Background: Pesticides are widely used in agricultural production to control insect pests and regulate plant growth in China, which may result in the presence of some pesticide residues in the vegetables. However, few studies of monitoring pesticides have been conducted in Henan Province. The aim of this study was to evaluate the level of pesticide residues in commonly consumed vegetables in the regions of Henan Province.
Methods: In this study, we collected 5,576 samples of 15 different vegetables in 17 areas from Henan Province during 2020. Eight kinds of pesticides were analyzed by gas chromatography-mass spectrometry (GC-MS), including procymidone, lambda-cyhalothrin, cypermethrin, pendimethalin, isocarbophos, isazophos, fenthion and deltamethrin. The chi-square test was used to compare the detection rates of pesticide residues in different regions.
Results: Of all the pesticides above, procymidone, lambda-cyhalothrin, cypermethrin, pendimethalin and isocarbophos were detected in vegetables, the detection rates were 27.0%, 16.2%, 11.4%, 3.5%, and 1.9%, respectively. However, isazophos, fenthion, and deltamethrin were not detected. In addition, procymidone, lambda-cyhalothrin, and cypermethrin were detected in urban areas, while pendimethalin was detected in rural areas. The detection rates of cypermethrin and pendimethalin in rural were 19.8% and 5.4%, respectively, which in urban were at relatively lower levels (13.7% and 1.9%, respectively) ( < 0.05). Compared the differences of pesticide detection rates among five areas of Henan province, we found that there were statistical differences in the detection rates of procymidone, cypermethrin and lambda-cyhalothrin in different regions (all < 0.05).
Conclusion: The results have revealed that the pesticide residues are present. Higher detection rates and more types of pesticides were found in rural areas than urban areas. In addition, there were higher detection rates in Eastern Henan. The findings provided valuable information on the current pesticide residues status, which can be a reference of pesticide supervision and management.
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http://dx.doi.org/10.3389/fpubh.2022.901485 | DOI Listing |
J Infect Dev Ctries
December 2024
Faculty of Medicine, Sakarya University, Sakarya, Türkiye.
Introduction: The frequency of scabies and its relationship with the coronavirus disease 2019 (COVID-19) pandemic is a current scientific curiosity in Turkey and worldwide. The data presented in this article will help raise awareness of dermatologists in situations such as pandemic-induced quarantines where scabies can spread rapidly.
Methodology: This was a retrospective study to compare patients who presented with scabies and were evaluated during the COVID-19 pandemic, with those who presented before and after the pandemic, in terms of the diagnosis ratios.
Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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January 2025
Whales Initiative, Ocean Wise Conservation Association, Vancouver, BC, Canada.
The expansion of drone-based aerial imagery has facilitated an increase in data obtained from free-ranging marine mammal populations, in particular cetacean species. This non-invasive approach allows for body condition assessments, including nutritional and reproductive health. Yet, existing methods of image analysis are time-consuming and lack the granularity to determine early-stage pregnancies and miscarriage rates.
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