-
Parasites & Vectors Jun 2024Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis caused by the SFTS virus (SFTSV). Understanding the prevalence of SFTSV RNA in... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis caused by the SFTS virus (SFTSV). Understanding the prevalence of SFTSV RNA in humans, vertebrate hosts and ticks is crucial for SFTS control.
METHODS
A systematic review and meta-analysis were conducted to determine the prevalence of SFTSV RNA in humans, vertebrate hosts and questing ticks. Nine electronic databases were searched for relevant publications, and data on SFTSV RNA prevalence were extracted. Pooled prevalence was estimated using a random effects model. Subgroup analysis and multivariable meta-regression were performed to investigate sources of heterogeneity.
RESULTS
The pooled prevalence of SFTSV RNA in humans was 5.59% (95% confidence interval [CI] 2.78-9.15%) in those in close contact (close contacts) with infected individuals (infected cases) and 0.05% (95% CI 0.00-0.65%) in healthy individuals in endemic areas. The SFTSV infection rates in artiodactyls (5.60%; 95% CI 2.95-8.96%) and carnivores (6.34%; 95% CI 3.27-10.23%) were higher than those in rodents (0.45%; 95% CI 0.00-1.50%). Other animals, such as rabbits, hedgehogs and birds, also played significant roles in SFTSV transmission. The genus Haemaphysalis was the primary transmission vector, with members of Ixodes, Dermacentor, and Amblyomma also identified as potential vectors. The highest pooled prevalence was observed in adult ticks (1.03%; 95% CI 0.35-1.96%), followed by nymphs (0.66%; 95% CI 0.11-1.50%) and larvae (0.01%; 95% CI 0.00-0.46%). The pooled prevalence in ticks collected from endemic areas (1.86%; 95% CI 0.86-3.14%) was higher than that in ticks collected in other regions (0.41%; 95% CI 0.12-0.81%).
CONCLUSIONS
Latent SFTSV infections are present in healthy individuals residing in endemic areas, and close contacts with SFTS cases are at a significantly higher risk of infection. The type of animal is linked to infection rates in vertebrate hosts, while infection rates in ticks are associated with the developmental stage. Further research is needed to investigate the impact of various environmental factors on SFTSV prevalence in vertebrate hosts and ticks.
Topics: Animals; Humans; Phlebovirus; Severe Fever with Thrombocytopenia Syndrome; Ticks; Vertebrates; Prevalence; RNA, Viral
PubMed: 38902842
DOI: 10.1186/s13071-024-06341-2 -
Journal of Cellular and Molecular... Mar 2024Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer...
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
Topics: Humans; Deep Learning; Lung; Neural Networks, Computer; Tomography, X-Ray Computed; Neoplasms; Diagnosis, Computer-Assisted
PubMed: 38426930
DOI: 10.1111/jcmm.18144