国外动物智能军事应用情报研究

目的：阐述动物智能在军事学的应用及意义。方法：以学术论文、新闻、报纸、智库报告等作为信息源，综合研究国外动物智能军事应用实例及内在关联，描述动物智能在未来战场应用场景。结果：动物智能军事应用主要是动物特殊能力的军事应用和动物智能的仿生武器应用。结论：动物部队、仿生作战部队和动物战术是未来战争不可忽视的重要组成部分，动物智能的隐匿、灵活、多变等特点正逐渐被广泛应用于战场。     

医学期刊编辑对生物医学研究伦理问题的处理方式调研及建议

【目的】 探讨医学期刊编辑处理生物医学研究伦理问题的方式,为推动确立医学期刊伦理审查规范提供参考。【方法】 通过问卷星向国内医学期刊编辑发放调查问卷,对审稿时和论文出版后处理医学伦理相关问题的方式展开调研。【结果】 共回收问卷230份。调查显示:在审稿阶段,在保护患者隐私方面,英文期刊、中英双语期刊的编辑更注重要求作者提供授权同意相关证明材料;中、英文期刊以及中英双语期刊的编辑对涉及人体试验伦理问题的处理方式都比较规范。但是,对于研究是否获得患者知情同意、动物实验研究是否经过伦理委员会审查、回顾性研究是否需要伦理审查、涉及人的研究是否在临床试验注册中心注册等问题,编辑还需要进一步重视。在论文出版后,编辑发现的医学伦理问题主要是“涉及人的研究未说明是否经过伦理委员会审查”。对论文出版后发现的没有保护患者隐私和没有在临床试验注册中心注册的问题,大部分期刊缺乏相关处理方案。【结论】 编辑应加强对论文知情同意、动物实验伦理、回顾性研究伦理的审查,并审查涉及人的研究是否在临床试验注册中心注册,以推动我国医学期刊的高质量发展。     

Variability in non-core vaccination rates of dogs and cats in veterinary clinics across the United States

Vaccination guidelines for dogs and cats indicate that core vaccines (for dogs, rabies, distemper, adenovirus, parvovirus; for cats, feline parvovirus, herpes virus-1, calicivirus) are essential to maintain health, and that non-core vaccines be administered according to a clinician’s assessment of a pet’s risk of exposure and susceptibility to infection. A reliance on individual risk assessment introduces the potential for between-practice inconsistencies in non-core vaccine recommendations. A study was initiated to determine non-core vaccination rates of dogs (Leptospira, Borrelia burgdorferi, Bordetella bronchiseptica, canine influenza virus) and cats (feline leukemia virus) in patients current for core vaccines in veterinary practices across the United States. Transactional data for 5,531,866 dogs (1,670 practices) and 1,914,373 cats (1,661 practices) were retrieved from practice management systems for the period November 1, 2016 through January 1, 2020, deidentified and normalized. Non-core vaccination status was evaluated in 2,798,875 dogs and 788,772 cats that were core-vaccine current. Nationally, median clinic vaccination rates for dogs were highest for leptospirosis (70.5%) and B. bronchiseptica (68.7%), and much lower for canine influenza (4.8%). In Lyme-endemic states, the median clinic borreliosis vaccination rate was 51.8%. Feline leukemia median clinic vaccination rates were low for adult cats (34.6%) and for kittens and 1-year old cats (36.8%). Individual clinic vaccination rates ranged from 0 to 100% for leptospirosis, B. bronchiseptica and feline leukemia, 0–96% for canine influenza, and 0–94% for borreliosis. Wide variation in non-core vaccination rates between clinics in similar geographies indicates that factors other than disease risk are driving the use of non-core vaccines in pet dogs and cats, highlighting a need for veterinary practices to address gaps in patient protection. Failure to implement effective non-core vaccination strategies leaves susceptible dogs and cats unprotected against vaccine-preventable diseases.     

骨质疏松症动物模型的构建及实验方法研究进展

骨质疏松症是一种全身性骨代谢病,由多种因素引起,其特点是骨量、骨密度和骨组织的显微结构恶化,骨脆性增强和易发生骨折。骨质疏松症已严重威胁人类健康,最终将导致患者日常活动减少,生活质量降低,死亡率增加。针对不同的研究对象,选择一个正确、理想的动物骨质疏松的模型和实验方法以尽可能再现人类骨质疏松的状态,是开展动物实验的关键。我们综述了当前原发性和继发性骨质疏松等动物模型的构建,以及利用这些模型进行给药实验的研究方法,并对研发治疗骨质疏松的新型药物的选择提供了一些参考建议,以期对后续关于药物的作用机制、研发新药、药物的改良等研究提供思路。     

Efficacy of recombinant Marek’s disease virus vectored vaccines with computationally optimized broadly reactive antigen (COBRA) hemagglutinin insert against genetically diverse H5 high pathogenicity avian influenza viruses

The genetic and antigenic drift associated with the high pathogenicity avian influenza (HPAI) viruses of Goose/Guangdong (Gs/GD) lineage and the emergence of vaccine-resistant field viruses underscores the need for a broadly protective H5 influenza A vaccine. Here, we tested experimental vector herpesvirus of turkey (vHVT)-H5 vaccines containing either wild-type clade 2.3.4.4A-derived H5 inserts or computationally optimized broadly reactive antigen (COBRA) inserts with challenge by homologous and genetically divergent H5 HPAI Gs/GD lineage viruses in chickens. Direct assessment of protection was confirmed for all the tested constructs, which provided clinical protection against the homologous and heterologous H5 HPAI Gs/GD challenge viruses and significantly decreased oropharyngeal shedding titers compared to the sham vaccine. The cross reactivity was assessed by hemagglutinin inhibition (HI) and focus reduction assay against a panel of phylogenetically and antigenically diverse H5 strains. The COBRA-derived H5 inserts elicited antibody responses against antigenically diverse strains, while the wild-type-derived H5 vaccines elicited protection mostly against close antigenically related clades 2.3.4.4A and 2.3.4.4D viruses. In conclusion, the HVT vector, a widely used replicating vaccine platform in poultry, with H5 insert provides clinical protection and significant reduction of viral shedding against homologous and heterologous challenge. In addition, the COBRA-derived inserts have the potential to be used against antigenically distinct co-circulating viruses and future drift variants.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.