In accordance with Article 6 of Regulation (EC) No 396/2005, the applicant BASF Agro B.V. Arnhem (NL) Freienbach Branch submitted a request to the competent national authority in Austria to modify the existing maximum residue levels (MRLs) for the active substance mefentrifluconazole in various crops and swine liver and other swine products. The data submitted in support of the request were found to be sufficient to derive MRL proposals. Adequate analytical methods for enforcement are available to control the residues of mefentrifluconazole in the plant commodities under consideration and in animal matrices at the validated limit of quantification (LOQ) of 0.01 mg/kg. New data relevant to the data gaps on storage stability and feeding studies of triazole derivative metabolites (TDMs), that were identified during the peer review of confirmatory data of the TDMs, were submitted in support of the present MRL application. Based on the risk assessment results, EFSA concluded that the short-term and long-term intake of residues resulting from the use of mefentrifluconazole according to the reported agricultural practices is unlikely to present a risk to consumer health. EFSA noted a narrow safety margin with regard to acute exposure to mefentrifluconazole residues from the intake of spinaches if residues occur at the level of the proposed MRL. EFSA also performed an indicative risk assessment for the TDMs based on uses of mefentrifluconazole only. The estimated exposure for TDMs did not exceed the toxicological reference values.
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http://dx.doi.org/10.2903/j.efsa.2023.8237 | DOI Listing |
Biomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
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PLoS One
January 2025
Department of Orthopedics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China.
Purpose: The present study is to explore the appropriate plantar support force for its effect on improving the collapse of the medial longitudinal arch with flexible flatfoot.
Methods: A finite element model with the plantar fascia attenuation was constructed simulating as flexible flatfoot. The appropriate plantar support force was evaluated.
PLoS One
January 2025
Colorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
This study aimed to identify radiotherapy dosimetric parameters related to local failure (LF)-free survival (LFFS) in patients with lung and liver oligometastases from colorectal cancer treated with stereotactic body radiotherapy (SBRT). We analyzed 75 oligometastatic lesions in 55 patients treated with SBRT between January 2014 and December 2021. There was no constraint or intentional increase in maximum dose.
View Article and Find Full Text PDFPLoS One
January 2025
Department of ENT, Head and Neck Surgery, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, India.
Aim: The perspectives and practices of healthcare professionals regarding ototoxicity in individuals with head and neck cancers are important for the implementation of ototoxicity monitoring. The current study aims to explore the oncologist's awareness and perspectives of ototoxicity and ototoxicity monitoring for individuals with head and neck cancer in a South-Indian district, using qualitative semi-structured interviews.
Method: The COnsolidated criteria for REporting Qualitative research (COREQ) Checklist was used to guide the method of the current qualitative study.
PLoS One
January 2025
Vocational Training Center, FoShan Open University, FoShan, Guangdong Province, China.
Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!