Mycotoxicosis – diagnosis,prevention and control: past practices and future perspectives

Abstract

Mycotoxins pose severe health hazards in animals, humans, and poultry birds. More than 400 chemically different mycotoxins have been identified to date. Twenty-five percent of world’s crops are potentially contaminated with mycotoxins. Luckily, nature has provided the ruminants a unique property of inactivating and detoxifying most of the mycotoxins with the help of microflora and microfauna present within their ruminal fluid; however, unfortunately avian species lack such ability putting them at high risk to the deleterious effects of mycotoxins. This review elaborates different strategies for diagnosis, prevention and control of mycotoxins.     

Lung function in children: a simple guide to performing and interpreting spirometry

Spirometry is the measurement of the volume and flow of air during expiration and inspiration. It is non-invasive and inexpensive and probably under-used in children. Whilst remaining a relatively simple test, it gives valuable information that can be used in the diagnosis and monitoring of respiratory conditions. Adequate spirometry requires good patient engagement, effort and technique. With practice, robust spirometry data can be collected in preschool children as young as 4 years of age. Increasing availability of portable spirometry equipment means that good quality data can be collected in a variety of clinical locations outside the lung function laboratory - even in the patient's own home. There are, however, a number of important considerations in both the performance and interpretation of spirometry. This review considers the equipment required, the basic techniques required and the essentials of interpretation of spirometry data.     

超声功能成像在脂肪性肝病研究中的应用进展

脂肪性肝病(Fatty Liver Disease)是我国目前最常见的肝脏病变,主要与过多脂肪蓄积在肝细胞内有关。患者早期常无明显临床表现,晚期有进展为肝硬化的风险。因此,应着眼于早期诊断和预后的判断。超声检查具有无创、便捷、实时、廉价及无辐射等特点,在临床工作中应用广泛。随着超声检查新技术的不断发展,以超声检查新技术为基础的功能成像在脂肪性肝病诊断方面显示出较大的优势,本文主要针对超声功能成像在脂肪性肝病中的应用予以总结分析。     

Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms:sourced from retrospective studies

Although intracranial hemorrhage in moyamoya disease can occur repeatedly, predicting the disease is difficult. Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors, evaluating the weight of different factors, and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease. To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes, we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling, including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes. Another 500 hemispheres with normal vessel appearance were selected as negative samples. We used deep residual neural network(Res Net-152) algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery, then trained and validated the model. The accuracy, sensitivity, and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64 ± 0.87%, 96.55 ± 3.44%, and 98.29 ± 0.98%, respectively. The area under the receiver operating characteristic curve was 0.990. We used a combined multi-view conventional neural network algorithm to integrate age, sex, and hemorrhagic factors with features of the digital subtraction angiography. The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69 ± 1.58% and the sensitivity and specificity were 94.12 ± 2.75% and 89.86 ± 3.64%, respectively. The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, China(approved No. 2014-278) on January 12, 2015.     

基因组拷贝数变异测序与染色体核型分析在胎儿遗传学诊断中的比较

目的比较基因组拷贝数变异测序(CNV-seq)技术和染色体核型分析和在产前胎儿遗传学诊断中的应用价值。方法收集来我院有产前诊断指征进行羊水穿刺的259例孕妇,取材后,送检染色体核型分析和CNV-seq,比较两种方法在产前诊断中的优缺点。结果259例标本中,共诊断异常染色体核型及微缺失微重复23例,总阳性诊断率8.88%(23/259),CNV-seq结果显示,共有22例染色体拷贝数异常(12例三体+9例微缺失微重复+1例三倍体),检出率为8.49%;染色体核型分析结果显示为:17例染色体异常(12例三体+3例结构异常+1例嵌合型+1例三倍体),检出率为6.56%。此外还检出染色体多态7例。结论CNV-seq与染色体核型分析对于染色体非整倍体的检测效力一致,CNV-seq能检测染色体微缺失微重复,染色体核型分析则能诊断出三体具体核型,在怀疑性染色体异常时,建议行两者联合检测。