Invited Commentary: Estimation and Bounds Under Data Fusion

In their recent article, Ogburn et al. (Am J Epidemiol. 2021;190(6):1142-1147) raised a cautionary tale for epidemiologic data fusion: Bias may occur if a variable that is completely missing in the primary data set is imputed according to a regression model estimated from an auxiliary data set. However, in some specific settings, a solution may exist. Focusing on a linear outcome regression model with a missing covariate, we show that the bias can be eliminated if the underlying imputation model for the missing covariate is nonlinear in the common variables measured in both data sets. Otherwise, we describe 2 alternative approaches existing in the data fusion literature that could partially resolve this issue: One fits the outcome model by leveraging an additional validation data set containing joint observations of the outcome and the missing covariate, and the other offers informative bounds for the outcome regression coefficients without using validation data. We justify these 3 methods in a linear outcome model and briefly discuss their extension to general settings.

Treatment of Severe Japanese Encephalitis Complicated With Hashimoto's Thyroiditis and Guillain-Barré Syndrome With Protein A Immunoadsorption: A Case Report

A severely comatose female patient was diagnosed with Japanese encephalitis (JE). Her condition was complicated by Hashimoto’s thyroiditis (HT) and Guillain-Barré syndrome (GBS). After antiviral, glucocorticoid, and immunoglobulin treatment, the patient’s consciousness was restored, and she could breathe spontaneously. Following this, new-onset, primarily demyelinating GBS developed, which progressed to demyelination combined with axonal injury. The patient was switched to protein A immunoadsorption (PAIA) therapy, and her Hughes score decreased rapidly, from 4 to 1 after 6 months. This patient is the first to receive PAIA combined with an antiviral-glucocorticoid-immunoglobulin regimen to treat encephalitis, meningitis, HT, and GBS caused by JE infection, thereby reflecting the importance of clinical application of PAIA in the treatment of immunological complications of JE.

The Effect of a Combined Ganciclovir, Methylprednisolone, and Immunoglobulin Regimen on Survival and Functional Outcomes in Patients With Japanese Encephalitis

Objective: There is currently no effective treatment for Japanese encephalitis, which has a high rate of morbidity and mortality. This study assessed the effectiveness of a ganciclovir, methylprednisolone, and immunoglobulin combination (TAGMIC) therapy in decreasing cognitive impairment and mortality among patients with Japanese encephalitis.

Methods: We retrospectively assessed the clinical data of 31 patients diagnosed with Japanese encephalitis, who were admitted to an intensive care unit. Patients were divided into the TAGMIC and non-TAGMIC group according to their treatment regime. We compared the 60-day, 6-month, and overall mortality and survival curves between groups. We also compared Barthel Index scores, Montreal Cognitive Assessment (MoCA) scores, and diffusion tensor imaging (DTI) results.

Results: There was no significant difference in the 30-day mortality rate or Kaplan–Meier survival curve between groups. The 60-day, 6-month, and overall mortality rates in the TAGMIC group were significantly reduced (P = 0.043, P = 0.018, and P = 0.018, respectively) compared with the non-TAGMIC group (0, 0, 0 vs. 31.25, 37.5, 37.5%, respectively). The 60-day, 6-month, and overall Kaplan–Meier survival curves were significantly different between groups (P = 0.020, P = 0.009, P = 0.009, respectively). There was no significant difference in the Barthel Index scores of surviving patients. Among the five patients who underwent MoCA and DTI, four had a score of 0/5 for delayed recall (no cue), while the remaining patient had a score of 2/5. All five patients were able to achieve a score of 5/5 with classification and multiple-choice prompts, and had sparse or broken corpus callosum (or other) fibre bundles.

Conclusion: TAGMIC treatment can reduce mortality due to severe Japanese encephalitis. The memory loss of surviving patients is mainly due to a disorder of the memory retrieval process, which may be related to the breakage of related fibre bundles.

Small cell carcinoma as an independent prognostic factor for cervical cancer patients: a population-based analysis

Aim: To compare cervical small cell carcinoma (SmCC) with squamous cell carcinoma (SCC) in patient characteristics and survival outcomes. Methods: Cervical SmCC and SCC patients in Surveillance, Epidemiology, and End Results database from 2004 to 2015 were enrolled. Propensity-score matching analysis (PSM) paired subjects with similar background variables. Cox regression, Kaplan-Meier and stratified analyses were conducted before and after PSM. Results: Cervical SmCC patients showed a higher rate of larger tumor size, advanced grade disease, lymph node involvement and distant metastasis (p < 0.001). Before and after PSM, SmCC histology and advanced Federation International of Gynecology and Obstetrics stages (p < 0.001) were principal prognostic factors of survival, and cervical SmCC was associated with worse survival in all stages (stage I-IV). Conclusion: SmCC was an independent poor prognostic factor in cervical cancer patients.

Rhodium-terpyridine Catalyzed Transfer Hydrogenation of Aromatic Nitro Compounds in Water

A rhodium terpyridine complex catalyzed transfer hydrogenation of nitroarenes to anilines with i-PrOH as hydrogen source and water as solvent has been developed. The catalytic system can work at a substrate/catalyst (S/C) ratio of 2000, with a turnover frequency (TOF) up to 3360 h^-1 , which represents one of the most active catalytic transfer hydrogenation systems for nitroarene reduction. The catalytic system is operationally simple and the protocol could be scaled up to 20 gram scale. The water-soluble catalyst bearing a carboxyl group could be recycled 15 times without significant loss of activity.

miR-146a aggravates cognitive impairment and Alzheimer disease-like pathology by triggering oxidative stress through MAPK signaling

INTRODUCTION

Mir-146a-5p has been widely recognized as a critical regulatory element in the immune response. However, recent studies have shown that miR-146a-5p may also be involved in the development of Alzheimer disease (AD). Regrettably, the related mechanisms are poorly understood. Here, we investigated the effects of miR-146a in mice models and SH-SY5Y cells treated with amyloid β (Aβ)1-42.

METHODS

To create a model of AD, SH-SY5Y cells were treated with Aβ1-42 and mice received intracerebroventricular injections of Aβ1-42. Then, the transcriptional levels of miR-146a were estimated by real-time PCR. We transiently transfected the miR-146a-5p mimic/inhibitor into cells and mice to study the role of miR-146a. The role of signaling pathways including p38 and reactive oxygen species (ROS) was studied by using specific inhibitors. Aβ and amyloid-beta precursor protein (APP)levels were measured by immunoblotting. Furthermore, Aβ expression was analyzed by immunofluorescence and histochemical examinations.

RESULTS

Aβ1-42-stimulated SH-SY5Y cells displayed increased transcriptional levels of miR-146a and APP. Moreover, the p38 MAPK signaling pathway and ROS production were activated upon stimulation with a miR-146a-5p mimic. However, treatment with a miR-146a-5p inhibitor decreased the levels of APP, ROS, and p-p38 MAPK. A similar phenomenon was also observed in the animals treated with Aβ1-42, in which miR-146a upregulation increased the expression of Aβ, p-p38, and ROS, while the inhibition of miR-146a had the opposite effect. This suggests that miR-146a increases Aβ deposition and ROS accumulation via the p-p38 signaling pathway.

CONCLUSIONS

Our research demonstrates that miR-146a-5pa increases Aβ deposition by triggering oxidative stress through activation of MAPK signaling.

A Selective Review of Negative Control Methods in Epidemiology

PURPOSE OF REVIEW

Negative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience and provides guidance on principled design and causal analysis based on a formal negative control framework.

RECENT FINDINGS

We review and summarize causal and statistical assumptions, practical strategies, and validation criteria that can be combined with subject-matter knowledge to perform negative control analyses. We also review existing statistical methodologies for the detection, reduction, and correction of confounding bias, and briefly discuss recent advances towards nonparametric identification of causal effects in a double-negative control design.

SUMMARY

There is great potential for valid and accurate causal inference leveraging contemporary healthcare data in which negative controls are routinely available. Design and analysis of observational data leveraging negative controls is an area of growing interest in health and social sciences. Despite these developments, further effort is needed to disseminate these novel methods to ensure they are adopted by practicing epidemiologists.

[Influence of comprehensive incubational measures on the perioperative treatment of extensively burned patients who underwent escharectomy and skin grafting]

Objective: To explore the influence of standardized and comprehensive incubational measures on perioperative treatment of extensively burned patients who underwent escharectomy and skin grafting. Methods: From January 2017 to November 2018, 50 patients with extensive burn who underwent escharectomy and skin grafting in the First Affiliated Hospital of Air Force Medical University and met the inclusion criteria of this study, were recruited in this retrospective cohort study. According to the incubational measures at that time, 20 patients (14 males and 6 females, aged (33.5±5.2) years) who received routine incubation during the perioperative period from January to October 2017 were set as routine incubation group, and 30 patients (23 males and 7 females, aged (35.8±1.4) years) who received standardized comprehensive incubational measures during the perioperative period from November 2017 to November 2018 were set as comprehensive incubation group. Their body temperature was controlled mainly in 4 stages: preoperative preparation and transfer from intensive care unit (ICU) to operating room, preoperative preparation in operating room, intraoperative operating room management, as well as postoperative transfer from operating room to ICU. The initial body temperature in operating room and intraoperative hypothermia duration, intraoperative blood loss, postoperative recovery time, postoperative chill, blister, and ulcer, and wound healing rate on post operation day (POD) 10 were recorded and calculated. Data were statistically analyzed with two independent samples t test and chi-square test. Results: (1) The initial body temperature in operating room of patients in comprehensive incubation group was (36.3±0.4) ℃, which was significantly higher than (35.6±0.4)℃ in routine incubation group, t=6.658, P<0.01; the intraoperative duration of hypothermia was (205±38) min, which was significantly shorter than (234±42) min in routine incubation group, t=2.564, P<0.05. (2) The intraoperative blood loss of patients in comprehensive incubation group was (323±114) mL, which was significantly less than (490±162) mL in routine incubation group, t=4.272, P<0.01; the postoperative recovery time was (36±8) min, which was significantly shorter than (49±17) min in routine incubation group, t=3.229, P<0.01. (3) The incidence of postoperative chill of patients in comprehensive incubation group was significantly lower than that in routine incubation group (χ(2)=28.626, P<0.01). The incidences of postoperative blister and ulcer of patients between the 2 groups were close. (4) On POD 10, the wound healing rate of patients in comprehensive incubation group was (78.08±0.06)%, which was significantly higher than (71.03±0.08)% in routine incubation group, t=3.694, P<0.01. Conclusions: The standardized and comprehensive incubational measures can effectively improve the initial body temperature of patients entering the operating room, shorten the intraoperative duration of hypothermia, reduce the amount of blood loss and postoperative complications, as well as shorten the postoperative recovery time, thus improve the wound healing rate.

IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE

In observational studies, treatments are typically not randomized and therefore estimated treatment effects may be subject to confounding bias. The instrumental variable (IV) design plays the role of a quasi-experimental handle since the IV is associated with the treatment and only affects the outcome through the treatment. In this paper, we present a novel framework for identification and inference using an IV for the marginal average treatment effect amongst the treated (ETT) in the presence of unmeasured confounding. For inference, we propose three different semiparametric approaches: (i) inverse probability weighting (IPW), (ii) outcome regression (OR), and (iii) doubly robust (DR) estimation, which is consistent if either (i) or (ii) is consistent, but not necessarily both. A closed-form locally semiparametric efficient estimator is obtained in the simple case of binary IV and outcome and the efficiency bound is derived for the more general case.

MicroRNA-195 reverses the resistance to temozolomide through targeting cyclin E1 in glioma cells

Glioma is the most common malignant tumor of the central nervous system with poor survival. Temozolomide (TMZ) is the first-line chemotherapy drug for initial and recurrent glioma treatment with a relatively good efficacy, which exerts its antitumor effects mainly through cell death induced by DNA double-strand breaks in the G1 and S phases. However, endogenous or acquired resistance to TMZ limits glioma patients’ clinical outcome and is also an important cause of glioma replase. MicroRNA-195 (miR-195) plays an important role in the regulation of G1-phase/S-phase transition, DNA damage repair, and apoptosis of tumor cells. We found that miR-195 expression was significantly decreased in TMZ-resistant glioma cells induced with TMZ and correlated to the resistance index negatively. Also, the exogenous expression of miR-195 reversed TMZ resistance and induced the apoptosis of TMZ-resistant glioblastoma cells. Further bioinformatics analysis showed cyclin E1 (CCNE1) was a potential target gene of miR-195. Knockdown of CCNE1 partially reversed the effect of decreased miR-195 on TMZ resistance. The data from The Cancer Genome Atlas – Cancer Genome further suggested that hsa-miR-195 could negatively regulate the expression of CCNE1 in glioma. In conclusion, miR-195 reverses the resistance to TMZ by targeting CCNE1 in glioma cells and it could act as a potential target for treatment in glioma with TMZ resistance.

Causal data fusion methods using summary-level statistics for a continuous outcome

In many empirical studies, there exist rich individual studies to separately estimate causal effect of the treatment or exposure variable on the outcome variable, but incomplete confounders are adjusted in each study. Suppose we are interested in the causal effect of a treatment or exposure on an outcome variable, and we have available rich datasets that contain different confounders. How to integrate summary-level statistics from multiple individual datasets to improve causal inference has become a main challenge in data fusion. We propose a novel method in this article to identify the causal effect of a treatment or exposure on the continuous outcome. We show that the causal effect is identifiable and can be estimated by combining summary-level statistics from multiple datasets containing subsets of confounders and an external dataset only containing complete confounding information. Simulation studies indicate the unbiasedness of causal effect estimate by our method and we apply our method to a study about the effect of body mass index on fasting blood glucose.

Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non-parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non-parametric model, and we propose multiply robust and locally efficient estimators when non-parametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post-licensure surveillance of vaccine safety among children.

Autophagy attenuates the oxidative stress-induced apoptosis of Mc3T3-E1 osteoblasts

OBJECTIVE

The oxidative stress-induced osteoblast apoptosis plays an important role in the pathological process of osteoporosis, but the roles of autophagy in oxidative stress and apoptosis of osteoblasts remain unclear. This study aimed to observe the role of autophagy in oxidative stress injury of osteoblasts and the relationship between autophagy and apoptosis.

MATERIALS AND METHODS

Mc3T3-E1 cells were stimulated with different concentrations (0.1, 0.5, and 1 mM) of hydrogen peroxide. The cell viability was detected via cell counting kit 8 (CCK8) at different time points (0, 2, 6, 8, and 12 h), the apoptosis was detected via Western blotting and flow cytometry, and the autophagy was detected via macrophage-derived chemokine (MDC) and transmission electron microscope. The changes in expression of autophagy-associated protein, Beclin1, and LC3II/I ratio, were detected via Western blotting. Moreover, the intracellular reactive oxygen species (ROS) level and extracellular superoxide dismutase (SOD) level were observed using the autophagy regulators, rapamycin (Rap) and 3-methyladenine (3-MA), so as to clarify the interaction between autophagy and cellular oxidation.

RESULTS

Hydrogen peroxide-induced apoptosis and autophagy of osteoblasts were in dose- and time-dependent manners; the hydrogen peroxide inhibitors could inhibit the autophagy level, and autophagy inhibitor (3-MA) could significantly enhance the hydrogen peroxide-induced ROS level and apoptosis rate in cells. Besides, Western blotting confirmed that the cleaved caspase-3 and cleaved poly adenosine diphosphate ribose polymerase (PARP) proteins were increased. The autophagy inducer (Rap) partially inhibited the hydrogen peroxide-induced oxidative stress and apoptosis.

CONCLUSIONS

Autophagy inhibits the oxidative stress-mediated apoptosis of osteoblasts, which is a potential target for the osteoporosis treatment.

Multiple target information fusion matching algorithm based on a line laser and a single plane array camera

In the testing process of the dynamic multitarget parameter of a weapon range, multiple targets often occur in the same plane simultaneously, so that the existing optical testing methods cannot recognize and match the spatial positions of all the targets. To address this problem, we propose a new optical testing method that combines a line laser with a plane array camera, establishing a multiparameter calculation model using the intersection of the imaging optical axis of a plane array camera and the two line-laser detection screens. Based on the established multiparameter model, the recognition model based on time-space information constraint relationship between the line laser and plane array camera is developed, and it provides a constraint condition discriminant function. We also introduce the recognition and matching method based on fuzzy information fusion, analyze the matching mechanism of correlation parameters of the data fusion algorithm, and provide the solution method of key parameters. On the basis of the inherent parameters of the proposed testing method, the multitarget images at different velocities are collected, and the test data under the contrast experimental conditions is obtained. The results show that the proposed testing method can effectively solve the matching problem when multitargets appear at the same time causing occlusion and overlapping. The test error meets the measurement requirements.

Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable

Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an instrumental variable (IV) is observed for all subjects which satisfies the exclusion restriction that the IV affects the missingness process without directly influencing the outcome. In this paper, we provide necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV. In addition, we give sufficient identification conditions that are more straightforward to verify in practice. For inference, we focus on estimation of a population outcome mean, for which we develop a suite of semiparametric estimators that extend methods previously developed for data missing at random. Specifically, we propose inverse probability weighted estimation, outcome regression-based estimation and doubly robust estimation of the mean of an outcome subject to MNAR. For illustration, the methods are used to account for selection bias induced by HIV testing refusal in the evaluation of HIV seroprevalence in Mochudi, Botswana, using interviewer characteristics such as gender, age and years of experience as IVs.