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Abstract Background Unlike in lower vertebrates, Müller glia (MG) in adult mammalian retinas lack the ability to reprogram into neurons after retinal injury or degeneration and exhibit reactive gliosis instead. Whether a transition in MG cell fate from gliosis to reprogramming would help preserve photoreceptors is still under exploration. Methods A mouse model of retinitis pigmentosa (RP) was established using MG cell lineage tracing mice by intraperitoneal injection of sodium iodate (SI). The critical time point for the fate determination of MG gliosis was determined through immunohistochemical staining methods. Then, bulk-RNA and single-cell RNA seq techniques were used to elucidate the changes in RNA transcription of the retina and MG at that time point, and new genes that may determine the fate transition of MG were screened. Finally, the selected gene was specifically overexpressed in MG cells through adeno-associated viruses (AAV) in the mouse RP model. Bulk-RNA seq technique, immunohistochemical staining methods, and visual function testing were used to elucidate and validate the mechanism of new genes function on MG cell fate transition and retinal function. Results Here, we found the critical time point for MG gliosis fate determination was 3 days post SI injection. Hmga2 was screened out as a candidate regulator for the cell fate transition of MG. After retinal injury caused by SI, the Hmga2 protein is temporarily and lowly expressed in MG cells. Overexpression of Hmga2 in MG down-regulated glial cell related genes and up-regulated photoreceptor related genes. Besides, overexpressing Hmga2 exclusively to MG reduced MG gliosis, made MG obtain cone’s marker, and retained visual function in mice with acute retinal injury. Conclusion Our results suggested the unique reprogramming properties of Hmga2 in regulating the fate transition of MG and neuroprotective effects on the retina with acute injury. This work uncovers the reprogramming ability of epigenetic factors in MG.

Himawari-8 satellite, equipped with an advanced Himawari imager (AHI), has been widely employed for cloud detection tasks due to its high-spatiotemporal resolution. In this article, we propose a deep learning model named dual-branch deformable convolutional network (DBDCN) to detect convective cloud from AHI data. Specifically, we first choose some infrared channels from AHI to compute their difference. These brightness temperature difference (BTD) data are sensitive to cloud detection, thus providing complementary information for AHI. Then, both AHI and BTD are used as inputs of DBDCN, which adopts a dual-branch structure to effectively combine them together. Considering that convective clouds often exist in variable shapes, we use deformable convolutions to extract convective clouds' features in each branch. In addition, in order to fuse the complementary features from both branches, we propose a crossbranch fusion block. To evaluate the performance of our proposed DBDCN, we collect a convective cloud dataset, covering most of regions in southern China (i.e., 97$^\circ$E to 122$^\circ$E, 20$^\circ$N to 35$^\circ$N). Experimental results show that DBDCN is able to achieve better detection performance than the widely used threshold methods and existing convolutional neural networks in most cases.

Background: Melanocytic nevus is mainly treated by complete or partial removal. However, predicting the risk of malignant transformation of melanocytic nevi and which treatment patients should receive, surgical or non-surgical management, to gain the best results and aesthetic outcomes is controversial. Methods: Global literature on melanocytic nevus treatment, published between 1997 and 2022, was scanned using the Web of Science Core Collection database. Microsoft Office Excel, CiteSpace V, VOSviewer, Scimago Graphica, Bibliometrix, and Biblioshiny packages in R were used for the bibliometric analysis to summarize the leading countries, institutions, professors, and research trends in this field. Results: This study included 1 723 articles. Publications and citations exhibited positive trends over the past 20 years. The United States had the most productive organizations and publications in the comprehensive worldwide cooperation network, and China was recently one of the most active major participants. Professor Giovanni Pellacani, whose H-index, G-index, and M-index ranked first in this field, founded a virtual biopsy using reflectance confocal microscopy. In addition, Krengel and Kinsler contributed significantly to diagnosing and treating melanocytic nevi. The top 25 keywords in recent years were mostly about the mechanisms and risk factors for the malignant transformation of nevi. Conclusion: The future trend for melanocytic nevi treatment is to specify genotype-phenotype and genotype-outcome correlations, choose proper therapy to reduce the risk of malignant transformation, and simultaneously achieve the best aesthetic outcomes.

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.

Abstract Multiple methods have been proposed for evaluating the symmetry of facial contour by utilizing the median sagittal plane of the skull as a reference and measuring the maxillofacial region. To replace the manual mark point analysis method, we used the anterior cerebral falx plane in MRI images as an indicator of the craniofacial midline. The MRI examination data of 30 individuals were analyzed with a MeVisLab workstation. Two independent examiners performed 15 anthropometric measurements (4 angular, 11 linear) and compared the MRI-based anterior cerebral falx plane with the manual mark point analysis of the craniofacial midline estimation. All measurements were repeated after 3 weeks. Statistical analyses included the repeatability and reproducibility of the 2 methods based on intra-observer and inter-observer correlation coefficients (ICCs), respectively. Precision was estimated by intergroup comparison of the coefficient of variation. The anterior falx plane derived from the MRI data resulted in an intra-observer ICC of 0.869 ± 0.065 (range 0.733–0.936) and inter-observer ICC of 0.876 ± 0.0417 (0.798–0.932) for all measurements, showing significant correlations with the ICC values obtained by the mark point method (p

Human life is affected by the intensity of near-surface wind fields， so obtaining high-precision wind field meteorological live products is essential. Precise and accurate wind field simulation can provide data support for real-time weather products. At present， the spatial-temporal resolution of the mesoscale model is low， which cannot meet the requirement of local fine-scale simulation. In this study， based on the large eddy simulation （LES） nested in the medium-scale Weather Research and Forecast Model （WRF）， we simulated the near-surface wind field in the eastern part of the Mentougou region， which has five layers of two-way nesting. Meanwhile， combined with ground meteorological observation， we designed sensitivity experiments of boundary layer and Subgrid-Scale （SGS） models to compare the effects of YSU and LES boundary layer schemes on inner layer nesting at 1 km resolution， and examined and evaluated the applicability of different SGS models in the real atmosphere. The results show that the YSU boundary layer can perform better simulation than the LES boundary scheme when turned on at the mesoscale transition resolution of 1 km， with fewer errors and the ability to capture turbulence details. In the different SGS models sensitivity experiments （1.5TKE， SMAG， 1.5TKE_NBA and SMAG_NBA）， there is little difference in the NBAs. SMAG had the slightest root-mean-square error and the highest correlation coefficient for statistical indicators. However， the wind speed simulation of four models still underestimates the high value and overestimates the low value.SMAG is closest to the observation in the wind vector distribution and probability density distribution of the wind. In the instantaneous spatial distribution of the wind field， SMAG_NBA shows a competitive advantage in capturing fine turbulence.

Abstract A20‐binding inhibitor of NF‐κB activation (ABIN1) is a polyubiquitin‐binding protein that regulates cell death and immune responses. Although Abin1 is located on chromosome 5q in the region commonly deleted in patients with 5q minus syndrome, the most distinct of the myelodysplastic syndromes (MDSs), the precise role of ABIN1 in MDSs remains unknown. In this study, mice with a mutation disrupting the polyubiquitin‐binding site (Abin1Q478H/Q478H) is generated. These mice develop MDS‐like diseases characterized by anemia, thrombocytopenia, and megakaryocyte dysplasia. Extramedullary hematopoiesis and bone marrow failure are also observed in Abin1Q478H/Q478H mice. Although Abin1Q478H/Q478H cells are sensitive to RIPK1 kinase–RIPK3–MLKL‐dependent necroptosis, only anemia and splenomegaly are alleviated by RIPK3 deficiency but not by MLKL deficiency or the RIPK1 kinase‐dead mutation. This indicates that the necroptosis‐independent function of RIPK3 is critical for anemia development in Abin1Q478H/Q478H mice. Notably, Abin1Q478H/Q478H mice exhibit higher levels of type I interferon (IFN‐I) expression in bone marrow cells compared towild‐type mice. Consistently, blocking type I IFN signaling through the co‐deletion of Ifnar1 greatly ameliorated anemia, thrombocytopenia, and splenomegaly in Abin1Q478H/Q478H mice. Together, these results demonstrates that ABIN1(Q478) prevents the development of hematopoietic deficiencies by regulating type I IFN expression.

Summary: Corneal alkali burn remains a clinical challenge in ocular emergency, necessitating the development of effective therapeutic drugs. Here, we observed the arachidonic acid metabolic disorders of corneas induced by alkali burns and aimed to explore the role of Prostaglandin E2 (PGE2), a critical metabolite of arachidonic acid, in the repair of alkali-burned corneas. We found a moderate dosage of PGE2 promoted the alkali-burned corneal epithelial repair, whereas a high dosage of PGE2 exhibited a contrary effect. This divergent effect is attributed to different dosages of PGE2 regulating ANXA1 expression differently. Mechanically, a high dosage of PGE2 induced higher GATA3 expression, followed by enhanced GATA3 binding to the ANXA1 promoter to inhibit ANXA1 expression. In contrast, a moderate dosage of PGE2 increased CREB1 phosphorylation and reduced GATA3 binding to the ANXA1 promoter, promoting ANXA1 expression. We believe PGE2 and its regulatory target ANXA1 could be potential drugs for alkali-burned corneas.

Background: To investigate the common symptoms after Covid-19 infection, characteristics of adverse events after vaccination, changes in clinical manifestations related to Neurofibromatosis type 1 (NF1), as well as the current vaccination status and factors related to vaccine hesitation among NF1 patients, in order to provide a basis for scientific protection and vaccine acceptance in NF1 individuals in the new phase of pandemic management. Methods: From December 29, 2022, to January 10, 2023, we conducted a self-assessment questionnaire survey among diagnosed NF1 patients. General data were provided including sex, age, main clinical presentations, and current treatment. This study mainly focused on the infection and vaccination status of Covid-19 among these patients with NF1. The data were statistically analyzed using SPSS26.0 software. Results: Of the 250 questionnaires distributed, 226 were valid. Among the 164 patients (72.6%) with Covid-19 infection, the most common infection symptoms and incidence of patients were not significantly different from those in the normal population (P>0.05), but the incidence of symptoms such as nasal congestion, headache, myalgia, sore throat, abdominal pain, diarrhea, and eye discomfort was higher than that in the normal population (P0.05), but the incidence of fatigue and headache was higher in NF1 patients (P

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

Introduction: Randomized, controlled trials of molnupiravir in real-world use during the Omicron wave are scarce. The frequency of hospitalization and death is low, so further research is needed to confirm the virological efficacy of molnupiravir.Methods: A single-center, randomized, controlled clinical trial was conducted, and 111 hospitalized coronavirus disease 2019 (COVID-19) patients were randomly assigned at a ratio of 1:1. Fifty-three patients in the molnupiravir group were administered 800 mg of molnupiravir twice daily for 5 days in addition to the standard therapy, and 58 patients in the control group only received the standard therapy in accordance with local guidelines. The antiviral effect and adverse events were evaluated during the follow-up.Results: The median viral clearance time in the molnupiravir group was significantly shorter than that in the control group (p = 0.003). Furthermore, patients who started molnupiravir therapy within 3 days had significantly shorter viral clearance time than the controls (p = 0.003). In the vaccinated subgroup, molnupiravir therapy was also associated with a shorter viral clearance time (p = 0.003). A total of three adverse events, which were minor, were reported in the molnupiravir group. One of the patients had mild liver function abnormalities, and all of them were resolved without intervention. However, the remission time was similar between the two tested groups.Conclusion: Molnupiravir exhibited good viral replication inhibitor efficacy in patients with Omicron variant vaccine breakthrough COVID-19 infection.Clinical Trial Registration: [https://www.chictr.org.cn/], identifier [ChiCTR2200059796].

Excessive light exposure can damage photoreceptors and lead to blindness. Oxidative stress serves a key role in photo-induced retinal damage. Free radical scavengers have been proven to protect against photo-damaged retinal degeneration. Fullerol, a potent antioxidant, has the potential to protect against ultraviolet-B (UVB)-induced cornea injury by activating the endogenous stem cells. However, its effects on cell fate determination of Müller glia (MG) between gliosis and de-differentiation remain unclear. Therefore, we established a MG lineage-tracing mouse model of light-induced retinal damage to examine the therapeutic effects of fullerol. Fullerol exhibited superior protection against light-induced retinal injury compared to glutathione (GSH) and reduced oxidative stress levels, inhibited gliosis by suppressing the TGF-β pathway, and enhanced the de-differentiation of MG cells. RNA sequencing revealed that transcription candidate pathways, including Nrf2 and Wnt10a pathways, were involved in fullerol-induced neuroprotection. Fullerol-mediated transcriptional changes were validated by qPCR, Western blotting, and immunostaining using mouse retinas and human-derived Müller cell lines MIO-M1 cells, confirming that fullerol possibly modulated the Nrf2, Wnt10a, and TGF-β pathways in MG, which suppressed gliosis and promoted the de-differentiation of MG in light-induced retinal degeneration, indicating its potential in treating retinal diseases.

Deep learning techniques, especially convolutional neural networks (CNNs), have dramatically boosted the performance of statistical downscaling. In this study, we propose a CNN-based 2 m air temperature downscaling model named Terrain-Guided Attention Network (TGAN), which aims at rebuilding 2 m air temperature distribution from 0.0625$^{\circ }$ to 0.01$^{\circ }$ over Southwest China. More concretely, TGAN utilizes two upsampling modules to progressively reconstruct the high-resolution temperature data from the low-resolution one. Then, to better recover the spatial detail of the low-resolution temperature data, an attentive-terrain block is proposed to introduce digital terrain model (DEM) information. It aggregates the temperature data and the corresponding-scale DEM information via the attention mechanism in a multiscale manner. Ultimately, the reconstruction module is employed to obtain the high-resolution temperature data. We use the 2019 data for training, and utilize the 2018 data to verify the effectiveness of the proposed TGAN. The experimental results showed that TGAN achieved the lowest root-mean-square error (1.12$\,^\circ$C) when incorporating DEM data by attentive-terrain blocks in a multiscale manner, followed by incorporating DEM data in a multiscale manner (TGAN-land, 1.31$\,^\circ$C) and only incorporating DEM data (SRCNN-land, 1.36$\,^\circ$C). Meanwhile, TGAN showed a competitive performance when compared with several advanced deep-learning-based super-resolution algorithms and reconstructed the texture details of 2 m air temperature fields more clearly. In general, among various deep learning approaches, TGAN achieves better downscaling results for 2 m air temperature reconstruction and provides a practical method and guidance for the back-calculation of high-resolution historical meteorological grid data.

This study aimed to comprehensively identify risk factors for the occurrence of prolonged air leak (PAL) in patients undergoing pulmonary surgery. Studies were retrieved from 3 databases, including PubMed, Web of Science, and EmBase up to 13 May 2020. We performed meta-analysis using Bayesian random effect models through divergence restricting conditional tessellation (DIRECT) algorithm. The effect size was expressed as odds ratio (OR) or mean difference (MD), each with 95% credible interval (CrI). The evidence quality was evaluated. Subgroup analyses and sensitivity analyses were conducted. Thirty-nine studies with 89006 patients were finally included. Pooled PAL incidence was 15%. Of 30 risk factors, 22 were significantly associated with increased PAL incidence. Five risk factors were ultimately selected with high evidence quality: smoking history (OR 1.84, 95%CrI 1.45 to 2.31, P

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model’s feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements.

Bisphenols, including Bisphenol A (BPA), Tetrabromobisphenol A (TBBPA), and Tetrabromobisphenol S (TBBPS), have been widely applied in the production of polycarbonate plastics and epoxy resins and have been detected in the environment worldwide. The frequent detection of bisphenols in maternal and fetal samples has raised concerns about their toxic effects on human embryonic development, especially on the development of the central nervous system. However, the effect of bisphenols on human retinal development is still unknown. In this study, to evaluate the toxicity of bisphenols on early retinal development, human embryonic stem cells were induced to differentiate into retinal organoids that responded to BPA, TBBPA, and TBBPS, at human exposure relevant concentrations. The global gene expression of retinal organoids was analyzed by RNA sequencing (RNA-seq). A set of retinal development-related biological processes, including neuron differentiation, phototransduction, axon guidance, and retina layer formation, were identified in retinal organoids corresponding to different developmental stages. The RNA-seq data also showed that BPA, TBBPA, and TBBPS influenced retinal development by interfering with the Cytokine-cytokine receptor interaction pathway. HSPA6, HIF1A-AS3, CDC20B, IL19, OAS1, HSPA7, and RN7SK were dysregulated by these chemicals. Additionally, BPA, TBBPA, and TBBPS exhibited different toxic effects on neural retina development, with TBBPA appearing to exert more toxicity than BPA and TBBPS. Furthermore, three bisphenols exhibited different effects at different stages of neural retina development. The sensitivity of retinal development to bisphenols depends on their developmental stage. This study provides new insights into the deep dissection of retinotoxicity after prenatal bisphenol exposure.

The eyes are highly susceptible to the oxidative stress induced by ultraviolet B (UVB, wavelength between 280 ∼ 320 nm), which could cause severe damage to the cornea. Fullerenols are effective antioxidants to alleviate UVB-induced injury, while their application for the eyes is still rare. In present study, we investigated the protective performance and mechanism of fullerenols on cornea under UVB radiation in vivo and in vitro. The synthesized fullerenols exhibited broad-spectrum free radical scavenging properties (applicable to both reactive oxygen species (ROS) and reactive nitrogen species (RNS)) and photo-stability. When compared with another widely used antioxidant glutathione (GSH), the administration of fullerenols markedly decreased the injured area, corneal edema, cell death, and increased the cell proliferation in UVB-induced rat cornea. The effects of fullerenols were confirmed in UVB-exposed human corneal epithelial cells (hCECs), where elevated cell viability and proliferation, decreased oxidative free radical production, repaired mitochondrial dysfunction and DNA lesions were observed. RNA sequencing (RNA-Seq) analysis demonstrated that fullerenol alleviated UVB-induced corneal injury through down-regulation of oxidative stress-related genes and up-regulation of proliferation-associated genes. Our results demonstrate the suitability of fullerenols as a potential exogenous treatment in ameliorating UVB-induced cornea damage.

Abstract Regeneration/repair after injury can be endowed by adult stem cells (ASCs) or lineage restricted and even terminally differentiated cells. In corneal epithelium, regeneration after a large wound depends on ASCs (limbal epithelial stem cells, LESCs), whereas repair after a small wound is LESCs-independent. Here, using rat corneal epithelial wounds with different sizes, we show that YAP activation promotes the activation and expansion of LESCs after a large wound, as well as the reprogramming of local epithelial cells (repairing epithelial cells) after a small wound, which contributes to LESCs-dependent and -independent wound healing, respectively. Mechanically, we highlight that the reciprocal regulation of YAP activity and the assembly of cell junction and cortical F-actin cytoskeleton accelerates corneal epithelial healing with different-sized wounds. Together, the common YAP activation and the underlying regulatory mechanism are harnessed by LESCs and lineage-restricted epithelial cells to cope with corneal epithelial wounds with different sizes.

Increasing evidence in animal models has suggested that polybrominated diphenyl ethers (PBDEs), a class of brominated flame retardants, can cause retinotoxicity. However, data on the influence of PBDE treatment on human retinal development are scarce due to the lack of appropriate models. In the present study, we report the utilization of human embryonic stem cell-derived retinal organoids (hESC-ROs) for toxicity assessment of the most common PBDE congener (BDE-47) during the early stages of retinal development. Exposure to BDE-47 decreased the thickness and area of the neural retina (NR) of hESC-ROs in a dose- and time-dependent manner. Abnormal retinal cell distributions, disordered NR structures, and neural rosette-like structures were found on hESC-ROs after low-level BDE-47 exposure. Moreover, BDE-47 exposure decreased cell proliferation, promoted cell apoptosis, and caused abnormal differentiation. Transcriptomic analysis demonstrated that differentially expressed genes, caused by BDE-47, were enriched in extracellular matrix organization. Metabolomic studies of hESC-ROs revealed significant changes in the metabolism of purine and glutathione after BDE-47 exposure for five weeks. This study clarifies the retinotoxicity of low-level BDE-47 treatment and highlights the powerfulness of the hESC-RO model, deepening our understanding of BDE-47-driven human early retina developmental toxicity.

The fluids with well-defined inner structure and intrinsic responsibility are demanding to the maintenance and regulation of normal functions in living systems. Inspired by this, responsive Janus droplets are constructed by small molecules of magneto-surfactants, and the traditional vortex mixing is applied to guarantee large-scale fabrication. Silicone oil (SO) and Vegetable oil (VO) as two immiscible oils are introduced as inner phases of the Janus emulsions. The topology and subsequently the magnetic responsive performance of Janus emulsion is controlled by adjusting the oil composition with various viscosity, the surfactant concentration and the mass ratio of two oils. Moreover, the remote control of the emulsion stability is realized by the external magnetic field, of which the demulsification mechanism is investigated. The anisotropic and responsive droplets with advantages of deformability, encapsulation capability, and biocompatibility would expand the application of emulsions in targeted therapy, drug delivery and magnetic materials science.

Remodeling of extracellular matrix (ECM) components of endothelial cells is the main cause of retinal vascular basement membrane (BM) thickening, which leads to the initiation and perpetuation of microvasculopathy of diabetic retinopathy (DR). Excessive amounts of glucocorticoids (GCs) are related to the presence and severity of DR, however transcriptional effects of GCs on the biology of human retinal capillary endothelial cells (HRCECs) and its impacts on DR are still unclear. Here, we showed that GC (hydrocortisone) treatment induced ECM component [fibronectin (FN) and type IV collagen (Col IV)] expression and morphological changes in HRCECs via the glucocorticoid receptor (GR), which depended on the nuclear translocation of YAP coactivator. Mechanistically, GCs induced stress fiber formation in HRCECs, while blocking stress fiber formation inhibited GC-induced YAP nuclear translocation. Overexpression of FN, but not Col IV, activated YAP through the promotion of stress fiber formation via ECM-integrin signaling. Thus, a feedforward loop is established to sustain YAP activity. Using mRNA sequencing of HRCECs with overexpressed YAP or GC treatment, we found a similarity in Gene Ontology (GO) terms, differentially expressed genes (DEGs) and transcription factors (TFs) between the two RNA-seq datasets. In vivo, YAP was activated in retina vascular ECs of STZ-induced diabetic mice, and TF prediction analysis of published RNA-seq data of dermal vascular ECs from T2DM patients showed that GR and TEAD (the main transcription factor for YAP) were enriched. Together, GCs activate YAP and promote ECM component (FN and Col IV) remodeling in retinal capillary endothelial cells, and the underlying regulatory mechanism may provide new insights into the vascular BM thickening of the retina in the early pathogenesis of DR.

Trametinib has been used in neurofibromatosis type 1 (NF1) patients, especially those with unresectable nerve tumors, but no systematic review based on the latest studies has been published. We conducted this meta-analysis to evaluate the effectiveness and safety of trametinib in treating NF1-related nerve tumors. Original articles reporting the efficacy and safety of trametinib in NF1 patents were identified in PubMed, EMBASE, and Web of Science up to 1 June 2022. Using R software and the ‘meta’ package, the objective response rates (ORRs) and disease control rates (DCRs) were calculated to evaluate the efficacy, and the pooled proportion of adverse events (AEs) was calculated. The Grading of Recommendations, Assessment, Development and Evaluation system was used to assess the quality of evidence. Eight studies involving 92 patients were included, which had a very low to moderate quality of evidence. The pooled ORR was 45.3% (95% CI: 28.9–62.1%, I2 = 0%), and the DCR was 99.8% (95% CI: 95.5–100%, I2 = 0%). The most common AEs was paronychia, with a pooled rate of 60.7% (95% CI: 48.8–72.7%, I2 = 0%). Our results indicate the satisfactory ability to stabilize tumor progression but a more limited ability to shrink tumors of trametinib in NF1-related nerve tumors. The safety profile of trametinib is satisfactory.

Increasing evidence demonstrated that PM2.5 could cross the placenta and fetal blood–brain barrier, causing neurotoxicity of embryonic development. The retina, an embryologic extension of the central nervous system, is extremely sensitive and vulnerable to environmental insults. The adverse effects of PM2.5 exposure on the retina during embryonic neurodevelopment are still largely unknown. Our goal was to investigate the effect of PM2.5 on human retinal development, which was recapitulated by human embryonic stem cell (hESC)-derived retinal organoids (hEROs). In the present study, using the hEROs as the model, the influences and the mechanisms of PM2.5 on the developing retina were analyzed. It demonstrated that the formation rate of the hERO-derived neural retina (NR) was affected by PM2.5 in a concentration dosage-dependent manner. The areas of hEROs and the thickness of hERO-NRs were significantly reduced after PM2.5 exposure at the concentration of 25, 50, and 100 μg/ml, which was due to the decrease of proliferation and the increase of apoptosis. Although we did not spot significant effects on retinal differentiation, PM2.5 exposure did lead to hERO-NR cell disarranging and structural disorder, especially retinal ganglion cell dislocation. Transcriptome analysis showed that PM2.5 treatment was significantly associated with the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT pathways and reduced the level of the fibroblast growth factors (FGFs), particularly FGF8 and FGF10. These results provided evidence that PM2.5 exposure potentially inhibited proliferation and increased apoptosis at the early development stage of the human NR, probably through the MAPK and PI3K/Akt pathway. Our study suggested that exposure to PM2.5 suppressed cell proliferation and promoted cell apoptosis, thereby contributing to abnormal human retinal development.

Retinal degeneration (RD) is one of the most common causes of visual impairment and blindness and is characterized by progressive degeneration of photoreceptors. Transplantation of neural stem/progenitor cells (NPCs) is a promising treatment for RD, although the mechanisms underlying the efficacy remain unclear. Accumulated evidence supports the notion that paracrine effects of transplanted stem cells is likely the major approach to rescuing early degeneration, rather than cell replacement. NPC-derived exosomes (NPC-exos), a type of extracellular vesicles (EVs) released from NPCs, are thought to carry functional molecules to recipient cells and play therapeutic roles. In present study, we found that grafted human NPCs (hNPCs) secreted EVs and exosomes in the subretinal space (SRS) of RCS rats, an RD model. And direct administration of mouse neural progenitor cell-derived exosomes (mNPC-exos) delayed photoreceptor degeneration, preserved visual function, prevented thinning of the outer nuclear layer (ONL), and decreased apoptosis of photoreceptors in RCS rats. Mechanistically, mNPC-exos were specifically internalized by retinal microglia and suppressed their activation in vitro and in vivo. RNA sequencing and miRNA profiling revealed a set of 17 miRNAs contained in mNPC-exos that markedly inhibited inflammatory signal pathways by targeting TNF-α, IL-1β, and COX-2 in activated microglia. The exosomes derived from hNPC (hNPC-exos) contained similar miRNAs to mNPC-exos that inhibited microglial activation. We demonstrated that NPC-exos markedly suppressed microglial activation to protect photoreceptors from apoptosis, suggesting that NPC-exos and their contents may be the mechanism of stem cell therapy for treating RD.

Bladder cancer is a fatal cancer that happens in the genitourinary tract with quite high morbidity and mortality annually. The high level of recurrence rate ranging from 50 to 80% makes bladder cancer one of the most challenging and costly diseases to manage. Faced with various problems in existing methods, a recently emerging concept for the measurement of imaging biomarkers and extraction of quantitative features called “radiomics” shows great potential in the application of detection, grading, and follow-up management of bladder cancer. Furthermore, machine-learning (ML) algorithms on the basis of “big data” are fueling the powers of radiomics for bladder cancer monitoring in the era of precision medicine. Currently, the usefulness of the novel combination of radiomics and ML has been demonstrated by a large number of successful cases. It possesses outstanding strengths including non-invasiveness, low cost, and high efficiency, which may serve as a revolution to tumor assessment and emancipate workforce. However, for the extensive clinical application in the future, more efforts should be made to break down the limitations caused by technology deficiencies, inherent problems during the process of radiomic analysis, as well as the quality of present studies.

Resident microglia are the main immune cells in the retina and play a key role in the pathogenesis of retinitis pigmentosa (RP). Many previous studies on the roles of microglia mainly focused on the neurotoxicity or neuroprotection of photoreceptors, while their contributions to synaptic remodeling of neuronal circuits in the retina of early RP remained unclarified. In the present study, we used Royal College of Surgeons (RCS) rats, a classic RP model characterized by progressive microglia activation and synapse loss, to investigate the constitutive effects of microglia on the synaptic lesions and ectopic neuritogenesis. Rod degeneration resulted in synapse disruption and loss in the outer plexiform layer (OPL) at the early stage of RP. Coincidentally, the resident microglia in the OPL increased phagocytosis and mainly engaged in phagocytic engulfment of postsynaptic mGluR6 of rod bipolar cells (RBCs). Complement pathway might be involved in clearance of postsynaptic elements of RBCs by microglia. We pharmacologically deleted microglia using a CSF1 receptor (CSF1R) inhibitor to confirm this finding, and found that it caused the accumulation of postsynaptic mGluR6 levels and increased the number and length of ectopic dendrites in the RBCs. Interestingly, the numbers of presynaptic sites expressing CtBP2 and colocalized puncta in the OPL of RCS rats were not affected by microglia elimination. However, sustained microglial depletion led to progressive functional deterioration in the retinal responses to light in RCS rats. Based on our results, microglia mediated the remodeling of RBCs by phagocytosing postsynaptic materials and inhibiting ectopic neuritogenesis, contributing to delay the deterioration of vision at the early stage of RP.

The multi-model ensemble approach is generally considered as the best way to explore the advantage and to avoid the weakness of each individual model, and ultimately to achieve the best climate projection. But the design of an optimal strategy and its practical implementation still constitutes a challenge. Here we use the random forest (RF) algorithm (from the category of machine learning) to explore the information offered by the multi-model ensemble simulations within the Coupled Model Intercomparison Project Phase 6. Our objective is to achieve a more reliable climate projection (mean climate and extremes) over China. RF is furthermore compared to two other ensemble-processing strategies of different nature, one is the basic arithmetic mean (AM), and another is the linear regression across the ensemble members. Our results indicate that RF effectively enhances the capability in capturing spatial climate characteristics. Regions with complex topography, such as the Tibetan Plateau and its periphery, show the most significant improvements. RF projects less future warming but enhanced wet conditions across China. It also produces larger spatial variability and more small-scale features. The most obvious increase of precipitation is in the northern part and the periphery of the Tibetan Plateau. The projected changes in RF for strong precipitation are almost twice higher than in AM, while in the northwestern area, weaker increases of precipitation are projected by RF, which indicates larger spatial inhomogeneity of its projection.

Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.