Diagnosis Of Infectious Diseases Research Articles

A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood.

Background: Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. Methods: A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-speed camera and a deep learning analysis section. Results: For training data, the sensitivity and specificity of the convolutional neural network model were 93.5% (92.7-94.2%) and 99.5% (99.1-99.5%), respectively. For validating data, the sensitivity and specificity were 81.3% (80.0-82.5%) and 99.4% (99.2-99.6%), respectively. Cryptococcal cells were found in 22.07% of blood samples. Conclusion: This high-speed microscopy system can analyze fungal pathogens in blood samples rapidly with high sensitivity and specificity and can help dramatically accelerate the diagnosis of fungal infectious diseases.

Prior infections are associated with smaller hippocampal volume in older women.

Accumulating evidence suggests that infections may play a major role in Alzheimer's disease (AD), however, mechanism is unclear, as multiple pathways may be involved. One possibility is that infections could contribute to neurodegeneration directly by promoting neuronal death. We explored relationships between history of infections and brain hippocampal volume (HV), a major biomarker of neurodegeneration, in a subsample of the UK Biobank (UKB) participants. Infectious disease diagnoses were based on ICD10 codes. The left/right HV was measured by the magnetic resonance imaging (MRI) in cubic millimeters and normalized. Analysis of variance (ANOVA), Welch test, and regression were used to examine statistical significance. We found that HV was significantly lower in women aged 60-75, as well as 65-80, years, with history of infections, compared to same age women without such history. The effect size increased with age faster for the left vs. right HV. Results for males didn't reach statistical significance. Results of our study support a major role of adult infections in neurodegeneration in women. The detrimental effect of infections on HV became stronger with age, in line with declining resilience and increasing brain vulnerability to stressors due to aging. The faster increase in the effect size observed for the left vs. right HV may indicate that female verbal memory degrades faster over time than visual-spatial memory. The observed sex difference may reflect a higher vulnerability of female brain to infection-related factors, which in turn may contribute to a higher risk of AD in women compared to men.

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

85 years of the Department of Epidemiology and Evidence-Based Medicine of Kazan State ­Medical University: from history to modern times

In 2023, it was 85 years since the founding of the Department of Epidemiology and Evidence-Based Medicine at Kazan State Medical University. The article describes the main stages of the history of the department, from its foundation to the present day. The first head of the department in 1938 was V.I. Popov. In addition to successful work in training specialists, the history of the department was marked by extensive scientific activity and work carried out in the direction of the infectious diseases prevention, including as part of eliminating outbreaks. Employees of the department have carried out scientific research devoted to improving the diagnosis of infectious diseases, improving epidemiological surveillance in order to further reduce the incidence and eliminate current infections, including diphtheria, typhus and typhoid fever, tuberculosis, smallpox, viral hepatitis, infection caused by the human immunodeficiency virus, etc. A significant part of the scientific work carried out by the staff of the department concerned the study of post-infectious and post-vaccination immunity, as well as the study of the influence of environmental risk factors on the immunological reactivity of the body. The change in the paradigm of modern epidemiology and the epidemic situation has necessitated modification of the educational process with the inclusion in the program and expansion of teaching in such areas as clinical epidemiology, hospital epidemiology, and military epidemiology. In 2016, the department was renamed the Department of Epidemiology and Evidence-Based Medicine.

A prospective study of nanopore-targeted sequencing in the diagnosis of central nervous system infections.

This study aims to prospectively evaluate the ability of nanopore-targeted sequencing (NTS) in the detection of pathogens in cerebrospinal fluid (CSF). It was the first time combining mNGS and microbial culture to verify the NTS-positive results also using 16S rDNA amplification with Sanger sequencing. Although microbial culture was thought to be the gold standard for pathogens detection and diagnosis of infectious diseases, this study suggested that microbial culture of CSF is not the most appropriate way for diagnosing central nervous system (CNS) infection. NTS should be recommended to be used in CSF for diagnosing CNS infection. When evaluating the value of NTS for diagnosis of CNS infections, the results of CSF TNC should be combined, and NTS-positive result is observed to be more reliable in patients with CSF TNC level >10 cells/μL.

On the role and perspectives of diagnostic improvement of infectious diseases in forensic medical practice

Postmortem verification of an infectious disease in forensic medical practice is crucial in solving several problems faced by both the doctor conducting the examination and healthcare system, from the regional to the international level. This review presents an analysis of scientific studies on the possibilities of postmortem diagnosis of infectious diseases. Foreign and domestic experience of forensic doctors in conditions of the threat and spread of highly contagious diseases and emerging infections is shown, as well as an overview of approaches to organizing safe working environment in the forensic medical examination bureau.

Nanotechnology in the Diagnosis and Treatment of Antibiotic-Resistant Infections.

The development of antimicrobial resistance (AMR), along with the relative reduction in the production of new antimicrobials, significantly limits the therapeutic options in infectious diseases. Thus, novel treatments, especially in the current era, where AMR is increasing, are urgently needed. There are several ongoing studies on non-classical therapies for infectious diseases, such as bacteriophages, antimicrobial peptides, and nanotechnology, among others. Nanomaterials involve materials on the nanoscale that could be used in the diagnosis, treatment, and prevention of infectious diseases. This review provides an overview of the applications of nanotechnology in the diagnosis and treatment of infectious diseases from a clinician's perspective, with a focus on pathogens with AMR. Applications of nanomaterials in diagnosis, by taking advantage of their electrochemical, optic, magnetic, and fluorescent properties, are described. Moreover, the potential of metallic or organic nanoparticles (NPs) in the treatment of infections is also addressed. Finally, the potential use of NPs in the development of safe and efficient vaccines is also reviewed. Further studies are needed to prove the safety and efficacy of NPs that would facilitate their approval by regulatory authorities for clinical use.

Simultaneous Detection of Infectious Diseases Using Aptamer-Conjugated Gold Nanoparticles in the Lateral Flow Immunoassay-Based Signal Amplification Platform.

Various platforms for the accurate diagnosis of infectious diseases have been studied because of the emergence of coronavirus disease (COVID-19) in 2019. Recently, it has become difficult to distinguish viruses with similar symptoms due to the continuous mutation of viruses, and there is an increasing need for a diagnostic method to detect them simultaneously. Therefore, we developed a paper-based rapid antigen diagnostic test using DNA aptamers for the simultaneous detection of influenza A, influenza B, and COVID-19. Aptamers specific for each target viral antigen were selected and attached to AuNPs for application in a rapid antigen diagnosis kit using our company's heterogeneous sandwich-type aptamer screening method (H-SELEX). We confirmed that the three viruses could be detected on the same membrane without cross-reactivity based on the high stability, specificity, and binding affinity of the selected aptamers. Further, the limit of detection was 2.89 pg·mL-1 when applied to develop signal amplification technology; each virus antigen was detected successfully in diluted nasopharyngeal samples. We believe that the developed simultaneous diagnostic kit, based on such high accuracy, can distinguish various infectious diseases, thereby increasing the therapeutic effect and contributing to the clinical field.

A CRISPR/Cas13a-powered catalytic hairpin assembly evanescent wave fluorescence biosensor for target amplification-free SARS-CoV-2 detection

The end of the coronavirus disease 2019 (COVID-19) pandemic still drives the development of rapid and accurate detection technology to prepare for diverse viral infections and emerging pandemics in the future. Herein, a CRISPR/Cas13a-powered catalytic hairpin assembly evanescent wave fluorescence biosensor (CAPABLE) was developed for amplification-free detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the ingenious design of trigger, the presence of SARS-CoV-2 activated the trans-cleavage activity of Cas13a, followed by the initiation of a catalytic hairpin assembly (CHA) system that exhibited excellent signal amplification efficiency. By integrating the biotin-avidin system, the detection sensitivity of the portable fluorescence platform was greatly improved. Quantitative SARS-CoV-2 detection was realized with a detection limit of 18.6 copies/μL within 50 min based on a fast extraction method. The proposed method exhibited a 100% positive detection rate for environmental water samples spiked with SARS-CoV-2 pseudovirus, which was consistent with standard RTqPCR results. Benefiting from amplification-free performance, the CAPABLE method holds great promise for rapid SARS-CoV-2 detection in field laboratories and primary facilities in resource-limited areas. With the inherent flexibility of the CRISPR/Cas system, the proposed method has the potential to be applied in the diagnosis of various infectious diseases.

Detecting and quantifying Veillonella by real-time quantitative PCR and droplet digital PCR.

Veillonella spp. are Gram-negative opportunistic pathogens present in the respiratory, digestive, and reproductive tracts of mammals. An abnormal increase in Veillonella relative abundance in the body is closely associated with periodontitis, inflammatory bowel disease, urinary tract infections, and many other diseases. We designed a pair of primers and a probe based on the 16S rRNA gene sequences of Veillonella and conducted real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR) to quantify the abundance of Veillonella in fecal samples. These two methods were tested for specificity and sensitivity using simulated clinical samples. The sensitivity of qPCR was 100 copies/μL, allowing for the accurate detection of a wide range of Veillonella concentrations from 103 to 108 CFU/mL. The sensitivity of ddPCR was 11.3 copies/μL, only allowing for the accurate detection of Veillonella concentrations from 101 to 104 CFU/mL because of the limited number of droplets generated by ddPCR. ddPCR is therefore more suitable for the detection of low-abundance Veillonella samples. To characterize the validity of the assay system, clinical samples from children with inflammatory bowel disease were collected and analyzed, and the results were verified using isolation methods. We conclude that molecular assays targeting the 16S rRNA gene provides an important tool for the rapid diagnosis of chronic and infectious diseases caused by Veillonella and also supports the isolation and identification of Veillonella for research purposes. KEY POINTS: • With suitable primer sets, the qPCR has a wider detection range than ddPCR. • ddPCR is suitable for the detection of low-abundance samples. • Methods successfully guided the isolation of Veillonella in clinical sample.

Antimicrobial Use-Related Problems Among Hospitalized Pediatric Patients: A Prospective Observational Study.

Antimicrobials are among the most commonly misused medications, leading to antimicrobial resistance, and therefore demand more attention. There are limited studies documenting "antimicrobial use-related problems" in developing countries, especially in pediatric patients. This study aimed to evaluate the prevalence of antimicrobial use-related problems and risk factors among hospitalized pediatric patients. A hospital-based prospective observational study was conducted in Ayder comprehensive specialized hospital (ACSH) in the Tigray region, Ethiopia. The participants of the study were pediatric patients aged ≤15 years who were admitted with a diagnosis of infectious disease between September 2019 and November 2019. A total of 232 pediatric patients were included in the study. Of these, 59.5% of the patients were male and the mean age (SD) of the patients was 5.8 (5.2) years. Of the 232 patients surveyed, a total of 177 antimicrobial use-related problems were identified. One or more antimicrobial use-related problems have occurred in more than half of the patients (53.9%). The commonest antimicrobial use problems were unnecessary antimicrobial therapy (22.8%) followed by need additional antimicrobial therapy (16.4%). In a multivariate logistic regression model, patients with comorbidities (Adjusted odds ratio (AOR): 1.84, 95% confidence interval (CI): 1.04-3.27) and hospital stays exceeding one week (AOR=1.88, 95% CI: 1.08-3.26) were predictors of antimicrobial use-related problems. Antimicrobial use-related problems were found in a significant proportion of pediatric patients. Addressing these issues necessitates collaborative efforts, emphasizing targeted education, strengthened antimicrobial stewardship, ensuring responsible antimicrobial use and enhancing pediatric care.

Diagnosis of infectious diseases: complexity to convenience

Transformation in diagnostic techniques, evolving from time-consuming and laboratory-based methods to rapid, POC options (left to right) – plate culture, microscopy, ELISA, PCR, smartphone-based ELISA, miniaturized PCR, photonic PCR, biosensors.

An overview of electrochemical biosensors used for COVID-19 detection.

This short review presents the latest advances in the field of electrochemical biosensors, focusing particularly on impedimetric biosensors for the direct measurement of analytes. As a source of study we have chosen to describe these advances in the latest global health crisis originated from the COVID-19 pandemic, initiated by the SARS-CoV-2 virus. In this period, the necessity for swift and precise detection methods has grown rapidly due to an imminent need for the development of an analytical method to identify and isolate infected patients as an attempt to control the spreading of the disease. Traditional approaches such as the enzyme-linked immunosorbent assay (ELISA), were extensively used during the SARS-CoV-2 pandemic, but their drawbacks, including slow response time, became evident. In this context, the potential of electrochemical biosensors as an alternative for COVID-19 detection was emphasized. These biosensors merge electrochemical technology with bioreceptors, offering benefits such as rapidity, accuracy, portability, and real-time result provision. Additionally, we present instances of electrochemical biosensors modified with conductive polymers, eliminating the necessity for an electrochemical probe. The adaptability of the developed materials and devices facilitated the prompt production of electrochemical biosensors during the pandemic, creating opportunities for broader applications in infectious disease diagnosis.

FARPA-based tube-array coupled with quick DNA extraction enables ultra-fast bedside-detection of antibiotic-resistant pathogens

Rapid and accurate detection of pathogens and antimicrobial resistance (AMR) genes in the pathogens is crucial for clinical diagnosis and efficient treatment of infectious diseases. However, the time-consuming step of...

Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

A microfluidic-based quantitative analysis system for the multiplexed genetic diagnosis of human viral infections using colorimetric loop-mediated isothermal amplification.

In this study, a microfluidic-based system utilizing colorimetric loop-mediated isothermal amplification (LAMP) is introduced for the quantitative analysis of nucleic acid targets. This system offers a user-friendly and cost-effective platform for the multiplexed genetic diagnosis of various infectious diseases across multiple samples. It includes time-lapse imaging equipment for capturing images of the microfluidic device during the LAMP assay and a hue-based quantitative analysis software to analyze the LAMP reaction, streamlining diagnostic procedures. An electric pipette was used to simplify the loading of samples and LAMP reagents into the device, allowing easy operation even by untrained individuals. The hue-based analysis software employs efficient image processing and post-processing techniques to calculate DNA amplification curves based on color changes in multiple reaction chambers. This software automates several tasks, such as identifying reaction chamber areas from time-lapse images, quantifying color information within each chamber, correcting baselines of DNA amplification curves, fitting experimental data to theoretical curves, and determining the threshold time for each curve. To validate the developed system, conventional off-chip LAMP assays were conducted with a 25 μL reaction mixture in 0.2 mL polymerase chain reaction (PCR) tubes using a real-time turbidimeter. The results indicated that the threshold time obtained using the colorimetric LAMP assay in the developed system is comparable to that obtained with real-time turbidity measurements in PCR tubes, demonstrating the system's capability for quantitative analysis of target nucleic acids, including those from human herpesviruses.

Application of quantum computing in image processing for recognition of infectious diseases of wheat

This study is devoted to the development and application of quantum methods in the field of diagnostics of infectious diseases of wheat. Taking into account the relevance of the problem of agriculture and the need to improve the efficiency of plant disease control, the work proposes a new approach based on the combined use of quantum computing, image processing and machine learning. Quantum image processing techniques have been applied to improve contrast, filter noise, and analyze key features of infectious diseases in the early stages of their development. The developed quantum machine learning models demonstrate high ac-curacy in image classification, which contributes to earlier and more accurate detection of diseases. The study results highlight the effectiveness of quantum methods in agriculture and provide new tools for more accurate diagnosis of infectious plant diseases. The prospects for introducing this approach into agriculture mean the possibility of improving yields, reducing the use of chemicals and ensuring food security.

The Applications of ELISpot in the Identification and Treatment of Various Forms of Tuberculosis and in the Cancer Immunotherapies.

ELISpot (enzyme-linked immunospot) is a powerful immunological tool for the detection of cytokine-secreting cells at a single-cell resolution. It is widely used for the diagnosis of various infectious diseases, e.g., tuberculosis and sarcoidosis, and it is also widely used in cancer immunotherapy research. Its ability to distinguish between active and latent forms of tuberculosis makes it an extremely powerful tool for epidemiological studies and contact tracing. In addition to that, it is a very useful tool for the research and development of cancer immunotherapies. ELISpot can be employed to assess the immune responses against various tumor-associated antigens, which could provide valuable insights for the development of effective therapies against cancers. Furthermore, it plays a crucial role to the evaluation of immune responses against specific antigens that not only could aid in vaccine development but also assist in treatment monitoring and development of therapeutic and diagnostic strategies. This chapter briefly describes some of the applications of ELISpot in tuberculosis and cancer research.

Molecular antimicrobial susceptibility testing in sepsis.

Rapidly detecting and identifying pathogens is crucial for appropriate antimicrobial therapy in patients with sepsis. Conventional diagnosticmethods have been a great asset to medicine, though they are timeconsuming and labor intensive. This workwill enable healthcare professionals to understand the bacterial community better and enhance their diagnostic capacity by using novel molecular methods that make obtaining quicker, more precise results possible. The authors discuss and critically assess the merits and drawbacks of molecular testing and the added value of these tests, including the shift turnaround time, the implication for clinicians' decisions, gaps in knowledge, future research directions and novel insights or innovations. The field of antimicrobial molecular testing has seen several novel insights and innovations to improve the diagnosis and management of infectious diseases.

A Deep Learning approach for the augmented diagnosis and prediction of infectious Lung Diseases

A Deep Learning approach for the augmented diagnosis and prediction of infectious Lung Diseases