Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type.

Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals. Convergent evidence suggests structural connectome abnormalities in specific brain regions are linked to AD progression. The biological basis underpinnings of these connectome changes, however, have remained elusive. We utilized an individual regional mean connectivity strength (RMCS) derived from a regional radiomics similarity network to capture altered morphological connectivity in 1654 participants (605 normal controls, 766 mild cognitive impairment [MCI], and 283 AD). Then, we also explored the biological basis behind these morphological changes through gene enrichment analysis and cell-specific analysis. We found that RMCS probes of the hippocampus and medial temporal lobe were significantly altered in AD and MCI, with these differences being spatially related to the expression of AD-risk genes. In addition, gene enrichment analysis revealed that the modulation of chemical synaptic transmission is the most relevant biological process associated with the altered RMCS in AD. Notably, neuronal cells were found to be the most pertinent cells in the altered RMCS. Our findings shed light on understanding the biological basis of structural connectome changes in AD, which may ultimately lead to more effective diagnostic and therapeutic strategies for this devastating disease.

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers.

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.

Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study.

Growing evidence indicates that amyloid-beta (Aß) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aß positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aß PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aß, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aß PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.

MiR-362-3p inhibits the proliferation and migration of vascular smooth muscle cells in atherosclerosis by targeting ADAMTS1.

Atherosclerosis is a progressive condition of the large arteries that can cause coronary artery disease (CAD). Growing amounts of evidence have indicated that microRNAs (miRNAs, miRs) can be used as diagnostic biomarkers in many cellular processes associated with CAD. MiR-362-3p has been implicated in many biological cellular functions. However, little is known about the role of miR-362-3p during atherosclerosis. In the present study, significant downregulation of miR-362-3p was observed in 110 atherosclerotic CAD patients and not in the 84 controls. The upregulation of miR-362-3p was demonstrated to inhibit vascular smooth muscle cell (VSMC) proliferation and migration, and impede the G1/S cell cycle transition. Bioinformatics analysis indicated that a disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) was a direct target of miR-362-3p. Subsequent experiments demonstrated that miR-362-3p binds to the 3'-untranslated region (UTR) of ADAMTS1 and decreases its levels of mRNA and protein expression. Overexpression of ADAMTS1 partially restored the miR-362-3p-mediated inhibition of VSMC proliferation, cell cycle, and migration. Upregulation of ADAMTS1 in plasma samples was detected in atherosclerotic CAD patients. Taken together, our findings suggested that miR-362-3p inhibits the proliferation and migration of VSMCs by directly targeting ADAMTS1, which might provide novel insight into the molecular mechanisms underlying the action of miR-362-3p in atherosclerosis.

Overexpression of MTERF4 promotes the amyloidogenic processing of APP by inhibiting ADAM10.

Alzheimer's disease (AD) is characterized by the deposition of ß-amyloid (Aß) peptide in the brain, which is produced by the proteolysis of ß-amyloid precursor protein (APP). Recently, the mitochondrial transcription factor 4 (MTERF4), a member of the MTERF family, was implicated in regulating mitochondrial DNA transcription and directly in controlling mitochondrial ribosomal translation. The present study identified a novel role for MTERF4 in shifting APP processing toward the amyloidogenic pathway. The levels of MTERF4 protein were significantly increased in the hippocampus of APP/PS1 mice. In addition, the overexpression of MTERF4 induced a significant increase in the levels of APP protein and secreted Aß42 in HEK293-APPswe cells compared with control cells. Further, MTERF4 overexpression shifted APP processing from α-to ß-cleavage, as indicated by decreased C83 levels and elevated C99 levels. Finally, the MTERF4 overexpression suppressed a disintegrin and metalloproteinase 10 (ADAM10) expression via a transcriptional mechanism. Taken together, these results suggest that MTERF4 promotes the amyloidogenic processing of APP by inhibiting ADAM10 in HEK293-APPswe cells; therefore, MTERF4 may play an important role in the pathogenesis of AD.

Attenuation of deregulated miR-369-3p expression sensitizes non-small cell lung cancer cells to cisplatin via modulation of the nucleotide sugar transporter SLC35F5.

Deregulation of the microRNAs (miRNAs), a cluster of important posttranscriptional regulators, has been frequently associated with lung cancer (LCa). However, the emerging mechanism for how miRNAs is linked causally in the development of LCa chemoresistance is poorly understood. Herein, we established for the time the up-regulation of miR-369-3p in cisplatin (DDP)-resistant nonsmall cell lung cancer (NSCLC) tissues and cells. Its deregulation was found to be correlated to the magnitude of malignancy in well-characterized LCa cells. Functionally, inhibition of miR-369-3p sensitized LCa cells to DDP and suppressed the invasive capability in the presence of DDP treatment, whereas miR-369-3p overexpression promoted DDP resistance and thereby enhanced LCa cells invasiveness. Mechanistically, bioinformatics coupled with luciferase and gain-of-function, loss-of-function assays revealed that miR-369-3p may regulate DDP chemoresistance by directly targeting the 3' untranslated region (UTR) of human solute carrier 35F5 (SLC35F5), as application of miR-369-3p inhibitors or reintroduction of epigenetically silenced SLC35F5 both individually sensitized LCa cells to DDP, but combined treatment with miR-369-3p inhibitors and SLC35F5 overexpression failed to sensitized LCa cells further to DDP-elicited cell death. Our results provide evidence that the oncomiR effect of miR-369-3p may be mediated through disrupting the nucleotide sugar transportation and that SLC35F5 is a key effector of this chemoresistance-promoting activity.

High-Resolution, Highly Transparent, and Efficient Quantum Dot Light-Emitting Diodes.

Aiming at next-generation displays, high-resolution quantum dot light-emitting diodes (QLEDs) with high efficiency and transparency are highly desired. However, there is limited study involving the improvements of QLED pixel resolution, efficiency, and transparency simultaneously, which undoubtedly restricts the practical applications of QLED for next-generation displays. Here, the strategy of electrostatic force-induced deposition (EF-ID) is proposed by introducing alternating polyethyleneimine (PEI) and fluorosilane patterns to synergistically improve the pixel accuracy and transmittance of QD patterns. More importantly, the leakage current induced by the void spaces between pixels that is usually reported for high-resolution QLEDs is greatly suppressed by substrate-assisted insulating fluorosilane patterns. Finally, high-performance QLEDs with high resolution ranging from 1104 to 3031 pixels per inch (PPI) and a high efficiency of 15.6% are achieved, among the best performances of high resolution QLEDs. Notably, the high resolution QD pixels greatly enhance the transmittance of the QD patterns, thus prompting an impressive transmittance of 90.7% for the transparent QLEDs (2116 PPI), which represents the highest transmittance of transparent QLED devices. Consequently, this work contributes an effective and general approach for high-resolution QLEDs with high efficiency and transparency.

An Attention Based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope.

The T-cell epitope prediction has always been a long-term challenge in immunoinformatics and bioinformatics. Studying the specific recognition between T-cell receptor (TCR) and peptide-major histocompatibility complex (p-MHC) complexes can help us better understand the immune mechanism, it's also make a signification contribution in developing vaccines and targeted drugs. Meanwhile, more advanced methods are needed for distinguishing TCRs binding from different epitopes. In this paper, we introduce a hybrid model composed of bidirectional long short-term memory networks (BiLSTM), attention and convolutional neural networks (CNN) that can identified the binding of TCRs to epitopes. The BiLSTM can more completely extract amino acid forward and backward information in the sequence, and attention mechanism can focus on amino acids at certain positions from complex sequences to capture the most important feature, then CNN was used to further extract salient features to predict the binding of TCR-epitope. In McPAS dataset, the AUC value (the area under ROC curve) of naive TCR-epitope binding is 0.974 and specific TCR-epitope binding is 0.887. The model has achieved better prediction results than other existing models (TCRGP, ERGO, NetTCR), and some experiments are used to analyze the advantages of our model. The algorithm is available at https://github.com/bijingshu/BiAttCNN.git.

A semi-supervised learning framework for micropapillary adenocarcinoma detection.

PURPOSE: Micropapillary adenocarcinoma is a distinctive histological subtype of lung adenocarcinoma with poor prognosis. Computer-aided diagnosis method has the potential to provide help for its early diagnosis. But the implementation of the existing methods largely relies on massive manually labeled data and consumes a lot of time and energy. To tackle these problems, we propose a framework that applies semi-supervised learning method to detect micropapillary adenocarcinoma, which aims to utilize labeled and unlabeled data better. METHODS: The framework consists of a teacher model and a student model. The teacher model is first obtained by using the labeled data. Then, it makes predictions on unlabeled data as pseudo-labels for students. Finally, high-quality pseudo-labels are selected and associated with the labeled data to train the student model. During the learning process of the student model, augmentation is added so that the student model generalizes better than the teacher model. RESULTS: Experiments are conducted on our own whole slide micropapillary lung adenocarcinoma histopathology image dataset and we selected 3527 patches for the experiment. In the supervised learning, our detector achieves a precision of 0.762 and recall of 0.884. In the semi-supervised learning, our method achieves a precision of 0.775 and recall of 0.896; it is superior to other methods. CONCLUSION: We proposed a semi-supervised learning framework for micropapillary adenocarcinoma detection, which has better performance in utilizing both labeled and unlabeled data. In addition, the detector we designed improves the detection accuracy and speed and achieves promising results in detecting micropapillary adenocarcinoma.

Accurate segmentation of green fruit based on optimized mask RCNN application in complex orchard.

Fruit and vegetable picking robots are affected by the complex orchard environment, resulting in poor recognition and segmentation of target fruits by the vision system. The orchard environment is complex and changeable. For example, the change of light intensity will lead to the unclear surface characteristics of the target fruit; the target fruits are easy to overlap with each other and blocked by branches and leaves, which makes the shape of the fruits incomplete and difficult to accurately identify and segment one by one. Aiming at various difficulties in complex orchard environment, a two-stage instance segmentation method based on the optimized mask region convolutional neural network (mask RCNN) was proposed. The new model proposed to apply the lightweight backbone network MobileNetv3, which not only speeds up the model but also greatly improves the accuracy of the model and meets the storage resource requirements of the mobile robot. To further improve the segmentation quality of the model, the boundary patch refinement (BPR) post-processing module is added to the new model to optimize the rough mask boundaries of the model output to reduce the error pixels. The new model has a high-precision recognition rate and an efficient segmentation strategy, which improves the robustness and stability of the model. This study validates the effect of the new model using the persimmon dataset. The optimized mask RCNN achieved mean average precision (mAP) and mean average recall (mAR) of 76.3 and 81.1%, respectively, which are 3.1 and 3.7% improvement over the baseline mask RCNN, respectively. The new model is experimentally proven to bring higher accuracy and segmentation quality and can be widely deployed in smart agriculture.

Single-cell transcriptomics reveals variable trajectories of CSPCs in the progression of osteoarthritis.

Osteoarthritis (OA) is characterised by cartilage destruction; however, there are no specific drugs available for its treatment. Cartilage-derived stem/progenitor cells (CSPCs) are multipotent cells that play an essential role in cartilage renewal and may provide critical insights into the medical needs for OA treatment. However, alterations in cell function and fate of CSPCs during OA progression have seldom been analysed, especially at the single-cell level. Additionally, it has been reported that CSPCs can migrate to the cartilage injury area, although the mechanism of migration remains elusive. Thus, understanding the changing patterns of CSPCs in the pathological process of OA is important in the effort to develop stem cell therapy for OA. Here, we downloaded single-cell transcriptomic data of patients with OA from the Gene Expression Omnibus (GEO) database and performed unbiased clustering of the cells based on gene expression patterns using the Seurat package. Using common stem cell markers and chondrogenic transcription factors, we traced CSPCs throughout all stages of OA. We further explored the dynamics of CSPCs in OA progression and validated the single-cell RNA sequencing data in vitro using qPCR, immunofluorescence, and western blotting. Specifically, we primarily explored the heterogeneity of CSPCs at the single-cell level and found that it was closely associated with OA progression. Our results indicate significantly reduced chondrogenic differentiation capacity in CSPCs during the late stage of OA, while their proliferation capacity tended to increase. We also found that genes implicated in fibrosis, cell motility, and extracellular matrix remodelling were upregulated in CSPCs during the progression of OA. Our study revealed the dynamics of stem cells in OA progression and may inform the development of stem cell therapy for OA.

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis.

Introduction: Manual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem. Methods: To solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called"Semi- supervised Histopathology Analysis Network"(Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training. Results: Our Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and three other different tumor subtypes, achieving an accuracy was 90%. The average AUC of cross-classification between tumors reached 0.893. Discussion: To overcome the limitations of visual inspection by pathologists for histopathology images, such as long time and low repeatability, we have developed a deep learning-based framework (Semi-His-Net) for automatic classification subdivision of the subtypes contained in the whole pathological images. This learning-based framework has great potential to improve the efficiency and repeatability of histopathological image diagnosis.

A systematic analysis of diagnostic performance for Alzheimer's disease using structural MRI.

Background: Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in the elderly. Although numerous structural magnetic resonance imaging (sMRI) studies have reported diagnostic models that could distinguish AD from normal controls (NCs) with 80-95% accuracy, limited efforts have been made regarding the clinically practical computer-aided diagnosis (CAD) system for AD. Objective: To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI. Methods: To systematically review the diagnostic models for AD based on sMRI, we identified relevant studies published in the past 15 years on PubMed, Web of Science, Scopus, and Ovid. To evaluate the heterogeneity and publication bias among those studies, we performed subgroup analysis, meta-regression, Begg's test, and Egger's test. Results: According to our screening criterion, 101 studies were included. Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies, accompanied by significant heterogeneity. Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI, which included but was not limited to the following aspects: (i) different datasets; (ii) different machine learning models, e.g. traditional machine learning or deep learning model; (iii) different cross-validation methods, e.g. k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation, but both overestimate the accuracy when compared to validation in independent samples; (iv) different sample sizes; and (v) the publication times. We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD. Conclusions: Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI. However, considering the many factors hindering clinical radiology practice, there would still be a long way to go to improve.

Electrocardiographic abnormalities in patients with intracerebral hemorrhage.

BACKGROUND: Stroke is frequently followed by electrocardiographic changes. Although electrocardiographic abnormalities are well known in ischemic stroke and subarachnoid hemorrhage, these changes have only rarely been investigated systematically in patients with intracerebral hemorrhage. The purpose of this study is to investigate the prevalence and characterization of ECG abnormalities in a consecutive series of ICH patients who had no history of heart disease. METHODS: The study was retrospective, and 304 intracerebral hemorrhage patients who met the study criteria were entered in the study. The ECG changes of the 304 acute hemorrhagic stroke patients without primary heart disease were analyzed. The relationship among the electrocardiographic abnormalities, the location of hematoma, and the clinical outcome were investigated to determine cardiac involvement in the cerebral hemorrhage in these patients. RESULTS: A total of 304 patients were included. Two hundred and four patients (67.1%) had one or more ECG abnormalities. These changes included morphological waveform changes and arrhythmias, such as QTc prolongation, ST-T morphological changes, sinus bradycardia, inverted T wave, and conduction block. These ECG changes were not related to the level of the cerebral lesion, but were related to its location and the outcome. CONCLUSIONS: Electrocardiographic abnormalities frequently occur after intracerebral hemorrhage, and these changes were not related to the level of the cerebral lesion, but were related to the location of the cerebral lesion and the outcome.

ECG change of acute subarachnoid hemorrhagic patients.

OBJECTIVE: This study intends to investigate whether the entry electrocardiographic (ECG) abnormalities of patients with acute subarachnoid hemorrhage (SAH) are related to the prognosis. METHODS: From 1998 to the present, 106 SAH patients who had no history of heart disease and were diagnosed with head CT were recruited. RESULTS: Abnormal ECG changes of acute subarachnoid hemorrhage patients were observed, with a total incidence rate of 63.2% (67/106). The incidence rate of allorhythmia was 22.6% (24/106), the repolarization abnormality was 14.2% (15/106), the conduction abnormality was 1.9% (2/106), and the combined abnormality was 21.7% (23/106). However, dividing the patients into two groups according to their entry consciousness state, no difference was observed between coma and alert groups (P=1.0000). In addition, the ECG changes had no relationship with the lesion degree and the outcome prognosis according to logistic regression analysis (P=0.0844). CONCLUSIONS: In 106 SAH patients, we could not identify any relationship between the ECG and lesion degree and outcome prognosis.

Saturated impulsive control for synchronization of coupled delayed neural networks.

The paper focuses on the synchronization problem for a class of coupled neural networks with impulsive control, where the saturation structure of impulse action is fully considered. The coupled neural networks under consideration are subject to mixed delays including transmission delay and coupled delay. The sector condition in virtue of a new constraint of set inclusion is given for a addressed network, based on which a sufficient condition for exponential synchronization problem is obtained by replacing saturation nonlinearity with a dead-zone function. In the framework of saturated impulses, our results relying on the domain of attraction can still achieve the synchronization of coupled delayed neural networks. In addition, the estimating domain of attraction is proposed as large as possible by solving an optimization problem. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the proposed results.

Deep multispectral image registration network.

Multispectral imaging (MSI) of the ocular fundus provides a sequence of narrow-band images to show the different depths in the retina and choroid. One challenge in analyzing MSI images comes from the image-to-image spatial misalignment, which occurs because the acquisition time of eye MSI images is commonly longer than the natural time scale of the eye's saccadic movement. It is necessary to align images because ophthalmologists usually overlay two of the images to analyze specific features when analyzing MSI images. In this paper, we propose a weakly supervised MSI image registration network, called MSI-R-NET, for multispectral fundus image registration. Compared to other deep-learning-based registration methods, MSI-R-NET utilizes the blood vessel segmentation label to provide spatial correspondence. In addition, we employ a feature equilibrium module to connect the aggregating layers better, and propose a multiresolution auto-context structure to adapt the registration task. In the testing stage, given a new pair of MSI images, the trained model can predict the pixelwise spatial correspondence without labeled blood vessel information. The experimental results demonstrate that the proposed segmentation-driven registration method is highly accurate.

Resistance Trends of Klebsiella pneumoniae Causing Urinary Tract Infections in Chongqing, 2011-2019.

PURPOSE: To analyze the characteristics and trends of drug resistance for Klebsiella pneumoniae (K. pneumoniae), isolated from urinary tract infections (UTIs), to common antibiotics used in clinics. METHODS: This retrospective study was conducted in a teaching hospital in Chongqing from 2011 to 2019. Laboratory data of isolated bacteria were collected and analyzed. RESULTS: Among the 17,966 non-repetitive strains isolated from the urine sample, a total of 1543 K. pneumoniae isolates were identified, with an isolation frequency secondary only to Escherichia coli (E. coli) and there was a peak in the K. pneumoniae isolates in the year 2013. During the period, the rate of extended-spectrum ß-lactamase (ESBL)-producing K. pneumoniae fell from 48.4% in 2011 to 32.9% in 2019, and a marked jump of resistance was seen in carbapenems from 2.2% to 18.0%. The peak of carbapenem resistance rate (22.6%) to K. pneumoniae was observed in 2017 along with a low ESBL-producing rate (30.9%). Piperacillin/tazobactam and cefepime resistance levels went up from 4.4% to 25.7% and from 18.2% to 30.5%, respectively. Moreover, the K. pneumoniae isolates resistance rate to carbapenems and amikacin gradually grew up, showing their peaks in 2017, and then dropped year by year. However, ceftazidime and aztreonam resistance levels were relatively stable, fluctuating between 21.8% and 35.6%, 32.2% and 39.4%, respectively. CONCLUSION: There is a significant upward tendency in carbapenem resistance rate and a downward tendency in ESBL-production rate in K. pneumoniae isolates from UTIs, and continuous surveillance is necessary in the future.

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis.

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.

In Vitro Activity of Auranofin in Combination With Aztreonam-Avibactam Against Metallo-ß-lactamase (MBL)-Producing Enterobacterales.

Objectives: To assess the efficacy of aztreonam-avibactam-auranofin (ATM-AVI-AUR) against a collection of 88 carbapenemase-producing Enterobacterales (CPE) clinical isolates and 6 in vitro selected ATM-AVI-resistant CPE with CMY-16 Tyr150Ser and Asn346His mutants or transformants. Methods: MICs of imipenem, ceftazidime-avibact8am (CAZ-AVI), ATM-AVI, CAZ-AVI-AUR and ATM-AVI-AUR were determined via the broth microdilution method. Genetic background and carbapenemase genes were determined by PCR and Sanger sequencing. Results: AUR alone showed little antibacterial activity with AUR MICs were greater than 64 µg/mL for all the 88 clinical CPE isolates. The addition of AUR (16 µg/mL) resulted in an 3-folding dilutions MIC reduction of ATM-AVI MIC50 (0.5 to 0.0625 µg/mL) and a 2-folding dilutions MIC reduction of MIC90 (1 to 0.25 µg/mL) against all 88 clinical CPE isolates, respectively. Notably, the reduced ATM-AVI MIC values were mainly found in MBL-producers, and the MIC50 and MIC90 reduced by 2-folding dilutions (0.25 to 0.0625 µg/mL) and 3-folding dilutions (2 to 0.25 µg/mL) respectively by AUR among the 51 MBL-producers. By contrast, the addition of AUR did not showed significant effects on ATM-AVI MIC50 (0.0625 µg/mL) and MIC90 (0.125 µg/mL) among single KPC-producers. Interestingly, the addition of AUR restored the ATM-AVI susceptibility against the 6 in vitro selected ATM-AVI-resistant CMY-16 Tyr150Ser and Asn346His mutants or transfromants, with the MICs reduced from ≥32 µg/mL (32->256 µg/mL) to ≤8 µg/mL (0.0625-8 µg/mL). Conclusions: Our results demonstrated that AUR potentiated the activities of CAZ-AVI and ATM-AVI against MBL-producing isolates in vitro. Importantly, AUR restored the ATM-AVI activity against ATM-AVI resistant mutant strains. As a clinically approved drug, AUR might be repurposed in combination with ATM-AVI to treat infections caused by highly resistant MBL-producing Enterobacterales.