Therapeutic drug monitoring of polymyxin B cerebrospinal fluid concentrations in patients with carbapenem-resistant Gram-negative bacteria-induced central nervous system infection.

OBJECTIVES: Central nervous system (CNS) infections caused by carbapenem-resistant Gram-negative bacteria (CR-GNB) present a major health and economic burden worldwide. This multicentre prospective study aimed to assess the feasibility and usefulness of CSF therapeutic drug monitoring (TDM) after intrathecal/intraventricular administration of polymyxin B in patients with CNS infections. METHODS: Forty-two patients treated with intrathecal/intraventricular administration of polymyxin B against CR-GNB-induced CNS infections were enrolled. CSF trough level (Cmin) was collected beginning on Day 2 post-polymyxin B initiation and thereafter. The primary outcomes were clinical cure and 28-day all-cause mortality. RESULTS: All patients started with intrathecal/intraventricular administration of polymyxin B at a dose of 5 g/day, corresponding to a median CSF Cmin of 2.93 mg/L (range, 0.21-25.74 mg/L). Clinical cure was 71.4%, and the median CSF Cmin of this group was higher than that of clinical failure group [3.31 (IQR, 1.73-5.62) mg/L versus 2.25 (IQR, 1.09-4.12) mg/L; P = 0.011]. In addition, with MICs ≤ 0.5 mg/L, maintaining polymyxin B CSF Cmin above 2.0 mg/L showed a higher clinical cure rate (P = 0.041). The 28-day all-cause mortality rate was 31.0% and had no association with CSF Cmin. CONCLUSIONS: After intrathecal/intraventricular administration of polymyxin B, CSF concentrations fluctuated considerably inter- and intra-individual. Polymyxin B CSF Cmin above 2.0 mg/L was associated with clinical cure when MICs were ≤ 0.5 mg/L, and the feasibility of TDM warrants additional clinical studies.

Doubly robust proximal synthetic controls.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

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.

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.

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.

Identification and functional study of detoxification-related genes in response to tolfenpyrad stress in Glyphodes pyloalis Walker (Lepidoptera: Pyralidae).

Glyphodes pyloalis Walker (G. pyloalis) is a common destructive mulberry pest. Due to the long-term and frequent use of insecticides, it has developed tolerance to commonly used insecticides. Tolfenpyrad (TFP) is a novel pyrazole heterocyclic insecticide. In order to understand the TFP detoxification mechanism of G. pyloalis larvae, we first estimated the LC30 dose of TFP for 3rd instar G. pyloalis larvae. Next, we identified genes that were differentially expressed in 3rd instar G. pyloalis larvae treated with TFP compared to the control group by transcriptome sequencing. In total, 86,949,569 and 67,442,028 clean reads were obtained from TFP-treated and control G. pyloalis larvae, respectively. A total of 5588 differentially expressed genes (DEGs) were identified in TFP-treated and control G. pyloalis larvae, of which 3084 genes were upregulated and 2504 genes were downregulated. We analyzed the expression of 43 candidate detoxification enzyme genes associated with insecticide tolerance using qPCR. According to the spatiotemporal expression pattern of DEGs, we found that CYP6ABE1, CYP333A36 and GST-epsilon8 were highly expressed in the midgut, while CarEs14 was strongly expressed in haemolymph. Furthermore, we successfully knocked down these genes by RNA interference. After silencing CYP6ABE1 and CYP333A36, bioassay showed that the mortality rate of TFP-treated G. pyloalis larvae was significantly higher compared to the control group. This study provides a theoretical foundation for understanding the sensitivity of G. pyloalis to TFP and establish the basis for the effective and green management of this pest.

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.

ß-Arylation of Racemic Secondary Benzylic Alcohols to Access Enantioenriched ß-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 ß-arylation of secondary alcohols with aryl bromides has been achieved in this study, which allows for the selective functionalization of a ß-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.

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.

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 Poly-ADP-ribose polymerase 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 homologous recombination deficiency (HRD) positive population. CONCLUSION: PARPi as first-line maintenance therapy significantly improves the progression-free survival in AOC, especially in the BRCAmut and HRD positive populations. PARPi has been becoming the standard first-line maintenance therapy for AOC.

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.

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach.

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

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.

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.

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.

Ginsenoside Rb1 Protects the Brain from Damage Induced by Epileptic Seizure via Nrf2/ARE Signaling.

BACKGROUND/AIMS: Ginsenoside Rb1 (Rb1) has been reported to have varieties of neuroprotective effects. This study aimed to evaluate the effects of Rb1 on pentylenetetrazol (PTZ)-induced rat brain injury and Mg2+ free-induced neuron injury and analyzed the detailed molecular mechanisms in vivo and in vitro. METHODS: Seizure duration and latency were measured in epilepsy kindled rat. The cognitive impairment was assessed by Morris water maze (MWM) test. Oxidative stress parameters, malondialdehyde (MDA) and glutathione (GSH) were measured by the 2-thiobarbituric acid methods and the DTNB-GSSG reductase recycling methods. Neuronal damage was assessed by hematoxylin and eosin (H&E) and Nissl staining. Neuronal apoptosis was measured by Annexin V-FITC and propidium iodide (PI) staining. Immunohistochemistry and immunofluorescence staining were performed to evaluate Nrf2 and HO-1 expressions. Expression of Nrf2, HO-1, Bcl-2, iNOS and LC3 were evaluated by western blot. RESULTS: The PTZ-injured rats presented longer seizure duration and shorter seizure latency. Rb1 ameliorated these effects, as well as the cognitive deficits caused by PTZ exposure. Besides, Rb1 dose-dependently increased GSH levels, decreased MDA levels and alleviated neuronal damage in PTZ-treated rats. In vitro, Rb1 increased cell viability and decreased neuronal apoptosis in a dose-dependent manner under Mg2+ free condition. Moreover, in vivo and in vitro, Rb1 enhanced both the Nrf2 and HO-1 expressions. Furthermore, upregulation of the expression of Bcl-2 and downregulation of the expression of iNOS and LC3 were observed. However, knockdown of Nrf2 adversely affected the protective effects of Rb1 in epileptic hippocampal neurons. CONCLUSION: Rb1 conferred neuroprotective effects against PTZ-induced brain damage and Mg2+ free-induced neuron injury by activating Nrf2/ARE signaling.

Functional interplay between SA1 and TRF1 in telomeric DNA binding and DNA-DNA pairing.

Proper chromosome alignment and segregation during mitosis depend on cohesion between sister chromatids. Cohesion is thought to occur through the entrapment of DNA within the tripartite ring (Smc1, Smc3 and Rad21) with enforcement from a fourth subunit (SA1/SA2). Surprisingly, cohesin rings do not play a major role in sister telomere cohesion. Instead, this role is replaced by SA1 and telomere binding proteins (TRF1 and TIN2). Neither the DNA binding property of SA1 nor this unique telomere cohesion mechanism is understood. Here, using single-molecule fluorescence imaging, we discover that SA1 displays two-state binding on DNA: searching by one-dimensional (1D) free diffusion versus recognition through subdiffusive sliding at telomeric regions. The AT-hook motif in SA1 plays dual roles in modulating non-specific DNA binding and subdiffusive dynamics over telomeric regions. TRF1 tethers SA1 within telomeric regions that SA1 transiently interacts with. SA1 and TRF1 together form longer DNA-DNA pairing tracts than with TRF1 alone, as revealed by atomic force microscopy imaging. These results suggest that at telomeres cohesion relies on the molecular interplay between TRF1 and SA1 to promote DNA-DNA pairing, while along chromosomal arms the core cohesin assembly might also depend on SA1 1D diffusion on DNA and sequence-specific DNA binding.

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.

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.