A non-coding variant in 5' untranslated region drove up-regulation of pseudo-kinase EPHA10 and caused non-syndromic hearing loss in humans.

Hereditary hearing loss has a genetic and phenotypic heterogeneity. However, it is still difficult to explain this heterogeneity perfectly with known deafness genes. Here, we report a novel causative gene EPHA10 as well as its non-coding variant in 5' untranslated region identified in a family with post-lingual autosomal dominant non-syndromic hearing loss from southern China. One affected member of this family had an ideal hearing restoration after cochlear implantation. We speculated that there were probable deafness-causing abnormalities in the cochlea according to clinical imaging and auditory evaluations. A heterozygous variant c.-81_-73delinsAGC was found co-segregating with hearing loss. Epha10 was expressed in mouse cochlea at both transcription and translation levels. The variant caused upregulation of EPHA10 which may result from promoter activity enhancement after sequence change. Overexpression of Eph (the homolog of human EPHA10) exerted effects on the structure and function of chordotonal organ in fly model. In summary, our study linked pseudo-kinase EPHA10 to hearing loss in humans for the first time.

Genetic insights, disease mechanisms, and biological therapeutics for Waardenburg syndrome.

Waardenburg syndrome (WS), also known as auditory-pigmentary syndrome, is the most common cause of syndromic hearing loss (HL), which accounts for approximately 2-5% of all patients with congenital hearing loss. WS is classified into four subtypes depending on the clinical phenotypes. Currently, pathogenic mutations of PAX3, MITF, SOX10, EDN3, EDNRB or SNAI2 are associated with different subtypes of WS. Although supportive techniques like hearing aids, cochlear implants, or other assistive listening devices can alleviate the HL symptom, there is no cure for WS to date. Recently major progress has been achieved in preclinical studies of genetic HL in animal models, including gene delivery and stem cell replacement therapies. This review focuses on the current understandings of pathogenic mechanisms and potential biological therapeutic approaches for HL in WS, providing strategies and directions for implementing WS biological therapies, as well as possible problems to be faced, in the future.

Treatment of inferior turbinate hypertrophy by plasma turbinate reduction with one-point-three-side way. / 一点三面法等离子鼻甲减容术治疗下鼻甲肥厚.

OBJECTIVES: Nasal congestion is often the main symptom of the patients with non-allergic rhinitis, who have inferior turbinate hypertrophy if getting poor treatment effect. Plasma treatment for inferior turbinate hypertrophy can effectively improve nasal obstruction. Generally, plasma treatment with multiple puncture sites, makes patients intraoperative painful and postoperative bleeding, which let patients often fear of surgery. Postoperative nasal adhesion or lower turbinate scar and other complications sometimes happened, and some patients still feel nasal obstruction due to severe mucosal damage and scar formation. We innovatively used one-point-three-side plasma turbinate volume reduction in the treatment of inferior turbinate hypertrophy, in order to reduce complication, improve symptoms, and enhance curative effect. METHODS: A total of 111 patients with non-allergic rhinitis with complete data due to hypertrophy of inferior turbinate and poor drug treatment from Nov. 2011 to Oct. 2019. The hypertrophic inferior turbinate of patients with non-allergic rhinitis was ablated by plasma turbinate volume reduction, and the symptom scores of patients were evaluated by visual analog scales (VAS) before surgery, 1 week, 1 month, 3 months, and 6 months after surgery. The intraoperative pain was scored by VAS. The pathological morphology of nasal mucosa was observed before and after operation in some patients. RESULTS: The nasal obstruction score of the patients was significantly lower at 1 week, 1 month, 3 months and 6 months after the operation (all P<0.05). The distribution of submucosal blood vessels and glands was improved by postoperative pathological observation. CONCLUSIONS: Plasma turbinate volume reduction with one-point-three-side is effective with minimally invasion, and less complication, which is worthy of clinical promotion.

Elmod3 knockout leads to progressive hearing loss and abnormalities in cochlear hair cell stereocilia.

ELMOD3, an ARL2 GTPase-activating protein, is implicated in causing hearing impairment in humans. However, the specific role of ELMOD3 in auditory function is still far from being elucidated. In the present study, we used the CRISPR/Cas9 technology to establish an Elmod3 knockout mice line in the C57BL/6 background (hereinafter referred to as Elmod3-/- mice) and investigated the role of Elmod3 in the cochlea and auditory function. Elmod3-/- mice started to exhibit hearing loss from 2 months of age, and the deafness progressed with aging, while the vestibular function of Elmod3-/- mice was normal. We also observed that Elmod3-/- mice showed thinning and receding hair cells in the organ of Corti and much lower expression of F-actin cytoskeleton in the cochlea compared with wild-type mice. The deafness associated with the mutation may be caused by cochlear hair cells dysfunction, which manifests with shortening and fusion of inner hair cells stereocilia and progressive degeneration of outer hair cells stereocilia. Our finding associates Elmod3 deficiencies with stereocilia dysmorphologies and reveals that they might play roles in the actin cytoskeleton dynamics in cochlear hair cells, and thus relate to hearing impairment.

Next-generation sequencing-based mutation analysis of genes associated with enlarged vestibular aqueduct in Chinese families.

OBJECTIVES: The identification of gene mutations enables more appropriate genetic counseling and proper medical management for EVA patients. The purpose of this study was to validate the accuracy and sensitivity of our method for comprehensive mutation detection in EVA, and summarize these data to explore a more accurate and convenient genetic diagnosis method. METHODS: A multiplex PCR sequencing panel was designed to capture the exons of three known EVA-associated genes (SLC26A4, KCNJ10, and FOXI1), and NGS was conducted in 17 Chinese families with EVA. RESULTS: A total of 16 SLC26A4 variants were found in 21 probands with bilateral EVA, including three novel variants (c.416G>A, c.823G>A and c.1027G>C), which were not reported in the dbSNP, gnomAD database, and ClinVar databases. One patient carried a FOXI1 variant (heterozygous, c.214C>A) and one patient carried a KCNJ10 variant (heterozygous, c.1054C>A), both of which were novel variants. Biallelic potential pathogenic variants were detected in 21/21patient samples, leading to a purported diagnostic rate of 100%. All results were verified by Sanger sequencing. CONCLUSION: This result supplemented the mutation spectrum of EVA, and supports that combined multiple PCR-targeted enrichment, and NGS is a valuable molecular diagnostic tool for EVA, and is suitable for clinical application.

Massively parallelization strategy for material simulation using high-dimensional neural network potential.

The potential energy surface (PES) calculation is the bottleneck for modern material simulation. The high-dimensional neural network (HDNN) technique emerged recently appears to be a problem solver for fast and accurate PES computation. The major cost of the HDNN lies at the computation of the structural descriptors that capture the geometrical environment of atoms. Here, we introduce a massive parallelization strategy optimized for our recently developed power-type structural descriptor. The method involves three-levels: from the top to the bottom the parallelization is over atoms first, then, over structural descriptors and finally over the n-body functions. We illustrate the parallelization method in a boron crystal system and show that the parallelization efficiency is maximally 100%, 58%, and 34% at each level. © 2018 Wiley Periodicals, Inc.

Ultrasmall Au clusters supported on pristine and defected CeO2: Structure and stability.

The atomistic simulation of supported metal catalysts has long been challenging due to the increased complexity of dual components. In order to determine the metal/support interface, efficient theoretical tools to map out the potential energy surface (PES) are generally required. This work represents the first attempt to apply the recently developed SSW-NN method, stochastic surface walking (SSW) global optimization based on global neural network potential (G-NN), to explore the PES of a highly controversial supported metal catalyst, Au/CeO2, system. By establishing the ternary Au-Ce-O G-NN potential based on first principles global dataset, we have searched for the global minima for a series of Au/CeO2 systems. The segregation and diffusion pathway for Au clusters on CeO2(111) are then explored by using enhanced molecular dynamics. Our results show that the ultrasmall cationic Au clusters, e.g., Au4O2, attaching to surface structural defects are the only stable structural pattern and the other clusters on different CeO2 surfaces all have a strong energy preference to grow into a bulky Au metal. Despite the thermodynamics tendency of sintering, Au clusters on CeO2 have a high kinetics barrier (>1.4 eV) in segregation and diffusion. The high thermodynamics stability of ultrasmall cationic Au clusters and the high kinetics stability for Au clusters on CeO2 are thus the origin for the high activity of Au/CeO2 catalysts in a range of low temperature catalytic reactions. We demonstrate that the global PES exploration is critical for understanding the morphology and kinetics of metal clusters on oxide support, which now can be realized via the SSW-NN method.

Advances in drug delivery-based therapeutic strategies for renal fibrosis treatment.

Renal fibrosis is the result of all chronic kidney diseases and is becoming a major global health hazard. Currently, traditional treatments for renal fibrosis are difficult to meet clinical needs due to shortcomings such as poor efficacy or highly toxic side effects. Therefore, therapeutic strategies that target the kidneys are needed to overcome these shortcomings. Drug delivery can be attained by improving drug stability and addressing controlled release and targeted delivery of drugs in the delivery category. By combining drug delivery technology with nanosystems, controlled drug release and biodistribution can be achieved, enhancing therapeutic efficacy and reducing toxic cross-wise effects. This review discusses nanomaterial drug delivery strategies reported in recent years. Firstly, the present review describes the mechanisms of renal fibrosis and anti-renal fibrosis drug delivery. Secondly, different nanomaterial drug delivery strategies for the treatment of renal injury and fibrosis are highlighted. Finally, the limitations of these strategies are also discussed. Investigating various anti-renal fibrosis drug delivery strategies reveals the characteristics and therapeutic effects of various novel nanosystem-derived drug delivery approaches. This will serve as a reference for future research on drug delivery strategies for renal fibrosis treatment.

Circular RNAs: Biomarkers of cancer.

Circular RNAs (circRNAs) are a class of single-stranded closed RNAs that are produced by the back splicing of precursor mRNAs. The formation of circRNAs mainly involves intron-pairing-driven circularization, RNA-binding protein (RBP)-driven circularization, and lariat-driven circularization. The vast majority of circRNAs are found in the cytoplasm, and some intron-containing circRNAs are localized in the nucleus. CircRNAs have been found to function as microRNA (miRNA) sponges, interact with RBPs and translate proteins, and play an important regulatory role in the development and progression of cancer. CircRNAs exhibit tissue- and developmental stage-specific expression and are stable, with longer half-lives than linear RNAs. CircRNAs have great potential as biomarkers for cancer diagnosis and prognosis, which is highlighted by their detectability in tissues, especially in fluid biopsy samples such as plasma, saliva, and urine. Here, we review the current studies on the properties and functions of circRNAs and their clinical application value.

Machine learning-based prognostic and metastasis models of kidney cancer.

Background: Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma, accounting for 20% of all urinary system tumors. Approximately 70% of cases are localized at diagnosis, and 30% are metastatic. Most localized kidney cancers can be cured by surgery, but most metastatic patients relapse after surgery and eventually die of kidney cancer. Therefore, accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis. Methods: This study used the data of 12,394 kidney cancer patients from the surveillance, epidemiology, and end results database to construct a research cohort related to kidney cancer survival and metastasis. Eight machine learning models (including support vector machines, logistic regression, decision tree, random forest, XGBoost, AdaBoost, K-nearest neighbors, and multilayer perceptron) were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators (accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic [AUROC]) were used to verify, evaluate, and optimize the models. Results: Among the eight machine learning models, Logistic Regression has the highest AUROC in both prediction scenarios. For 3-year survival prediction, the Logistic Regression model had an accuracy of 0.684, a sensitivity of 0.702, a specificity of 0.670, a precision of 0.686, an F1 score of 0.683, and an AUROC of 0.741. For tumor metastasis prediction, the Logistic Regression model had an accuracy of 0.800, a sensitivity of 0.540, a specificity of 0.830, a precision of 0.769, an F1 score of 0.772, and an AUROC of 0.804. Conclusion: In this study, we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer. The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients.

A Fast-Response Driving Waveform Design Based on High-Frequency Voltage for Three-Color Electrophoretic Displays.

Three-color electrophoretic displays (EPDs) have the characteristics of colorful display, reflection display, low power consumption, and flexible display. However, due to the addition of red particles, response time of three-color EPDs is increased. In this paper, we proposed a new driving waveform based on high-frequency voltage optimization and electrophoresis theory, which was used to shorten the response time. The proposed driving waveform was composed of an activation stage, a new red driving stage, and a black or white driving stage. The response time of particles was effectively reduced by removing an erasing stage. In the design process, the velocity of particles in non-polar solvents was analyzed by Newton's second law and Stokes law. Next, an optimal duration and an optimal frequency of the activation stage were obtained to reduce ghost images and improve particle activity. Then, an optimal voltage which can effectively drive red particles was tested to reduce the response time of red particles. Experimental results showed that compared with a traditional driving waveform, the proposed driving waveform had a better performance. Response times of black particles, white particles and red particles were shortened by 40%, 47.8% and 44.9%, respectively.

Atomic structure of boron resolved using machine learning and global sampling.

Boron crystals, despite their simple composition, must rank top for complexity: even the atomic structure of the ground state of ß-B remains uncertain after 60 years' study. This makes it difficult to understand the many exotic photoelectric properties of boron. The presence of self-doping atoms in the crystal interstitial sites forms an astronomical configurational space, making the determination of the real configuration virtually impossible using current techniques. Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of ß-B, revealing 15 293 distinct configurations out of the 2 × 105 minima visited, and reveal the key rules governing the filling of the interstitial sites. This advance is only allowed by the construction of an accurate and efficient neural network (NN) potential using a new series of structural descriptors that can sensitively discriminate the complex boron bonding environment. We show that, in contrast to the conventional views on the numerous energy-degenerate configurations, only 40 minima of ß-B are identified to be within 7 meV per atom in energy above the global minimum of ß-B, most of them having been discovered for the first time. These low energy structures are classified into three types of skeletons and six patterns of doping configurations, with a clear preference for a few characteristic interstitial sites. The observed ß-B and its properties are influenced strongly by a particular doping site, the B19 site that neighbors the B18 site, which has an exceptionally large vibrational entropy. The configuration with this B19 occupancy, which ranks only 15th at 0 K, turns out to be dominant at high temperatures. Our results highlight the novel SSW-NN architecture as the leading problem solver for complex material phenomena, which would then expedite substantially the building of a material genome database.

Revealing isoelectronic size conversion dynamics of metal nanoclusters by a noncrystallization approach.

Atom-by-atom engineering of nanomaterials requires atomic-level knowledge of the size evolution mechanism of nanoparticles, which remains one of the greatest mysteries in nanochemistry. Here we reveal atomic-level dynamics of size evolution reaction of molecular-like nanoparticles, i.e., nanoclusters (NCs) by delicate mass spectrometry (MS) analyses. The model size-conversion reaction is [Au23(SR)16]- → [Au25(SR)18]- (SR = thiolate ligand). We demonstrate that such isoelectronic (valence electron count is 8 in both NCs) size-conversion occurs by a surface-motif-exchange-induced symmetry-breaking core structure transformation mechanism, surfacing as a definitive reaction of [Au23(SR)16]- + 2 [Au2(SR)3]- → [Au25(SR)18]- + 2 [Au(SR)2]-. The detailed tandem MS analyses further suggest the bond susceptibility hierarchies in feed and final Au NCs, shedding mechanistic light on cluster reaction dynamics at atomic level. The MS-based mechanistic approach developed in this study also opens a complementary avenue to X-ray crystallography to reveal size evolution kinetics and dynamics.

Material discovery by combining stochastic surface walking global optimization with a neural network.

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO2, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.