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Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Abstract In ophthalmology, early fundus screening is an economical and effective way to prevent blindness from eye diseases. Because clinical evidence does not exist, manual detection is time-consuming and may cause the situation to be delayed clinically. With the development of deep learning, a wide variety of eye diseases have shown promising results; however, most of these studies focus on only one disease. Therefore, focusing on multi-disease classification based on fundus images is an effective approach. Consequently, this paper presents a method based on the multilevel glowworm swarm optimization convolutional neural network (MGSCNN) for the classification of multiple diseases. It is proposed that the proposed system has two stages, namely preprocessing and classification. In the beginning, the images are normalized, smoothed, and resized to prepare them for preprocessing. After pre-processing, the images are fed to the MGSCNN classifier to classify an image as normal or abnormal (covering 39 different types of diseases). In the CNN classifier, with the help of Glowworm Swarm Optimizer (GSO), we optimally detect the structure and hyperparameters of CNN simultaneously. This approach achieves an excellent accuracy of 95.09% based on various metrics.

In ophthalmology, early fundus screening is an economical and effective way to prevent blindness from eye diseases. Because clinical evidence does not exist, manual detection is time-consuming and may cause the situation to be delayed clinically. With the development of deep learning, a wide variety of eye diseases have shown promising results; however, most of these studies focus on only one disease. Therefore, focusing on multi-disease classification based on fundus images is an effective approach. Consequently, this paper presents a method based on the multilevel glowworm swarm optimization convolutional neural network (MGSCNN) for the classification of multiple diseases. It is proposed that the proposed system has two stages, namely preprocessing and classification. In the beginning, the images are normalized, smoothed, and resized to prepare them for preprocessing. After pre-processing, the images are fed to the MGSCNN classifier to classify an image as normal or abnormal (covering 39 different types of diseases). In the CNN classifier, with the help of Glowworm Swarm Optimizer (GSO), we optimally detect the structure and hyperparameters of CNN simultaneously. This approach achieves an excellent accuracy of 95.09% based on various metrics. [ABSTRACT FROM AUTHOR]

Abstract Background Physio-psycho-socioeconomical health comprehensively declines during aging, the complexity of which is challenging to measure. Among the complexity, multiple chronic disorders continuously cumulated during aging, further aggravating the challenge. Methods A population-based survey on Comprehensive Ageing Health Assessment was conducted in older adults (age > = 60) enrolled from hospital settings and community settings in 13 working centers in six subnational regions in China. Cross-sectional datasets of 8,093 older participants with approximately complete assessment results were collected for the present analysis. Individual’s multi-disease or multi-symptom was respectively scored by summing coexistent multiple diseases or multiple symptoms by respective weighting efficient for Self-Rated Health (SRH). Individual’s age-dependent health decline was further summed of four SRH-weighted scores for daily function (activity of daily life, ADL), physical mobility (an average of three metrics), cognitive function (mini mental state examination, MMSE) and mental being (geriatric depression scale, GDS) plus multi-disease score (MDS) and multi-symptom score (MSS).Multi-disease patten among 18 diseases or multi-symptom pattern among 15 symptoms was latent-clustered in the older adults, the optimal outcome of which was categorized into high, moderate or low aging-associated clusters, respectively. Percentage distribution was compared between overall health decline score and multi-disease pattern cluster or multi-symptom patten cluster. A new variable of difference between MDS and MSS (hereinafter terming DMM) that displayed linear variation with socioeconomic factors was further fitted using multilevel regression analyses by substantial adjustments on individual confounders (level-1) and subnational region variation (level-2). Results Consistent gradient distribution was shown between health decline and multimorbidity pattern cluster in the older adults. DMM was found linearly varied with personal education attainment and regional socioeconomic status. Using optimally fitted stratification of DMM (DMM interval = 0.02), an independent U-shaped interrelated tendency was shown between health decline, multi-disease and multi-symptom, which could be well explained by regional disparities in socioeconomic status. Conclusion Newly developed metrics for age-dependent health decline and aging-associated multimorbidity patten were preliminarily validated from within. The new variable of optimally fitted categorization of DMM might function as a practical indicator aiding in improving the cost-effectiveness and reduce inequity of healthcare delivery for older adults in developing countries.

Background: Physio-psycho-socioeconomical health comprehensively declines during aging, the complexity of which is challenging to measure. Among the complexity, multiple chronic disorders continuously cumulated during aging, further aggravating the challenge. Methods: A population-based survey on Comprehensive Ageing Health Assessment was conducted in older adults (age > = 60) enrolled from hospital settings and community settings in 13 working centers in six subnational regions in China. Cross-sectional datasets of 8,093 older participants with approximately complete assessment results were collected for the present analysis. Individual's multi-disease or multi-symptom was respectively scored by summing coexistent multiple diseases or multiple symptoms by respective weighting efficient for Self-Rated Health (SRH). Individual's age-dependent health decline was further summed of four SRH-weighted scores for daily function (activity of daily life, ADL), physical mobility (an average of three metrics), cognitive function (mini mental state examination, MMSE) and mental being (geriatric depression scale, GDS) plus multi-disease score (MDS) and multi-symptom score (MSS).Multi-disease patten among 18 diseases or multi-symptom pattern among 15 symptoms was latent-clustered in the older adults, the optimal outcome of which was categorized into high, moderate or low aging-associated clusters, respectively. Percentage distribution was compared between overall health decline score and multi-disease pattern cluster or multi-symptom patten cluster. A new variable of difference between MDS and MSS (hereinafter terming DMM) that displayed linear variation with socioeconomic factors was further fitted using multilevel regression analyses by substantial adjustments on individual confounders (level-1) and subnational region variation (level-2). Results: Consistent gradient distribution was shown between health decline and multimorbidity pattern cluster in the older adults. DMM was found linearly varied with personal education attainment and regional socioeconomic status. Using optimally fitted stratification of DMM (DMM interval = 0.02), an independent U-shaped interrelated tendency was shown between health decline, multi-disease and multi-symptom, which could be well explained by regional disparities in socioeconomic status. Conclusion: Newly developed metrics for age-dependent health decline and aging-associated multimorbidity patten were preliminarily validated from within. The new variable of optimally fitted categorization of DMM might function as a practical indicator aiding in improving the cost-effectiveness and reduce inequity of healthcare delivery for older adults in developing countries. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing number of vaccines and vaccine-preventable diseases, the pressure to generate multi-valent and multi-pathogen vaccines grows. Combining individual established vaccines to generate single-shot formulations represents an established path, with significant ensuing public health and cost benefits. Poxvirus-based vector systems have the capacity for large recombinant payloads and have been widely used as platforms for the development of recombinant vaccines encoding multiple antigens, with considerable clinical trials activity and a number of registered and licensed products. Areas covered: Herein we discuss design strategies, production processes, safety issues, regulatory hurdles and clinical trial activities, as well as pertinent new technologies such as systems vaccinology and needle-free delivery. Literature searches used PubMed, Google Scholar and clinical trials registries, with a focus on the recombinant vaccinia-based systems, Modified Vaccinia Ankara and the recently developed Sementis Copenhagen Vector. Expert commentary: Vaccinia-based platforms show considerable promise for the development of multi-valent and multi-pathogen vaccines, especially with recent developments in vector technologies and manufacturing processes. New methodologies for defining immune correlates and human challenge models may also facilitate bringing such vaccines to market.

Introduction: With the increasing number of vaccines and vaccine-preventable diseases, the pressure to generate multi-valent and multi-pathogen vaccines grows. Combining individual established vaccines to generate single-shot formulations represents an established path, with significant ensuing public health and cost benefits. Poxvirus-based vector systems have the capacity for large recombinant payloads and have been widely used as platforms for the development of recombinant vaccines encoding multiple antigens, with considerable clinical trials activity and a number of registered and licensed products. Areas covered: Herein we discuss design strategies, production processes, safety issues, regulatory hurdles and clinical trial activities, as well as pertinent new technologies such as systems vaccinology and needle-free delivery. Literature searches used PubMed, Google Scholar and clinical trials registries, with a focus on the recombinant vaccinia-based systems, Modified Vaccinia Ankara and the recently developed Sementis Copenhagen Vector. Expert commentary: Vaccinia-based platforms show considerable promise for the development of multi-valent and multi-pathogen vaccines, especially with recent developments in vector technologies and manufacturing processes. New methodologies for defining immune correlates and human challenge models may also facilitate bringing such vaccines to market. [ABSTRACT FROM AUTHOR]

Neglected tropical diseases (NTDs) cause serious health, social and economic burdens in the countries of the World Health Organization Western Pacific Region. Among the NTDs, helminth infections are particularly prominent with regard to the number of infected individuals and health impact. Co-endemicity is common among impoverished and marginalized populations. To achieve effective and sustainable control of helminth NTDs, a deeper understanding of the social-ecological systems governing their endemicity and strategies beyond preventive chemotherapy are required to tackle the multiple causes of infection and re-infection. We discuss the feasibility of implementing multi-disease, multi-sectoral intervention packages for helminth NTDs in the Western Pacific Region. After reviewing the main determinants for helminth NTD endemicity and current control strategies, key control activities that involve or concern other programmes within and beyond the health sector are discussed. A considerable number of activities that have an impact on more than one helminth NTD are identified in a variety of sectors, suggesting an untapped potential for synergies. We also highlight the challenges of multi-sectoral collaboration, particularly of involving non-health sectors. We conclude that multi-sectoral collaboration for helminth NTD control is feasible if the target diseases and sectors are carefully selected. To do so, an incentive analysis covering key stakeholders in the sectors is crucial, and the disease-control strategies need to be well understood. The benefits of multi-disease, multi-sectoral approaches could go beyond immediate health impacts by contributing to sustainable development, raising educational attainment, increasing productivity and reducing health inequities. [ABSTRACT FROM AUTHOR]

Objectives: We studied whether individual socioeconomic and neighborhood factors such as living in a poor community independently affected health screening participation. Methods: We studied 3 blocks of public-rental flats (the poorer neighborhood) adjacent to 3 blocks of owner-occupied public housing (the better-off neighborhood) in a precinct in Taman Jurong, Singapore. Demographic details and reasons for not having regular hypertension, diabetes mellitus, hyperlipidemia and colorectal cancer screening were collected from 2009 to 2010. An access-enhancing intervention was implemented in both neighborhoods to raise health screening rates. Results: Participation rates for rental flats and owner-occupied flats were 89.0% (356/400) and 70.2% (351/500) respectively. Living in a better-off neighborhood was independently associated with diabetes mellitus (66% vs. 35%, adjusted odds ratio (AOR)=2.12, p<0.01), hyperlipidemia (53% vs. 26%, AOR=4.34, p<0.01) and colorectal cancer screening (17% vs. 6%, AOR=15.43, p<0.01), as were individual socioeconomic factors such as employment, need for financial aid and household income. Uptake of all screening modalities significantly increased in the poorer neighborhood post-intervention (all p<0.05). Cost was cited more commonly as a barrier to health screening in the poorer neighborhood. Conclusion: Differing neighborhoods within one geographical location, as well as individual socioeconomic factors, were independently associated with differences in health screening. [Copyright &y& Elsevier]

Abstract: A large research project on the subject of computer-aided diagnosis (CAD) entitled “Intelligent Assistance in Diagnosis of Multi-dimensional Medical Images” was initiated in Japan in 2003. The objective of this research project is to develop a multi-organ, multi-disease CAD system that incorporates anatomical knowledge of the human body and diagnostic knowledge of various types of diseases. The present paper provides an overview of the project and clarifies the trend of future CAD technologies in Japan. [Copyright &y& Elsevier]