Peripheral nerve injury associated with JEV infection in high endemic regions, 2016-2020: a multicenter retrospective study in China

Previously, we reported a cohort of Japanese encephalitis (JE) patients with Guillain-Barré syndrome. However, the evidence linking Japanese encephalitis virus (JEV) infection and peripheral nerve injury (PNI) remains limited, especially the epidemiology, clinical presentation, diagnosis, treatment, and outcome significantly differ from traditional JE. We performed a retrospective and multicenter study of 1626 patients with JE recorded in the surveillance system of the Chinese Center for Disease Control and Prevention, spanning the years 2016-2020. Cases were classified into type 1 and type 2 JE based on whether the JE was combined with PNI or not. A comparative analysis was conducted on demographic characteristics, clinical manifestations, imaging findings, electromyography data, laboratory results, and treatment outcomes. Among 1626 laboratory confirmed JE patients, 230 (14%) were type 2 mainly located along the Yellow River in northwest China. In addition to fever, headache, and disturbance of consciousness, type 2 patients experienced acute flaccid paralysis of the limbs, as well as severe respiratory muscle paralysis. These patients presented a greater mean length of stay in hospital (children, 22 years [range, 1-34]; adults, 25 years [range, 0-183]) and intensive care unit (children, 16 years [range, 1-30]; adults, 17 years [range, 0-102]). The mortality rate was higher in type 2 patients (36/230 [16%]) compared to type 1 (67/1396 [5%]). The clinical classification of the diagnosis of JE may play a crucial role in developing a rational treatment strategy, thereby mitigating the severity of the disease and potentially reducing disability and mortality rates among patients.

Nomogram to Predict the Prognosis of Oligodendroglioma Patients Undergoing Postoperative Adjuvant Chemoradiotherapy

OBJECTIVE

The aim of this study was to develop a prognostic nomogram for predicting the prognosis of oligodendroglioma patients receiving combined chemoradiotherapy (CRT) after surgery.

METHODS

The study used data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. The patients were randomly divided into a development cohort (700 patients) and a validation cohort (244 patients) in a 7:3 ratio. The Cox hazards regression model was used to identify predictors, and a nomogram was constructed to visualize the prognosis. The performance of the prognostic nomogram was evaluated using the consistency index (C-index), clinical net benefit, and calibration.

RESULTS

The nomogram included 5 variables: age, marital status, tumor size, site of lesions, and surgery type. The C-index of the training set and validation set were 0.77 and 0.68, respectively. The calibration plots showed that the nomogram was in good agreement with the actual observation. The clinical decision curve indicated that the nomogram had a good clinical net benefit in oligodendroglioma patients receiving CRT after surgery.

CONCLUSIONS

This study established and verified a prognostic nomogram for a large cohort of oligodendroglioma patients receiving CRT after surgery based on the SEER database. The nomogram may help clinicians provide personalized treatment services and clinical decisions for patients.

The triglyceride glucose index predicts short-term mortality in non-diabetic patients with acute heart failure

BACKGROUND

The triglyceride glucose index (TyG) has previously been considered a reliable indicator of insulin resistance (IR) and an independent prognostic predictor in heart failure (HF).

OBJECTIVES

To clarify the association between the TyG and short-term death in non-diabetic patients admitted for acute heart failure (AHF).

MATERIAL AND METHODS

We examined 886 out of 1620 consecutive AHF patients who were admitted to Shunde Hospital, Southern Medical University, Foshan, China, from June 1, 2014, to June 1, 2022. The median of the patientsf TyG values was used to divide them into 2 groups. The following formula was used to calculate the TyG: ln [fasting triglycerides (mg/dL) ~ fasting glucose (mg/dL)/2]. The data on all-cause mortality of AHF patients during their hospital stay were collected. The 30-day Enhanced Feedback for Effective Cardiac Treatment (EFFECT) death risk score was used to assess the risk of death.

RESULTS

The TyG level was positively correlated with a poor AHF prognostic marker (N-terminal B-type natriuretic peptide (NT-proBNP)) (Ď = 0.207, p < 0.001) and negatively correlated with a protective marker (serum albumin) (Ď = .0.43, p < 0.001). Higher TyG values were associated with an elevated EFFECT score and hospital mortality (p < 0.001). According to multivariate logistic regression analysis, higher TyG levels raised the risk of death in hospital (odds ratio (OR) = 1.73; 95% confidence interval (95% CI): 1.03.3.27; p = 0.031) after adjusting for multiple variables, including age, EFFECT score and NT-proBNP. The TyG had a greater area under the receiver operating characteristic (ROC) curve (AUC: 0.688) for predicting hospital death compared to NT-proBNP (AUC: 0.506).

CONCLUSIONS

Our findings show that the TyG is associated with the short-term mortality rate of non-diabetic patients admitted to the hospital for AHF. The TyG testing could be a useful prognostic indicator for these patients.

A self-censoring model for multivariate nonignorable nonmonotone missing data

We introduce an itemwise modeling approach called "self-censoring" for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We also provide a novel and flexible global sensitivity analysis procedure anchored at the self-censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.

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.

Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution

We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for n -estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires the existence of a function as a solution to a representer equation that connects the shadow variable to the mean functional. Solutions to the representer equation may not be unique, which presents substantial challenges for non-parametric estimation, and standard theories for non-parametric sieve estimators are not applicable here. We construct a consistent estimator of the solution set and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator of an appropriately chosen solution. The estimator is asymptotically normal, locally efficient and attains the semi-parametric efficiency bound under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.

Proximal Causal Inference without Uniqueness Assumptions

We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference. Proximal causal inference requires existence of solutions to at least one of two integral equations. We motivate the existence of solutions to the integral equations from proximal causal inference by demonstrating that, assuming the existence of a solution to one of the integral equations, n -estimability of a mean functional of that solution requires the existence of a solution to the other integral equation. Solutions to the integral equations may not be unique, which complicates estimation and inference. We construct a consistent estimator for the solution set for one of the integral equations and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator for a uniquely defined solution. A debiased estimator is shown to be root-n consistent, regular, and semiparametrically locally efficient under additional regularity conditions.

Predictive effect of the decline in CD4+ T cell levels in blood on infection in patients with severe hemorrhagic stroke and mechanism

Objective

The purpose of this research was to evaluate the influence of immunity on infection in patients with severe hemorrhagic stroke and explore the mechanism underlying this connection.

Methods

Clinical data obtained from 126 patients with severe hemorrhagic stroke were retrospectively analyzed, and the factors affecting infection were screened by multivariable logistic regression models. Nomograms, calibration curves, the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis were used to examine the effectiveness of the models in evaluating infection. The mechanism underlying the reduction in CD4⁺ T-cell levels in blood was explored by analysis of lymphocyte subsets and cytokines in cerebrospinal fluid (CSF) and blood.

Results

The results showed that CD4⁺ T-cell levels of <300/μL was an independent risk factor for early infection. The models for multivariable logistic regression involving the CD4⁺ T-cell levels and other influencing factors had good applicability and effectiveness in evaluating early infection. CD4⁺ T-cell levels decreased in blood but increased in CSF. Similarly, interleukin (IL)-6 and IL-8 levels in CSF had a significant increase, generating a substantial concentration gradient between the CSF and the blood.

Conclusion

Reduced blood CD4⁺ T-cell counts among patients who had severe hemorrhagic stroke increased the risk of early infection. CSF IL-6 and IL-8 may be involved in inducing the migration of CD4⁺ T cells into the CSF and decreasing blood CD4⁺ T-cell levels.

β-Arylation of Racemic Secondary Benzylic Alcohols to Access Enantioenriched β-Arylated Alcohols

The transformation of alcohols into value-added products is of great importance, as simple alcohols are widespread and can be easily derived from both fossil fuels and biomass. The selective functionalization of a sp3 C-H bond on the alkyl side chain of an alcohol over its hydroxyl group would offer an expedient route to expand the chemical space of alcohols but it remains a challenging task. Harnessing the borrowing hydrogen strategy, the β-arylation of secondary alcohols with aryl bromides has been achieved in this study, which allows for the selective functionalization of a β-Csp3-H bond in an alcohol substrate. Under the catalysis of a Pd complex, secondary alcohols reacted with aryl bromides to afford 1,2-diaryl alcohols with broad substrate scope in the presence of a ketone additive. Furthermore, the enantioconvergent version of the reaction has also been realized, transforming racemic secondary alcohols into enantioenriched chiral 1,2-diaryl alcohols under the cooperative Pd and Ru catalysis. Mechanism studies indicate that the reactions are enabled by borrowing hydrogen catalysis.

A robust fusion-extraction procedure with summary statistics in the presence of biased sources

Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption, or model misspecification. This calls for robust data combination methods with biased sources. In this paper, a robust data fusion-extraction method is proposed. In contrast to existing methods, the proposed method can be applied to the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and only employs summary statistics, and hence can be applied to many different fields, e.g., meta-analysis, Mendelian randomization, and distributed systems. The proposed estimator is consistent even if many data sources are biased and is asymptotically equivalent to the oracle estimator that only uses unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to the existing meta-analysis methods, the theoretical properties are guaranteed even if the number of data sources and the dimension of the parameter diverges as the sample size increases. Furthermore, the proposed method provides a consistent selection for unbiased data sources with probability approaching one. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The proposed method is applied to a meta-analysis dataset to evaluate the surgical treatment for moderate periodontal disease and to a Mendelian randomization dataset to study the risk factors of head and neck cancer.

Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness

The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In a TND study, individuals who experience symptoms and seek care are recruited and tested for the infectious disease which defines cases and controls. Despite TND's potential to reduce unobserved differences in healthcare seeking behavior (HSB) between vaccinated and unvaccinated subjects, it remains subject to various potential biases. First, residual confounding bias may remain due to unobserved HSB, occupation as healthcare worker, or previous infection history. Second, because selection into the TND sample is a common consequence of infection and HSB, collider stratification bias may exist when conditioning the analysis on testing, which further induces confounding by latent HSB. In this paper, we present a novel approach to identify and estimate vaccine effectiveness in the target population by carefully leveraging a pair of negative control exposure and outcome variables to account for potential hidden bias in TND studies. We illustrate our proposed method with extensive simulation and an application to study COVID-19 vaccine effectiveness using data from the University of Michigan Health System.

Improving efficiency of inference in clinical trials with external control data

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.

Nile tilapia DNA sensor STING is involved in the IFN-β and AP-1 signaling pathways in the immune response dependent on DDX41

Stimulator of interferon genes (STING) plays important roles in innate immunology. In this study, we isolated the STING gene in Nile tilapia, termed OnSTING. Using quantitative RT-PCR, we explored the expression patterns of the OnSTING gene. Using dual-luciferase reporter assays, we revealed the effect of STING overexpression on nuclear factor κB (NF-κB), IFN and AP activation in HEK 293 cells. Using coimmunoprecipitation, the interaction of STING and TRIF was studied. The effect of OnSTING overexpression on the antibacterial activity in tilapia was investigated. The results showed that upon stimulation with Streptococcus agalactiae, the OnSTING transcript was upregulated in all the tested tissues. OnSTING mRNA levels were very stable from 2.5 to 8.5 dpf. Moreover, OnSTING, OnIFN and IRF3 expression was induced by LPS, Poly (I:C), S. agalactiae WC1535 and DCPS in Nile tilapia macrophages. Overexpression of OnSTING and OnDDX41 increased NF-κB activation in HEK293T cells and slightly increased IFN-β activation but had no effect on AP-1 activation. OnSTING interacted with OnDDX41 and OnTBK1. However, OnSTING did not interact with TRIF. OnSTING overexpression in vivo decreased the sensitivity of tilapia to S. agalactiae infection. These results are helpful for clarifying the innate immune response against bacterial infection in Nile tilapia.

Data Integration in Causal Inference

Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

Human parvovirus B19 infection in hospitalized patients suspected of infection with pathogenic microorganism

Background

Human parvovirus B19 (HPV B19) is a single-stranded DNA virus. The detection rate of HPV B19 in the blood of healthy blood donors using PCR technology was reported to be 6.323/100000. However, that among hospitalized patients suspected of being infected with a pathogenic microorganism is unknown.

Methods

A retrospective analysis was conducted on 2,182 high-throughput NGS results for 1,484 inpatients admitted to the First Affiliated Hospital of Zhengzhou University from January 2020 to October 2021 who were suspected of being infected with a pathogenic microorganism, as well as on clinical data of some HPV B19-positive patients.

Results

Human parvovirus B19 was detected in 39 samples from 33 patients. The positivity rate was 2.22% among patients and 1.78% among samples. HPV B19 was detected in 20 cerebrospinal fluid samples, 13 blood samples, 3 alveolar lavage fluid samples, 2 tissue samples, and 1 throat swab. Based on clinical symptoms and NGS results, 16 patients were diagnosed with HPV B19 infection. The number of HPV B19 sequences in these patients was greater than 6, and the patients showed common symptoms such as fever (14 cases), anemia (11 cases), and severe nervous system symptoms such as meningoencephalitis (9 cases) and Guillain-Barré syndrome with peripheral motor and sensory nerve axon damage (4 cases). All 16 patients had experienced events likely to lead to decreased immunity (11 had a history of trauma/surgery/major disease, 4 had a history of precursor infection, and 3 had used immunosuppressants) and 7 had a history of blood transfusion during hospitalization. After treatment with antiviral drugs (12 cases) and intravenous human immunoglobulin (3 cases), of the 16 patients, 14 patients improved.

Conclusion

The HPV B19 infection rate in hospitalized patients suspected of microbial infection was 2.22%. Most patients with HPV B19 infection had a history of low immunity and blood transfusion. HPV B19 could be detected in various bodily fluids and tissues (especially cerebrospinal fluid) using NGS. Patients with severe HPV B19 infection may have nervous system damage such as Guillain-Barré syndrome and meningoencephalitis. Early diagnosis using NGS and treatment with antiviral drugs and immunoglobulin can improve prognosis.

Neuroanatomy and Functional Connectivity in Patients with Parkinson's Disease with or without Restless Legs Syndrome

INTRODUCTION

Restless legs syndrome (RLS) is a common non-motor symptom in Parkinson's disease (PD), but its pathogenesis remains unclear. This study aimed to explore the potential neural substrates of RLS in a large sample of patients with PD.

METHODS

A total of 42 patients with PD with RLS and 124 patients with PD without RLS were prospectively recruited at our hospital between February 2019 and October 2020 and underwent structural and resting-state functional magnetic resonance imaging. Differences between the two patient groups were assessed using voxel-based morphometry and functional connectivity analysis. PD duration, Part III of the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) score, and levodopa equivalent daily dose were treated as covariates.

RESULTS

Patients with PD with RLS had significantly larger gray matter volume in the bilateral posterior cingulate cortex than patients with PD without RLS (FDR-adjusted P < 0.05). Compared to patients without RLS, those with RLS had significantly lower functional connectivity between the left central opercular cortex and the bilateral precentral gyri and postcentral gyri (FDR-adjusted P < 0.001).

CONCLUSION

Our study provides the first evidence that in patients with PD, RLS is associated with significantly larger gray matter volume in the posterior cingulate cortex and lower resting-state functional connectivity within the sensorimotor network. Our results may help clarify the pathophysiology of RLS in PD and identify possible therapeutic targets.

Advances in Potential Cerebrospinal Fluid Biomarkers for Autoimmune Encephalitis: A Review

Autoimmune encephalitis (AE) is a severe inflammatory disease of the brain. Patients with AE demonstrate amnesia, seizures, and psychosis. Recent studies have identified numerous associated autoantibodies (e.g., against NMDA receptors (NMDARs), LGI1, etc.) involved in the pathogenesis of AE, and the levels of diagnosis and treatment are thus improved dramatically. However, there are drawbacks of clinical diagnosis and treatment based solely on antibody levels, and thus the application of additional biomarkers is urgently needed. Considering the important role of immune mechanisms in AE development, we summarize the relevant research progress in identifying cerebrospinal fluid (CSF) biomarkers with a focus on cytokines/chemokines, demyelination, and nerve damage.

PARP inhibitors rising as an epoch-making strategy in first-line maintenance therapy of ovarian cancer: A systematic review and meta-analysis

Background To illustrate the accurate location of PARP inhibitor (PARPi) as the first-line maintenance therapy in advanced ovarian cancer (AOC).Methods Search for eligible studies and calculate clinical outcomes.Results PARPi as a first-line maintenance treatment significantly prolonged the BRCAmut population and the HRD positive population.Conclusion PARPi as first-line maintenance therapy significantly improves the PFS in AOC, especially in the BRCAmut and HRD positive populations. PARPi has been becoming the standard first-line maintenance therapy for AOC.

Cat swarm optimization algorithm based on the information interaction of subgroup and the top-N learning strategy

Because of the lack of interaction between seeking mode cats and tracking mode cats in cat swarm optimization (CSO), its convergence speed and convergence accuracy are affected. An information interaction strategy is designed between seeking mode cats and tracking mode cats to improve the convergence speed of the CSO. To increase the diversity of each cat, a top-N learning strategy is proposed during the tracking process of tracking mode cats to improve the convergence accuracy of the CSO. On ten standard test functions, the average values, standard deviations, and optimal values of the proposed algorithm with different N values are compared with the original CSO algorithm and the adaptive cat swarm algorithm based on dynamic search (ADSCSO). Experimental results show that the global search ability and the convergence speed of the proposed algorithm are significantly improved on all test functions. The proposed two strategies will improve the convergence accuracy and convergence speed of CSO greatly.

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.

Identification and inference with nonignorable missing covariate data

We study identification of parametric and semiparametric models with missing covariate data. When covariate data are missing not at random, identification is not guaranteed even under fairly restrictive parametric assumptions, a fact that is illustrated with several examples. We propose a general approach to establish identification of parametric and semiparametric models when a covariate is missing not at random. Without auxiliary information about the missingness process, identification of parametric models is strongly dependent on model specification. However, in the presence of a fully observed shadow variable, which is correlated with the missing covariate but otherwise independent of its missingness, identification is more broadly achievable, including in fairly large semiparametric models. With a shadow variable, special consideration is given to the generalized linear models with the missingness process unrestricted. Under such a setting, the outcome model is identified for familiar generalized linear models, and we provide counterexamples when identification fails. For estimation, we describe an inverse probability weighted estimator that incorporates the shadow variable to estimate the missingness process, and we evaluate its performance via simulations.