Defining an ageing-related pathology, disease or syndrome: International Consensus Statement

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased health span. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand and meet the healthcare, workforce, well-being and socioeconomic needs of ageing populations, whilst supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19, 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥ 70% for approval. The accepted criteria for an ageing-related pathology, disease or syndrome were (1) develops and/or progresses with increasing chronological age; (2) should be associated with, or contribute to, functional decline or an increased susceptibility to functional decline and (3) evidenced by studies in humans. Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes

Defining an ageing-related pathology, disease or syndrome: International Consensus Statement

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased health span. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand and meet the healthcare, workforce, well-being and socioeconomic needs of ageing populations, whilst supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19, 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥ 70% for approval. The accepted criteria for an ageing-related pathology, disease or syndrome were (1) develops and/or progresses with increasing chronological age; (2) should be associated with, or contribute to, functional decline or an increased susceptibility to functional decline and (3) evidenced by studies in humans. Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes

Characterization of the cytotoxic factor produced in the spleen of dengue virus-infected mice.

Data presented in the study show that the cytotoxic factor (CF) produced in the spleen of (dengue-virus) infected mice can be purified by agarose-gel electrophoresis and Sephadex G-100 gel filtration. CF is non-dialysable, heat-labile, trypsin-sensitive, unstable at acidic and alkaline pH, a macromolecular substance which sediments on ultracentrifugation and is retained by a Millipore filter of 0.45 micron size. Its approximate molecular weight is 1.15 (+/- 0.34) x 10(5) as determined by gel filtration

Studies on dengue virus-induced cytotoxic factor

This article does not have an abstract

Inhibition of production of dengue virus induced cytotoxic factor by treatment with cycloheximide & mitomycin C

This article does not have an abstract

11th Asia-Pacific Conference on Combustion, ASPACC 2017

We present results from direct numerical simulations (DNS) of laminar lifted two-dimensional (2D) n-dodecane flames at thermochemical conditions corresponding to the Engine Combustion Network (ECN) target flame known as Spray A, which canonically represents conditions in a diesel engine. Simulations were performed for three distinct inlet velocities. Results indicate that the flames present a multibrachial structure. The number of branches depends on the inlet velocity and changes from five branches for the 2.5 m/s case to four branches for the 0.6 m/s case. For the 2.5 m/s case the flame consists of a typical triple flame (consisting of a diffusion, a rich premixed, and a lean premixed flames) with additional upstream low temperature chemistry (LTC) and high temperature chemistry (HTC) branches. With decreasing inlet velocity, the triple flame moves closer to the upstream HTC branch, ultimately overtaking it for an inlet velocity of 0.6 m/s. This indicates that the triple flame propagation velocity is O(1 m/s). These results provide an important baseline to compare against a 3D turbulent Spray A flame and investigate how turbulence changes the flame structure and stabilisation mechanism

An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions

A principal component analysis (PCA) and artificial neural network (ANN) based chemistry tabulation approach is presented. ANNs are used to map the thermochemical state onto a low-dimensional manifold consisting of five control variables that have been identified using PCA. Three canonical configurations are considered to train the PCA-ANN model: a series of homogeneous reactors, a nonpremixed flamelet, and a two-dimensional lifted flame. The performance of the model in predicting the thermochemical manifold of a spatially-developing turbulent jet flame in diesel engine thermochemical conditions is a priori evaluated using direct numerical simulation (DNS) data. The PCA-ANN approach is compared with a conventional tabulation approach (tabulation using ad hoc defined control variables and linear interpolation). The PCA-ANN model provides higher accuracy and requires several orders of magnitude less memory. These observations indicate that the PCA-ANN model is superior for chemistry tabulation, especially for modelling complex chemistries that present multiple combustion modes as observed in diesel combustion. The performance of the PCA-ANN model is then compared to the optimal estimator, i.e. the conditional mean from the DNS. The results indicate that the PCA-ANN model gives high prediction accuracy, comparable to the optimal estimator, especially for major species and the thermophysical properties. Higher errors are observed for the minor species and reaction rate predictions when compared to the optimal estimator. It is shown that the prediction of minor species and reaction rates can be improved by using training data that exhibits a variation of parameters as observed in the turbulent flame. The output of the ANN is analysed to assess mass conservation. It is observed that the ANN incurs a mean absolute error of 0.05% in mass conservation. Furthermore, it is demonstrated that this error can be reduced by modifying the cost function of the ANN to penalise for deviation from mass conservation