Out of the boxes,out of the silos: The need of interdisciplinary collaboration to reduce poor-quality medical products in the supply chain

In this paper, we argue that understanding and addressing the problem of poor-quality medical products requires a more interdisciplinary approach than has been evident to date. While prospective studies based on rigorous standardized methodologies are the gold standard for measuring the prevalence of poor-quality medical products and understanding their distribution nationally and internationally, they should be complemented by social science research to unpack the complex set of social, economic, and governance factors that underlie these patterns. In the following sections, we discuss specific examples of prospective quality surveys and of social science studies, highlighting the value of cross-sector partnerships in driving high-quality, policy-relevant research in this area.     

Utility of Differential White Cell Count and Cell Population Data for Ruling Out COVID-19 Infection in Patients With Community-Acquired Pneumonia

IntroductionThe main aim of this study was to assess the utility of differential white cell count and cell population data (CPD) for the detection of COVID-19 in patients admitted for community-acquired pneumonia (CAP) of different etiologies.MethodsThis was a multicenter, observational, prospective study of adults aged ≥18 years admitted to three teaching hospitals in Spain from November 2019 to November 2021 with a diagnosis of CAP. At baseline, a Sysmex XN-20 analyzer was used to obtain detailed information related to the activation status and functional activity of white cells.ResultsThe sample was split into derivation and validation cohorts of 1065 and 717 patients, respectively. In the derivation cohort, COVID-19 was confirmed in 791 patients and ruled out in 274 patients, with mean ages of 62.13 (14.37) and 65.42 (16.62) years, respectively (p < 0.001). There were significant differences in all CPD parameters except MO-Y. The multivariate prediction model showed that lower NE-X, NE-WY, LY-Z, LY-WY, MO-WX, MO-WY, and MO-Z values and neutrophil-to-lymphocyte ratio were related to COVID-19 etiology with an AUC of 0.819 (0.790, 0.846). No significant differences were found comparing this model to another including biomarkers (p = 0.18).ConclusionsAbnormalities in white blood cell morphology based on a few cell population data values as well as NLR were able to accurately identify COVID-19 etiology. Moreover, systemic inflammation biomarkers currently used were unable to improve the predictive ability. We conclude that new peripheral blood biomarkers can help determine the etiology of CAP fast and inexpensively.     

红外光谱数据融合对栽培滇重楼产地鉴别*

目的 多样的环境因素使得不同产地栽培滇重楼的化学成分也丰富多样，不同居群栽培滇重楼的甾体皂苷类成分具有很大的差异，多源数据融合分析能更全面的表征药材化学信息，建立一个高效而准确的产地鉴别模型，为其资源合理开发利用提供依据。方法 以来自云南和四川的8个产地（保山、楚雄、大理、红河、丽江、成都、文山、玉溪）共366份栽培滇重楼根茎为实验材料，采集其傅里叶变换近红外光谱（FT-NIR）和衰减全反射-傅里叶变换中红外光谱（ATR-FTMIR）数据。采用Kennard-Stone算法将不同产地的样品分为2/3的训练集和1/3的预测集，基于4种特征变量提取方法（CARS、VIP、SPA、SO-Covsel）结合2种数据融合策略（低级数据融合和中级数据融合），建立偏最小二乘产地判别分析模型。根据模型参数交叉验证均方根误差（RMSECV）和预测均方根误差（RMSEP）评估模型的稳定性，模型训练集和预测集准确率（ACC）评估模型分类性能。结果 近红外光谱和中红外光谱均能反应不同产地栽培滇重楼的化学成分差异，在中级数据融合中，基于VIP和SPA提取的特征变量建立的模型正确率均大于94%。相较于中级数据融合，低级数据融合模型得到了最为满意的结果，其预测集分类正确率达到100%。结论 根据近红外和中红外数据建立的低级数据融合PLS-DA模型，能够用于栽培滇重楼的产地鉴别分析。     

Non-canonical role of Hippo tumor suppressor serine/threonine kinase 3 STK3 in prostate cancer

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Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

BACKGROUND Esophageal squamous cell carcinoma(ESCC)is one of the most prevalent malignancies that seriously threaten people’s health worldwide.DEAD-box helicase 51(DDX51)is a member of the DEAD-box(DDX)RNA helicase family,and drives or inhibits tumor progression in multiple cancer types.AIM To determine whether DDX51 affects the biological behavior of ESCC.METHODS The expression of DDX51 in ESCC tumor tissues and adjacent normal tissues was detected by Immunohistochemistry(IHC)analyses and quantitative PCR(qPCR).We knocked down DDX51 in ESCC cell lines by using a small interfering RNA(siRNA)transfection.The proliferation,apoptosis,and mobility of DDX51 siRNAtransfected cells were detected.The effect of DDX51 on the phosphoinositide 3-kinase(PI3K)/AKT pathway was investigated by western blot analysis.A mouse xenograft model was established to investigate the effects of DDX51 knockdown on ESCC tumor growth.RESULTS DDX51 exhibited high expression in ESCC tissues compared with normal tissues and represented a poor prognosis in patients with ESCC.Knockdown of DDX51 induced inhibition of ESCC cell proliferation and promoted apoptosis.Moreover,DDX51 siRNA-expressing cells also exhibited lower migration and invasion rates.Investigations into the underlying mechanisms suggested that DDX51 knock down induced inactivation of the PI3K/AKT pathway,including decreased phosphorylation levels of phosphate and tensin homolog,PI3K,AKT,and mammalian target of rapamycin.Rescue experiments demonstrated that the AKT activator insulin-like growth factor 1 could reverse the inhibitory effects of DDX51 on ESCC malignant development.Finally,we injected DDX51 siRNA-transfected TE-1 cells into an animal model,which resulted in slower tumor growth.CONCLUSION Our study suggests for the first time that DDX51 promotes cancer cell proliferation by regulating the PI3K/AKT pathway;thus,DDX51 might be a therapeutic target for ESCC.     

Artificial intelligence and clinical data suggest the T cell-mediated SARS-CoV-2 nonstructural protein intranasal vaccines for global COVID-19 immunity

Advanced computational methodologies suggested SARS-CoV-2, nonstructural proteins ORF1AB, ORF3a, as the source of immunodominant peptides for T cell presentation. T cell immunity is long-lasting and compatible with COVID-19 pathology. Based on the supporting clinical data, nonstructural SARS-CoV-2 protein vaccines could provide global immunity against COVID-19.