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.

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.

Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments.

Objectives: To investigate the risk factors of pulmonary infection in patients with severe myelitis and construct a prediction model. Methods: The clinical data of 177 patients with severe myelitis at admission from the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were retrospectively analyzed. The predicting factors associated with pulmonary infection were screened by multivariate logistic regression analysis, and the nomogram model was constructed, and the predictive efficiency of the model was evaluated, which was verified by calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis. Results: Of the 177 patients with severe myelitis, 38 (21.5%) had pulmonary infection. Multivariate logistic regression analysis showed that neutrophil percentage to albumin ratio (NPAR) (OR = 6.865, 95%CI:1.746-26.993, p = 0.006) and high cervical cord lesion (OR = 2.788, 95%CI:1.229-6.323, p = 0.014) were independent risk factors for pulmonary infection, and the combined nomogram could easily predict the occurrence of pulmonary infection, with a C-index of 0.766 (95% CI: 0.678-0.854). The calibration curve, Hosmer-Lemeshow goodness-of-fit test (χ2 = 9.539, p = 0.299) and decision curve analysis showed that the model had good consistency and clinical applicability. Conclusion: The nomogram model constructed based on NPAR combined with high cervical cord lesion at admission has good clinical application value in predicting pulmonary infection in patients with severe myelitis, which is conducive to clinicians' evaluation of 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)) (D = 0.207, p < 0.001) and negatively correlated with a protective marker (serum albumin) (D = .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.

Moxibustion enables protective effects on rheumatoid arthritis-induced myocardial injury transforming growth factor beta1 signaling and metabolic reprogramming.

OBJECTIVE: To examine the effects of moxibustion on myocardial injury and myocardial metabolomics in rats with rheumatoid arthritis (RA) based on the transforming growth factor beta1 (TGF-ß1)/Smads signaling pathway. METHODS: One hundred rats were treated with saline [normal control (NC) group] or complete Freund's adjuvant (CFA) by right plantar injection for the RA model group, and the latter were randomly divided into 4 groups. Tripterygium wilfordii polyglycoside tablets (, TPT) have anti-inflammatory and are widely used in the clinical treatment of RA, therefore serving as a positive control group. Three days post injection rats were given TPT tablet (TPT group), acupuncture therapy (APT group), and moxibustion treatment (MOX group) for 15 consecutive days, while NC group and model group were equally grasped and fixed and received normal saline. Rat joint swelling scores and arthritis index (AI) were evaluated in each group before the CFA challenge, therapy and after receiving therapy. Myocardial ultrastructure was observed by electron microscope. Enzyme-linked immunosorbent assay was used to detect cardiac troponin I (cTnI) levels in rat myocardial tissue. Quantitative reverse transcription polymerase chain reaction and Western blotting analysis were used to measure the mRNA and protein levels of TGF-ß signaling molecules including TGF-ß1, Smad2, Smad3, Smad4, and Smad7. Myocardial metabolomics was analyzed using gas chromatography-mass spectrometer. RESULTS: Compared with model group, RA model rats receiving TPT, acupuncture, or moxibustion therapy all showed reduced joint swelling scores and AI (all P < 0.01) and improved myocardial damage, whereas rats treated with moxibustion were found to be more marked. Consistently, the expressions of cTnI, TGF-ß1, Smad2, Smad3, and Smad4 were found to be elevated in model rat group in contrast to NC rats and were significantly downregulated in TPT, APT and MOX group when compared with model group, while the levels of Smad7 showed the opposite result (all P < 0.01). Moreover, the dissection of metabolomics suggested a novel metabolite biomarker panel including D-Xylulose 5-phosphate, dihydroxyacetone phosphate, arachidonic acid, etc was defined and implicated in amino acid, glucose, and fatty acid metabolic processes as revealed by principal component analysis and partial least squares discriminant analysis. CONCLUSION: Moxibustion prevents RA-induced inflammatory response and offers potent therapeutic effects on myocardial dysfunctions. The protective effects might be associated with its role in TGF-ß1 inactivation and metabolic reprogramming.

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.

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.

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.

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.

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.

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.

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.

Nile tilapia DNA sensor STING is involved in the IFN-ß 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-ß 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.

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.

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.

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.