Development of an IgG-Fc fusion COVID-19 subunit vaccine,AKS-452

AKS-452 is a biologically-engineered vaccine comprising an Fc fusion protein of the SARS-CoV-2 viral spike protein receptor binding domain antigen (Ag) and human IgG1 Fc (SP/RBD-Fc) in clinical development for the induction and augmentation of neutralizing IgG titers against SARS-CoV-2 viral infection to address the COVID-19 pandemic. The Fc moiety is designed to enhance immunogenicity by increasing uptake via Fc-receptors (FcγR) on Ag-presenting cells (APCs) and prolonging exposure due to neonatal Fc receptor (FcRn) recycling. AKS-452 induced approximately 20-fold greater neutralizing IgG titers in mice relative to those induced by SP/RBD without the Fc moiety and induced comparable long-term neutralizing titers with a single dose vs. two doses. To further enhance immunogenicity, AKS-452 was evaluated in formulations containing a panel of adjuvants in which the water-in-oil adjuvant, Montanide™ ISA 720, enhanced neutralizing IgG titers by approximately 7-fold after one and two doses in mice, including the neutralization of live SARS-CoV-2 virus infection of VERO-E6 cells. Furthermore, ISA 720-adjuvanted AKS-452 was immunogenic in rabbits and non-human primates (NHPs) and protected from infection and clinical symptoms with live SARS-CoV-2 virus in NHPs (USA-WA1/2020 viral strain) and the K18 human ACE2-trangenic (K18-huACE2-Tg) mouse (South African B.1.351 viral variant). These preclinical studies support the initiation of Phase I clinical studies with adjuvanted AKS-452 with the expectation that this room-temperature stable, Fc-fusion subunit vaccine can be rapidly and inexpensively manufactured to provide billions of doses per year especially in regions where the cold-chain is difficult to maintain.     

Correlation between cow’s milk protein allergy and otitis media: a systematic review

ObjectivesTo review the evidence pertaining to the association between cow’s milk protein allergy and recurrent acute otitis media and otitis media with effusion.MethodsThe CENTRAL, Web of Science, EMBASE, MEDLINE, LILACS databases, and gray literature were searched.ResultsFour studies were included, identifying the prevalence rates: 0.2% of delayed speech due to chronic otitis media with effusion in 382 children with cow’s milk protein allergy, 10.7% of cow’s milk protein allergy in 242 children who underwent ENT procedures, 40% of cow’s milk protein allergy in 25 children with recurrent otitis media with effusion and higher tendency to otitis media in children with cow’s milk protein allergy of 186 children (1.5 + 0.6 vs. 0.4 + 0.1; p < 0.1).ConclusionConsidering the characteristics and methodological variations of the identified studies, it is not possible to state that there is reliable evidence of an association between cow’s milk protein allergy and otitis media.     

行为-结构化-关系干预模式对短期住院孤独症谱系障碍儿童的疗效分析

目的 探讨以行为-结构化-关系(BSR)干预模式对短期住院孤独症谱系障碍(ASD)儿童的干预疗效,为改善ASD儿童的预后提供参考依据。方法 选取2015年12月－2016年12月确诊为ASD的2~6岁儿童141例,随机分为治疗组和对照组。治疗组ASD儿童均接受BSR模式的短期课程训练,训练课程包括个别辅导、游戏课、运动课、音乐课等,每天训练时间6 h,持续1个月;对照组ASD儿童处于干预等待,接受随访观察和评估。所有ASD儿童干预前、后均接受儿童心理教育评估(第三版)(PEP-3)来进行各方面能力变化的评估。结果 治疗组、对照组干预前各副测验的原积分差异无统计学意义(P>0.05)。两组ASD儿童干预后认知、语言理解、模仿、情感表达、社会互动、行为特征-非语言、适应行为的原积分比较,差异均有统计学意义(t=2.41、2.02、4.14、3.69、4.42、2.69、2.96,P<0.05);但小肌肉、大肌肉、自理的原积分比较,差异无统计学意义(t=-1.13、-1.05、-0.84,P>0.05)。结论 BSR干预模式能够有效改善短期住院ASD儿童的预后,可推广用于儿童ASD的治疗。     

疏肝健脾法治疗腹泻型肠易激综合征1例

肠易激综合征（IBS）是一种常见的功能性疾病，中医病名为"肠郁"。郭朋教授根据IBS生理、病理特点，以疏肝健脾法调和肝脾，辨证施治，取得良好疗效。文章从病因病机、辨证论治及经典案例介绍等几个方面对郭教授治疗IBS经验进行系统阐述，以期为中医药治疗IBS提供理论和方法学参考。     

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.