Mapping global zoonotic niche and interregional transmission risk of monkeypox: a retrospective observational study.

BACKGROUND: Outbreaks of monkeypox have been ongoing in non-endemic countries since May 2022. A thorough assessment of its global zoonotic niche and potential transmission risk is lacking. METHODS: We established an integrated database on global monkeypox virus (MPXV) occurrence during 1958 - 2022. Phylogenetic analysis was performed to examine the evolution of MPXV and effective reproductive number (Rt) was estimated over time to examine the dynamic of MPXV transmissibility. The potential ecological drivers of zoonotic transmission and inter-regional transmission risks of MPXV were examined. RESULTS: As of 24 July 2022, a total of 49 432 human patients with MPXV infections have been reported in 78 countries. Based on 525 whole genome sequences, two main clades of MPXV were formed, of which Congo Basin clade has a higher transmissibility than West African clade before the 2022-monkeypox, estimated by the overall Rt (0.81 vs. 0.56), and the latter significantly increased in the recent decade. Rt of 2022-monkeypox varied from 1.14 to 4.24 among the 15 continuously epidemic countries outside Africa, with the top three as Peru (4.24, 95% CI: 2.89-6.71), Brazil (3.45, 95% CI: 1.62-7.00) and the United States (2.44, 95% CI: 1.62-3.60). The zoonotic niche of MPXV was associated with the distributions of Graphiurus lorraineus and Graphiurus crassicaudatus, the richness of Rodentia, and four ecoclimatic indicators. Besides endemic areas in Africa, more areas of South America, the Caribbean States, and Southeast and South Asia are ecologically suitable for the occurrence of MPXV once the virus has invaded. Most of Western Europe has a high-imported risk of monkeypox from Western Africa, whereas France and the United Kingdom have a potential imported risk of Congo Basin clade MPXV from Central Africa. Eleven of the top 15 countries with a high risk of MPXV importation from the main countries of 2022-monkeypox outbreaks are located at Europe with the highest risk in Italy, Ireland and Poland. CONCLUSIONS: The suitable ecological niche for MPXV is not limited to Africa, and the transmissibility of MPXV was significantly increased during the 2022-monkeypox outbreaks. The imported risk is higher in Europe, both from endemic areas and currently epidemic countries. Future surveillance and targeted intervention programs are needed in its high-risk areas informed by updated prediction.

Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.

OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenatal diagnosis. METHODS: Normal and abnormal fetal ultrasound heart images, including five standard views, were collected according to the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) Practice guidelines. You Only Look Once version 5 (YOLOv5) models were trained and tested. An excellent model was screened out after comparing YOLOv5 with other classic detection methods. RESULTS: On the training set, YOLOv5n performed slightly better than the others. On the validation set, YOLOv5n attained the highest overall accuracy (90.67 %). On the CHD test set, YOLOv5n, which only needed 0.007 s to recognize each image, had the highest overall accuracy (82.93 %), and YOLOv5l achieved the best accuracy on the abnormal dataset (71.93 %). On the VSD test set, YOLOv5l had the best performance, with a 92.79 % overall accuracy rate and 92.59 % accuracy on the abnormal dataset. The YOLOv5 models achieved better performance than the Fast region-based convolutional neural network (RCNN) & ResNet50 model and the Fast RCNN & MobileNetv2 model on the CHD test set (p<0.05) and VSD test set (p<0.01). CONCLUSIONS: YOLOv5 models are able to accurately distinguish normal and abnormal fetal heart ultrasound images, especially with respect to the identification of VSD, which have the potential to assist ultrasound in prenatal diagnosis.

Pt modified NiMoO4-GO/NF nanorods withstrong metal-support interaction as efficient bifunctional catalysts for overall water splitting.

Catalysts for the electrolysis of water are critical in the production of hydrogen for the energy industry. The use of strong metal-support interactions (SMSI) to modulate the dispersion, electron distribution, and geometry of active metals is an effective strategy for improving catalytic performance. However, in currently used catalysts, the supporting effect does not significantly contribute directly to catalytic activity. Consequently, the continued investigation of SMSI, using active metals to stimulate the supporting effect for catalytic activity, remains very challenging. Herein, the atomic layer deposition technique was employed to prepare an efficient catalyst composed of platinum nanoparticles (Pt NPs) deposited on nickel-molybdate (NiMoO4) nanorods. Nickel-molybdate's oxygen vacancies (Vo) not only help anchor highly-dispersed Pt NPs with low loading but also strengthen the SMSI. The valuable electronic structure modulation between Pt NPs and Vo resulted in a low overpotential of the hydrogen and oxygen evolution reactions, returning results of 190 mV and 296 mV, respectively, at a current density of 100 mA cm-2 in 1 M KOH. Ultimately, an ultralow potential (1.515 V) for the overall decomposition of water was achieved at 10 mA cm-2, outperforming state-of-art catalysts based on the Pt/C || IrO2 couple (1.668 V). This work aims to provide reference and a concept for the design of bifunctional catalysts that apply the SMSI effect to achieve a simultaneous catalytic effect from the metal and its support.

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.

Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the prognosis of the fetus to a certain extent. At present, prenatal diagnosis of CHD mainly uses 2D ultrasound to directly evaluate the development and function of fetal heart and main structures in the second trimester of pregnancy. Artificial recognition of fetal heart 2D ultrasound is a highly complex and tedious task, which requires a long period of prenatal training and practical experience. Compared with manual scanning, computer automatic identification and classification can significantly save time, ensure efficiency, and improve the accuracy of diagnosis. In this paper, an effective artificial intelligence recognition model is established by combining ultrasound images with artificial intelligence technology to assist ultrasound doctors in prenatal ultrasound fetal heart standard section recognition. The method data in this paper were obtained from the Second Affiliated Hospital of Fujian Medical University. The fetal apical four-chamber heart section, three vessel catheter section, three vessel trachea section, right ventricular outflow tract section, and left ventricular outflow tract section were collected at 20-24 weeks of gestation. 2687 image data were used for model establishment, and 673 image data were used for model validation. The experiment shows that the map value of this method in identifying different anatomical structures reaches 94.30%, the average accuracy rate reaches 94.60%, the average recall rate reaches 91.0%, and the average F1 coefficient reaches 93.40%. The experimental results show that this method can effectively identify the anatomical structures of different fetal heart sections and judge the standard sections according to these anatomical structures, which can provide an auxiliary diagnostic basis for ultrasound doctors to scan and lay a solid foundation for the diagnosis of congenital heart disease.