Synthetic musks in the natural environment: Sources, occurrence, concentration, and fate-A review of recent developments (2010-2023).

Synthetic musks (SMs) have served as cost-effective substitutes for natural musk compounds in personal care and daily chemical products for decades. Their widespread use has led to their detection in various environmental matrices, raising concerns about potential risks. Despite numerous studies on SM levels in different natural environments, a systematic review of their contemporary presence is lacking. This review aims to address this gap by summarising recent research developments on SMs in diverse natural environments, including river water, lake water, seawater, estuarine water, groundwater, snow, meltwater, sediments, aquatic suspended matter, soils, sands, outdoor air, and atmospheric particulate matter. Covering the period from 2010 to 2023, the review focuses on four SM categories: nitro, polycyclic, macrocyclic, and alicyclic. It systematically examines their sources, occurrences, concentrations, spatial and temporal variations, and fate. The literature reveals widespread detection of SMs in the natural environment (freshwater and sediments in particular), with polycyclic musks being the most studied group. Both direct (e.g., wastewater discharges) and indirect (e.g., human recreational activities) sources contribute to SM presence. Levels of SMs vary greatly among studies with higher levels observed in certain regions, such as sediments in Southeast Asia. Spatial and temporal variations are also evident. The fate of SMs in the environment depends on their physicochemical properties and environmental processes, including bioaccumulation, biodegradation, photodegradation, adsorption, phase exchange, hydro-dilution effects. Biodegradation and photodegradation can decrease SM levels, but may produce more persistent and eco-toxic products. Modelling approaches have been employed to analyse SM fate, especially for indirect processes like photodegradation or long-distance atmospheric transport. Future studies should further investigate the complex fate if SMs and their environmental influence. This review enhances understanding of SM status in the natural environment and supports efforts to control environmental contamination.

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video).

BACKGROUND AND AIMS: Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. METHODS: Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. RESULTS: The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. CONCLUSIONS: The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study.

BACKGROUND AND AIM: Diagnosis of esophageal squamous cell carcinoma (ESCC) is complicated and requires substantial expertise and experience. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC under multiple endoscopic imaging modalities. METHODS: Endoscopic images were retrospectively collected from West China Hospital, Sichuan University as a training dataset and an independent internal validation dataset. Images from other four hospitals were used as an external validation dataset. The AI system was compared with 11 experienced endoscopists. Furthermore, videos were collected to assess the performance of the AI system. RESULTS: A total of 53 933 images from 2621 patients and 142 videos from 19 patients were used to develop and validate the AI system. In the internal and external validation datasets, the performance of the AI system under all or different endoscopic imaging modalities was satisfactory, with sensitivity of 92.5-99.7%, specificity of 78.5-89.0%, and area under the receiver operating characteristic curves of 0.906-0.989. The AI system achieved comparable performance with experienced endoscopists. Regarding superficial ESCC confined to the epithelium, the AI system was more sensitive than experienced endoscopists on white-light imaging (90.8% vs 82.5%, P = 0.022). Moreover, the AI system exhibited good performance in videos, with sensitivity of 89.5-100% and specificity of 73.7-89.5%. CONCLUSIONS: We developed an AI system that showed comparable performance with experienced endoscopists in detecting superficial ESCC under multiple endoscopic imaging modalities and might provide valuable support for inexperienced endoscopists, despite requiring further evaluation.

Optical characterization of Chinese hybrid rice using laser-induced fluorescence techniques-laboratory and remote-sensing measurements: publisher's note.

This publisher's note corrects the author listing and a figure caption in Appl. Opt.57, 3481 (2018)APOPAI0003-693510.1364/AO.57.003481.

Peptide toxin components of Amanita exitialis basidiocarps.

Eight peptide toxins were isolated and purified from basidiocarps of Amanita exitialis with high performance liquid chromatography and were subjected to ultraviolet, nuclear magnetic resonance and mass spectrometry. We identified seven peptide toxins, α-amanitin, ß-amanitin, amaninamide, phallacin, phallacidin, phallisacin and desoxoviroidin. The molecular weight (729.5 Da) of the eighth compound did not match that of any reported Amanita toxins and, although the UV absorption spectrum indicated it to be a phallotoxin, further studies are required to identify this component. This is the first report of amaninamide, phallacin, phallisacin and desoxoviroidin in this lethal mushroom species.