Restoring susceptibility to ß-lactam antibiotics in methicillin-resistant Staphylococcus aureus.

Infections by Staphylococcus aureus have been treated historically with ß-lactam antibiotics. However, these antibiotics have become obsolete in methicillin-resistant S. aureus by acquisition of the bla and mec operons. The presence of the ß-lactam antibiotic is detected by the sensor domains of BlaR and/or MecR, and the information is transmitted to the cytoplasm, resulting in derepression of the antibiotic-resistance genes. We hypothesized that inhibition of the sensor domain would shut down this response system, and ß-lactam susceptibility would be restored. An in silico search of 11 million compounds led to a benzimidazole-based hit and, ultimately, to the boronate 4. The X-ray structure of 4 is covalently engaged with the active-site serine of BlaR. Compound 4 potentiates by 16- to 4,096-fold the activities of oxacillin and of meropenem against methicillin-resistant S. aureus strains. The combination of 4 with oxacillin or meropenem shows efficacy in infected mice, validating the strategy.

A dual-action antibiotic that kills Clostridioides difficile vegetative cells and inhibits spore germination.

Clostridioides difficile infection (CDI) is the most lethal of the five CDC urgent public health treats, resulting in 12,800 annual deaths in the United States alone [Antibiotic Resistance Threats in the United States, 2019 (2019), www.cdc.gov/DrugResistance/Biggest-Threats.html]. The high recurrence rate and the inability of antibiotics to treat such infections mandate discovery of new therapeutics. A major challenge with CDI is the production of spores, leading to multiple recurrences of infection in 25% of patients [C. P. Kelly, J. T. LaMont, N. Engl. J. Med. 359, 1932-1940 (2008)], with potentially lethal consequence. Herein, we describe the discovery of an oxadiazole as a bactericidal anti-C. difficile agent that inhibits both cell-wall peptidoglycan biosynthesis and spore germination. We document that the oxadiazole binds to the lytic transglycosylase SleC and the pseudoprotease CspC for prevention of spore germination. SleC degrades the cortex peptidoglycan, a critical step in the initiation of spore germination. CspC senses germinants and cogerminants. Binding to SleC is with higher affinity than that to CspC. Prevention of spore germination breaks the nefarious cycles of CDI recurrence in the face of the antibiotic challenge, which is a primary cause of therapeutic failure. The oxadiazole exhibits efficacy in a mouse model of recurrent CDI and holds promise in clinical treatment of CDI.

First-Principles Calculations of TiB4 and TiB5 as Anodes with High Capacity for Na-Ion Batteries.

The electrochemical properties of TiB4 and TiB5 monolayers in Na-ion batteries (NIBs) were studied by using the first-principles calculation method based on density functional theory. The TiB4/TiB5 monolayer showed excellent Na storage capacity, capable of adsorbing two layers of Na with theoretical capacities of 1176.77 and 1052.05 mA g-1, respectively. The average operating voltages of the TiB4 and TiB5 monolayers are 0.073 and 0.042 eV, respectively, indicating that they can be used as anode materials for NIBs. More interestingly, the exposed B surface not only brings a high theoretical capacity but also provides a relatively small diffusion barrier of 0.16 (for TiB4) and 0.33 eV (for TiB5), enhancing their rate capability in NIBs.

Prediction of hypertension risk based on multiple feature fusion.

OBJECTIVE: In the application of machine learning to the prediction of hypertension, many factors have seriously affected the classification accuracy and generalization performance. We propose a pulse wave classification model based on multi-feature fusion for accuracy prediction of hypertension. METHODS AND MATERIALS: We propose an ensemble under-sampling model with dynamic weights to decrease the influence of class imbalance on classification, further to automatically classify of hypertension on inquiry diagnosis. We also build a deep learning model based on hybrid attention mechanism, which transforms pulse waves to feature maps for extraction of in-depth features, so as to automatically classify hypertension on pulse diagnosis. We build the multi-feature fusion model based on dynamic Dempster/Shafer (DS) theory combining inquiry diagnosis and pulse diagnosis to enhance fault tolerance of prediction for multiple classifiers. In addition, this study calculates feature importance ranking of scale features on inquiry diagnosis and temporal and frequency-domain features on pulse diagnosis. RESULTS: The accuracy, sensitivity, specificity, F1-score and G-mean after 5-fold cross-validation were 94.08%, 93.43%, 96.86%, 93.45% and 95.12%, respectively, based on the hypertensive samples of 409 cases from Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We find the key factors influencing hypertensive classification accuracy, so as to assist in the prevention and clinical diagnosis of hypertension. CONCLUSION: Compared with the state-of-the-art models, the multi-feature fusion model effectively utilizes the patients' correlated multimodal features, and has higher classification accuracy and generalization performance.

A pilot study of the association between leukoaraiosis and cerebral atherosclerosis using synthetic magnetic resonance imaging.

BACKGROUND: Leukoaraiosis is a type of lesion characterized by tissue rarefaction or myelin pallor resulting from axons loss and gliosis. Synthetic magnetic resonance imaging (MRI) could yield quantitative T1, T2, proton density (PD) values of leukoaraiosis in addition to information on the volume of the lesion. PURPOSE: To investigate the feasibility of quantifying leukoaraiosis using synthetic MRI and to explore the association between leukoaraiosis and cerebral small vascular diseases and cerebral atherosclerosis. MATERIAL AND METHODS: Patients with acute ischemic stroke were enrolled in this study. All participants underwent a conventional T2-weighted image, brain volume, CUBE fluid attenuated inversion recovery, and synthetic MRI acquisition using a 3.0-T MR system. A time-of-flight magnetic resonance angiography was also obtained. We evaluated the T1, T2, PD values and leukoaraiosis volume. RESULTS: Analysis of the leukoaraiosis volume ratios demonstrated a positive association with T2 values, a negative association with T1 values, and no association with PD values. Leukoaraiosis volume ratios were independently correlated with age (P < 0.001), lacunes (P = 0.022), and cerebral microbleeds (P = 0.010). A statistical association was found between both age (P < 0.001) and lacunes (P = 0.047) and leukoaraiosis T2 values. CONCLUSION: Synthetic MRI may enhance the evaluation of leukoaraiosis, in addition to providing information on its volume. Leukoaraiosis may represent a type of cerebral small vascular disease rather than cerebral atherosclerosis and may share the same pathological mechanism as lacunes and cerebral microbleeds.

CodnNet: A lightweight CNN architecture for detection of COVID-19 infection.

The application of Convolutional Neural Network (CNN) on the detection of COVID-19 infection has yielded favorable results. However, with excessive model parameters, the CNN detection of COVID-19 is low in recall, highly complex in computation. In this paper, a novel lightweight CNN model, CodnNet is proposed for quick detection of COVID-19 infection. CodnNet builds a more effective dense connections based on DenseNet network to make features highly reusable and enhances interactivity of local and global features. It also uses depthwise separable convolution with large convolution kernels instead of traditional convolution to improve the range of receptive field and enhances classification performance while reducing model complexity. The 5-Fold cross validation results on Kaggle's COVID-19 Dataset showed that CodnNet has an average precision of 97.9%, recall of 97.4%, F1score of 97.7%, accuracy of 98.5%, mAP of 99.3%, and mAUC of 99.7%. Compared to the typical CNNs, CodnNet with fewer parameters and lower computational complexity has achieved better classification accuracy and generalization performance. Therefore, the CodnNet model provides a good reference for quick detection of COVID-19 infection.

First-principles prediction of layered MoO2and MoOSe as promising cathode materials for magnesium ion batteries.

Magnesium ion battery is one of the promising next-generation energy storage systems. Nevertheless, lack of appropriate cathode materials to ensure massive storage and efficient migration of Mg cations is a big obstacle for development of Mg-ion batteries. Herein, by means of first principles calculations, the geometric structure, electronic structure, Mg intercalation behavior and Mg diffusion behavior of the layered MoO2and two MoOSe (MoOSe(I) and MoOSe(V)) were systematically investigated. Layered MoO2shows semiconductor properties, while MoOSe displays metallic characteristics which ensure higher conductivity. The Mg cations tend to intercalate into octahedral sites for both MoO2and MoOSe. The maximum Mg-storage phases of the layered MoO2, MoOSe(I) and MoOSe(V) correspond to Mg0.666MoO2, Mg0.666MoOSe(I) and Mg0.666MoOSe(V), with theoretical specific capacities of 279, 191 and 191 mAh g-1, respectively. The calculated discharge plateaus of MoO2and two MoOSe are all about 1 V, which ensure that the layered MoO2and MoOSe electrodes can act as cathodes for Mg-ion batteries in the early stage. Moreover, comparing with other cathodes, the diffusion barrier of Mg cations and volume expansion during Mg intercalation process are competitive. The results suggest that layered MoO2and MoOSe are the promising cathode materials for Mg-ion batteries.

Midurethral sling position and surgical outcome: A meta-analysis.

There is still ongoing debate as to whether the outcome of the sling is determined by the position of the midurethral sling. In order to evaluate the potential impact of sling position on the outcomes of retropubic and transobturator sling procedures for stress urinary incontinence, it is necessary to conduct further investigations. We conducted a comprehensive search across various electronic databases such as PubMed, EMBASE, MEDLINE. Relevant data were extracted, organized in a table format, and analyzed using RevMan software for further analysis. This review comprised a total of 9 studies. The findings indicate that patients with TVT tape placement in the midurethra exhibited a slightly higher cure rate compared to those with proximal placement. Conversely, tape placement in the midurethra was associated with a significantly higher cure rate compared to distal placement [RR = 0.84, 95% CI (0.74-0.95), P < .05]. In the case of transobturator slings, tape positioning beneath the midurethra or distal urethra yielded better outcomes compared to placement near the bladder neck [RR = 0.74, 95% CI (0.57-0.94), P = .02; RR = 0.61, 95% CI (0.39-0.96), P = .03]. Based on 2D and 3D ultrasound imaging, differences in TVT placement appear to have minimal impact on the cure rate. However, the highest rate of failure after transobturator sling surgery is closely linked to the positioning.

Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map.

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.

Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning.

Common clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to the other methods, ARF-OOBEE has better evaluation indicator and is more effective for the early clinical diagnosis of rhinitis.

A Potent and Narrow-Spectrum Antibacterial against Clostridioides difficile Infection.

Clostridioides difficile is an anaerobic Gram-positive bacterium that colonizes the gut of patients treated with broad-spectrum antibiotics. The normal gut microflora prevents C. difficile colonization; however, dysbiosis by treatment with broad-spectrum antibiotics causes recurrent C. difficile infection (CDI) in 25% of patients. There are no fully effective antibiotics for multiple recurrent CDIs. We report herein that oxadiazole antibiotics exhibit bactericidal activity against C. difficile vegetative cells. We screened a library of 75 oxadiazoles against C. difficile ATCC 43255. The findings from this collection served as the basis for the syntheses of an additional 58 analogs, which were tested against the same strain. We report a potent (MIC50 = 0.5 µg/mL and MIC90 = 1 µg/mL values for 101 C. difficile strains) and narrow-spectrum oxadiazole (3-(4-(cyclopentyloxy)phenyl)-5-(4-nitro-1H-imidazol-2-yl)-1,2,4-oxadiazole; compound 57), which is not active against common gut bacteria or other tested organisms. Compound 57 is selectively bactericidal against C. difficile and targets cell-wall synthesis.

Dimeric 2-aminoimidazoles are highly active adjuvants for gram-positive selective antibiotics against Acinetobacter baumannii.

The Centers for Disease Control and Prevention (CDC) reports that hospital acquired infections have increased by 65% since 2019. One of the main contributors is the gram-negative bacterium Acinetobacter baumannii. Previously, we reported aryl 2-aminoimidazole (2-AI) adjuvants that potentiate macrolide antibiotics against A. baumannii. Macrolide antibiotics are typically used to treat infections caused by gram-positive bacteria, but are ineffective against most gram-negative bacteria. We describe a new class of dimeric 2-AIs that are highly active macrolide adjuvants, with lead compounds lowering minimum inhibitory concentrations (MICs) to or below the gram-positive breakpoint level against A. baumannii. The parent dimer lowers the clarithromycin (CLR) MIC against A. baumannii 5075 from 32 µg/mL to 1 µg/mL at 7.5 µM (3.4 µg/mL), and a subsequent structure activity relationship (SAR) study identified several compounds with increased activity. The lead compound lowers the CLR MIC to 2 µg/mL at 1.5 µM (0.72 µg/mL), far exceeding the activity of both the parent dimer and the previous lead aryl 2-AI. Furthermore, these dimeric 2-AIs exhibit considerably reduced mammalian cell toxicity compared to aryl-2AI adjuvants, with IC50s of the two lead compounds against HepG2 cells of >200 µg/mL, giving therapeutic indices of >250.

CrodenseNet: An efficient parallel cross DenseNet for COVID-19 infection detection.

Purpose At present, though the application of Convolution Neural Network (CNN) to detect COVID-19 infection significantly enhance the detection performance and efficiency, it often causes low sensitivity and poor generalization performance. Methods In this article, an effective CNN, CrodenseNet is proposed for COVID-19 detection. CrodenseNet consists of two parallel DenseNet Blocks, each of which contains dilated convolutions with different expansion scales and traditional convolutions. We employ cross-dense connections and one-sided soft thresholding to the layers for filtering of noise-related features, and increase information interaction of local and global features. Results Cross-validation experiments on COVID-19x dataset shows that via CrodenseNet the COVID-19 detection attains the precision of 0.967 ± 0.010, recall of 0.967 ± 0.010, F1-score of 0.973 ± 0.005, AP (area under P-R curve) of 0.991 ± 0.002, and AUC (area under ROC curve) of 0.996 ± 0.001. Conclusion CrodenseNet outperforms a variety of state-of-the-art models in terms of evaluation metrics so it assists clinicians to prompt diagnosis of COVID-19 infection.

Activation of ozone by peroxymonosulfate for selective degradation of 1,4-dioxane: Limited water matrices effects.

The presence of 1,4-dioxane in various water streams poses a threat to the health of human beings. In this study, the oxidative combination of ozone with peroxymonosulfate (PMS) was for the first time used to remove 1,4-dioxane from water. Near complete abatement of 1,4-dioxane was achieved by ozone-PMS after reaction of only 15 min and the degradation kinetics was found to be positively correlated with doses of PMS and ozone. Ozone-PMS oxidation had the optimum performance at slight base pH values. Both sulfate radicals and hydroxyl radicals were generated in ozone-PMS oxidation and these radicals resulted in the degradation of 1,4-dioxane. The effects of common water constituents and real water matrices were investigated. It was found that bicarbonate ions with a concentration up to 10 mM had a slight promoting effect, while either chloride ions or natural organic matter inhibited only slightly the degradation. Meanwhile, no obvious difference in the degradation of 1,4-dioxane was found among the real water matrices and deionized water, which demonstrates that ozone-PMS oxidation has high tolerance and stability. The results from this study demonstrate that ozone-PMS may be a promising technology for the removal of 1,4-dioxane from various water matrices.

Experimental study on the permeability of Pb-contaminated silt solidified by CFG.

The permeability of soil is a fundamental parameter in hydrological and geotechnical studies, nevertheless, the investigations on the silt especially the heavy mental contaminated silt have rarely been reported. The research introduces the effect of the different factors, including the osmotic pressure, curing time, the content of binders and the concentration of lead, on the permeability and microscopic properties of lead-contaminated silt, which is solidified by a novel curing agent, formed by mixing cement, fly ash and desulphurization gypsum (CFG). According to the tests of the permeability and scanning electron microscope (SEM), the permeability of samples is investigated under different influencing factors. The results demonstrate that the permeability coefficient of the contaminated silt increases with the increasing osmotic pressure and lead ion concentration, while decreases with the increasing CFG content and the curing time, additonally, there are interactions among various factors, which jointly affect the permeability of samples. Moreover, the statistical analysis shows that osmotic pressure has the most significant effect on permeability among various factors. Besides, the influence of lead ion concentration, osmotic pressure and CFG content on the permeability coefficient at the curing time of 14d is more significant than that of 28d, and the influence order of factors was slightly different at different curing time. Furthermore, the SEM test proves the conclusion of the statistical analysis, which also explains the common reasons for the enhancement of the impermeability and unconfined compressive strength (UCS) of the lead-contaminated silt with the increasing curing time.

Graphene Modified Polyaniline-Hydrogel Based Stretchable Supercapacitor with High Capacitance and Excellent Stretching Stability.

Conjugated polymers have been widely adopted as active materials in hydrogel-based stretchable supercapacitors, but the relatively low conductivity and poor structural stability limit their applications. Herein, highly conductive graphene was incorporated as a substrate to anchor polyaniline (PANI) in a hydrogel-based stretchable electrode. Graphene not only provided an effective conducting network in the electrode, but also stabilized PANI during repeating charge-discharge processes due to strong π-π interaction between graphene and PANI. The obtained electrode showed high capacitance of 500.13 mF cm-2 and 100 % capacitance retention after 10000 charge-discharge cycles. The symmetrical supercapacitor using this novel stretchable electrode showed a high capacitance of 218.26 mF cm-2 , high capacitance retention of 43 % even when stretched at 150 % strain, and no capacitance decay when stretched to 100 % and then released to 0 % repeatedly for 2000 cycles, all of which were much better than the device based on the electrode without adding graphene. Such outstanding electrochemical performance shows the great application potential of highly conductive graphene in conjugated polymer-based stretchable energy storage devices.

Study on mechanical and permeability characteristics of nickel-copper-contaminated soil solidified by CFG.

The study introduces the potential use of a novel curing agent formed by mixing cement, fly ash, and desulfurization gypsum (CFG) for the stabilization/solidification of nickel-copper-contaminated soil. According to the tests of the unconfined compressive strength (UCS), permeability, and scanning electron microscope (SEM), the samples' characteristics, which include UCS, deformation, and permeability, are investigated under different curing time, ion concentration, and curing agent content. The correlation between the UCS and permeability of samples is estimated. The results demonstrates that the gradual increase of the UCS and impermeability of the samples can be recognized with the increase of curing age; however, the failure strain decreased gradually but the trend is reversed with the increase of heavy metal concentration. Besides, greater amounts of CFG results in better mechanical and impermeability properties. The correlation between UCS and permeability coefficient reveals that the UCS of contaminated soil is significantly negatively correlated with permeability coefficient, and the prediction formulas of permeability coefficient based on age and CFG content are given respectively. Moreover, the microscopic analysis demonstrates that the hydration products calcium silicate hydrate(C-S-H) and ettringite (AFt) of CFG curing agent are the main reasons for the enhancement of the UCS and impermeability of contaminated soil. However, the increase of nickel and copper concentration leads to the decrease of hydration products and the compactness of solidified soil, which is the reason for the weakening of the compressive strength and impermeability of contaminated soil.

Lysis of Microcystis aeruginosa with extracts from Chinese medicinal herbs.

Boiling water extracts of 66 selected Chinese medicinal herbs were screened for their anticyanobaterial activity against Microcystis aeruginosa by the soft-agar overlayer (SAO) method. Results indicated that extracts from 16 materials could inhibit the growth of this bacterial species. Among these anticyanobacterial samples, eight extracts showed low minimum inhibitory concentrations (MIC), including four extracts with MICs between 1 and 6 mg/mL, and four extracts with MICs < 1 mg/mL which could be considered useful to prevent the outbreak of cyanobacteria before the appearance of cyanobacterial blooms. Further study showed that three extracts with MIC values < 1 mg/mL induced intensive chlorophyll-a lysis within 7 days at the MIC. The results suggested that highly efficient anticyanobacterial compounds must be involved in the inhibitory activities. The final results indicated these three extracts (from Malaphis chinensis, Cynips gallae-tinctoriae and Fructus mume) had the potential to be developed as algicides due to their remarkably anticyanobacterial activities.

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

BACKGROUND AND OBJECTIVE: Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. METHODS: In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. RESULTS: DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. CONCLUSIONS: The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances.

Motor function rebuilding of limbs based on communication principle and electronic system.

In this paper, we report our novel idea on the function rebuilding for hemiplegic limbs and the primary experiments. The main concept is to connect the control-lost nerves or neuromuscular junctions by using a multi-channel micro- electronic neural bridge (MENB), regenerate the nervous signal, and rebuild the motor functions of the related limb. Since the injured nervous system in stroke-related hemiplegia is located in the brain and difficult to be identified and operate on, we use another nervous system functioning as a new signal source to supply similar neural signals. In these cases, that means, two independent nervous systems are connected by a MENB. As preclinical experiments, we have made a series of tests on bodies of animals and healthy human. The principle, the system construction and the experimental results will be given.