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Deer antlers are unique because they are cast and regenerate each year. They are the fastest-growing structure, reaching an astonishing growth rate of up to 2.75 cm/day in length and more than 20 cm2/day of skin. Surprisingly, no study so far has assessed the metabolic rate of the antler. High metabolic rate needs highly efficient (or large) mitochondria, and it involves a high creation or reactive oxygen species (ROS), origin of oxidative stress. The speed of creation of ROS and the oxidative stress are inversely related to ageing and many diseases such as cancer or age-related diseases. However, antler must have the most efficient anti-oxidant system, as it rarely shows any departure from a perfect growth. This paper examines recent studies showing surprising applications in medicine of growing-antler extracts, or the information regarding its physiology. A recent study (Wang et al. (2019), Science 364, eaav6335) has shown that antlers have evolved a speed of growth faster than cancer, based on high expression of proto-oncogenes. As a result, deer has evolved tumour-suppression genes to control the high risk of developing cancer. This may explain why several studies have found in vitro and in vivo anti-cancer effects of deer velvet-antler extract in human tumours, such as cell cultures and animal models of cancers such as brain cancer (glioblastoma), prostate cancer, and others. We will also discuss findings in the study of the cyclic osteoporosis of the deer, with unexpected similarities in their proteomics and gene expression with that of the human pathological osteoporosis. Last, we will examine potential applications based on having the highest metabolic rate. If the future studies establish the antler as the tissue having the fastest metabolism and the best antioxidant system, this may have implications for understanding how to fight oxidative stress, which, in turn, will have direct implications for aging and age-related diseases (and others, from cancer to osteoporosis and Alzheimer’s for example). It may also show that velvet-antler extract is a general anti-cancer compound, and this may show the path to find an anti-cancer medicine that has no secondary toxic effects in healthy cells.

Gelatin and chitosan nanoparticles have been widely used in pharmaceutical, biomedical, and nanofood applications due to their high biocompatibility and biodegradability. This study proposed a highly efficient synthesis method for type B gelatin and low-molecular-weight (LMW) chitosan nanoparticles. Gelatin nanoparticles (GNPs) were synthesized by the double desolvation method and the chitosan nanoparticles (CNPs) by the ionic gelation method. The sizes of the obtained CNPs and GNPs (373 ± 71 nm and 244 ± 67 nm, respectively) and zeta potential (+36.60 ± 3.25 mV and −13.42 ± 1.16 mV, respectively) were determined via dynamic light scattering. Morphology and size were verified utilizing SEM and TEM images. Finally, their biocompatibility was tested to assure their potential applicability as bioactive molecule carriers and cell-penetrating agents.

Biomaterials engineering and biotechnology have advanced significantly towards probiotic encapsulation with encouraging results in assuring sufficient bioactivity. However, some major challenges remain to be addressed, and these include maintaining stability in different compartments of the gastrointestinal tract (GIT), favoring adhesion only at the site of action, and increasing residence times. An alternative to addressing such challenges is to manufacture encapsulates with stimuli-responsive polymers, such that controlled release is achievable by incorporating moieties that respond to chemical and physical stimuli present along the GIT. This review highlights, therefore, such emerging delivery matrices going from a comprehensive description of addressable stimuli in each GIT compartment to novel synthesis and functionalization techniques to currently employed materials used for probiotic’s encapsulation and achieving multi-modal delivery and multi-stimuli responses. Next, we explored the routes for encapsulates design to enhance their performance in terms of degradation kinetics, adsorption, and mucus and gut microbiome interactions. Finally, we present the clinical perspectives of implementing novel probiotics and the challenges to assure scalability and cost-effectiveness, prerequisites for an eventual niche market penetration.

BACKGROUND: The former prisoner of war (FPOW) population is mandated to “receive the highest quality care and benefit services” from the US Department of Veterans Affairs (VA). Each VA medical facility is required to have a special Care and Benefits Team to meet this policy goal. METHODS: In South Texas, 40% of FPOWs had no VA primary care or clinic assignment. In consideration of the commitment of the VA to care for FPOWs, the unique POW-related medical and psychological issues, the geriatric age of many FPOWs, and the surprising number of FPOWs currently not receiving VA care, the South Texas Veterans Health Care System in San Antonio incorporated the concept of geriatric evaluation and management into its cognitive behavioral therapy team to create a specialized interdisciplinary FPOW Clinic. The main purpose of this project was to advise FPOWs of VA benefits and services as well as to facilitate the identification of overlooked conditions with a presumption of service connection, for example, exposure to Agent Orange. RESULTS: As most FPOWs are aged > 65 years, the FPOW Clinic was designed as an interdisciplinary team similar to that proven successful in geriatric medicine. Overlooked FPOW presumptive conditions were identified for 34% of FPOWs. CONCLUSIONS: FPOW veterans are rapidly dwindling in numbers and may live in rural areas. Consistent with the VA’s desire to adopt novel technological approaches, we propose to modify our FPOW Clinic by adopting telehealth.

The unique lignocellulosic and solvent-extractive chemical constituents of most natural fibers are rich in natural polymers and bioactive molecules that can be exploited for biomaterial formulation. However, although natural fibers’ main constituents have been already incorporated as material reinforcement and improve surface bioactivity of polymeric materials, the use of the whole natural fibers as bioactive fillers remains largely unexplored. Thus, we put forward the formulation of natural fiber filling and functionalization of biomaterials by studying the chemical composition of cocoa bean shells (CBS) and proposing the fabrication and characterization of polylactic acid (PLA) and CBS-based composite by solvent-casting. As was expected from previous studies of agro-industrial wastes, the main components of CBS were to cellulose (42.23 wt.%), lignin (22.68 wt.%), hemicellulose (14.73 wt.%), and solvent extractives (14.42 wt.%). Structural analysis (FTIR) confirms the absence of covalent bonding between materials. Thermal degradation profiles (DSC and TGA) showed similar mass losses and thermal-reaction profiles for lignocellulosic-fibers-based composites. The mechanical behavior of the PLA/CBS composite shows a stiffer material behavior than the pristine material. The cell viability of Vero cells in the presence of the composites was above 94%, and the hemolytic tendency was below 5%, while platelet aggregation increased up to 40%. Antioxidant activity was confirmed with comparable 2,2-diphe-277 nyl-1-picryl-hydrazyl-hydrate (DPPH) free-radical scavenging than Vitamin C even for PLA/CBS composite. Therefore, the present study elucidates the significant promise of CBS for bioactive functionalization in biomaterial-engineering, as the tested composite exhibited high biocompatibility and strong antioxidant activity and might induce angiogenic factors’ release. Moreover, we present an eco-friendly alternative to taking advantage of chocolate-industry by-products.

Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better under- standing of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country mi- gration with correlations of up to 0.91, and can also predict migration in other coun- tries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting.

Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%–79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

1Local and regional control is complicated by the facts that no vaccine has yet been licensed for broad-scale application and that dengue epidemiology is complex. Not only are there four different virus serotypes with potentially different ecologies, but transmission dynamics are also influenced by variation in the behavior and population dynamics of mosquito vectors and human hosts and the nature of their interactions with the environment and each other. 2 Mathematical and computer simulation models provide a systematic way to explore and analyze the complexity of dengue transmission and can be useful tools for devising more effective disease surveillance and control strategies. Models can integrate data from different sources and at different spatiotemporal scales, identify and prioritize gaps in data or knowledge, and address questions that are too complex, expensive, or dangerous to address in other ways. 3, 4 Models, therefore, provide a basic theoretical framework so that the intricate transmission processes of dengue can be represented in a simplified form that can be analyzed in ways that are not possible in the natural system itself. To explore the complexity of infectious disease systems, both simple 5 and complex 6– 8 models have been developed. More complex disease models tend to be more realistic and can be used to develop location-specific control strategies. However, the behavior of complex models can be difficult to explore analytically, 9 and as the number of model parameters increases, the likelihood also increases that parameters for which little empirical data exist are included. Evaluating the suitability, accuracy, and performance of complex disease models can be difficult. Sensitivity analysis provides a way to explore the behavior of complex disease models by identifying how variation in model parameters affects model output. 9 Model outputs obtained with different parameter values are evaluated to identify the parameters to which the model is most sensitive. In this way, sensitivity analysis can be useful not only for understanding model behavior, but also for identifying the biological processes that may be most important in determining pathogen transmission. Obtaining accurate empirical estimates of the parameters capturing the most important biological processes may then improve the accuracy of disease models and the ability of models to inform surveillance and control strategies. In this study, we conducted a sensitivity analysis of the complex dengue simulation models originally developed by Focks and others. 7, 8

La crisis financiera colombiana de finales de la década de 1990 causó graves traumatismos a la economía del país, pero también dejó importantes lecciones para el manejo de la política macroeconómica y la regulación financiera. Sin embargo, hoy en día no hay consenso sobre el impacto que tuvieron las medidas macro- prudenciales sobre las principales variables económicas. Esto se debe, en parte, por la ausencia de un recuento detallado de las acciones de política adoptadas por los diversos entes gubernamentales y del contexto en que estas se desarrollaron. Este artículo pretende contribuir a la literatura proporcionando un listado exhaustivo de los cambios regulatorios en el sistema financiero durante los últimos 25 años. Específicamente, nos centramos en tres grandes tópicos financieros: i) encajes bancarios, ii) derivados financieros y posición propia, y iii) riesgos de crédito, de mercado y de liquidez. Por consiguiente, el aporte principal de este trabajo es de servir como insumo de futuras evaluaciones de impacto que se realicen sobre las medidas macro-prudenciales en Colombia. Classification JEL: E50, F36, G10, G18

La crisis financiera colombiana de finales de la decada de 1990 causo graves traumatismos a la economia del pais, pero tambien dejo importantes lecciones para el manejo de la politica macroeconomica y la regulacion financiera. Sin embargo, hoy en dia no hay consenso sobre el impacto que tuvieron las medidas macroprudenciales sobre las principales variables economicas. Esto se debe, en parte, por la ausencia de un recuento detallado de las acciones de politica adoptadas por los diversos entes gubernamentales y del contexto en que estas se desarrollaron. Este articulo pretende contribuir a la literatura proporcionando un listado exhaustivo de los cambios regulatorios en el sistema financiero durante los ultimos 25 anos. Especificamente, nos centramos en tres grandes topicos financieros: i) encajes bancarios, ii) derivados financieros y posicion propia, y iii) riesgos de credito, de mercado y de liquidez. Por consiguiente, el aporte principal de este trabajo es de servir como insumo de futuras evaluaciones de impacto que se realicen sobre las medidas macro-prudenciales en Colombia.

Background Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available. Methodology/Principal Findings Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites. Conclusions/Significance A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited., Author Summary Maps have long been used to not only visualise, but also to inform infectious disease control efforts, identify and predict areas of greatest risk of specific diseases, and better understand the epidemiology of disease over various spatial scales. In spite of the utilities of such outputs, globally comprehensive maps have been produced for only a handful of infectious diseases. Due to limited resources, it is necessary to define a framework to prioritise which diseases to consider mapping globally. This paper outlines a framework which compares each disease’s global burden with its associated interest from the policy community in a data-driven manner which can be used to determine the relative priority of each condition. Malaria, HIV and TB are, unsurprisingly, ranked highest due to their considerable health burden, while the other priority diseases are dominated by neglected tropical diseases and vector-borne diseases. For some conditions, global mapping efforts are already in place, however, for many neglected conditions there still remains a need for high resolution spatial surveys.

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests., Author Summary Outbreaks of zoonoses can have large costs to society through public health and agricultural impacts. Because many zoonoses co-occur in multiple animal populations simultaneously, detection of zoonotic outbreaks can be especially difficult. We evaluated how to design sampling strategies for the early detection of disease outbreaks of vector-borne diseases. We built a framework to integrate epidemiological dynamical models with a sampling process that accounts for budgetary constraints, such as those faced by many management agencies. We illustrate our approach using West Nile virus, a globally-spreading zoonotic arbovirus that has significantly affected North American bird populations. Our results suggest that simple formulas can often make robust predictions about the proper sampling procedure, though we also illustrate how computational methods can be used to extend our framework to more realistic modeling scenarios when these simple predictions break down.

Background Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected. Methods National census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region. Results The spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations. Conclusion This was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.

Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.

Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross-Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross-Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross-Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process., Journal of The Royal Society Interface, 10(81), 20120921; 2013

Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.

The development of methods for the off–on switching of immobilization or presentation of cell-adhesive peptides and proteins during cell culture is important because such surfaces are useful for the analysis of the dynamic processes of cell adhesion and migration. This paper describes a chemically functionalized gold substrate that captures a genetically tagged extracellular matrix protein in response to light. The substrate was composed of mixed self-assembled monolayers (SAMs) of three disulfide compounds containing (i) a photocleavable poly(ethylene glycol) (PEG), (ii) nitrilotriacetic acid (NTA) and (iii) hepta(ethylene glycol) (EG7). Although the NTA group has an intrinsic high affinity for oligohistidine tag (His-tag) sequences in its Ni2+-ion complex, the interaction was suppressed by the steric hindrance of coexisting PEG on the substrate surface. Upon photoirradiation of the substrate to release the PEG chain from the surface, this interaction became possible and hence the protein was captured at the irradiated regions, while keeping the non-specific adsorption of non-His-tagged proteins blocked by the EG7 underbrush. In this way, we selectively immobilized a His-tagged fibronectin fragment (FNIII7–10) to the irradiated regions. In contrast, when bovine serum albumin—a major serum protein—was added as a non-His-tagged protein, the surface did not permit its capture, with or without irradiation. In agreement with these results, cells were selectively attached to the irradiated patterns only when a His-tagged FNIII7-10 was added to the medium. These results indicate that the present method is useful for studying the cellular behavior on the specific extracellular matrix protein in cell-culturing environments.