Askar Nametov, Rashid Karmaliyev, Bekzhassar Sidikhov
et al.
The increasing number of owned and stray dogs in large cities is becoming a pressing issue due to rising population densities, urban conditions, and poor control over animal reproduction. This situation poses serious epidemiological risks, as dogs can act as reservoirs and transmitters of infectious and parasitic diseases dangerous to humans. This study aimed to investigate the prevalence and carriage of infectious and parasitic diseases in stray dogs in the city of Uralsk as a factor of epidemiological risk. In 2024, 1213 stray dogs were captured from different city districts and examined at the veterinary clinic and laboratory of Zhangir Khan University. Biological samples (blood, urine, feces) from 10% of the animals were analyzed using molecular (PCR), serological (ELISA), and helminthological methods. Serological and molecular analyses revealed the widespread circulation of bacterial pathogens. Antibodies to additional bacterial agents, including Pasteurella multocida, Mycobacterium spp., Listeria monocytogenes, and Leptospira spp., were detected in the samples, indicating an unfavorable sanitary and epidemiological situation in the urban environment. An enzyme-linked immunosorbent assay (ELISA) identified antibodies against Toxocara canis in 50.9% of the dogs and against Echinococcus granulosus in 76.4%, reflecting both active and past infections. The polymerase chain reaction (PCR) results showed the presence of Brucella canis DNA in blood and urine samples, while antibodies to Brucella spp. were detected in 57.8% of the examined dogs, underscoring the significant zooanthroponotic importance of this pathogen and its potential threat to human health. Additionally, T. canis DNA was found in 39.2% of the samples and E. granulosus DNA in 16.6%. A helminthological examination using the Fülleborn method revealed a high rate of helminth infection: Ancylostoma caninum—35.3%, T. canis—32.3%, and Toxascaris leonina—29.4%. The obtained results highlight the significant role of stray dogs as epizootiological and epidemiological reservoirs of zooanthroponotic infections. This poses a serious threat to public health and necessitates the implementation of effective control and prevention measures for infectious and parasitic diseases within urban fauna.
David Singleton Nyasvisvo, Tamuka Nhiwatiwa, Rudo Sithole
et al.
Background & objectives:
Area-specific identification and studies of Anopheles breeding habitat diversity, distribution, and productivity in different seasons are important in designing and advancing effective malaria vector control according to the local context and needs. This study identified and characterized Anopheles breeding habitats for targeted control of malaria vectors in Mazowe and Shamva districts of Zimbabwe.
Methods:
Repeated cross-sectional surveys were conducted in Mazowe and Shamva districts between April and December 2023. Habitat productivity and physicochemical parameters were measured. Anopheles larvae were collected, reared to adults, and identified using morphological keys. SPSS software was used for data analysis. One-way ANOVA, Fisher’s exact, Pearson’s correlation, and simple linear regression tests were conducted.
Results:
Seven different types of Anopheles breeding habitats were identified from 56 sites. The highest mean density of larvae was recorded in stream edge pools during the post-rainfall period. Anopheles pretoriensis (67.4%), An. gambiae s.l. (23.1%), An. rufipes (9.2%) and An. coustani (0.38%) were breeding in the study area. An. pretoriensis bred in all habitat types, An. coustani in swamps only while An. gambiae s.l. and An. rufipes preferred stream edge pools, roadside pools, and hoof prints. There was a significant positive correlation between larval density and dissolved oxygen (r = 0.535; p < 0.001) and conductivity (r = 0.288; p = 0.032).
Interpretation & conclusion:
Size, origin, and type of breeding habitat were positive indicators for different Anopheles species in the study area. Potential malaria vector breeding habitats should be targeted for larval control under the current malaria control and elimination phases in the two districts.
Subtype H10 viruses are known to infect humans in Africa, Oceania, and Asia. In 2021, 2022, and recently in April 2024, a novel H10N3 subtype avian influenza virus was found cause human infection with severe pneumonia. Herein, we comprehensively studied the phylogenetic evolution and biological characteristics of the newly emerged influenza A(H10N3) virus. We found that the human isolated H10N3 virus was generated in early 2019 in domestic poultry. The viruses bound to salic acid α2, 3 receptors, indicating their insufficient ability to infect humans. Although a low pathogenic avian influenza virus, the human isolated H10N3 virus exhibited robust pathogenicity in both BALB/c and C57BL/6 mice, with MLD50 1000 times higher than a homologous environmental isolate. The human isolated H10N3 also showed respiratory droplet transmissibility in ferrets. Considering the continuous circulation in avian populations and repeated transmission to humans, strengthened surveillance of H10 subtype viruses in poultry should be put into effect.
Networked SIR models have become essential workhorses in the modeling of epidemics, their inception, propagation and control. Here, and building on this venerable tradition, we report on the emergence of a remarkable self-organization of infectiousness in the wake of a propagating disease front. It manifests as a cascading power-law distribution of disease strength in networked SIR simulations, and is then confirmed with suitably defined kinetics, then stochastic modeling of surveillance data. Given the success of the networked SIR models which brought it to light, we expect this scale-invariant feature to be of universal significance, characterizing the evolution of disease within and across transportation networks, informing the design of control strategies, and providing a litmus test for the soundness of disease propagation models.
This article presents a mathematical model for identifying the safest travel routes during a pandemic by minimizing disease contraction risks, such as COVID-19. We formulate this as the LEAST INFECTION PROBABILITY PATH (LIPP) problem, which optimizes routes between two nodes in a transportation network based on minimal disease transmission probability. Our model evaluates risk factors including environmental density, likelihood of encountering carriers, and exposure duration across multiple transportation modes (walking, subway, BRT, buses, and cars). The probabilistic framework incorporates additional variables such as ventilation quality, activity levels, and interpersonal distances to estimate transmission risks. Applied to Tehran's transportation network using routing applications (Neshan and Balad), our model demonstrates that combined pedestrian-subway-BRT routes exhibit significantly lower infection risks compared to car or bus routes, as illustrated in our case study of peak-hour travel between Sadeghiyeh Square and Amirkabir University. We develop a practical routing algorithm suitable for integration with existing navigation software to provide pandemic-aware path recommendations. Potential future extensions include incorporating additional variables like waiting times and line changes, as well as adapting the model for other infectious diseases. This research offers a valuable tool for urban travelers seeking to minimize infection risks during pandemic conditions.
India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.
Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33\% improvement in recall and 7.63\% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.
For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important transmission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling the outbreak.
Candida auris is a fungus with pathogenic potential, first isolated in 2009 in Japan. Since then, sporadic cases have been reported, which have increased significantly in recent years. In Bulgaria, for the first time in 2025, we described almost simultaneously three cases with Candida auris strains isolated from hospitalized patients. All of them were immunocompromised, mainly with cardiovascular problems, but in none of them a fungal isolate from blood stream was obtained. It is probably a question of colonization. In the National Reference Laboratory of Mycoses at the NCIPD, Candida auris fungal strains were confirmed and tested for susceptibility to antifungals. All showed resistance to Fluconazole. Given the frequent misidentification of Candida auris, adequate microbiological examination and subsequent appropriate antifungal therapy are necessary, because this fungus is associated with a high mortality rate in immunocompromised individuals, even with initiated treatment.
Background Addressing HIV-related stigma among healthcare workers is vital in the era of contemporary antiretroviral therapy (cART). It is equally important for stimulating early diagnosis, and for meeting the complex medical needs of people living with HIV (PLHIV). Material and Methods. We analyzed the results of anonymous local survey comprised of 18 closed-choice questions on the knowledge and attitude towards HIV infection and PLHIV. The survey was conducted among 91 Bulgarian healthcare workers as a part of a large cross-national study in Europe and Central Asia, launched by ECDC and EACS in 2023. Results. The respondents were predominantly women (65,7%) working mostly as medical doctors (34%), 23% - in a specialized unit for PLHIV, 42% - with over 20 years of experience. The most important results were: lack of training on PrEP (in 62%), on stigma and discrimination (in 75%). Low level of knowledge on HIV transmission and prevention (in 40%) was associated with anxiety, exaggerated preventive measures and no professional experience with PLHIV. As compared to the mean EACS survey results, specific for Bulgaria were the high prevalence of the misconception “HIV is a result of irresponsible behavior, the ignorance of PrEP, the low rate of administrative sanctions related to PLHIV discrimination, and the low awareness of the availability of PEP. Conclusions There is an urgent need of targeted and tailored educational programs on HIV-related issues among the different healthcare workers groups. Those should be combined with legislative and administrative measures to assure the implementation of UNAIDS 2030 goals.
Abstract Background Cryptosporidium spp. is a ubiquitous, globally distributed intestinal protozoan infecting humans and at least 260 animal hosts. Due to close human contact with pet dogs and identification of zoonotic Cryptosporidium species and subtypes in these animals, dog health is not only a veterinarian issue but also a public health issue. This study aimed to understand occurrence and genetic characterization at both genotype and subtype levels in pet dogs in Yunnan Province, China. Results A total of 589 fresh fecal specimens were collected from adult pet dogs in the rural areas of eight cities/autonomous prefectures of Yunnan Province, China. 16 fecal specimens were positive for Cryptosporidium spp. by polymerase chain reaction (PCR) amplification and sequence analysis of the small subunit ribosomal RNA (SSU rRNA) gene, with an average occurrence rate of 2.7% (16/589) being observed. Three zoonotic Cryptosporidium species were identified: C. parvum (n = 7), C. suis (n = 5) and C. canis (n = 4). At the 60-kDa glycoprotein (gp60) locus, only three C. parvum and two C. canis specimens were successfully amplified and sequenced, with subtype IIaA17G2R1 (n = 3) and subtypes XXa4 (n = 1) and XXa5 (n = 1) being identified, respectively. Conclusions The present finding of three zoonotic Cryptosporidium species in dogs implied that dogs infected with Cryptosporidium spp. may pose a threat to human health. C. suis was identified in dogs in this study for the first time, expanding the host range of this species. Identification of C. parvum subtype IIaA17G2R1 and C. canis subtypes XXa4 and XXa5 will be helpful to explore the source attribution of infection/contamination and assess the transmission dynamics of C. parvum and C. canis in the investigated areas in the future.
Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.
Anna-Sophia Kruppenbacher, Elisabeth Müller, Matthew L. Aardema
et al.
Abstract Background Anaplasma phagocytophilum is a Gram-negative obligate intracellular bacterium that replicates in neutrophil granulocytes. It is transmitted by ticks of the Ixodes ricinus complex and causes febrile illness called granulocytic anaplasmosis primarily in humans, horses, dogs, sheep, cattle and goats. In comparison, clinically apparent disease has been described rarely in cats especially compared to dogs and horses. It is currently unknown whether cats are less susceptible to A. phagocytophilum or whether granulocytic anaplasmosis might be underdiagnosed in cats. Methods To address this question, we examined clinical signs and laboratory findings in seven A. phagocytophilum infected cats from Germany and Switzerland. We then genetically characterized feline A. phagocytophilum strains and compared them to those from other hosts showing clinically apparent disease. For this purpose, ankA-based, groEL-based and multilocus sequence typing (MLST) were applied. Furthermore, the concordance between these typing methods was assessed. Results Fever, lethargy and anorexia were the most common clinical signs in cats suffering from granulocytic anaplasmosis. The most frequent laboratory finding was thrombocytopenia. All three typing methods consistently indicated that the A. phagocytophilum strains found infecting cats are the same as those that cause disease in humans, dogs and horses. In general, the three typing methods applied exhibited high concordance. Conclusions The genetic characterization of the feline A. phagocytophilum strains indicates that strain divergence is not the explanation for the fact that granulocytic anaplasmosis is much less frequently diagnosed in cats than in dogs and horses. Otherwise, it may be possible that cats are less susceptible to the same strains than dogs and horse are. However, due to the unspecific clinical signs, it should be considered that granulocytic anaplasmosis may be under-diagnosed in cats. Graphical Abstract
Hiromu Sugiyama, Mitsuko Shiroyama, Ikuyo Yamamoto
et al.
Using data from 2018–2019 health insurance claims, we estimated the average annual incidence of anisakiasis in Japan to be 19,737 cases. Molecular identification of larvae revealed that most (88.4%) patients were infected with the species Anisakis simplex sensu stricto. Further insights into the pathogenesis of various anisakiasis forms are needed.
Abstract Background The reported incidence of sexually transmitted infections (STIs) in China has been increasing over the last decades, especially among drug users, which has become one of the main burdens of public health in China. This study was conducted to estimate the prevalence and associated factors of STIs among non-injecting methamphetamine (MA) users in Eastern China. Methods A cross-sectional survey was conducted among 632 MA users in Eastern China in 2017. Demographic characteristics, sexual behaviors, behaviors of MA use and sexual health knowledge were collected through questionnaire. First pass urine specimens were collected and detected for deoxyribonucleic acid (DNA) of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) with Nucleic Acid Amplification Technology (NAAT), while blood specimens were collected and detected for antibodies of Human immunodeficiency virus (HIV), Herpes simplex virus type-2 (HSV-2), and syphilis with enzyme-linked immune sorbent assay (ELISA). Results Among the 632 MA users, 464 (73.42%) were males, 60.92% were < 35 years of age, 546 (86.39%) were Shandong residents. 317 (50.16%, 95% CI 46.26–54.06%) participants were tested positive for at least one kind of STIs, including 242 (38.29%, 95% CI 34.50–42.08%) for HSV-2, 107 (16.93%, 95% CI 14.01–19.85%) for active syphilis, 46 (7.28%, 95% CI 5.25–9.31%) for treated syphilis, 40 (6.33%, 95% CI 4.43–8.23%) for CT, 6 (0.95%, 95% CI 0.19–1.71%) for HIV, and 3 (0.47%, 95% CI 0.06–1.00%) for NG infection. 99 (15.66%, 95% CI 12.83–18.49%) participants were co-infected with two kinds of STIs, including 91 (14.40%, 95% CI 11.66–17.14%) participants were co-infected with HSV-2 and syphilis. 14 (2.22%, 95% CI 1.07–3.37%) participants were co-infected with three kinds of STIs, and 4 HIV positive participants were co-infected with both syphilis and HSV-2. In the multiple logistic regression analysis, the results showed that females (adjusted OR [AOR] = 7.30, 95% CI 4.34–12.30) and individuals ≥ 35 years of age (AOR = 2.97, 95% CI 2.04–4.32) were more likely to test positive for STIs among MA users, whereas participants who acquired sexual health knowledge primarily from the Internet (AOR = 0.57, 95% CI 0.40–0.82) and those whose regular partners did not use drugs (AOR = 0.59, 95% CI 0.37–0.94) were less likely. Conclusions This study found that the prevalence of HSV-2 and syphilis are alarming high among non-injecting MA users in Shandong Province in Eastern China. The prevention and control intervention of STIs among MA users in Shandong were needed, especially on females and MA users ≥ 35 years of age.
Matthaios Papadimitriou-Olivgeris, Fevronia Kolonitsiou, Sotiria Kefala
et al.
Background: Patients with severe Coronavirus Disease 2019 (COVID-19) are treated with corticosteroids. Aim: We aimed to evaluate the role of corticosteroid treatment in candidemia development during the COVID-19 pandemic. Methods: This retrospective study was conducted in a Greek ICU, from 2010 to August 2021, encompassing a pre-pandemic and a pandemic period (pandemic period: April 2020 to August 2021). All adult patients with candidemia were included. Results: During the study period, 3,572 patients were admitted to the ICU, 339 patients during the pandemic period, of whom 196 were SARS-CoV-2-positive. In total, 281 candidemia episodes were observed in 239 patients, 114 in the pandemic period. The majority of candidemias in both periods were catheter-related (161; 50.4%). The incidence of candidemia in the pre-pandemic period was 5.2 episodes per 100 admissions, while in the pandemic period was 33.6 (p < 0.001). In the pandemic period, the incidence among COVID-19 patients was 38.8 episodes per 100 admissions, while in patients without COVID-19 incidence was 26.6 (p = 0.019). Corticosteroid administration in both periods was not associated with increased candidemia incidence. Conclusions: A significant increase of candidemia incidence was observed during the pandemic period in patients with and without COVID-19. This increase cannot be solely attributed to immunosuppression (corticosteroids, tocilizumab) of severe COVID-19 patients, but also to increased workload of medical and nursing staff.
Perla Mayo, Nantheera Anantrasirichai, Thanarat H. Chalidabhongse
et al.
Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ensure a prompt treatment to patients, and off-load excessive work from experts' shoulders. Advances in deep learning inspired us to exploit successful architectures for detection, adapting them to tackle a different domain. We propose a framework that exploits two such state-of-the-art models. Specifically, we demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection respectively, on microscopy images of varying quality. The use of these techniques yields encouraging results, though further improvements are still needed for certain egg types whose detection still proves challenging. As a result, a new dataset has been created and made publicly available, providing an even wider range of classes and variability.
Arnab Chattopadhyay, Swarnendu Banerjee, Amit Samadder
et al.
The study of infectious disease has been of interest to ecologists since long. The initiation of epidemic and the long term disease dynamics are largely influenced by the nature of the underlying consumer (host)-resource dynamics. Ecological traits of such systems may be often modulated by toxins released in the environment due to ongoing anthropogenic activities. This, in addition to toxin-mediated alteration of epidemiological traits, has a significant impact on disease progression in ecosystems which is quite less studied. In order to address this, we consider a mathematical model of disease transmission in consumer population where multiple traits are affected by environmental toxins. Long term dynamics show that the level of environmental toxin determines disease persistence, and increasing toxin may even eradicate the disease in certain circumstances. Furthermore, our results demonstrate bistability between different ecosystem states and the possibility of an abrupt transition from disease-free coexistence to disease-induced extinction of consumers. Overall the results from this study will help us gain fundamental insights into disease propagation in natural ecosystems in the face of present anthropogenic changes.
A. Yu. Popova, T. A. Ruzhentsova, D. A. Khavkina
et al.
The results of the joint work of a panel of experts from Rospotrebnadzor and healthcare professionals of the Republic of Uzbekistan on organizing activities to counter the spread of the SARS-CoV-2 virus are described in the paper.The goal of the study was to determine the main driving forces of COVID-19 spread in the Republic of Uzbekistan and develop an action plan to reduce the incidence of coronavirus infection caused by the SARS-CoV-2 virus.Materials and methods. The organization of work in 14 health care institutions in Tashkent and Samarkand, as well as in Tashkent and Samarkand Regions, was analyzed: in 7 laboratories, 6 hospitals and 1 polyclinic. The routes for the movement of personnel, the demarcation of green and red zones, the features of disinfection and the use of personal protective equipment were studied. Attention is drawn to the diagnosis of COVID-19, the use of therapy aimed at reducing the period of virus shedding, the criteria for lifting quarantine restrictions for patients.Results and discussion. The main factors in the organization of work of institutions that contribute to the spread of COVID-19 among medical personnel and the population have been identifed: the lack of equipped gateways between the red and green zones with the accessibility of adequate disinfection, the wrong choice of personal protective equipment, monitoring of contact persons for 10 days, discharge from hospitals based on clinical improvement. The incorrect use of antiviral therapy, the lack of differentiated approaches to the selection of optimal regimens have been noted. Proposals are formulated for organizing the work of healthcare institutions, taking into account the requirements of biological safety. The introduction of targeted measures in addition to those previously adopted has led to a signifcant improvement in the epidemic situation: the total number of active cases in the Republic of Uzbekistan, despite the increase in testing volumes, decreased from 3,686 people on August 23 to 2335 on October 27. Towards December 20, 2020, 97 % of patients recovered completely. All diagnostic triage centers in the Republic of Uzbekistan are closed due to the absence of patients with COVID-19, most of the country’s medical institutions previously re-profled for patients with coronavirus infection have returned to the routine operations.