This paper presents a novel concept for electrically small antenna arrays incorporating chiral parasitic elements of opposite handedness. This configuration mitigates the detrimental effects of electromagnetic mutual coupling, which in conventional arrays causes a 180 degree phase shift between adjacent antenna currents when the element spacing is less than half a wavelength. The proposed approach is experimentally validated using a seven-element monopole ESAA with compact dimensions, specifically below half-wavelength in cross-section and one-sixth to one-fourth of a wavelength in vertical range. The antenna elements are spaced less than one-sixth wavelength apart, ensuring a highly compact footprint. Measurements show a minus 10 dB return loss, a fractional bandwidth of 5 to 15 per cent, and a realised gain of 5 to 9 dBi, along with full 360 degrees of azimuthal beam steering. The results confirm that employing oppositely handed chiral parasitic elements can significantly enhance performance in densely packed, electrically small antenna arrays.
Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://github.com/Emory-Melody/EpiLearn.
Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis
Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.
Samadhi Patamatamkul, Narittee Sukswai, Onjira Mangkalamanee
et al.
Herpes simplex virus (HSV) is a common cause of recurrent oropharyngeal ulcers or stomatitis resulting from the reactivation of latent infection since childhood. Extensive ulceration and dissemination to vital organs such as pneumonitis or colitis is mostly encountered among hematologic malignancy or hematologic stem cell transplants. We hereby reported a case with osteosarcoma who developed disseminated HSV infection during neutropenia after chemotherapy.
Veja Widdershoven, Eveline C.H. van Eerd, Marije Pfeyffer
et al.
Abstract Background Healthcare professionals (HCPs) play a significant role in the decision-making process of pregnant women on maternal vaccinations. Whereas a high proportion of HCPs discuss maternal vaccinations with pregnant women, confidence in discussing maternal vaccinations is lacking and HCPs experience inadequate training to discuss maternal vaccinations with pregnant women. Furthermore, different practical barriers might influence the consultation process, such as lack of time. More studies on the barriers, as well as facilitators, to discussing maternal vaccinations is needed and will help us to better understand and support HCPs in discussing maternal vaccinations. Methods This qualitative study involved semi-structured interviews with fourteen HCPs working as midwives or gynaecologists in the Netherlands. An integrated theoretical approach was used to inform data collection and analysis. Thematic analysis was conducted using inductive and deductive approaches. This study followed the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines. Results The thematic analysis of the data pointed to the following five themes of HCP counselling: the consultation process, attitude, perceived norm, perceived control and improvement ideas. Most HCPs follow a similar approach in maternal pertussis vaccination consultations, beginning by assessing clients’ understanding, providing basic information, and addressing questions. However, consultation timing and prioritization vary among HCPs. Challenges in consultations include client requests for clear advice, with HCPs trained to remain neutral, emphasizing client autonomy in decision-making. Most HCPs acknowledge the importance of their consultations in informing pregnant women about maternal pertussis vaccination. Conclusions This study offers a confirmation of the awareness of the pivotal role of HCPs in informing pregnant women about the maternal pertussis vaccination. HCPs stress the importance of neutral counselling, enabling pregnant women to make well-informed decisions independently. Because of upcoming vaccine hesitancy nowadays, HCPs must be equipped with the knowledge and confidence to navigate difficult conversations. Continuous education and training might help to increase HCPs’ confidence in handling difficult consultations. Additionally, making the information materials for pregnant women available in multiple languages and incorporating more visuals to enhance comprehension could support HCPs in reaching a broader group of pregnant women.
Vianney Brouard, Cornelia Pokalyuk, Marco Seiler
et al.
In this paper we study invasion probabilities and invasion times of cooperative parasites spreading in spatially structured host populations. The spatial structure of the host population is given by a random geometric graph on $[0,1]^n$, $n\in \mathbb{N}$, with a Poisson($N$)-distributed number of vertices and in which vertices are connected over an edge when they have a distance of at most $r_N\in Θ\left(N^{\frac{β-1}{n}}\right)$ for some $0<β<1$ and $N\rightarrow \infty$. At a host infection many parasites are generated and parasites move along edges to neighbouring hosts. We assume that parasites have to cooperate to infect hosts, in the sense that at least two parasites need to attack a host simultaneously. We find lower and upper bounds on the invasion probability of the parasites in terms of survival probabilities of branching processes with cooperation. Furthermore, we characterize the asymptotic invasion time. An important ingredient of the proofs is a comparison with infection dynamics of cooperative parasites in host populations structured according to a complete graph, i.e. in well-mixed host populations. For these infection processes we can show that invasion probabilities are asymptotically equal to survival probabilities of branching processes with cooperation. Furthermore, we build in the proofs on techniques developed in [BP22], where an analogous invasion process has been studied for host populations structured according to a configuration model. We substantiate our results with simulations.
The spread of many infectious diseases is modeled using variants of the SIR compartmental model, which is a coupled differential equation. The coefficients of the SIR model determine the spread trajectories of disease, on whose basis proactive measures can be taken. Hence, the coefficient estimates must be both fast and accurate. Shaier et al. in the paper "Disease Informed Neural Networks" used Physics Informed Neural Networks (PINNs) to estimate the parameters of the SIR model. There are two drawbacks to this approach. First, the training time for PINNs is high, with certain diseases taking close to 90 hrs to train. Second, PINNs don't generalize for a new SIDR trajectory, and learning its corresponding SIR parameters requires retraining the PINN from scratch. In this work, we aim to eliminate both of these drawbacks. We generate a dataset between the parameters of ODE and the spread trajectories by solving the forward problem for a large distribution of parameters using the LSODA algorithm. We then use a neural network to learn the mapping between spread trajectories and coefficients of SIDR in an offline manner. This allows us to learn the parameters of a new spread trajectory without having to retrain, enabling generalization at test time. We observe a speed-up of 3-4 orders of magnitude with accuracy comparable to that of PINNs for 11 highly infectious diseases. Further finetuning of neural network inferred ODE coefficients using PINN further leads to 2-3 orders improvement of estimated coefficients.
We consider a cell population subject to a parasite infection. Cells divide at a constant rate and, at division, share the parasites they contain between their two daughter cells. The sharing may be asymmetrical, and its law may depend on the quantity of parasites in the mother. Cells die at a rate which may depend on the quantity of parasites they carry, and are also killed when this quantity explodes. We study the survival of the cell population as well as the mean quantity of parasites in the cells, and focus on the role of the parasites partitioning kernel at division.
Antje Glass, Andrea Springer, Marie-Kristin Raulf
et al.
Lyme borreliosis, caused by Borrelia burgdorferi sensu lato (s.l.) spirochaetes, is the most common tick-borne disease (TBD) in the Northern Hemisphere. Rising incidences indicate that its epidemiology may be affected by global changes. Therefore, the current study aimed to assess changes in tick infection rates with Borrelia spp. over a 15-year monitoring period in the city of Hanover, Germany, as a follow-up to previous prevalence studies (years 2005, 2010 and 2015). To assess the epidemiological risk, ticks of the Ixodes ricinus/inopinatus-complex were sampled from April to October 2020 by the flagging method at 10 frequently visited recreation areas in Hanover. Analysis by quantitative real-time PCR of 2100 individual ticks revealed an overall Borrelia prevalence of 25.5% (535/2100). Regarding different tick developmental stages, nymphs showed a significantly lower Borrelia prevalence (18.4% [193/1050]) than adult ticks (32.6% [342/1050]). Comparison with previous years revealed a stable total Borrelia prevalence along with consistent infection rates in the different developmental stages over the 15-year monitoring period. Borrelia species differentiation by Reverse Line Blot was successful in 67.3% of positive ticks collected in 2020, with B. afzelii being the dominating species (59.2% of the differentiated infections), besides B. burgdorferi sensu stricto (s.s.), B. garinii, B. valaisiana, B. spielmanii, B. bavariensis and B. bissettiae and the relapsing fever spirochaete B. miyamotoi. Additionally, the proportion of infections attributed to B. afzelii showed a significant increase in 2020 compared to 2005 and 2015 (59.2% vs. 37.6% and 32.0% of successfully differentiated infections, respectively). Coinfections with Anaplasma phagocytophilum and Rickettsia spp. stayed stable comparing 2020 with previous years. Therefore, although changes in the Borrelia prevalence in questing ticks were not observed throughout the 15-year monitoring period, shifts in Borrelia species distribution may alter the epidemiological risk.
Vanessa M. Howieson, Joy Zeng, Joachim Kloehn
et al.
Toxoplasma gondii is a pervasive apicomplexan parasite that can cause severe disease and death in immunocompromised individuals and the developing foetus. The treatment of toxoplasmosis often leads to serious side effects and novel drugs and drug targets are therefore actively sought. In 2014, Mageed and colleagues suggested that the T. gondii pantothenate synthetase, the enzyme responsible for the synthesis of the vitamin B5 (pantothenate), the precursor of the important cofactor, coenzyme A, is a good drug target. Their conclusion was based on the ability of potent inhibitors of the M. tuberculosis pantothenate synthetase to inhibit the proliferation of T. gondii tachyzoites. They also reported that the inhibitory effect of the compounds could be antagonised by supplementing the medium with pantothenate, supporting their conclusion that the compounds were acting on the intended target. Contrary to these observations, we find that compound SW314, one of the compounds used in the Mageed et al. study and previously shown to be active against M. tuberculosis pantothenate synthetase in vitro, is inactive against the T. gondii pantothenate synthetase and does not inhibit tachyzoite proliferation, despite gaining access into the parasite in situ. Furthermore, we validate the recent observation that the pantothenate synthetase gene in T. gondii can be disrupted without detrimental effect to the survival of the tachyzoite-stage parasite in the presence or absence of extracellular pantothenate. We conclude that the T. gondii pantothenate synthetase is not essential during the tachyzoite stage of the parasite and it is therefore not a target for drug discovery against T. gondii tachyzoites.
ABSTRACTNovel ecological antimicrobial approaches to dental caries focus on inhibiting cariogenic pathogens while enhancing the growth of health-associated commensal communities or suppressing cariogenic virulence without affecting the diversity of oral microbiota, which emphasize the crucial role of establishing a healthy microbiome in caries prevention. Considering that the acidified cariogenic microenvironment leads to the dysbiosis of microecology and demineralization of enamel, exploiting the acidic pH as a bioresponsive trigger to help materials and medications target cariogenic pathogens is a promising strategy to develop novel anticaries approaches. In this study, a pH-responsive antimicrobial peptide, LH12, was designed utilizing the pH-sensitivity of histidine, which showed higher cationicity and stronger interactions with bacterial cytomembranes at acidic pH. Streptococcus mutans was used as the in vitro caries model to evaluate the inhibitory effects of LH12 on the cariogenic properties, such as biofilm formation, biofilm morphology, acidurance, acidogenicity, and exopolysaccharides synthesis. The dual-species model of Streptococcus mutans and Streptococcus gordonii was established in vitro to evaluate the regulation effects of LH12 on the mixed species microbial community containing both cariogenic bacteria and commensal bacteria. LH12 suppressed the cariogenic properties and regulated the bacterial composition to a healthier condition through a dual-functional mechanism. Firstly, LH12-targeted cariogenic pathogens in response to the acidified microenvironment and suppressed the cariogenic virulence by inhibiting the expression of multiple virulence genes and two-component signal transduction systems. Additionally, LH12 elevated H2O2 production of the commensal bacteria and subsequently improved the ecological competitiveness of the commensals. The dual-functional mechanism made LH12 a potential bioresponsive approach to caries management.
N. Eberhardt, Gastón Bergero, Yanina L. Mazzocco Mariotta
et al.
Infectious diseases are caused by the invasion of pathogenic microorganisms such as fungi, bacteria, viruses, and parasites. After infection, disease progression relies on the complex interplay between the host immune response and the microorganism evasion strategies. The host’s survival depends on its ability to mount an efficient protective anti-microbial response to accomplish pathogen clearance while simultaneously preventing tissue injury by keeping under control the excessive inflammatory process. The purinergic system has the dual function of regulating the immune response and triggering effector antimicrobial mechanisms. This review provides an overview of the current knowledge of the modulation of innate and adaptive immunity driven by the purinergic system during parasitic, bacterial and viral infections.
This essay aims to present and discuss the theoretical framework for the COVID-19 syndemic. The first part presents the foundations and principles of syndemic theory. For the purposes of this essay, syndemic was defined as a process of synergic interaction between two or more diseases, in which the effects are mutually enhanced. We discussed the three principal typologies of syndemic interaction: mutually causal epidemics; epidemics interacting synergically; and serial causal epidemics. In the second part, COVID-19 is analyzed as a syndemic resulting from the interaction between various groups of diseases and the socioeconomic context. The theoretical model considered the interaction between COVID-19 and chronic noncommunicable diseases, infectious and parasitic diseases, and mental health problems. The essay addressed how social iniquities and conditions of vulnerability act at various levels to increase the effect of COVID-19 and other pandemics. The last section discusses the need for comprehensive, multisector, and integrated responses to COVID-19. A model for intervention was presented that involves the patient care and socioeconomic dimensions. In the sphere of patient care, the authors defend the structuring of strong and responsive health systems, accessible to the entire population. The economic and social dimension addressed the issue of reclaiming the ideals of solidarity, the health promotion strategy, and emphasis on social determinants of health. In conclusion, the lessons learned from the syndemic approach to COVID-19 call on government and society to develop policies that link clinical, sanitary, socioeconomic, and environmental interventions.
In the last three decades, the aquaculture sector has experienced a 527% growth, producing 82 million tons for a first sale value estimated at 250 billion USD. Infectious diseases caused by bacteria, viruses, or parasites are the major causes of mortality and economic losses in commercial aquaculture. Some pathologies, especially those of bacterial origin, can be treated with commercially available drugs, while others are poorly managed. In fact, despite having been recognized as a useful preventive measure, no effective vaccination against many economically relevant diseases exist yet, such as for viral and parasitic infections. The objective of the present review is to provide the reader with an updated perspective on the most significant and innovative vaccine research on three key aquaculture commodities. European sea bass (Dicentrarchus labrax), Nile tilapia (Oreochromis niloticus), and Atlantic salmon (Salmo salar) were chosen because of their economic relevance, geographical distinctiveness, and representativeness of different culture systems. Scientific papers about vaccines against bacterial, viral, and parasitic diseases will be objectively presented; their results critically discussed and compared; and suggestions for future directions given.
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based split transform merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.997), which suggest it to be utilized for malaria parasite screening.
More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus (exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as a "virus" attacking a host cell. To increase infectivity, the "virus" mutates to bind better to the host's receptor. 2 algorithms were attempted - greedy search and beam search. The strength of this variant-host binding was then assessed by a transformer network we developed, with a high accuracy of 90%. With both components, beam search eventually proposed more infectious variants. Therefore, this approach can potentially enable researchers to develop vaccines that provide protection against future infectious variants before they emerge, pre-empting outbreaks and saving lives.
A. Markowska, Joanna Kaysiewicz, J. Markowska
et al.
Chemotherapy is one of the standard methods for the treatment of malignant tumors. It aims to cause lethal damage to cellular structures, mainly DNA. Noteworthy, in recent years discoveries of novel anticancer agents from well-known antibiotics have opened up new treatment pathways for several cancer diseases. The aim of this review article is to describe new applications for the following antibiotics: doxycycline (DOX), salinomycin (SAL), monensin (MON) and ivermectin (IVR) as they are known to show anti-tumor activity, but have not yet been introduced into standard oncological therapy. To date, these agents have been used for the treatment of a broad-spectrum of bacterial and parasitic infectious diseases and are widely available, which is why they were selected. The data presented here clearly show that the antibiotics mentioned above should be recognised in the near future as novel agents able to eradicate cancer cells and cancer stem cells (CSCs) across several cancer types.
Background Research regarding zoonotic diseases often focuses on infectious diseases animals have given to humans. However, an increasing number of reports indicate that humans are transmitting pathogens to animals. Recent examples include methicillin-resistant Staphylococcus aureus, influenza A virus, Cryptosporidium parvum, and Ascaris lumbricoides. The aim of this review was to provide an overview of published literature regarding reverse zoonoses and highlight the need for future work in this area. Methods An initial broad literature review yielded 4763 titles, of which 4704 were excluded as not meeting inclusion criteria. After careful screening, 56 articles (from 56 countries over three decades) with documented human-to-animal disease transmission were included in this report. Findings In these publications, 21 (38%) pathogens studied were bacterial, 16 (29%) were viral, 12 (21%) were parasitic, and 7 (13%) were fungal, other, or involved multiple pathogens. Effected animals included wildlife (n = 28, 50%), livestock (n = 24, 43%), companion animals (n = 13, 23%), and various other animals or animals not explicitly mentioned (n = 2, 4%). Published reports of reverse zoonoses transmission occurred in every continent except Antarctica therefore indicating a worldwide disease threat. Interpretation As we see a global increase in industrial animal production, the rapid movement of humans and animals, and the habitats of humans and wild animals intertwining with great complexity, the future promises more opportunities for humans to cause reverse zoonoses. Scientific research must be conducted in this area to provide a richer understanding of emerging and reemerging disease threats. As a result, multidisciplinary approaches such as One Health will be needed to mitigate these problems.