Illness and death from diseases caused by contaminated food are a constant threat to public health and a significant impediment to socio-economic development worldwide. To measure the global and regional burden of foodborne disease (FBD), the World Health Organization (WHO) established the Foodborne Disease Burden Epidemiology Reference Group (FERG), which here reports their first estimates of the incidence, mortality, and disease burden due to 31 foodborne hazards. We find that the global burden of FBD is comparable to those of the major infectious diseases, HIV/AIDS, malaria and tuberculosis. The most frequent causes of foodborne illness were diarrheal disease agents, particularly norovirus and Campylobacter spp. Diarrheal disease agents, especially non-typhoidal Salmonella enterica, were also responsible for the majority of deaths due to FBD. Other major causes of FBD deaths were Salmonella Typhi, Taenia solium and hepatitis A virus. The global burden of FBD caused by the 31 hazards in 2010 was 33 million Disability Adjusted Life Years (DALYs); children under five years old bore 40% of this burden. The 14 subregions, defined on the basis of child and adult mortality, had considerably different burdens of FBD, with the greatest falling on the subregions in Africa, followed by the subregions in South-East Asia and the Eastern Mediterranean D subregion. Some hazards, such as non-typhoidal S. enterica, were important causes of FBD in all regions of the world, whereas others, such as certain parasitic helminths, were highly localised. Thus, the burden of FBD is borne particularly by children under five years old–although they represent only 9% of the global population–and people living in low-income regions of the world. These estimates are conservative, i.e., underestimates rather than overestimates; further studies are needed to address the data gaps and limitations of the study. Nevertheless, all stakeholders can contribute to improvements in food safety throughout the food chain by incorporating these estimates into policy development at national and international levels.
Here, we propose a new strategy for the treatment of early cancerous lesions and advanced metastatic disease, via the selective targeting of cancer stem cells (CSCs), a.k.a., tumor-initiating cells (TICs). We searched for a global phenotypic characteristic that was highly conserved among cancer stem cells, across multiple tumor types, to provide a mutation-independent approach to cancer therapy. This would allow us to target cancer stem cells, effectively treating cancer as a single disease of “stemness”, independently of the tumor tissue type. Using this approach, we identified a conserved phenotypic weak point – a strict dependence on mitochondrial biogenesis for the clonal expansion and survival of cancer stem cells. Interestingly, several classes of FDA-approved antibiotics inhibit mitochondrial biogenesis as a known “side-effect”, which could be harnessed instead as a “therapeutic effect”. Based on this analysis, we now show that 4-to-5 different classes of FDA-approved drugs can be used to eradicate cancer stem cells, in 12 different cancer cell lines, across 8 different tumor types (breast, DCIS, ovarian, prostate, lung, pancreatic, melanoma, and glioblastoma (brain)). These five classes of mitochondrially-targeted antibiotics include: the erythromycins, the tetracyclines, the glycylcyclines, an anti-parasitic drug, and chloramphenicol. Functional data are presented for one antibiotic in each drug class: azithromycin, doxycycline, tigecycline, pyrvinium pamoate, as well as chloramphenicol, as proof-of-concept. Importantly, many of these drugs are non-toxic for normal cells, likely reducing the side effects of anti-cancer therapy. Thus, we now propose to treat cancer like an infectious disease, by repurposing FDA-approved antibiotics for anti-cancer therapy, across multiple tumor types. These drug classes should also be considered for prevention studies, specifically focused on the prevention of tumor recurrence and distant metastasis. Finally, recent clinical trials with doxycycline and azithromycin (intended to target cancer-associated infections, but not cancer cells) have already shown positive therapeutic effects in cancer patients, although their ability to eradicate cancer stem cells was not yet appreciated.
Garazi Retegui, María Dolores Ugarte, Jaione Etxeberria
et al.
To date, we have seen the emergence of a large literature on multivariate disease mapping. That is, incidence of (or mortality from) multiple diseases is recorded at the scale of areal units where incidence (mortality) across the diseases is expected to manifest dependence. The modeling involves a hierarchical structure: a Poisson model for disease counts (conditioning on the rates) at the first stage, and a specification of a function of the rates using spatial random effects at the second stage. These random effects are specified as a prior and introduce spatial smoothing to the rate (or risk) estimates. What we see in the literature is the amount of smoothing induced under a given prior across areal units compared with the observed/empirical risks. Our contribution here extends previous research on smoothing in univariate areal data models. Specifically, for three different choices of multivariate prior, we investigate both within prior smoothing according to hyperparameters and across prior smoothing. Its benefit to the user is to illuminate the expected nature of departure from perfect fit associated with these priors since model performance is not a question of goodness of fit. We propose both theoretical and empirical metrics for our investigation and illustrate with both simulated and real data.
Abstract Marburg virus (MARV) is a highly pathogenic zoonotic filovirus. The Orthomarburgvirus marburgense species includes MARV and Ravn virus (RAVV), which differs from MARV by 21% at the nucleotide level and 22% at the protein level. This review offers fresh discussions of the epidemiology, genetics and natural reservoir transmission of RAVV, summarizes experimental animal models, outlines current vaccine development and raises outstanding questions about RAVV life history, transmission and pathogenicity.
Infectious and parasitic diseases, Biology (General)
China's extraordinary economic growth, industrialization, and urbanization, coupled with inadequate investment in basic water supply and treatment infrastructure, have resulted in widespread water pollution. In China today approximately 700 million people--over half the population--consume drinking water contaminated with levels of animal and human excreta that exceed maximum permissible levels by as much as 86% in rural areas and 28% in urban areas. By the year 2000, the volume of wastewater produced could double from 1990 levels to almost 78 billion tons. These are alarming trends with potentially serious consequences for human health. This paper reviews and analyzes recent Chinese reports on public health and water resources to shed light on what recent trends imply for China's environmental risk transition. This paper has two major conclusions. First, the critical deficits in basic water supply and sewage treatment infrastructure have increased the risk of exposure to infectious and parasitic disease and to a growing volume of industrial chemicals, heavy metals, and algal toxins. Second, the lack of coordination between environmental and public health objectives, a complex and fragmented system to manage water resources, and the general treatment of water as a common property resource mean that the water quality and quantity problems observed as well as the health threats identified are likely to become more acute. We thank John Sheer for his contribution to this paper. All interpretations and findings set forth in this paper are solely those of the authors, and do not represent the opinions or policies of their host institutions.
A. C. Costa-da-Silva, D. O. Nascimento, J. R. Ferreira
et al.
Leishmaniasis is a parasitic, widespread, and neglected disease that affects more than 90 countries in the world. More than 20 Leishmania species cause different forms of leishmaniasis that range in severity from cutaneous lesions to systemic infection. The diversity of leishmaniasis forms is due to the species of parasite, vector, environmental and social factors, genetic background, nutritional status, as well as immunocompetence of the host. Here, we discuss the role of the immune system, its molecules, and responses in the establishment, development, and outcome of Leishmaniasis, focusing on innate immune cells and Leishmania major interactions.
More than a decade ago, type 2 innate lymphoid cells (ILC2s) were discovered to be members of a family of innate immune cells consisting of five subsets that form a first line of defence against infections before the recruitment of adaptive immune cells. Initially, ILC2s were implicated in the early immune response to parasitic infections, but it is now clear that ILC2s are highly diverse and have crucial roles in the regulation of tissue homeostasis and repair. ILC2s can also regulate the functions of other type 2 immune cells, including T helper 2 cells, type 2 macrophages and eosinophils. Dysregulation of ILC2s contributes to type 2-mediated pathology in a wide variety of diseases, potentially making ILC2s attractive targets for therapeutic interventions. In this Review, we focus on the spectrum of ILC2 phenotypes that have been described across different tissues and disease states with an emphasis on human ILC2s. We discuss recent insights in ILC2 biology and suggest how this knowledge might be used for novel disease treatments and improved human health. Type 2 innate lymphoid cells (ILC2s) have diverse phenotypes across different tissues and disease states. Recent insights into ILC2 biology raise new possibilities for the improved treatment of cancer and of metabolic, infectious and chronic inflammatory diseases.
Martijn H. H. Schoot Uiterkamp, Willian J. van Dijk, Hans Heesterbeek
et al.
In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available.
Dana R. Bowers, Clara Secaira, Nancy Sandoval
et al.
Abstract
Objective:
To describe antimicrobial prescribing practices in 4 hospitals in Guatemala to guide the development of an ongoing antimicrobial stewardship (AS) project.
Design:
A cross-sectional mixed methodologies descriptive study design.
Participants and setting
Practicing physicians from 4 hospitals (2 tertiary public hospitals and 2 specialty referral hospitals) within Guatemala City.
Methods:
All participants responded to a survey to ascertain 3 key areas of antimicrobial prescription practices: identify key players, communication among key players, and perceptions and behaviors regarding antimicrobial prescribing. A subset of respondents participated in semi-structured interviews to further explore experiences with AS team dynamics and communication.
Results:
One hundred and ten participants completed the survey (n = 110/145, 75.8%), and 79 completed the interview (n = 79/110, 71.8%). Antimicrobial prescribing is led by physicians who are responsible for maintaining communication with infectious disease physicians. The limited role of the pharmacist and the more predominant role of the microbiologist in antimicrobial selection were notable despite similar levels of training. Efficient communication about prescribing was perceived primarily among physicians, although existing hierarchies within the healthcare system negatively influenced decision-making strategies. Participants reported difficulty in choosing an antibiotic and indicated a preference for broad-spectrum antimicrobial use.
Conclusions:
The existing structure between physicians in hospitals facilitates antimicrobial prescribing practices. However, optimization of antimicrobial use may occur if multidisciplinary teams participate in antimicrobial selection activities. The results of this study provide valuable insight and can be used as a starting point toward the implementation of effective AS strategies within Guatemala and other similar countries in Central America and the Caribbean.
Infectious and parasitic diseases, Public aspects of medicine
Aleksandra Torbica, Deepak Sharma, Duilio Balsamo
et al.
Introduction Negative public sentiment towards vaccination (PSV) poses significant challenges to the effectiveness of immunisation programmes, with dramatic effects on morbidity and mortality for vaccine-preventable diseases. Yet, health research is often shaped by economic and geopolitical factors rather than countries’ epidemiological or healthcare needs. This study examines global patterns and drivers of PSV research on five vaccines—polio, measles, human papillomavirus, influenza and SARS-CoV-2—and evaluates the COVID-19 pandemic’s impact on research volume, focus and distribution.Methods We conducted a machine-learning-assisted literature search on PSV without geographical, language or time constraints. Using natural language processing, network and statistical analyses, we examined the global PSV research landscape and identified geographical, epidemiological and economic drivers.Results We analysed 13 287 articles and detected consistent literature growth from 1980 onwards, with vaccine-specific peaks following key licensing events. After 2020, publication volumes rose above projections for influenza (32%; 95% CI 20% to 46%) but declined for polio (−56%; 95% CI −68% to −26%) and measles (−17%; 95% CI −33% to 9%). Although PSV research had global coverage, its distribution was markedly imbalanced, largely shaped by country-specific economic factors. A few high-income countries (HICs) produced 72% of publications and the likelihood of a country being studied varied by income and vaccine. Foreign authorship also increased as the income of the studied country decreased (over 75% in low-income and middle-income countries vs below 50% in HIC).Conclusions PSV research reveals persistent inequities, with a misalignment between countries leading research and populations most in need of its outcomes. These inequities, further exacerbated by COVID-19 disruptions, reflect systematic imbalances in global health. Our findings underscore the need to decolonise research by fostering leadership, agenda-setting and accountability that centre on the necessities of affected communities. Achieving this will require funding and publication reforms that promote equitable collaborations and elevate local priorities alongside long-standing global health objectives.
Medicine (General), Infectious and parasitic diseases
Echinococcosis is a parasitic disease that affects humans, caused by the larval stage of the Echinococcus tapeworm. The disease is a major health problem in many parts of the world, including Bulgaria. It has a long incubation period and can affect various organs, but most commonly the liver and lungs. In this article, we present cases of echinococcosis diagnosed in all members of the same family, highlighting the importance of early diagnosis and the need for effective prophylactic measures. Regardless of the degree of endemicity, cases of familial echinococcosis are rare in medical practice. Therefore, a comprehensive epidemiological study is needed to establish the causes of such a phenomenon. In conclusion, seroepidemiological research on echinococcosis and imaging (ultrasound and X-ray) of seropositive individuals should be performed among risk groups to establish hidden morbidity, particularly among communities, where familial echinococcosis is more prevalent.
S. A. Abdul Rahman, Jasvinder Singh Bhatti, Suresh Thareja
et al.
Malaria is among one of the most devastating and deadliest parasitic disease in the world claiming millions of lives every year around the globe. It is a mosquito-borne infectious disease caused by various species of the parasitic protozoan of the genus Plasmodium. The indiscriminate exploitation of the clinically used antimalarial drugs led to the development of various drug-resistant and multidrug-resistant strains of plasmodium which severely reduces the therapeutic effectiveness of most frontline medicines. Therefore, there is urgent need to develop novel structural classes of antimalarial agents acting with unique mechanism of action(s). In this context, design and development of hybrid molecules containing pharmacophoric features of different lead molecules in a single entity represents a unique strategy for the development of next-generation antimalarial drugs. Research efforts by the scientific community over the past few years has led to the identification and development of several heterocyclic small molecules as antimalarial agents with high potency, less toxicity and desired efficacy. Triazole derivatives have become indispensable units in the medicinal chemistry due to their diverse spectrum of biological profiles and many triazole based hybrids and conjugates have demonstrated potential in vitro and in vivo antimalarial activities. The manuscript compiled recent developments in the medicinal chemistry of triazole based small heterocyclic molecules as antimalarial agents and discusses various reported biologically active compounds to lay the groundwork for the rationale design and discovery of triazole based antimalarial compounds. The article emphasised on biological activities, structure activity relationships, and molecular docking studies of various triazole based hybrids with heterocycles such as quinoline, artemisinins, naphthyl, naphthoquinone, etc. as potential antimalarial agents which could act on the dual stage and multi stage of the parasitic life cycle.
Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they highly rely on medical data, which are scarce for most patients in the early stage of the chronic diseases. In this paper, we aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data, thus, enable the early detection of 3H without using medical data collected in clinical settings. Specifically, we collected daily behavioral data from 629 participants over a 3-month study period, and trained various ML models after data preprocessing. Experimental results show that only using the participants' uploaded behavioral data, we can achieve accurate 3H diagnoses: 80.2\%, 71.3\%, and 81.2\% for diabetes, hyperlipidemia, and hypertension, respectively. Furthermore, we conduct Shapley analysis on the trained models to identify the most influential features for each type of diseases. The identified influential features are consistent with those reported in the literature.
Lemons and oranges, both are the most economically significant citrus fruits globally. The production of lemons and oranges is severely affected due to diseases in its growth stages. Fruit quality has degraded due to the presence of flaws. Thus, it is necessary to diagnose the disease accurately so that we can avoid major loss of lemons and oranges. To improve citrus farming, we proposed a disease classification approach for lemons and oranges. This approach would enable early disease detection and intervention, reduce yield losses, and optimize resource allocation. For the initial modeling of disease classification, the research uses innovative deep learning architectures such as VGG16, VGG19 and ResNet50. In addition, for achieving better accuracy, the basic machine learning algorithms used for classification problems include Random Forest, Naive Bayes, K-Nearest Neighbors (KNN) and Logistic Regression. The lemon and orange fruits diseases are classified more accurately (95.0% for lemon and 99.69% for orange) by the model. The model's base features were extracted from the ResNet50 pre-trained model and the diseases are classified by the Logistic Regression which beats the performance given by VGG16 and VGG19 for other classifiers. Experimental outcomes show that the proposed model also outperforms existing models in which most of them classified the diseases using the Softmax classifier without using any individual classifiers.
We analyze a disease transmission model that allows individuals to acquire fear and change their behaviour to reduce transmission. Fear is acquired through contact with infected individuals and through the influence of fearful individuals. We analyze the model in two limits: First, an Established Disease Limit (EDL), where the spread of the disease is much faster than the spread of fear, and second, a Novel Disease Limit (NDL), where the spread of the disease is comparable to that of fear. For the EDL, we show that the relative rate of fear acquisition to disease transmission controls the size of the fearful population at the end of a disease outbreak, and that the fear-induced contact reduction behaviour has very little impact on disease burden. Conversely, we show that in the NDL, disease burden can be controlled by fear-induced behaviour depending on the rate of fear loss. Specifically, fear-induced behaviour introduces a contact parameter $p$, which if too large prevents the contact reduction from effectively managing the epidemic. We analytically identify a critical prophylactic behaviour parameter $p=p_c$ where this happens leading to a discontinuity in epidemic prevalence. We show that this change in disease burden introduces delayed epidemic waves.
Maria Pishmisheva-Peleva, Stanislav Kotsev, Elitsa Golkocheva-Markova
et al.
BACKGROUND: Hepatitis E is a global health issue, only partially understood. Bulgarian record started in 2019 and data is not sufficient. AIM: This research aims to analyze extrahepatic manifestation of acute hepatitis E in patients with hepatitis E from Pazardzhik region, between 2014 – 2022. MATERIALS AND METHODS: The analysis includes 247 patients with acute hepatitis E, treated at the Department of Infectious Diseases of Pazardzhik Multiprofile Hospital for Active Treatment, Bulgaria between 2014 – 2022. The methodology includes clinical observation, laboratory tests and medical imaging. The diagnosis was established by serological /ELISA for anti-HEV IgM, IgG detection/and molecular-biological tests /RT-PCR for HEV RNA detection/. RESULTS: We observed extrahepatic manifestations in 19% (47/247) of the cases. In 60% (28/47) comorbidities were present, and 9% (4/47) were with underlying acute/chronic coinfection with another hepatotropic virus. Thrombocytopenia was found in 83% (39/47) of the patients; asymptomatic creatine kinase elevation – in 13% (6/47), acute pancreatitis – in 9% (4/47), transitory renal impairent – in 6% (3/47); 2% (1/47) had Guillain-Barré syndrome (GBS), 2% (1/47) – arrhythmia and 13% (6/47) – multiorgan involvement. While 91% (43/47) of the patients recovered, in 9% (4/47) the outcome was fatal. CONCLUSION: Extrahepatic manifestations might prevail, potentially delaying diagnosis of HEV-infection. Symptoms associated with comorbidities might also impede the final diagnosis. A diagnostic algorithm is needed to enhance the accurate diagnosis of HEV in patients with dubious symptoms.
The yellow mealworm, Tenebrio molitor, is currently one of the most important insect species produced for livestock feed and human consumption. High-density rearing conditions make the risk of disease and infections by parasitic symbionts a challenge in the mass production of these insects. However, certain symbionts are beneficial and should be favoured in order to promote healthy insect populations. Knowledge of parasitic symbionts and their management is essential for the insect rearing industry and its associated research. Here we review the documented microbial infectious agents, invertebrate parasites, and beneficial symbionts occurring in T. molitor. Furthermore, we discuss detection, prevention, and treatment methods for disease management in T. molitor production systems to inform future management and decision making in T. molitor rearing.
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery. A comprehensive database of various plant species, exhibiting numerous diseases, was compiled from the Internet and utilized as the training and test dataset. A Convolutional Neural Network (CNN), renowned for its performance in image classification tasks, was employed as our primary predictive model. The CNN model, trained on this rich dataset, demonstrated superior proficiency in crop disease categorization and detection, even under challenging imaging conditions. For field implementation, we deployed a prototype drone model equipped with a high-resolution camera for live monitoring of extensive agricultural fields. The captured images served as the input for our trained model, enabling real-time identification of healthy and diseased plants. Our approach promises an efficient and scalable solution for improving crop health monitoring systems.
Shaylee Anderson, Nicholas Bennett, Laura Aragon
et al.
This study assessed outcomes prior to and after electronic medical record-based clinical decision support implementation combined with prospective audit in patients with COVID-19. This multimodal stewardship intervention was associated with a decrease in antibiotic exposure for patients with COVID-19 (44.4% vs 61.8%, p = 0.002) within the first 7 days of hospitalization.
Infectious and parasitic diseases, Public aspects of medicine