Auto-inhibition and activation of a short Argonaute-associated TIR-APAZ defense system.

Short prokaryotic Ago accounts for most prokaryotic Argonaute proteins (pAgos) and is involved in defending bacteria against invading nucleic acids. Short pAgo associated with TIR-APAZ (SPARTA) has been shown to oligomerize and deplete NAD+ upon guide-mediated target DNA recognition. However, the molecular basis of SPARTA inhibition and activation remains unknown. In this study, we determined the cryogenic electron microscopy structures of Crenotalea thermophila SPARTA in its inhibited, transient and activated states. The SPARTA monomer is auto-inhibited by its acidic tail, which occupies the guide-target binding channel. Guide-mediated target binding expels this acidic tail and triggers substantial conformational changes to expose the Ago-Ago dimerization interface. As a result, SPARTA assembles into an active tetramer, where the four TIR domains are rearranged and packed to form NADase active sites. Together with biochemical evidence, our results provide a panoramic vision explaining SPARTA auto-inhibition and activation and expand understanding of pAgo-mediated bacterial defense systems.

EVLncRNAs 3.0: an updated comprehensive database for manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse biological processes and diseases. While high-throughput sequencing has enabled lncRNA discovery, functional characterization remains limited. The EVLncRNAs database is the first and exclusive repository for all experimentally validated functional lncRNAs from various species. After previous releases in 2018 and 2021, this update marks a major expansion through exhaustive manual curation of nearly 25 000 publications from 15 May 2020, to 15 May 2023. It incorporates substantial growth across all categories: a 154% increase in functional lncRNAs, 160% in associated diseases, 186% in lncRNA-disease associations, 235% in interactions, 138% in structures, 234% in circular RNAs, 235% in resistant lncRNAs and 4724% in exosomal lncRNAs. More importantly, it incorporated additional information include functional classifications, detailed interaction pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and organoid-related lncRNAs. The web interface was substantially improved for browsing, visualization, and searching. ChatGPT was tested for information extraction and functional overview with its limitation noted. EVLncRNAs 3.0 represents the most extensive curated resource of experimentally validated functional lncRNAs and will serve as an indispensable platform for unravelling emerging lncRNA functions. The updated database is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.

EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning.

Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html, and the data and source code can be freely available along with the web server.

AIBI Modified Mesoporous Copper Sulfide Nanocomposites for Efficient Non-Oxygen Dependent Free Radicals-Assisted Photothermal Therapy in Uveal Melanoma.

Uveal melanoma (UM) is an ocular cancer predominantly affecting adults, characterized by challenging diagnostic outcomes. This research endeavors to develop an innovative multifunctional nanocomposite system sensitive to near-infrared (NIR) radiation, serving as both a non-oxygen free-radical generator and a photothermal agent. The designed system combines azobis isobutyl imidazoline hydrochloride (AIBI) with mesoporous copper sulfide (MCuS) nanoparticles. MCuS harnesses NIR laser energy to induce photothermal therapy, converting light energy into heat to destroy cancer cells. Simultaneously, AIBI is activated by the NIR laser to produce alkyl radicals, which induce DNA damage in remaining cancer cells. This distinctive feature equips the designed system to selectively eliminate cancers in the hypoxic tumor microenvironment. MCuS is also beneficial to scavenge the overexpressed glutathione (GSH) in the tumor microenvironment. GSH generally consumes free radicals and hiders the PDT effect. To enhance control over AIBI release in cancer cells, 1-tetradecyl alcohol (TD), a phase-changing material, is introduced onto the surface of MCuS nanoparticles to create the final AMPT nanoparticle system. In vitro and in vivo experiments confirm the remarkable anti-tumor efficacy of AMPT. Notably, the study introduces an orthotopic tumor model for UM, demonstrating the feasibility of precise and effective targeted treatment within the ocular system.

Adjusting Catalytic Activity of ß-Amyrin Synthase GgBAS by Utilizing the Plasticity Residues of an Active Site.

ß-Amyrin synthase (bAS) is a representative plant oxidosqualene cyclase (OSC), and previous studies have identified many functional residues and mutants that can alter its catalytic activity. However, the regulatory mechanism of the active site architecture for adjusting the catalytic activity remains unclear. In this study, we investigate the function of key residues and their regulatory effects on the catalytic activity of Glycyrrhiza glabra ß-amyrin synthase (GgbAS) through molecular dynamics simulations and site-directed mutagenesis experiments. We identified the plasticity residues located in two active site regions and explored the interactions between these residues and tetracyclic/pentacyclic intermediates. Based on computational and experimental results, we further categorize these plasticity residues into three types: effector, adjuster, and supporter residues, according to their functions in the catalytic process. This study provides valuable insights into the catalytic mechanism and active site plasticity of GgbAS, offering important references for the rational enzyme engineering of other OSC enzyme.

Constructing mesoporous biochar derived from waste carton: Improving multi-site adsorption of dye wastewater and investigating mechanism.

The development of cost-efficient biochar adsorbent with a simple preparation method is essential to constructing efficient wastewater treatment system. Here, a low-cost waste carton biochar (WCB) prepared by a simple two-step carbonization was applied in efficiently removing Rhodamine B (RhB) in aqueous environment. The maximum ability of WCB for RhB adsorption was 222 mg/g, 6 and 10 times higher than both of rice straw biochar (RSB) and broadbean shell biochar (BSB), respectively. It was mainly ascribed to the mesopore structure (3.0-20.4 nm) of WCB possessing more spatial sites compared to RSB (2.2 nm) and BSB (2.4 nm) for RhB (1.4 nm✕1.1 nm✕0.6 nm) adsorption. Furthermore, external mass transfer (EMT) controlled mass transfer resistance (MTR) of the RhB sorption process by WCB which was fitted with the Langmuir model well. Meanwhile, the adsorption process was dominated by physisorption through van der Waals forces and π-π interactions. A mixture of three dyes in river water was well removed by using WCB. This work provides a straightforward method of preparing mesoporous biochar derived from waste carton with high-adsorption capacity for dye wastewater treatment.

An Airborne Arc Array Synthetic Aperture Radar Vibration Error Compensation Method.

Compared to conventional radars, arc array synthetic aperture radar (SAR) enables wide-area observation under ideal conditions. However, helicopters carrying arc array SAR platforms are generally smaller in size and more sensitive to vibration, which has a greater impact on the imaging quality. In this paper, the vibration error of the arc array SAR platform is investigated, and a vibration error model of the arc array SAR platform is established. Based on the study of the vibration error model, a vibration phase estimation and compensation algorithm based on the delayed conjugate multiplication method is proposed. In the first step, distance pulse pressure processing is performed on the echo signal. In the second step, the pulse pressure signals and their delays in the same distance unit are subjected to conjugate multiplication, and the phase of the signal after conjugate multiplication is extracted. The extracted phase is then amplitude- and phase-compensated to estimate the vibration phase. In the third step, the vibration phase is compensated in the azimuthal direction of the distance pulse pressure signal, and the pairwise echo is eliminated, which completes the compensation of the airborne arc array SAR vibration platform.

Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning.

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR.

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images.

The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels' amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel's probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Isomerization-Controlled Proton-Electron Coupling in a π-Planar Metal Complex.

Proton-coupled electron transfer (PCET) is a ubiquitous and fundamental process in biochemistry and electrochemistry performed by transition-metal complexes. Most synthetic efforts have been devoted to selecting the components, that is, metal ions and ligands, to control the proton-electron coupling. Here, we show the first example of controlling the proton-electron coupling using the cis-trans metal-ligand isomerization in a π-planar platinum complex, Pt(itsq)2 (itsq1-: o-iminothiosemiquinonate). Both the isomers, which were obtained separately, were characterized by single-crystal X-ray diffraction, and the cis-to-trans isomerization was achieved by immersing in organic solvents. Theoretical calculations predicted that the proton-electron coupling evaluated from the energetic stabilization of the lowest unoccupied molecular orbital by protonation varies greatly depending on the geometrical configuration compared to the metal substitution.

Examining the change of half-period voltage in longitudinal electro-optic modulation and residual stress of potassium dihydrogen phosphate crystal.

In this paper, the voltage-transmittance curvesKDP crystals were measured accurately between two crossed or parallel polarizers using longitudinal electro-optic effect. The end faces of rectangular KDP samples were coated with ring-shaped electrodes using conductive silver paint (CSP). The change of half period voltage U i has been investigated. A method for quantitative characterization of residual stress has been proposed, based on deviation voltage U d. The results demonstrate that loading voltage is close to the integration of electric field intensity in crystal along the optical path when the CSP ring electrodes have a large outer radius R, small inner radius r, and long-distance d. The half-period voltage U i is also close to longitudinal half-wave voltage U π in these circumstances. The unclamped electro-optic coefficient γ63σ of KDP crystal at room temperature was measured as 10.24±0.05p m/V at the wavelength of 632.8 nm.

EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas.

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.

A Modified Keystone-Based Forward-Looking Arc Array Synthetic Aperture Radar 3D Imaging Method.

An arc array synthetic aperture radar (AA-SAR) is a new type of omnidirectional observation and imaging system. Based on linear array 3D imaging, this paper introduces a keystone algorithm combined with the arc array SAR 2D imaging method and proposes a modified 3D imaging algorithm based on keystone transformation. The first step is to discuss the target azimuth angle, retain the far-field approximation method of the first-order term, analyze the influence of the forward motion of the platform on the along-track position, and realize the two-dimensional focusing of the target slant range-azimuth direction. The second step is to redefine a new azimuth angle variable in the slant-range along-track imaging and use the keystone-based processing algorithm in the range frequency domain to eliminate the coupling term generated by the array angle and the slant-range time. The corrected data are used to perform along-track pulse compression to obtain the focused image of the target and realize the three-dimensional imaging of the target. Finally, in this article, the spatial resolution of the AA-SAR system in the forward-looking state is analyzed in detail, and the change in the spatial resolution of the system and the effectiveness of the algorithm are verified through simulation.

System Design and Echo Preprocessing of Spaceborne Squinted Two-Dimensional Beam Scanning Synthetic Aperture Radar.

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode.

High-quality, large-scale, semi-thin, & ultra-thin sections of the optic nerve in large animals: An optimized procedure.

Large animal model of optic nerve (ON) injury is an essential tool for translational medicine. Perfusion fixation with paraformaldehyde is mainly used for preparing the semi-thin (1-2 µm thick) and ultra-thin (<0.5 µm thick) sections of the ON tissues. However, this conventional fixation technique in large animals needs a large volume of fixatives, which increases the risk of toxic exposure and is environmentally unfriendly. Additionally, fixed residual ON cannot be used for other tests that require fresh tissue samples. Although conventional immersion fixation is feasible for preparing a semi-thin section of the ON in small animals (0.2-0.6 mm in diameter), it faces technical challenges when fixing the ON of large animals (3 mm in diameters), as increased diameter limits the permeability of the fixatives into deeper tissue. Therefore, we optimized the immersion-fixation method to obtain high-quality, large-scale, semi-thin, and ultra-thin sections for the ON of goat and rhesus macaques. Using this optimized technique, the ON microstructure was well preserved throughout the entire area of 1.5*1.5 square millimeters, allowing confident quantification of axon density/diameter on semi-thin section and identification of specific organelles and glial cells on ultra-thin sections. Furthermore, the optimized technique is a quick, simple, and environmentally friendly fixation method. Notably, the ON regions of large animals with or without an intact neurovascular system can be prepared for light and electron microscopy. In contrast, the residual unfixed ON from the same animal can be further utilized for experiments such as tissue culture and biomolecular tests.

Chemotherapy or Allogeneic Stem Cell Transplantation as Salvage Therapy for Patients with Refractory Acute Myeloid Leukemia: A Multicenter Analysis.

INTRODUCTION: The overall outcome of patients with refractory AML (rAML) remains poor. Though allogeneic hematopoietic stem cell transplantation (allo-HSCT) is considered as the only curative therapy, it is routinely recommended only for patients after remission with salvage chemotherapy. OBJECTIVE: In this study, we evaluated the impact of salvage chemotherapy or allo-HSCT on the overall outcome in rAML. METHODS: We collected the clinical data of 220 patients from 4 medical centers and performed retrospective analysis of prognosis factors, including salvage chemotherapy, intensity of chemotherapy, and allo-HSCT. RESULTS: A total of 29 patients received allo-HSCT directly without salvage chemotherapy, 26 patients achieved complete remission (CR) or complete remission with incomplete hematological recovery (CRi) after transplantation and 4-year leukemia-free survival (LFS) and overall survival (OS) were 45.0 ± 10.7 and 51.0 ± 10.6%, respectively. Another 191 patients received salvage chemotherapy and 81 (42.2%) achieved CR or CRi. Thirty-four patients among them underwent subsequent allo-HSCT with 4-year LFS and OS of 46.0 ± 8.8 and 46.2 ± 9.0%. The 4-year LFS and OS in 26 patients who failed to obtain CR or CRi but received allo-HSCT with active disease were 32.9 ± 10.0 and 36.9 ± 10.8%, respectively. For patients who received salvage chemotherapy but not allo-HSCT, few of them became long-term survivors. In multivariate analysis, salvage chemotherapy and the intensity of chemotherapy failed to have significant impact on both OS and LFS. Allo-HSCT was the only prognostic factor for improved OS and LFS in multivariate analysis. CONCLUSIONS: These results indicate the benefit of allo-HSCT in patients with rAML and direct allo-HSCT without salvage chemotherapy could be treatment option.

Abnormal Functional Connectivity Density in Patients with Dysthyroid Optic Neuropathy.

OBJECTIVE: Functional connectivity density (FCD) mapping was used to investigate abnormalities and factors related to brain functional connectivity in cortical regions of patients with dysthyroid optic neuropathy (DON) and to analyze the pathogenesis of DON further. METHODS: Patients diagnosed with thyroid-associated opthalmology (TAO) in the Eye Hospital were enrolled. All patients underwent comprehensive eye examinations and best-corrected visual acuity, visual field (VF) test. MRI data collection and analysis were completed in the 2nd Affiliated Hospital of Wenzhou Medical University. The patients were divided into 2 groups: the DON group, with an average VF, mean deviation (MD) of both eyes < -5 dB, and the non-DON group (nDON group), with an average VF MD of both eyes ≥ -2 dB. RESULTS: A total of 30 TAO patients (14 men, 16 women) with complete data who met the experimental requirements were enrolled. The average age was 48.79 (40-57) years. There were 16 patients in the DON group and 14 patients in the nDON group. No significant differences in age, gender, education level, and the maximum horizontal diameter of either medial rectus muscle were found between the 2 groups. The difference of brain FCD between the 2 groups showed significant abnormal connectivity in the right orbital gyri of the frontal lobe (Frontal_Inf_Orb_R) and the left precuneus in the DON group compared with the nDON group. As demonstrated by decreased FCD values in the right inferior frontal gyrus/orbital part, the relevant brain regions were the left middle temporal gyrus, left precuneus, left middle frontal gyrus, right postcentral gyrus, and brain gyri (excluding the supramarginal gyrus and angular gyrus) below the left parietal bone. The FCD associated with the left precuneus was increased, and the relevant brain areas were the left middle temporal gyrus, right cuneus, superior occipital gyrus, and right fusiform gyrus. A significant correlation was identified between the MD of the binocular VF and brain FCD. CONCLUSION: The abnormal FCD in the cortex of DON patients suggests that a central nervous system mechanism may be related to the pathogenesis of the DON.