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Natural heterogeneity in patient populations can make it very hard to develop treatments that benefit all patients. As a result, an important goal of precision medicine is identification of patient subgroups that respond to treatment at a much higher (or lower) rate than the population average. Despite there being many subgroup identification methods, there is no comprehensive comparative study of their statistical properties. We review 13 methods and use real-world and simulated data to compare the performance of their publicly available software using seven criteria:(a) bias in selection of subgroup variables, (b) probability of false discovery, (c) probability of identifying correct predictive variables, (d) bias in estimates of subgroup treatment effects, (e) expected subgroup size, (f) expected true treatment effect of subgroups, and (g) subgroup stability. The results show that many methods fare poorly on at least one criterion.

Cell entry by SARS-CoV-2 requires the binding between the receptor-binding domain (RBD) of the viral Spike protein and the cellular angiotensin-converting enzyme 2 (ACE2). As such, RBD has become the major target for vaccine development, while RBD-specific antibodies are pursued as therapeutics. Here, we report the development and characterization of SARS-CoV-2 RBD-specific VHH/nanobody (Nb) from immunized alpacas. Seven RBD-specific Nbs with high stability were identified using phage display. They bind to SARS-CoV-2 RBD with affinity KD ranging from 2.6 to 113 nM, and six of them can block RBD-ACE2 interaction. The fusion of the Nbs with IgG1 Fc resulted in homodimers with greatly improved RBD-binding affinities (KD ranging from 72.7 pM to 4.5 nM) and nanomolar RBD-ACE2 blocking abilities. Furthermore, the fusion of two Nbs with non-overlapping epitopes resulted in hetero-bivalent Nbs, namely aRBD-2-5 and aRBD-2-7, with significantly higher RBD binding affinities (KD of 59.2 pM and 0.25 nM) and greatly enhanced SARS-CoV-2 neutralizing potency. The 50% neutralization dose (ND50) of aRBD-2-5 and aRBD-2-7 was 1.22 ng/mL (∼0.043 nM) and 3.18 ng/mL (∼0.111 nM), respectively. These high-affinity SARS-CoV-2 blocking Nbs could be further developed into therapeutics as well as diagnostic reagents for COVID-19. Importance To date, SARS-CoV-2 has caused tremendous loss of human life and economic output worldwide. Although a few COVID-19 vaccines have been approved in several countries, the development of effective therapeutics, including SARS-CoV-2 targeting antibodies, remains critical. Due to their small size (13-15 kDa), high solubility, and stability, Nbs are particularly well suited for pulmonary delivery and more amenable to engineer into multivalent formats than the conventional antibody. Here, we report a series of new anti-SARS-CoV-2 Nbs isolated from immunized alpaca and two engineered hetero-bivalent Nbs. These potent neutralizing Nbs showed promise as potential therapeutics against COVID-19.

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There are many methods of scoring the importance of variables in prediction of a response but not much is known about their accuracy. This paper partially fills the gap by introducing a new method based on the GUIDE algorithm and comparing it with 11 existing methods. For data without missing values, eight methods are shown to give biased scores that are too high or too low, depending on the type of variables (ordinal, binary or nominal) and whether or not they are dependent on other variables, even when all of them are independent of the response. Among the remaining four methods, only GUIDE continues to give unbiased scores if there are missing data values. It does this with a self-calibrating bias-correction step that is applicable to data with and without missing values. GUIDE also provides threshold scores for differentiating important from unimportant variables with 95 and 99 percent confidence. Correlations of the scores to the predictive power of the methods are studied in three real data sets. For many methods, correlations with marginal predictive power are much higher than with conditional predictive power.

Monitoring the levels of SARS-CoV-2 specific antibodies such as IgG, M and A in COVID-19 patient is an alternative method for diagnosing SARS-CoV-2 infection and an simple way to monitor immune responses in convalescent patients and after vaccination. Here, we assessed the levels of SARS-CoV-2 RBD specific antibodies in twenty-seven COVID-19 convalescent patients over 28-99 days after hospital discharge. Almost all patient who had severe or moderate COVID-19 symptoms and a high-level of IgG during the hospitalization showed a significant reduction at revisit. The remaining patients who had a low-level IgG during hospitalization stayed low at revisit. As expected, IgM levels in almost all convalescent patients reduced significantly or stayed low at revisit. The RBD-specific IgA levels were also reduced significantly at revisit. We also attempted to estimate decline rates of virus-specific antibodies using a previously established exponential decay model of antibody kinetics after infection. The predicted days when convalescent patients' RBD-specific IgG reaches to an undetectable level are approximately 273 days after hospital discharge, while the predicted decay times are 150 days and 108 days for IgM and IgA, respectively. This investigation and report will aid current and future studies to develope SARS-CoV-2 vaccines that are potent and long-lasting.

Raking is widely used for categorical data modeling and calibration in survey practice but faced with methodological and computational challenges. We develop a Bayesian paradigm for raking by incorporating the marginal constraints as a prior distribution via two main strategies: (1) constructing solution subspaces via basis functions or the projection matrix and (2) modeling soft constraints. The proposed Bayes-raking estimation integrates the models for the margins, the sample selection and response mechanism, and the outcome as a systematic framework to propagate all sources of uncertainty. Computation is done via Stan, and codes are ready for public use. Simulation studies show that Bayes-raking can perform as well as raking with large samples and outperform in terms of validity and efficiency gains, especially with a sparse contingency table or dependent raking factors. We apply the new method to the longitudinal study of well-being study and demonstrate that model-based approaches significantly improve inferential reliability and substantive findings as a unified survey inference framework.

This study was set to determine the expression of microRNA-449a in cancer tissue and serum of non-small cell lung cancer (NSCLC) patients and explore the underlying mechanisms of its tumor suppressor functions. We selected 50 NSCLC patients in our hospital as the lung cancer group and 50 healthy volunteers as the control group. RT-PCR was performed to detect the expression levels of microRNA-449a in NSCLC tissue and the plasma of NSCLC patients. Further, we transfected microRNA-449a mimic and inhibitor in lung cancer cell line A549, and used western blot to determine the expression level of apoptosis-related molecules Bcl-2 and p53. Compared with the surrounding tissue, microRNA-449a exhibited significantly reduced mRNA expression, which was statistically different (P < 0.05). microRNA-449a exhibited significantly lower expression in NSCLC patients' plasma than the healthy volunteers, which was statistically different (P < 0.05). Spearman correlation analysis showed that microRNA-449a expression levels in NSCLC tissue and plasma of NSCLC patients were reversely correlated with lung cancer differentiation (P < 0.05). But microRNA-449a expression in the surrounding tissue was not significantly correlated with lung cancer differentiation (P > 0.05). Compared with negative control, cell proliferation and p53 and Bcl-2 expression significantly decreased after microRNA-449a mimic transfection (P < 0.05). However, after transfection of microRNA-449a inhibitor, cell proliferation and p53 and Bcl-2 expression significantly increased after microRNA-449a mimic transfection (P < 0.05). microRNA-449a expression levels in NSCLC are significantly lower than those in the surrounding tissue, and its expression levels in NSCLC patients are also lower than those in healthy volunteers. The tumor suppression role of microRNA-449a could be due to its promotion of tumor cell proliferation and its inhibition of tumor cell apoptosis.