Identification of SLC7A11-AS1/SLC7A11 pair as a ferroptosis-related therapeutic target for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a prevalent malignancy worldwide, poses significant challenges in terms of prognosis, necessitating innovative therapeutic approaches. Ferroptosis offers notable advantages over apoptosis, holding promise as a novel therapeutic approach for HCC complexities. Moreover, while the interaction between long non-coding RNAs (lncRNAs) and mRNAs is pivotal in various physiological and pathological processes, their involvement in ferroptosis remains relatively unexplored. In this study, we constructed a ferroptosis-related lncRNA-mRNA correlation network in HCC using Pearson correlation analysis. Notably, the SLC7A11-AS1/SLC7A11 pair, exhibiting high correlation, was identified. Bioinformatics analysis revealed a significant correlation between the expression levels of this pair and key clinical characteristics of HCC patients, including gender, pathology, Ishak scores and tumour size. And poor prognosis was associated with high expression of this pair. Functional experiments demonstrated that SLC7A11-AS1, by binding to the 3'UTR region of SLC7A11 mRNA, enhanced its stability, thereby promoting HCC cell growth and resistance to erastin- induced ferroptosis. Additionally, in vivo studies confirmed that SLC7A11-AS1 knockdown potentiated the inhibitory effects of erastin on tumour growth. Overall, our findings suggest that targeting the SLC7A11-AS1/SLC7A11 pair holds promise as a potential therapeutic strategy for HCC patients.

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally found along the utero-placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. PURPOSE: To develop a DL tool for antenatal diagnosis of PAS using T2-weighted MR images. STUDY TYPE: Retrospective. SUBJECTS: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. FIELD STRENGTH/SEQUENCE: Sagittal T2-weighted fast spin echo sequence at 1.5 T and 3 T. ASSESSMENT: An nnU-Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero-placental boundary region. The UPB image was then fed into DenseNet-PAS for PAS diagnosis. DenseNet-PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. STATISTICAL TESTS: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann-Whitney U-test or the chi-squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. RESULTS: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737-0.770). DATA CONCLUSION: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

BACKGROUND: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images. PURPOSE: To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. STUDY TYPE: Retrospective. POPULATION: Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system. ASSESSMENT: EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort. STATISTICAL TESTS: Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. RESULTS: EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). DATA CONCLUSION: The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Role of epidural fat in the local milieu: what we know and what we don't.

PURPOSE: Traditionally, the epidural fat (EF) is known as a physical buffer for the dural sac against the force and a lubricant facilitating the relative motion of the latter on the osseous spine. Along with the development of the studies on EF, controversies still exist on vital questions, such as the underlying mechanism of the spinal epidural lipomatosis. Meanwhile, the scattered and fragmented researches hinder the global insight into the seemingly dispensable tissue. METHODS: Herein, we reviewed literature on the EF and its derivatives to elucidate the dynamic change and complex function of EF in the local milieu, especially at the pathophysiological conditions. We start with an introduction to EF and the current pathogenic landscape, emphasizing the interlink between the EF and adjacent structures. We generally categorize the major pathological changes of the EF into hypertrophy, atrophy, and inflammation. RESULTS AND CONCLUSIONS: It is acknowledged that not only the EF (or its cellular components) may be influenced by various endogenic/exogenic and focal/systematic stimuli, but the adjacent structures can also in turn be affected by the EF, which may be a hidden pathogenic clue for specific spinal disease. Meanwhile, the unrevealed sections, which are also the directions the future research, are proposed according to the objective result and rational inference. Further effort should be taken to reveal the underlying mechanism and develop novel therapeutic pathways for the relevant diseases.

Perception and memory retrieval states are reflected in distributed patterns of background functional connectivity.

The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity (1) was fully independent from stimulus-related variance in the signal and, further, (2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

Ordered Van der Waals Hetero-nanoribbon from Pressure-Induced Topochemical Polymerization of Azobenzene.

Pressure-induced topochemical polymerization of molecular crystals with various stackings is a promising way to synthesize materials with different co-existing sub-structures. Here, by compressing the azobenzene crystal containing two kinds of intermolecular stacking, we synthesized an ordered van der Waals carbon nanoribbon (CNR) heterostructure in one step. Azobenzene polymerizes via a [4 + 2] hetero-Diels-Alder (HDA) reaction of phenylazo-phenyl in layer A and a para-polymerization reaction of phenyl in layer B at 18 GPa, as evidenced by in situ Raman and IR spectroscopies, X-ray diffraction, as well as gas chromatography-mass spectrometry and the solid-state nuclear magnetic resonance of the recovered products. The theoretical calculation shows that the obtained CNR heterostructure has a type II (staggered) band gap alignment. Our work highlights a high-pressure strategy to synthesize bulk CNR heterostructures.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Genome-wide, integrative analysis of circular RNA dysregulation and the corresponding circular RNA-microRNA-mRNA regulatory axes in autism.

Circular RNAs (circRNAs), a class of long noncoding RNAs, are known to be enriched in mammalian neural tissues. Although a wide range of dysregulation of gene expression in autism spectrum disorder (ASD) have been reported, the role of circRNAs in ASD remains largely unknown. Here, we performed genome-wide circRNA expression profiling in postmortem brains from individuals with ASD and controls and identified 60 circRNAs and three coregulated modules that were perturbed in ASD. By integrating circRNA, microRNA, and mRNA dysregulation data derived from the same cortex samples, we identified 8170 ASD-associated circRNA-microRNA-mRNA interactions. Putative targets of the axes were enriched for ASD risk genes and genes encoding inhibitory postsynaptic density (PSD) proteins, but not for genes implicated in monogenetic forms of other brain disorders or genes encoding excitatory PSD proteins. This reflects the previous observation that ASD-derived organoids show overproduction of inhibitory neurons. We further confirmed that some ASD risk genes (NLGN1, STAG1, HSD11B1, VIP, and UBA6) were regulated by an up-regulated circRNA (circARID1A) via sponging a down-regulated microRNA (miR-204-3p) in human neuronal cells. Particularly, alteration of NLGN1 expression is known to affect the dynamic processes of memory consolidation and strengthening. To the best of our knowledge, this is the first systems-level view of circRNA regulatory networks in ASD cortex samples. We provided a rich set of ASD-associated circRNA candidates and the corresponding circRNA-microRNA-mRNA axes, particularly those involving ASD risk genes. Our findings thus support a role for circRNA dysregulation and the corresponding circRNA-microRNA-mRNA axes in ASD pathophysiology.

Integrated metabolomic and transcriptomic strategies to reveal alkali-resistance mechanisms in wild soybean during post-germination growth stage.

MAIN CONCLUSION: The keys to alkali-stress resistance of barren-tolerant wild soybean lay in enhanced reutilization of reserves in cotyledons as well as improved antioxidant protection and organic acid accumulation in young roots. Soil alkalization of farmlands is increasingly serious, adversely restricting crop growth and endangering food security. Here, based on integrated analysis of transcriptomics and metabolomics, we systematically investigated changes in cotyledon weight and young root growth in response to alkali stress in two ecotypes of wild soybean after germination to reveal alkali-resistance mechanisms in barren-tolerant wild soybean. Compared with barren-tolerant wild soybean, the dry weight of common wild soybean cotyledons under alkali stress decreased slowly and the length of young roots shortened. In barren-tolerant wild soybean, nitrogen-transport amino acids asparagine and glutamate decreased in cotyledons but increased in young roots, and nitrogen-compound transporter genes and genes involved in asparagine metabolism were significantly up-regulated in both cotyledons and young roots. Moreover, isocitric, succinic, and L-malic acids involved in the glyoxylate cycle significantly accumulated and the malate synthetase gene was up-regulated in barren-tolerant wild soybean cotyledons. In barren-tolerant wild soybean young roots, glutamate and glycine related to glutathione metabolism increased significantly and the glutathione reductase gene was up-regulated. Pyruvic acid and citric acid involved in pyruvate-citrate metabolism increased distinctly and genes encoding pyruvate decarboxylase and citrate synthetase were up-regulated. Integrated analysis showed that the keys to alkali-stress resistance of barren-tolerant wild soybean lay in enhanced protein decomposition, amino acid transport, and lipolysis in cotyledons as well as improved antioxidant protection and organic acid accumulation in young roots. This study provides new ideas for the exploitation and utilization of wild soybean resources.

From Biomass to Functional Crystalline Diamond Nanothread: Pressure-Induced Polymerization of 2,5-Furandicarboxylic Acid.

2,5-Furandicarboxylic acid (FDCA) is one of the top-12 value-added chemicals from sugar. Besides the wide application in chemical industry, here we found that solid FDCA polymerized to form an atomic-scale ordered sp3-carbon nanothread (CNTh) upon compression. With the help of perfectly aligned π-π stacked molecules and strong intermolecular hydrogen bonds, crystalline poly-FDCA CNTh with uniform syn-configuration was obtained above 11 GPa, with the crystal structure determined by Rietveld refinement of the X-ray diffraction (XRD). The in situ XRD and theoretical simulation results show that the FDCA experienced continuous [4 + 2] Diels-Alder reactions along the stacking direction at the threshold C···C distance of ∼2.8 Å. Benefiting from the abundant carbonyl groups, the poly-FDCA shows a high specific capacity of 375 mAh g-1 as an anode material of a lithium battery with excellent Coulombic efficiency and rate performance. This is the first time a three-dimensional crystalline CNTh is obtained, and we demonstrated it is the hydrogen bonds that lead to the formation of the crystalline material with a unique configuration. It also provides a new method to move biomass compounds toward advanced functional carbon materials.

Distance-Selected Topochemical Dehydro-Diels-Alder Reaction of 1,4-Diphenylbutadiyne toward Crystalline Graphitic Nanoribbons.

Solid-state topochemical polymerization (SSTP) is a promising method to construct functional crystalline polymeric materials, but in contrast to various reactions that happen in solution, only very limited types of SSTP reactions are reported. Diels-Alder (DA) and dehydro-DA (DDA) reactions are textbook reactions for preparing six-membered rings in solution but are scarcely seen in solid-state synthesis. Here, using multiple cutting-edge techniques, we demonstrate that the solid 1,4-diphenylbutadiyne (DPB) undergoes a DDA reaction under 10-20 GPa with the phenyl as the dienophile. The crystal structure at the critical pressure shows that this reaction is "distance-selected". The distance of 3.2 Å between the phenyl and the phenylethynyl facilitates the DDA reaction, while the distances for other DDA and 1,4-addition reactions are too large to allow the bonding. The obtained products are crystalline armchair graphitic nanoribbons, and hence our studies open a new route to construct the crystalline carbon materials with atomic-scale control.

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.

OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). METHODS: We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. RESULTS: In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. CONCLUSION: In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. KEY POINTS: • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.

Integrative transcriptome sequencing reveals extensive alternative trans-splicing and cis-backsplicing in human cells.

Transcriptionally non-co-linear (NCL) transcripts can originate from trans-splicing (trans-spliced RNA; 'tsRNA') or cis-backsplicing (circular RNA; 'circRNA'). While numerous circRNAs have been detected in various species, tsRNAs remain largely uninvestigated. Here, we utilize integrative transcriptome sequencing of poly(A)- and non-poly(A)-selected RNA-seq data from diverse human cell lines to distinguish between tsRNAs and circRNAs. We identified 24,498 NCL events and found that a considerable proportion (20-35%) of them arise from both tsRNAs and circRNAs, representing extensive alternative trans-splicing and cis-backsplicing in human cells. We show that sequence generalities of exon circularization are also observed in tsRNAs. Recapitulation of NCL RNAs further shows that inverted Alu repeats can simultaneously promote the formation of tsRNAs and circRNAs. However, tsRNAs and circRNAs exhibit quite different, or even opposite, expression patterns, in terms of correlation with the expression of their co-linear counterparts, expression breadth/abundance, transcript stability, and subcellular localization preference. These results indicate that tsRNAs and circRNAs may play different regulatory roles and analysis of NCL events should take the joint effects of different NCL-splicing types and joint effects of multiple NCL events into consideration. This study describes the first transcriptome-wide analysis of trans-splicing and cis-backsplicing, expanding our understanding of the complexity of the human transcriptome.

Secrecy Performance Analysis of Wireless Powered Sensor Networks Under Saturation Nonlinear Energy Harvesting and Activation Threshold.

In this paper, we investigate the impact of saturation nonlinear energy harvesting (EH) and activation threshold on the multiuser wireless powered sensor networks (WPSNs) from the physical layer security (PLS) perspective. In particular, for improving the secrecy performance, the generalized multiuser scheduling (GMS) scheme is exploited, in which the Kth strongest sensor is chosen based on the legitimate link. For evaluating the impact of various key parameters on the security of system, we obtain the exact closed-form expressions for secrecy outage probability (SOP) under linear EH (LEH), saturation nonlinear EH (SNEH) and saturation nonlinear EH with activation threshold (SNAT), respectively, and solve the maximization problem of secure energy efficiency (SEE). Simulation results demonstrate that: (1) the number of source sensors, the EH efficiency and the transmit power of power beacon (PB) all have positive impact on SOP, and the smaller generalized selection coefficient is advantageous for secrecy performance; (2) LEH is an ideal situation for SNEH when the saturation threshold is large enough and SNEH is a special situation for SNAT when the activation threshold is low enough; (3) the time-switching factor and the activation threshold both have an important impact on the secrecy performance, which are worth considering carefully.

SpyTag/SpyCatcher cyclization enhances the thermostability and organic solvent tolerance of L-phenylalanine aldolase.

OBJECTIVES: To improve the thermostability and organic solvent tolerance of L-phenylserine aldolase, the in vivo SpyTag/SpyCatcher cyclization strategy was applied in this work. RESULTS: The in vivo cyclization of L-phenylserine aldolase was achieved by fusing the tags of SpyCatcher and SpyTag to the N- and C-termini of the enzyme, respectively. The kcat values and the circular dichroism spectra of the linear and cyclized LPAs are very similar, indicating that the cyclized LPA can be folded appropriately like the wild type. The cyclized enzyme has better thermostability and organic solvent tolerance than does the wild type. The half-life of L-phenylserine aldolase after cyclization was increased by 8.3 times at 70 °C, and the T50 also increased from 56.8 to 67.1 °C. The cyclized enzyme showed a remarkably higher tolerance to organic solvents (e.g., methanol, ethanol and acetone). CONCLUSIONS: These results suggest that the in vivo cyclization using SpyTag/SpyCatcher is an effective strategy to improve the stability of enzymes, which potentially could be applied in industrial bioconversion.

CCDC134 ameliorated experimental autoimmune encephalomyelitis by suppressing Th1 and Th17 cells.

CCDC134 (coiled-coil domain containing 134), a cytokine-like molecule, was previously reported to exert antitumor effects by augmenting CD8+ T-cell mediated immunity. However, the dynamic changes in CCDC134 expression patterns in the spinal cord that may be involved in the progression of experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis, remains unclear. In this study, we found that CCDC134 expression was markedly increased in the spinal cord during the progression of EAE. Furthermore, we demonstrated that CCDC134 significantly reduced the severity and slowed the progression of EAE, which correlated with reduced spinal cord inflammation and demyelination. The underlying mechanism of CCDC134-induced effects involved inhibition of T helper (Th)-1 and Th17 cell differentiation and secretion of its key effector molecules IFN-γ and IL-17A via regulation of JAK/STAT signaling. These findings indicate that CCDC134 exerts potent anti-inflammatory effects through the selective modulation of pathogenic Th1 and Th17 cells by targeting critical signaling pathways. The study provides insights into the role of CCDC134 as a unique therapeutic agent for the treatment of autoimmune diseases.

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

[Micro-dissection testicular sperm extraction for non-obstructive azoospermia patients with the history of secondary testicular injury].

Objective: To investigate the value of micro- dissection testicular sperm extraction (micro-TESE) in the treatment of non-obstructive azoospermia (NOA) in patients with the history of secondary testicular injury. METHODS: Totally, 121 NOA patients with the history of secondary testicular injury underwent micro-TESE in our hospital from September 2014 to December 2017. We analyzed the correlation of the sperm retrieval rate with the causes of testicular injury and compared the outcomes of the ICSI cycles with the sperm retrieved from the NOA males by micro-TESE (the micro-TESE group) and those with the sperm ejaculated from severe oligospermia patients (sperm concentration <1×106/ml, the ejaculate group). Comparisons were also made between the two groups in the female age, two-pronucleus (2PN) fertilization rate, transferrable embryos on day 3 (D3), D3 high- quality embryos, D14 blood HCG positive rate, embryo implantation rate, and clinical pregnancy rate. RESULTS: Testicular sperm were successfully retrieved by micro-TESE in 86.0% of the patients (104/121), of whom 98.4% had the history of orchitis, 75.5% had been treated surgically for cryptorchidism, and 63.6% had received chemo- or radiotherapy. No statistically significant differences were observed between the micro-TESE and ejaculate groups in the 2PN fertilization rate (59.4% vs 69.3%, P > 0.05), D14 blood HCG positive rate (44.6% vs 57.9%, P > 0.05), embryo implantation rate (31.8 %% vs 32.6%, P > 0.05) and clinical pregnancy rate (41.5% vs 48.7%, P > 0.05). However, the rate D3 transferrable embryos was significantly lower in the micro-TESE than in the ejaculate group (40.5% vs 52.2%,P < 0.05), and so was that of D3 high-quality embryos (32.5% vs 42.1%, P < 0.05). CONCLUSIONS: Micro-TESE can be applied as the first choice for NOA patients with the history of secondary testicular injury, but more effective strategies are to be explored for the improvement of ICSI outcomes with the sperm retrieved by micro- TESE.

Transcriptome analysis of the effect of Vibrio alginolyticus infection on the innate immunity-related TLR5-mediated induction of cytokines in Epinephelus lanceolatus.

Epinephelus lanceolatus, considered to be an aquaculture fish species of high economic value in East Asia, is one of the largest groupers in the Epinephelus genus. Vibrio alginolyticus is a bacterial species that causes high morbidity in marine fish; infection can cause exophthalmia, ulcers, septicemia, and corneal opaqueness in fish. Epinephelus lanceolatus larvae infected with Vibrio alginolyticus were subjected to transcriptome analysis to study the immune regulation pathway. Grouper larvae were injected with 2.6 × 10(4) CFU/fish in 20 µl of V. alginolyticus and control larvae were injected with TSB; RNA samples were then collected at 4, 6, 8, 10, 12, 16, 24, and 48 h after infection. Extracted RNA was subjected to reverse transcription, and used to examine the immune gene response of E. lanceolatus by Real-time PCR. Samples taken at 6 h were subjected to next-generation sequencing, resulting in a total read value of 28,705,411 and total base number of 2,152,905,850. The unigene number was 100,848, and 5913 unigenes were filtered using FPKM>0.3, 2FC, p < 0.05. Gene Ontology (GO) analysis of the filtered genes revealed a total of 30 GO numbers in the cellular component, and 58 GO numbers for both biological processes and molecular functions. Of the GO group related to immune pathways, 27 unigenes related to biological processes involving the immune response, 31 related to the immune system, 9 related to the inflammatory response, and 43 related to the response to stress were identified. KEGG pathway analysis only detected 1 to 4 genes, and as such, we selected the GO analysis results for further analysis using GeneSpring. This demonstrated that V. alginolyticus probably stimulates TLR5 activity via the bacterial flagellum, through an MyD88-dependent pathway; the resulting production of IL-1ß and IL-8 through the NFκB pathway induces pro-inflammatory and/or chemotactic effects. Alternatively, serum amyloid A may stimulate neutrophils that induce the secretion of MMP9 from infected tissues, resulting in the cleavage and activation of IL-8. IL-8, in turn, would enhance neutrophil chemotaxis. Infection also induced expression of genes encoding C3, C6, C7, C8, and C9, which induce the complement system and form the membrane attack complex to lyse the bacteria membrane. The qPCR results indicated that TLR5 is significantly increased between 10 and 16 h, IL-1ß between 8 and 16 h, IL-8 between 8 and 12 h, and C6 between 4 and 16 h, as compared to levels in the control. One antimicrobial peptide, hepcidin, was also strongly expressed between 4 and 10 h in infected fish. The results indicate that V. alginolyticus infection probably induces an immune response via TLR5-mediated regulation of down-stream cytokine gene expression. A second possibility is that the complement system and hepcidin may be involved in the immune response. These results may be applied by examining the immune effects of feeding E. lanceolatus larvae on a recombinant protein mixture based on the up-regulated genes.