Artemisinin and its derivatives in treating protozoan infections beyond malaria.

Parasitic protozoan diseases continue to rank among the world's greatest global health problems, which are also common among poor populations. Currently available drugs for treatment present drawbacks, urging the need for more effective, safer, and cheaper drugs. Artemisinin (ART) and its derivatives are some of the most important classes of antimalarial agents originally derived from Artemisia annua L. However, besides the outstanding antimalarial and antischistosomal activities, ART and its derivatives also possess activities against other parasitic protozoa. In this paper we review the activities of ART and its derivatives against protozoan parasites in vitro and in vivo, including Leishmania spp., Trypanosoma spp., Toxoplasma gondii, Neospora caninum, Eimeria tenella, Acanthamoeba castellanii, Naegleria fowleri, Cryptosporidium parvum, Giardia lamblia, and Babesia spp. We conclude that ART and its derivatives may be good alternatives for treating other non-malarial protozoan infections in developing countries, although more studies are necessary before they can be applied clinically.

Incidence and Characteristics of Retinopathy of Prematurity Patients With Late Gestational Age and Large Birth Weight in South China.

Purpose: To investigate the incidence and characteristics of retinopathy of prematurity (ROP) premature infants with late gestational age (GA) and large birth weight (BW) and show a 7-year trend of ROP incidence in South China. Methods: This retrospective, cross-sectional study included premature infants who received ROP screening in a 7-year period (from 2010 to 2016) at the Sun Yat-sen Memorial Hospital (SYSMH), Guangzhou, South China. Infants were screened if they had GA <37 weeks or BW <2,500 g. All screened infants were divided into two groups: Group 1 (with both GA ≥ 35 weeks and BW ≥ 1,750 g) and Group 2 (others). The characteristics of ROP infants in Group 1 were analyzed and compared with those in Group 2. Results: A total of 911 premature infants were screened, with 282 infants in Group 1 and 629 in Group 2. Both the incidences of any ROP (6.7 vs. 8.3%, p = 0.50) and Type 1 ROP (1.4 vs. 1.7%, p = 0.72) in Group 1 were comparable with those in Group 2. Lower proportions of respiratory distress (15.8 vs. 71.2%, p < 0.001), blood transfusion (5.3 vs. 32.7%, p = 0.028), and oxygen administration (31.6 vs. 86.5%, p < 0.001) among ROP patients in Group 1 than those in Group 2 were revealed. Vaginal delivery [OR: 4.73 (1.83-12.26)] was identified as a factor associated with ROP among the infants in Group 1. Forty percent (6/15) of Type 1 ROP in this study would have been missed under the current screening criteria in China (GA ≤ 34 weeks and/or BW ≤ 2,000 g). Trends of increased incidence of Type 1 ROP and decreased BW were exhibited in the 7-year study period. Conclusions: These findings indicate that even the premature infants with late GA and large BW also have a high risk of developing ROP, especially for those delivered by vagina. The findings may provide a significant reference for ROP screening and neonatal care in South China and other regions with similar conditions.

Computerized assisted evaluation system for canine cardiomegaly via key points detection with deep learning.

Cardiomegaly is the main imaging finding for canine heart diseases. There are many advances in the field of medical diagnosing based on imaging with deep learning for human being. However there are also increasing realization of the potential of using deep learning in veterinary medicine. We reported a clinically applicable assisted platform for diagnosing the canine cardiomegaly with deep learning. VHS (vertebral heart score) is a measuring method used for the heart size of a dog. The concrete value of VHS is calculated with the relative position of 16 key points detected by the system, and this result is then combined with VHS reference range of all dog breeds to assist in the evaluation of the canine cardiomegaly. We adopted HRNet (high resolution network) to detect 16 key points (12 and four key points located on vertebra and heart respectively) in 2274 lateral X-ray images (training and validation datasets) of dogs, the model was then used to detect the key points in external testing dataset (396 images), the AP (average performance) for key point detection reach 86.4 %. Then we applied an additional post processing procedure to correct the output of HRNets so that the AP reaches 90.9 %. This result signifies that this system can effectively assist the evaluation of canine cardiomegaly in a real clinical scenario.

Systemically modeling the relationship between climate change and wheat aphid abundance.

Climate change influences all living beings. Wheat aphids deplete the nutritional value of wheat and affect the production of wheat in changing climate. In this study, we attempt to explain the ecological mechanisms of how climate change affects wheat aphids by simulating the relationship between climate and the abundance of wheat aphids, which will not only aid in improving wheat aphid forecasting and the effectiveness of prevention and treatment, but also help mitigate food crises. Fuzzy cognitive maps (FCM) are an effective tool for portraying complex systems. Using Sitobion avenae and climatological data collected in China, we made use of differential evolution (DE) algorithms to construct FCM models that directly illustrate the effect of climate on wheat aphid abundance. The relationships among climate and wheat aphids at different growth stages (I-III instar larvae, IV instar larvae with wings, IV instar larvae without wings, adult with wings, adult without wings) were established. The analysis results from the FCM models show that temperature positively influences wheat aphids most. Moreover, these models can be used to determine the numerical value of each climate factor and the abundance of wheat aphids quantitatively. Furthermore, the two overall relationship models between climate and wheat aphids were constructed and the experimental results show that natural enemies and highest daily temperature affect wheat aphids most. Natural enemies and highest daily temperature exert negative and positive impacts on wheat aphids respectively. Some interrelationships among wheat aphids at all growth stages and the internal relationships among climate factors were also shown.

Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.

Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs and LSTM (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model's performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths. The quantitative results demonstrated that our proposed model offers exceptional performance with mean accuracy (92.22), sensitivity (88.55), specificity (94.31) and AUC (97.18). Moreover, the model achieves real-time prediction with only 27.6ms for single sequence, and simultaneously predicts sequence data with lengths of 3-5. Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields.