There is a growing proportion of people with several disease conditions ("multimorbidity"), placing increasing demands on healthcare systems. One hypothesis is that clusters of diseases may arise from shared underlying disease processes (shared "pathogenesis"), whereby the presence of one disease indicates the state of disease progression to several related disease types. This article explains how this hypothesis can be tested using observational data for disease incidence. Specifically, a multistage model is used to test whether two diseases can have a "shared stage" or "step", before either disease can occur, and how the unobserved rate of this step can be determined. The approach offers a simple method for studying multiple diseases and identifying shared underlying causes of multiple conditions, and is illustrated with published data and numerical examples. The fundamental mathematical model is analysed to compare key statistical properties such as the expectation and variance with those of independent diseases. The main results do not need an understanding of the underlying mathematics and can be appreciated by a non-expert. Significance: It is widely believed that there are shared underlying pathways that can lead to several disease types (shared "pathogenesis"), and this may explain observed clusters of disease types. This article shows how this hypothesis can be tested for a pair or cluster of diseases, using observational data of disease incidence.
Vaccines remain one of the most effective tools in combating infectious diseases, though traditional platforms are constrained by limitations including suboptimal immunogenicity, safety concerns, and manufacturing complexity. Circular RNA (circRNA) vaccines have recently emerged as a novel vaccine modality, demonstrating unique advantages including high stability, low innate immunogenicity, and sustained antigen expression. Although early research has predominantly focused on viral targets, accumulating evidence now supports the application potential of circRNA vaccines against diverse pathogens, particularly antibiotic-resistant bacteria. Through encoding critical antigens or virulence factors, these circRNA vaccines demonstrate capability to induce protective immune responses, presenting a viable alternative to conventional antimicrobial strategies. This review highlights recent advances in circRNA vaccine development, spanning synthetic circularization techniques, delivery approaches, and immunological mechanisms. We emphasize their potential against viral, bacterial, fungal, and parasitic infections, while addressing current challenges and future research directions of this emerging platform. Collectively, these insights underscore circRNA’s multifaceted versatility and its expanding relevance in next-generation vaccine innovation.
Evgenii Filippov, Jessica Bass, Jiana Blaha
et al.
Abstract Background Eosinophilia, defined as an absolute eosinophil count (AEC) ≥500 cells/μL, is common in the setting of helminth infection, though the relationship between the degree of eosinophilia and the likelihood of an underlying helminth infection remains unstudied. To begin to address this question, we performed a retrospective analysis of data from a large cohort of patients referred to the National Institutes of Health for parasitic disease screening. Methods Patients evaluated at the NIH from September 2001 to March 2021 on clinical protocols designed to study suspected parasitic infections (NCT00001230 and NCT00001645) and for whom clinical and laboratory information was available in a research database were included. Demographic information, clinical manifestations, final diagnosis, and laboratory values were reviewed. Peak AEC was recorded and categorized as mild (500-1499 cells/μL), moderate (1500-4999 cells/μL), or severe (>5000 cells/μL). Results A total of 196 of the 459 included participants (43%) had documented eosinophilia, of whom 61% (n=120), 34% (n=66) and 5% (n=10) had mild, moderate, or severe peak AEC elevations, respectively. Most of the eosinophilic patients were diagnosed with one or more helminth infections (n=150). The remaining patients (n=46) were found to have myiasis (n=1), a non-infectious or no cause identified (n=43) or were lost to follow up (n=2). Among the helminth-infected patients, GM AEC was 1412 cells/μL (range 500-12,800). Most patients likely acquired infection in Africa (n=88) or Central America (n=25). Three patients had no history of travel outside the US. Strongyloidiasis (n=64) and loiasis (n=43) were the most common helminth diagnoses, and 29 individuals had multiple helminths identified. Of the 10 patients in the total patient cohort with peak AEC >5000 cells/μL, 5 were diagnosed with helminth infection (loiasis or strongyloidiasis), none of whom had AEC >20,000 cells/μL. Conclusion The study underscores that helminths, particularly strongyloidiasis and loiasis, are commonly associated with mild to moderate eosinophilia. Severe eosinophilia (AECs >5000 cells/μL) is less common and extremely high counts (AEC ≥20,000 cell/μL) should prompt investigation of non-helminth etiologies. Disclosures All Authors: No reported disclosures
Zhengqing Fang, Shuowen Zhou, Zhouhang Yuan
et al.
Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorporate the "black box" into clinicians' workflows. To make the diagnostic patterns learned from data understandable by clinicians, we develop an interpretable model, knowledge-guided diagnosis model (KGDM), that provides a visualized reasoning process containing AI-based biomarkers and retrieved cases that with the same diagnostic patterns. It embraces clinicians' prompts into the interpreted reasoning through human-AI interaction, leading to potentially enhanced safety and more accurate predictions. This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness. The classification performance of KGDM is evaluated on a prospective validation dataset, an external testing dataset, and an publicly available testing dataset. The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit consistent diagnostic patterns with clinic experience. Moreover, a human-AI collaborative diagnosis test is conducted and the participants with collaboration achieved a performance exceeding that of both humans and AI. By synergistically integrating interpretability and interaction, this study facilitates the convergence of clinicians' expertise and data-driven intelligence. The promotion of inexperienced ophthalmologists with the aid of AI-based biomarkers, as well as increased AI prediction by intervention from experienced ones, demonstrate a promising diagnostic paradigm for infectious keratitis using KGDM, which holds the potential for extension to other diseases where experienced medical practitioners are limited and the safety of AI is concerned.
Mohsen Asghari Ilani, Saba Moftakhar Tehran, Ashkan Kavei
et al.
The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its impact on public health systems. In response, this study delves into the application of advanced machine learning (ML) techniques for predicting COVID-19 infection probability. We conducted a rigorous investigation into the efficacy of various ML models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with 3200 allocated for training and 800 for testing, our experiment offers comprehensive insights into the performance of these models in COVID-19 prediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. This underscores the efficacy of deep learning approaches in leveraging complex data patterns to identify COVID-19 infections accurately. This study underscores the critical role of machine learning, particularly deep learning methodologies, in augmenting early detection efforts amidst the ongoing pandemic. The success of DNN in accurately predicting COVID-19 infection probability highlights the importance of continued research and development in leveraging advanced technologies to combat infectious diseases.
In the modeling of parasite transmission dynamics, understanding the reproductive characteristics of these parasites is crucial. This paper presents a mathematical model that explores the reproductive behavior of dioecious parasites and its impact on transmission dynamics. Specifically, the study focuses on the investigation of various reproductive variables such as the mating probability and the fertilized egg production in the case of helminth parasites. While previous studies have commonly assumed Poisson and negative binomial distributions to describe the distribution of parasites among hosts, this study adopts an arbitrary distribution model and examines its consequences on some reproductive variables. These variables include mean number of fertile females, mean egg production, mating probability and mean fertilized egg production. In addition, the study of these variables takes into account the sex distribution of the parasites and whether male and female parasites are considered to be distributed together or separately. We show that the models obtained for the case of male and female parasites distributed separately in the hosts are ecologically unrealistic. We present the results obtained for some specific models and we tested the models obtained in this work using Monte Carlo simulations.
Nocardia microorganisms are saprophytes, either non-pathogenic or pathogenic, causing nocardiosis. The clinically significant disease occurs in immunocompromised people, most often as pneumonia with cough, dyspnea, and fever. Antibiotic therapy, which is longer in time, is necessary. The main treatment is with sulfonamides, but the sensitivity of these bacteria varies. Therefore, the antibiotic susceptibility of the respective strain is important to apply combined therapy if needed. The risk of death without treatment is high, especially if the infection disseminates and the brain is involved. Antibacterial prophylaxis is therefore recommended in patients at high risk of nocardiosis. Our clinical case concerns an immunocompromised patient with isolated Nocardia from bronchoalveolar lavage (BAL).
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. However, these models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones. To the best of our knowledge, limited work has been done on identifying, let alone reducing, model bias in skin disease classification and segmentation. In this paper, we examine DL fairness and demonstrate the existence of bias in classification and segmentation models for subpopulations with darker skin tones compared to individuals with lighter skin tones, for specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we propose a novel preprocessing, data alteration method, called EdgeMixup, to improve model fairness with a linear combination of an input skin lesion image and a corresponding a predicted edge detection mask combined with color saturation alteration. For the task of skin disease classification, EdgeMixup outperforms much more complex competing methods such as adversarial approaches, achieving a 10.99% reduction in accuracy gap between light and dark skin tone samples, and resulting in 8.4% improved performance for an underrepresented subpopulation.
The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field. Graphical Abstract
Epilepsy is a major public health concern in low and middle-income countries (LMICs) and comorbidities aggravate the burden associated with the disease. The epidemiology of these comorbidities has not been well described, although, identifying the main comorbidities of epilepsy, and their relative importance, is crucial for improving the quality of care. Comorbidities were defined as disorders coexisting with or preceding epilepsy, or else compounded or directly attributed to epilepsy or to its treatment. A meta-analysis of the proportion of main comorbidities by subcontinent as well as overall was also conducted. Out of the 2,300 papers identified, 109 from 39 countries were included in this systematic review. Four groups of comorbidities were identified: parasitic and infectious diseases (44% of comorbid conditions), somatic comorbidities (37%), psychosocial (11%), as well as psychiatric comorbidities (8%). Heterogeneity was statistically significant for most variables then random effect models were used. The most frequently studied comorbidities were: neurocysticercosis (comorbid proportion: 23%, 95% CI: 18–29), head trauma (comorbid proportion: 9%, 95% CI: 5–15) malnutrition (comorbid proportion: 16%, 95% CI: 28–40), stroke (comorbid proportion: 1.3%, 95% CI: 0.2–7.0), and discrimination for education (comorbid proportion: 34%, 95% CI: 28–40). Many comorbidities of epilepsy were identified in LMICs, most of them being infectious.
Nanomaterials have wide-ranging biomedical applications in prevention, treatment and control of diseases. Nanoparticle based vaccines have proven prodigious prophylaxis of various infectious and non-infectious diseases of human and animal concern. Nano-vaccines outnumber the conventional vaccines by virtue of plasticity in physio-chemical properties and ease of administration. The efficacy of nano-based vaccines may be attributed to the improved antigen stability, minimum immuno-toxicity, sustained release, enhanced immunogenicity and the flexibility of physical features of nanoparticles. Based on these, the nano-based vaccines have potential to evoke both cellular and humoral immune responses. Targeted and highly specific immunological pathways required for solid and long lasting immunity may be achieved with specially engineered nano-vaccines. This review presents an insight into the prevention of infectious diseases (of bacterial, viral and parasitic origin) and non-infectious diseases (cancer, auto-immune diseases) using nano-vaccinology. Additionally, key challenges to the effective utilization of nano-vaccines from bench to clinical settings have been highlighted as research domains for future.
Aimaiti Yasen, Abudusalamu Aini, Wending Li
et al.
Single-cell sequencing (SCS) is a next-generation sequencing method that is mainly used to analyze differences in genetic and protein information between cells, to obtain genetic information on microorganisms that are difficult to cultivate at a single-cell level and to better understand their specific roles in the microenvironment. By sequencing the whole genome, transcriptome and epigenome of a single cell, the complex heterogeneous mechanisms involved in disease occurrence and progression can be revealed, further improving disease diagnosis, prognosis prediction and monitoring of the therapeutic effects of drugs. In this study, we mainly summarized the methods and application fields of SCS, which may provide potential references for its future clinical applications, including the analysis of embryonic and organ development, the immune system, cancer progression, and parasitic and infectious diseases as well as stem cell research, antibody screening, and therapeutic research and development.
Key Points Question Is there an association of silica dust exposure and cigarette smoking with mortality? Findings In this cohort study of 44 708 adults who worked in mines or pottery factories in China and were followed up for a median of 34.9 years, the combination of silica dust exposure and cigarette smoking was found to be associated with mortality among individuals with lung cancer, certain infectious and parasitic diseases, respiratory tuberculosis, diseases of the respiratory system, and pneumoconiosis. Meaning Smoking cessation and control of silica dust concentrations may be associated with reduced risk of mortality among individuals exposed to silica dust.
Abstract A major advance in antimalarial drug discovery has been the shift towards cell-based phenotypic screening, with notable progress in the screening of compounds against the asexual blood stage, liver stage, and gametocytes. A primary method for drug target deconvolution in Plasmodium falciparum is in vitro evolution of compound-resistant parasites followed by whole-genome scans. Several of the most promising antimalarial drug targets, such as translation elongation factor 2 (eEF2) and phenylalanine tRNA synthetase (PheRS), have been identified or confirmed using this method. One drawback of this method is that if a mutated gene is uncharacterized, a substantial effort may be required to determine whether it is a drug target, a drug resistance gene, or if the mutation is merely a background mutation. Thus, the availability of high-throughput, functional genomic datasets can greatly assist with target deconvolution. Studies mapping genome-wide essentiality in P. falciparum or performing transcriptional profiling of the host and parasite during liver-stage infection with P. berghei have identified potentially druggable pathways. Advances in mapping the epigenomic regulation of the malaria parasite genome have also enabled the identification of key processes involved in parasite development. In addition, the examination of the host genome during infection has identified novel gene candidates associated with susceptibility to severe malaria. Here, we review recent studies that have used omics-based methods to identify novel targets for interventions against protozoan parasites, focusing on malaria, and we highlight the advantages and limitations of the approaches used. These approaches have also been extended to other protozoan pathogens, including Toxoplasma, Trypanosoma, and Leishmania spp., and these studies highlight how drug discovery efforts against these pathogens benefit from the utilization of diverse omics-based methods to identify promising drug targets.
The CW RF test of 1.3 GHz 9-cell cavity in liquid helium bath at 2 K is a very important key point in the cavity procurement. Some problems can be found through the test, according which to optimized and improve the process of cavity. Recently, Medium temperature (mid-T) furnace bake of 1.3 GHz 9-cell cavities have been carried out at IHEP. Through the proceed of mid-T bake, the quality factor of cavity has been greatly improved. While the excitation of the parasitic modes in the high Q cavities CW cold test has been encountered, which implies an error source for the cavity gradient and quality factor determination. In order to ensure the testing accuracy of superconducting cavity, we have improved the testing system. Finally, the parasitic mode is completely suppressed and the CW RF cold test of high Q cavity is guaranteed.
Manuel Ponce-Alonso, Borja M. Fernández-Félix, Ana Halperin
et al.
ABSTRACT: Objective: Men have been considered to have a higher incidence of infectious diseases, with controversy over the possibility that sex could influence the prognosis of the infection. This study aimed to explore this assumption in patients admitted to the intensive care unit (ICU) with septic bacteremia. Methods: A retrospective analysis (2006-2017) of septic patients with microbiologically confirmed bacteremia (n=440) was performed. Risk of ICU and in-hospital mortality in males versus females was compared by univariate analysis and a propensity score analysis integrating their clinical characteristics. Results: Sepsis more frequently occurred in males (80.2% vs 76.1%) as well as in-hospital (48.0% vs 41.3%) and ICU (39.9% vs 36.5%) mortality. Univariate analyses showed that males had a higher Charlson comorbidity index and worse McCabe prognostic score. However, the propensity score in 296 matched patients demonstrated that females had higher risk of both ICU (OR 1.39; 95% CI 0.89-2.19) and in-hospital mortality (OR 1.18; 95% CI 0.77-1.83), but without statistical significance. Conclusion: Males with sepsis had worse clinical characteristics when admitted to the ICU, but sex had no influence on mortality. These data contribute to helping reduce the sex-dependent gap present in healthcare provision.
Druti Hazra, Kiran Chawla, Vishnu P. Shenoy
et al.
Background: The recent COVID-19 pandemic became a looming catastrophe over global public health and severely disrupted essential healthcare services like tuberculosis (TB). This study estimated the impact of the COVID-19 in the diagnosis of TB, a microbiology laboratory-based overview. Method: This ambispective observational study was conducted at the Department of Microbiology in a tertiary care hospital in South Karnataka from January 2019 to December 2020. A standardized data collection sheet was prepared to collect the month-wise total number of suspected TB and confirmed TB samples. Data were analyzed using EZR 3.4.3 (R, open-source). Categorical variables were expressed in frequency and percentage. The Chi-square test was performed to test the difference in proportions and p < 0.05 indicated statistical significance. Results: In this study, a significant drop was observed in suspected TB specimens in 2020 compared to 2019, i.e. 54.8% for microscopy, along with 34.2% and 49.7% for Xpert MTB/RIF and MGIT culture respectively. Also, a sharp decline in confirmed TB samples was noted in 2020 with 49%, 43.8%, and 59.7% reduction with microscopy, Xpert MTB/RIF, and MGIT culture respectively, compared to 2019. Another major finding from this study reveals the PTB: EPTB proportion changed from 2.7:1 in 2019 to 2.1:1 in 2020, divulging an overall increase in EPTB sample proportion in 2020 (p = 0.0385). Conclusion: The COVID-19 pandemic adversely impacted the TB diagnostic services, resulting in a significant reduction of active TB case detection. It highlights an urgent need to revise the strategies to control and eliminate TB in this hour of the pandemic crisis.
Infectious and parasitic diseases, Public aspects of medicine
E. V. Putintseva, S. K. Udovichenko, N. V. Boroday
et al.
The paper presents an analysis of West Nile Fever incidence in the Russian Federation in 2020, summarizes the results and identifies problematic issues of the pathogen monitoring. Manifestations of West Nile Fever in 2020 were characterized by a low incidence rate (10 times lower than the average long-term value) with sporadic cases registration in the endemic areas of the Southern (9 cases) and Central (1 case) Federal Districts. A discrepancy between the morbidity structure (distribution by age, sex, social status) and the trends that have developed in Russia in recent years is shown. The analysis of officially recorded cases doesn’t characterize the epidemic process of West Nile fever in Russia during 2020-season as a whole. The generalized results of monitoring of the West Nile virus circulation in environmental objects in 2020 indicate a decrease in the effectiveness of its implementation and a low detectability of pathogen markers. A decrease in the volume of diagnostic studies for the active detection of patients with West Nile fever in the epidemic season (5.7 times lower compared to 2019), as well as serological screening of healthy population samples (1.6 times) has been established. The results of a molecular-genetic study of the pathogen showed that lineage 2 of the pathogen was circulating in the European part of Russia. The circulation of the lineage 4 of the virus in the enzootic cycle in the Republic of Kalmykia was found out. The genome sequences of 11 West Nile virus isolates allocated in 2019 and 2020 were obtained. Phylogenetic analysis revealed that all isolates allocated in the Volgograd Region and isolates from the Rostov and Astrakhan regions belong to the Volgograd clade of the lineage 2 of the West Nile virus. Based on the assessment of abiotic and biotic factors, possible local increases in the incidence of West Nile fever in 2021 in the regions of the Southern and North Caucasian Federal Districts and in the south of Western Siberia have been substantiated.
The incidence of infections caused by Cryptococcus neoformans has increased significantly in recent years, especially in the settings of immune deficiency (HIV infection transplantation, etc.). Most often after inhalation of spores dissemination of yeast to the brain parenchyma occurs, leading to meningitis (meningo-encephalitis). Our clinical case, is a patient with cryptococcal meningitis after liver transplantation , who died despite the onset of antifungal therapy. This is further evidence of the severe prognosis of CNS cryptococcosis, especially in immunocompromised patients.