Exogenous neuritin restores auditory following cochlear spiral ganglion neuron denervation of gerbils.

Spiral ganglion neurons (SGNs) transmit sound signals received by hair cells to the auditory center to produce hearing. The quantity and function are important for maintaining normal hearing function. Limited by the regenerative capacity, SGNs are unable to regenerate spontaneously after injury. Various neurotrophic factors play an important role in the regeneration process. Neuritin is a neurite growth factor that plays an important role in neural plasticity and nerve injury repair. In this study, we used bioinformatics analysis to show that neuritin was negatively correlated with cochlear damage. Then, we aimed to establish a cochlear spiral ganglion-specific sensorineural deafness model in gerbils using ouabain and determine the effects of exogenous neuritin protein in protecting damaged cochlear SGNs and repairing damaged auditory nerve function. The provides a new research strategy and scientific basis for the prevention and treatment of sensorineural deafness caused by the loss of SGNs. We were discovered that neuritin is expressed throughout the development of the gerbil cochlea, primarily in the SGNs and Corti regions. The expression of neuritin was negatively correlated with the sensorineural deafness induced by ouabain. In vitro and in vivo revealed that neuritin significantly maintained the number and arrangement of SGNs and nerve fibers in the damaged cochlea and effectively protected the high-frequency listening function of gerbils.

Retrospective comparative study of the efficacy of JAK inhibitor (tofacitinib) in the treatment of systemic sclerosis-associated interstitial lung disease.

The oral Janus kinases inhibitor (JAKi) has improved the management of skin manifestations in systemic sclerosis (SSc), and our study aimed to explore the efficacy of non-selective JAKi tofacitinib in ameliorating interstitial lung disease (ILD) in the patients with SSc. The hospitalization data of the SSc-ILD patients from April 2019 to April 2021 were collected, and the changes of pulmonary function and the radiological findings in pulmonary high-resolution CT (HRCT) from the 9 patients who received tofacitinib for at least 6 months and a matched group of 35 SSc-ILD patients treated with conventional immunosuppressants or glucocorticoids, were compared and analyzed. There were no significant differences in demographic data and clinical characteristics between the tofacitinib-treated group (tofa-group) and the matched group. However, in the tofa-group, the changes in serum lactate dehydrogenase (LDH) concentration and serum interleukin-6 levels were significantly lower than those in the matched group. Moreover, the tofa-group showed amelioration in decreased diffusing capacity of the lung for carbon monoxide (DLCO) (62.05 ± 9.47 vs. 66.61 ± 12.39, p = 0.046), reductions in ground-glass attenuation involvement (1.00 ± 0.86 vs. 0.33 ± 0.50, p = 0.024) and irregular pleural thickening (1.33 ± 0.50 vs. 0.67 ± 0.51, p = 0.004) in pulmonary HRCTs, alleviated modified Rodnan skin score (mRSS) of skin sclerosis (9.22 ± 3.81 vs. 7.11 ± 3.92, p = 0.048), and reduced HRCT scores of pulmonary fibrosis (15.00 ± 3.87 vs. 12.66 ± 4.92, p = 0.009). Logistic regression analysis showed that the involvement of ground-glass attenuation (OR 11.43) and the add-on therapy of tofacitinib (OR 9.98) were the relevant factors in the amelioration of HRCT. Our results indicate that the use of JAKi (tofacitinib) may be relevant to significant improvement of the sclerosis and early radiological abnormalities in SSc-ILD patients. Further studies are needed to confirm these findings and to explore its efficacy more precisely. Key Points • The currently available therapies for SSc-ILD have limited therapeutic benefits. • The add-on therapy of the oral JAK inhibitor is available in the real world. • The tofacitinib was promising in the improvement of the sclerosis and early radiological abnormalities in SSc-ILD patients.

Transcutaneous auricular vagus nerve stimulation would be an alternative to implantable cervical vagus nerve stimulation in some situation.

Implantable cervical vagus nerve stimulation (iVNS) is a representative and promising neuromodulation. However, the invasive nature restricts its application. Traditional auricular acupuncture treatment has a long history. The auricular branch of the vagus nerve (ABVN) is a branch on the surface of the ear. Some studies demonstrates that transcutaneous auricular vagus nerve stimulation (taVNS) would achieve similar effects as iVNS. TaVNS and iVNS share a common anatomical basis and acting mechanism. In this article, we made a comparison between iVNS and taVNS in indications and efficacy. The recent studies have revealed similar clinical efficacy of taVNS, taVNS would expand the indication of iVNS. High-quality clinical evidences are needed before taVNS become be an alternative of iVNS.

Alginic Acid Beads Containing Fluorescent Solvatochromic Dyes Display an Emission Color Response to a Cationic Surfactant.

Lipophilic fluorescent dyes can be employed as sensors for surfactants present in concentrations above the critical micellar concentration (CMC) where the dyes are monodispersed in micelles. However, the surfactant concentration range over which these dyes are effective is narrowed because by the sigmoidal nature of their responses. To overcome this limitation, we developed a novel sensor material comprised of a labeled fluorescent solvatochromic dye covalently bonded to alginate gel, which is known to strongly adsorb cationic surfactants. We hypothesized that the dye-alginate conjugate would undergo fluorescent color changes in response to binding of surfactants which alter the polarity of the surrounding environment. Indeed, addition of the representative cationic surfactant, cetylpyridinium chloride (CPC), to an aqueous solution of the alginate conjugated fluorescent solvatochromic dye leads to a visible fluorescent color change when the concentration of CPC is below the CMC. The average values of the color appearance parameter, referred to as a hue, of light emitted from gels, calculated by analysis of fluorescence microscopy images using ImageJ software, were found to be approximately linearly dependent on the concentration of CPC encapsulated in the alginate-fluorescent dye complex. This finding shows that absorbed CPC can be quantitatively determined over a wide concentration range in the form of simple fluorescence wavelength or visible responses.

Brain Tumor Identification by Hybrid CNN-SWT Model.

OBJECTIVE: Detecting brain tumor using the segmentation technique is a big challenge for researchers and takes a long time in medical image processing. Magnetic resonance image analysis techniques facilitate the accurate detection of tissues and abnormal tumors in the brain. The size of a brain tumor can vary with the individual and the specifics of the tumor. Radiologists face great difficulty in diagnosing and classifying brain tumors. METHOD: This paper proposed a hybrid model-based convolutional neural network with a stationary wavelet trans-form named "CNN-SWT" to segment brain tumors using MR brain big data. We utilized 7 layers for classification in the proposed model that include 3 convolutional and 3 ReLU. Firstly, the input MR image is divided into multiple patches, and then the central pixel value of each patch is provided to the CNN-SWT. Secondly, the pre-processing stage is per-formed using the mean filter to remove the noise. Then the convolution neural network-layer approach is utilized to segment brain tumors. After segmentation, robust feature extraction such as information-extraction methods is used for the feature extraction process. Finally, a CNN-based hybrid scheme based on the stationary wavelet transform technique is used to detect tumors using MR brain images. MATERIALS: These experiments were obtained using 11500 MR brain images data from the hospital national of oncology. RESULTS: It was proved that the proposed hybrid achieved a high classification accuracy of (98.7 %) as compared with existing methods. CONCLUSION: The advantage of the hybrid novelty of the model and the ability to detect the tumor area achieved excellent overall performance using different values.

Beta-carotene regulates the biological activity of EGF in IEC6 cells by alleviating the inflammatory process.

Epidermal growth factor (EGF) has many important biological functions. It plays an important role in regulating the growth, survival, migration, apoptosis, proliferation, and differentiation of intestinal tissues and cells. However, until now, the effect of inflammation on the biological activity of EGF in intestinal cells or tissues is still unclear. For this reason, in the current research, we have conducted a detailed study on this issue. Using the rat small intestinal crypt epithelial cell line (IEC6) was used as an in vitro model, and Confocal laser scanning microscope (CLSM), Flow cytometry (FCM), Indirect immunofluorescence assay (IFA), Western-blotting (WB), and Quantitative real-time RT-PCR (QRT-PCR) methods were used to explore the effects of inflammation on EGF/EGFR biological activity and signal transduction profiles. We found that the EGF/EGFR nuclear signal almost disappeared in the inflammatory state, and the phosphorylation levels of EGFR, AKT, and STAT3 were all significantly down-regulated. In addition, we also studied the effect of ß-carotene on the biological activity of EGF, and found that when cells were pretreated with ß-carotene, the cellular behavior, biological activity, and nuclear signal of EGF/EGFR under inflammation stimulation were partially restored. In summary, the current study shows that inflammation can disrupt EGF/EGFR-mediated signaling in IEC6 cells, suggesting that inflammation negatively regulates the biological activity of EGF/EGFR. Furthermore, we found that ß-carotene not only attenuated lipopolysaccharide (LPS)-induced inflammation but also partially restored the biological activity of EGF in IEC6 cells, laying a solid foundation for studying the biological functions of EGF and ß-carotene.

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model.

The process of diagnosing brain tumors is very complicated for many reasons, including the brain's synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named "DWAE model", employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices' quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification.

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.

OBJECTIVE: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy. MATERIALS: The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. METHODS: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. RESULTS: The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. CONCLUSION: Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Cu-induced assembly of methanobactin-modified gold nanoparticles and its peroxidase mimic activity.

Methanobactin (Mb) is a small copper-chelating molecule that functions as an agent for copper acquisition, uptake and copper-containing methane monooxygenase catalysis in methane-oxidising bacteria. The UV-visible spectral and fluorescence spectral suggested that Mb/Cu coordination complex as a monomer (Mb-Cu), dimmer (Mb2-Cu) and tetramer (Mb4-Cu) could be obtained at different ratios of Mb to Cu (II). The kinetics of the oxidation of hydroquinone with hydrogen peroxide catalysed by the different Mb/Cu coordination complex were investigated. The results suggested that Mb2-Cu coordination form has highest catalytic capacity. Further, Mb-modified gold nanoparticles (AuNPs) were obtained by ligand exchange and assembled into two- and three-D nanocluster structure by metal-organic coordination as driving force. It has been found that AuNPs increased the catalytic activity of Mb2-Cu on AuNPs. The more significant catalytic activity was exhibited by the nanocluster assembly with multi-catalytic centres. This may be attributed to the multivalent collaborative characteristics of the catalytic active centres in the nanocluster network assembly. The assembly of Mb-modified AuNPs can act as excellent nanoenzyme models for imitating peroxidase.

[Clinical application of cone beam computed tomography combined with micro-ultrasound technique in treating three mesial canals in mandibular first molars].

Objective The aim of this study is to investigate the influence of cone beam computed tomography (CBCT) and micro-ultrasound technique for the treatment of three mesial canals in mandibular first molars. The three mesial canals according to Pomeranz's classification were characterized. Methods A total of 75 permanent mandibular first molars for root canal treatment were randomly selected from patients belonging to the age group of 14-60 years. After preparing the access cavity and locating the main canals, the middle mesial canal orifices in all teeth were determined with an endodontic explorer under direct vision (StageⅠ), under magnification with the aid of micro-ultrasound (Stage Ⅱ), and with the combined use of CBCT and micro-ultrasound to remove the dentin wall and calcifications (Stage Ⅲ). Results Middle mesial canals were detected in 4.0%, 18.7%, and 22.7% of the teeth in StagesⅠ-Ⅲ, respectively. Statistical analysis showed significant differences (P<0.05) between StagesⅠand Ⅱ with regard to middle mesial canal detection. The number of Stage Ⅲ was more than that of Stage Ⅱ. The difference between the two stages was no significant. Among the 17 middle mesial canals, "confluent", "fin" and "independent" anatomies were 52.9%, 35.3%, and 11.8%. Conclusion When used with adjunctive aids, including CBCT, micro-ultrasound facilitates dental clinicians in the location and treatment of middle mesial canals.

Lipidomics and RNA-Seq Study of Lipid Regulation in Aphis gossypii parasitized by Lysiphlebia japonica.

The cotton-melon aphid, Aphis gossypii Glover, is a major insect pest worldwide. Lysiphlebia japonica (Ashmead) is an obligate parasitic wasp of A. gossypii, and has the ability to regulate lipid metabolism of the cotton-melon aphid. Lipids are known to play critical roles in energy homeostasis, membrane structure, and signaling. However, the parasitoid genes that regulate fat metabolism and lipid composition in aphids are not known. 34 glycerolipids and 248 glycerophospholipids were identified in this study. We have shown that a 3-day parasitism of aphids can induce significant changes in the content and acyl chain composition of triacylglycerols (TAGs) and subspecies composition of glycerophospholipids content and acyl chains. It also upregulate the expression of several genes involved in triacylglycerol synthesis and glycerophospholipid metabolism. Pathway analysis showed that a higher expression of genes involved in the tricarboxylic acid cycle and glycolysis pathways may contribute to TAGs synthesis in parasitized aphids. Interestingly, the higher expression of genes in the sphingomyelin pathway and reduced sphingomyelin content may be related to the reproductive ability of A. gossypii. We provide a comprehensive resource describing the molecular signature of parasitized A. gossypii particularly the changes associated with the lipid metabolism and discuss the biological and ecological significance of this change.

Dioxin and dioxin-like compounds suppress acetylcholinesterase activity via transcriptional downregulations in vitro.

Recently, acetylcholinesterase (AChE, EC 3.1.1.7) has received increased attention in the field of environmental sciences. Evaluation of the effects of environmental contaminants on AChE enzymatic activity not only can reflect, to some extent, the interference with the nervous system, but also can be used for monitoring pollution. Our previous study showed that 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) suppressed neuronal AChE enzymatic activity via transcriptional downregulations mediated by aryl hydrocarbon receptor. In the present study, the effects of several other dioxin-like compounds (DLCs) on neuronal AChE activity were determined, including 1,2,3,7,8-pentachlorodibenzo-p-dioxin, 2,3,7,8-tetrachlorodibenzofuran, 2,3,4,7,8-pentachlorodibenzofuran, and 2,3,7,8-tetrabromodibenzo-p-dioxin. The results showed that the enzymatic activity of AChE was significantly decreased by approximately 15-30 % after exposure to a certain concentrations of the DLCs, whereas incubating neuronal cell lysates directly with these DLCs did not inhibit AChE enzyme. Subsequent molecular mechanism study showed that these chemicals could decrease ACHE promoter activity, as well as AChE T mRNA expression, thereby suggesting the involvements of transcriptional regulation in these effects. These findings on DLCs are similar with those on 2,3,7,8-TCDD, pointing to the possibility that exposure to dioxin and DLCs, which frequently coexist in the contaminated environments, may concurrently interfere with the cholinergic functions via AChE.

Performance of multi-soil-layering system (MSL) treating leachate from rural unsanitary landfills.

The widely spread rural unsanitary landfills in South China pose an environmental threat to water bodies and soil. Although various processing technologies have been utilized for treatment of landfill leachate, their application to the landfills in rural areas is restricted by the availability of skilled professionals and high operation costs. In this experiment, four MSLs with altered soil mixed block (SMB) and different hydraulic load rate (HLR) were applied in the experiment to investigate the treatment of the landfill leachate without aeration or under low aeration supply. The experiment results showed that the improved MSL could effectively treat the chemical oxygen demand (COD), NH(4)-N and P. COD and NH(4)-N removal efficiencies of MSL were 97.4%, 82.4% and 72.0%, 62.0%, respectively under HLRs of 200 and 400L/(m(2)·d) without aeration; COD and NH(4)-N removal efficiencies of M800 and M1600 were 62.3%, 53.4% and 45.3%, 35.3% respectively under intermittent aeration. N removal efficiency was low due to a strong nitrification effect, and the nitrogen removal capacity of the MSL was greatly reduced at the end of the experiment. P removal efficiency of MSL was 75.6 to 91.9% under HLR 200 and 400L/(m(2)·d). The intermittent aeration was helpful to remove the clogging of MSLs, after they were clogged under HLRs of 800 and 1600L/(m(2)·d). MSL is promising as an appealing nitrifying biofilm reactor.