An aptasensor for chloramphenicol determination based on dual signal output of photoelectrochemistry and colorimetry.

In the present work, we developed an aptasensor to determine chloramphenicol (CAP) based on the dual signal output of photoelectrochemistry (PEC) and colorimetry. The Fe3+-doped porous tungsten trioxide was prepared by sol-gel method and coated on the ITO conductive glass to form ITO/p-W(Fe)O3. After assembling the captured DNA (cDNA) and the aptamer of CAP (apt) successively, the constructed ITO/p-W(Fe)O3-cDNA/apt aptasensor exhibited excellent photocurrent response under visible light irradiation in the presence of glucose, which provided the feasibility for PEC measurement with high sensitivity. In the presence of CAP, the apt left the ITO/p-W(Fe)O3 surface and AuNPs linked on the probe DNA would be assembled on it, which led to the decrease of photocurrent. Thanks to the oxidase-mimic catalytic performance of AuNPs and the recycling catalytic hydrolysis by exonuclease I, the measurement signal of the aptasensor could be amplified significantly, and the photocurrent decrease of the aptasensor was linearly related to the concentration of CAP in the range of 1.0 pM-10.0 nM and low detection limit was 0.36 pM. Meanwhile, the H2O2 produced from catalytic oxidation of glucose could oxidize TMB to blue oxTMB under HRP catalysis, which absorbance at 652 nm was linearly related to the concentration of CAP in the range of 5.0 pM-10.0 nM and low detection limit was 1.72 pM. Therefore, an aptasensor that determine CAP in real samples was successfully constructed with good precision of the relative standard deviation less than 5.7 % for PEC method and 7.3 % for colorimetric method, which can meet the analysis needs in different scenarios.

Bibliometric analysis of hotspots and trends of global myopia research.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Superlubricity Achieved by a Transparent Poly(vinylpyrrolidone) Composite Hydrogel with Glycerol Ethoxylate in Ocular Conditions.

Efficient and stable ocular lubrication is pivotal in safeguarding eye tissues from wear, especially under repetitive strain due to frequent blinking. Hydrogels have been reported to possess adjustable mechanical properties, biocompatibility, durability, and elevated water content and extensive utilization in medical fields. In this work, a kind of visible photo-cross-linking poly(vinylpyrrolidone) (PVP) hydrogel was designed and synthesized using 1-vinyl-2-pyrrolidone (NVP) and poly(ethylene glycol) diacrylate (PEGDA). To optimize the structure and improve the lubrication performance of hydrogels, we prepared and investigated glycerol ethoxylate (GE)-introduced composite hydrogels (GE/PVP). The results show that the addition of 3 wt % GE helped the hydrogel to form a uniform and dense porous matrix and reduce the frictional coefficient (COF) by over 50%, achieving superlubricity (COF ≈ 0.005). However, with the excessive increase of GE (6 wt %), the structure of the hydrogel is destroyed, inducing pore walls to thin and expand. After that, a lubrication mechanism of the GE/PVP composite hydrogel was proposed, in which the addition of GE reduced the surface tension of the hydrogel, enhanced the hydration ability of the hydrogel, and thus decreased the friction between sliding surfaces. Besides, the cytotoxicity tests show that the composite hydrogels possess good biocompatibility. Overall, the as-synthesized hydrogels hold great potential as lubricating medium for use in ocular applications.

Toward Low-Voltage and High-Sensitivity Direct X-ray Detectors Based on Thick Bulk Heterojunction Organic Device.

Organic semiconducting materials are promising for the fabrication of flexible ionizing radiation detectors for imaging because of their tissue equivalence, simple large-scale processing, and mass production. However, it is challenging to achieve high-sensitivity detection for organic direct detectors prepared by low-cost solution processing because of the compromise between thickness and carrier transport. In this study, high-performance organic direct X-ray detectors were fabricated by building a micrometer-thick bulk heterojunction (BHJ) using poly(3-hexylthiophene-2,5-diyl) (P3HT):(6,6)-phenyl c71 butyric acid methyl ester. A 5 µm BHJ film was fabricated by drop-casting and enhanced crystallization of P3HT using binary solvents and high-boiling-point additives to improve the charge carrier mobility. Furthermore, this organic direct X-ray detector has a sensitivity of >654.26 µC Gyair s-1 and a self-powered response. Because of the architecture of the thick active layer and the energy cascade in this diode detector, it has a very low dark current of 46.26 pA at -2 V. A fast and efficient approach was developed for fabricating thick, highly mobile organic BHJ films for high-performance direct X-ray detectors. It has great potential for application in a new generation of flexible and portable large-area flat-panel detectors.

A photoelectrochemical aptasensor for omethoate determination based on a photocatalysis of CeO2@MnO2 heterojunction for glucose oxidation.

In the present work, we developed a photoelectrochemical aptasensor to determine omethoate (OMT) based on the dual signal amplification of CeO2@MnO2 photocatalysis for glucose oxidation and exonuclease I-assisted cyclic catalytic hydrolysis. CeO2@MnO2 heterojunction material prepared by hydrothermal method was linked with captured DNA (cDNA) and then assembled on the ITO conductive glass to form ITO/CeO2@MnO2-cDNA, which exhibited significant photocurrent response and good photocatalytic performance for glucose oxidation under visible light irradiation, providing the feasibility for sensitive determining OMT. After binding with the aptamer of OMT (apt), the formation of rigid double stranded cDNA/apt kept CeO2@MnO2 away from ITO surface, which ensured a low photocurrent background for the constructed ITO/CeO2@MnO2-cDNA/apt aptasensor. In the presence of target OMT, the restoration of the cDNA hairpin structure and the exonuclease I-assisted cyclic catalytic hydrolysis led to the generation and amplification of measurement photocurrent signals, and allowed the aptasensor to have an ideal quantitative range of 0.01-10.0 nM and low detection limit of 0.0027 nM. Moreover, the aptasensor has been applied for selective determination of OMT in real samples with good precision of the relative standard deviation less than 6.2 % and good accuracy of the recoveries from 93 % to 108 %. What's more, the aptasensor can be used for other target determination only by replacing the captured DNA and corresponding aptamer.

A colorimetric aptasensor for CA125 determination based on dual catalytic performance of CeO2 nanozyme confined in macroporous silica foam.

A portable colorimetric aptasensor was constructed based on the dual catalytic performance of CeO2 nanozyme to determine carbohydrate antigen 125 (CA125). Firstly, CeO2 nanozyme was synthesized by calcination and ultrasonically dispersed in a macroporous silica foam (MSF) to form CeO2@MSF. Then the aptamer of CA125 (apt) and complementary DNA (c-DNA) were successively assembled on the CeO2@MSF to construct a CeO2@MSF/apt/c-DNA colorimetric aptasensor, which exhibited excellent oxidase-mimic performance and phosphatase-mimic activity simultaneously. In the presence of CA125, the apt specifically binds to target CA125, and the single-strand c-DNA leaves the CeO2@MSF/apt surface, which is catalytically hydrolyzed by exonuclease I. The produced phosphate ions inhibit the phosphatase-mimic activity of CeO2 nanozyme. Thus, the absorbance at 652 nm of 3,3',5,5'-tetramethylbenzidine solution containing ascorbic acid-2-phosphate increases with the concentration of CA125. The response is linearly related to the logarithm of CA125 concentration from 1.0 to 10.0 U/mL under optimal experimental conditions. Based on this, the constructed colorimetric aptasensor has a high sensitivity, good selectivity, and high accuracy for CA125 determination in real human serum sample.

Automatic measurement of exophthalmos based orbital CT images using deep learning.

Introduction: Objective, accurate, and efficient measurement of exophthalmos is imperative for diagnosing orbital diseases that cause abnormal degrees of exophthalmos (such as thyroid-related eye diseases) and for quantifying treatment effects. Methods: To address the limitations of existing clinical methods for measuring exophthalmos, such as poor reproducibility, low reliability, and subjectivity, we propose a method that uses deep learning and image processing techniques to measure the exophthalmos. The proposed method calculates two vertical distances; the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the outer edge of the orbit in axial CT images and the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the upper and lower outer edges of the orbit in sagittal CT images. Results: Based on the dataset used, the results of the present method are in good agreement with those measured manually by clinicians, achieving a concordance correlation coefficient (CCC) of 0.9895 and an intraclass correlation coefficient (ICC) of 0.9698 on axial CT images while achieving a CCC of 0.9902 and an ICC of 0.9773 on sagittal CT images. Discussion: In summary, our method can provide a fully automated measurement of the exophthalmos based on orbital CT images. The proposed method is reproducible, shows high accuracy and objectivity, aids in the diagnosis of relevant orbital diseases, and can quantify treatment effects.

Virtual reality technology enhances the cognitive and social communication of children with autism spectrum disorder.

Objective: We aimed to explore the impact of using virtual reality technology to intervene in and encourage the developmental behavior areas of cognition, imitation, and social interaction in children with autism spectrum disorder. Methods: Forty-four children with autism spectrum disorder were divided randomly into an intervention group and a control group, with each group consisting of 22 participants. Incorporating conventional rehabilitation strategies, virtual reality technology was used with the intervention group to conduct rehabilitation training in areas including cognition, imitation, and social interaction. The control group was provided conventional/routine clinical rehabilitation training. The children's cognitive development was evaluated before and 3 months after intervention. Results: After intervention, the developmental abilities of both groups of children in the areas of cognition, imitation, and social interaction were improved over their abilities measured before the intervention (P < 0.05). However, post-intervention score differences between the two groups demonstrated that the intervention group levels were better than the control group levels only in the areas of cognition and social interaction (P < 0.05). Conclusion: Combining virtual reality with conventional rehabilitation training improved the cognitive and social development of children with autism spectrum disorder and supported the goal of improving the rehabilitation effect.

Bibliometric analysis of research themes and trends in childhood autism spectrum disorders from 2012 to 2021.

Background: Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental conditions that affect people worldwide. Early diagnosis and clinical support help achieve good outcomes. However, medical system structure and restricted resource availability create challenges that increase the risk of poor outcomes. Understanding the research progress of childhood ASD in recent years, based on clinical literature reports, can give relevant researchers and rehabilitation therapists more resonable research guides. Objective: This bibliometric study aimed to summarize themes and trends in research on childhood ASD and to suggest directions for future enquiry. Methods: Citations were downloaded from the Web of Science Core Collection database on childhood ASD published from 1 January 2012, to 31 December 2021. The retrieved information was analyzed using CiteSpace.5.8. R3, and VOS viewer. Results: A total of 7,611 papers were published across 103 areas. The United States was the leading source of publications. The clusters that have continued into 2020 include coronavirus disease 2019, gut microbiota, and physical activity, which represent key research topics. Keywords with frequency spikes during 2018-2021 were "disabilities monitoring network," "United States," and "caregiver." Conclusions: The Autism and Developmental Disabilities Monitoring Network in the United States can be used as a reference for relevant workers worldwide. An intelligent medical assistant system is being developed. Further studies are required to elucidate challenges associated with caring for a child with ASD.

EEG microstate in first-episode drug-naive adolescents with depression.

A growing number of studies have revealed significant abnormalities in electroencephalography (EEG) microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far. Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train an SVM for classification of patients with depression. Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained. Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging.

Background: Autism spectrum disorder (ASD) is a chronic developmental disability caused by differences in the brain. The gold standard for the diagnosis of this condition is based on behavioral science, but research on the application of neurological detection to diagnose the atypical nervous system of ASD is ongoing. ASD neuroimaging research involves the examination of the brain's structure, functional connections, and neurometabolic. However, limited medical resource and the unique heterogeneity of ASD have resulted in many challenges when neuroimaging is utilized. Objective: This bibliometric study is aimed at summarizing themes and trends in research on autism spectrum disorder neuroimaging and at proposing potential directions for future inquiry. Methods: Citations were downloaded from the Web of Science Core Collection database on neuroimaging published from January 1, 2012, to December 31, 2021. The retrieved information was analyzed using Bibliometric.com, CiteSpace.5.8. R3, and VOS viewer. Results: A total of 1,363 papers were published across 58 regions. The United States was the leading source of publications. The League of European Research Universities published the largest number of articles (171). Burst keywords from 2018 to 2021 include identification and network. The clusters of references that continued into 2020 included graph theory, functional connectivity, and classification, which represent key research topics. Conclusions: Imaging data is being used to identify neuro-network models with higher accuracy for ASD discrimination. Functional near-infrared imaging is advantageous compared to other neuroimaging. In the future, research on systematic and accurate computer-aided diagnosis technology should be encouraged. Moreover, the study of neuroimaging of ASD in different psychological and behavioral states can inspire new ideas about the diagnosis and intervention training of ASD and should be explored.

Advances in artificial intelligence applications for ocular surface diseases diagnosis.

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Sequential samples reveal significant variation of mercury isotope ratios during single rainfall events.

Although the investigation of mercury (Hg) isotopes in precipitation has largely improved our knowledge of the source and transformation of Hg in the atmosphere, rainwater investigated in previous studies were integrated samples collected over an event and could obscure key information about the physiochemical transformation and deposition dynamics of Hg (and its isotopes) in short precipitation events. In this study, we investigated Hg isotopic composition of filtered (HgF) and particulate Hg (HgPM) in sequential rain samples from three single rainfall events in Guiyang, China. All samples showed a decrease of total Hg concentration, as well as HgF and HgPM with time in each rainfall event, and large variation of both mass-dependent fractionation (MDF) and mass-independent fractionation of odd Hg isotopes (odd-MIF) for both phases. Isotopic data indicated variable contributions of different sources triggered by the instant change of meteorological conditions, rather than internal atmospheric processes. The rapid response of MDF and odd-MIF of precipitation samples to the incense burning on the Tomb Sweeping Day implied that Hg isotopic composition was very sensitive to the momentary anthropogenic emission, which could have at least a regional short-lived effect and should be taken into account in future studies. Hg isotopes are a powerful tool for investigating both atmospheric transformation and instant deposition dynamic of Hg, and like stable H and O isotopes, could provide useful information about local or regional meteorological changes.

Atomic-resolution imaging of size-selected platinum clusters on TiO(2)(110) surfaces.

Size-selected Pt(n) (n=4,7-10,15) clusters were deposited on TiO(2)(110)-(1x1) surfaces and imaged at atomic resolution using an ultrahigh-vacuum scanning tunneling microscope with a carbon nanotube tip. Clusters smaller than Pt(7) lay flat on the surface with a planar structure and a planar-to-three-dimensional transition occurred at n=8 for Pt(n) clusters on TiO(2). However, both Pt(8) and Pt(9) had two types of geometric structures. The geometric structures depend strongly on the number of atoms in the deposited cluster possibly because of the differences in binding energies in different-sized clusters and different degrees of interaction with the surface. We obtained atomic-resolution images of size-selected clusters on surfaces for the first time, enabling the identification of atomic alignments in the clusters on the surface.