which healthcare spending is going to absorb a greater fraction of income than Canadians have experienced to date under two scenarios: a baseline scenario drawn from historical experience, and an optimistic scenario, which assumes an unprecedented improvement in the efficiency and effectiveness of the healthcare system and large improvement in potential economic growth. Canadians must choose some combination of: 1) a sharp reduction in public services, other than health care; 2) increased taxes to finance the public share of healthcare spending; 3) increased individual spending on healthcare services currently insured by provinces, through some form of co-payment or through delisting of services that are currently publicly financed; 4) or a degradation of publicly insured healthcare standards – longer queues, and services of poorer quality.The Health Papers, healthcare spending, Canada