Utility of QuantiFERON®-TB Gold In-Tube Test Compared with Tuberculin Skin Test in Diagnosing Tuberculosis in Indian Children with Malnutrition

This prospective cohort study was conducted to compare the accuracy of QuantiFERON®-TB (QFT) Gold In-Tube test and tuberculin skin test (TST) in diagnosing tuberculosis (TB) in predominantly bacille Calmette–Guerin-vaccinated children with a high incidence of malnutrition. The sensitivity of the QFT versus the TST was 69.6% versus 52.9% for WHO-defined TB, with specificity of 86% versus 78.3%, respectively. The concordance of the TST and QFT was 79% overall (κ = 0.430), 62.5% in those with WHO-defined TB and 85.7% in those without TB. Majority of the QFT+/TST − discordance was seen in children with TB, whereas majority of the TST+/QFT − discordance was seen in those without TB. The TST was more likely to be negative in children with moderate-to-severe malnutrition (P = 0.003) compared to the QFT, which was more likely to be positive in younger children. The significantly better performance of the QFT in malnourished children and those at younger ages supports its use for TB diagnosis in these subpopulations.     

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.     

Germline gain-of-function MMP11 variant results in an aggressive form of colorectal cancer

Matrix metalloproteinase-11 (MMP11) is an enzyme with proteolytic activity against matrix and nonmatrix proteins. Although most MMPs are secreted as inactive proenzymes and are later activated extracellularly, MMP11 is activated intracellularly by furin within the constitutive secretory pathway. It is a key factor in physiological tissue remodeling and its alteration may play an important role in the progression of epithelial malignancies and other diseases. TCGA colon and colorectal adenocarcinoma data showed that upregulation of MMP11 expression correlates with tumorigenesis and malignancy. Here, we provide evidence that a germline variant in the MMP11 gene (NM_005940: c.232C>T; p.(Pro78Ser)), identified by whole exome sequencing, can increase the tumorigenic properties of colorectal cancer (CRC) cells. P78S is located in the prodomain region, which is responsible for blocking MMP11's protease activity. This variant was detected in the proband and all the cancer-affected family members analyzed, while it was not detected in healthy relatives. In silico analyses predict that P78S could have an impact on the activation of the enzyme. Furthermore, our in vitro analyses show that the expression of P78S in HCT116 cells increases tumor cell invasion and proliferation. In summary, our results show that this variant could modify the structure of the MMP11 prodomain, producing a premature or uncontrolled activation of the enzyme that may contribute to an early CRC onset in these patients. The study of this gene in other CRC cases will provide further information about its role in CRC development, which might improve patient treatment in the future.     

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.