Multi-omics Mendelian randomization integrating GWAS, eQTL and pQTL data revealed GSTM4 as a potential drug target for migraine.

INTRODUCTION: Migraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine. METHOD: We used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications. RESULT: The MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93-0.96); p = 2.70e - 10; Replication cohort: OR(95%CI) = 0.93(0.91-0.94); p = 4.21e - 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87-0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85-0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83-0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target. CONCLUSION: This study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma.

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition.

Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. METHODS: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. RESULTS: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. CONCLUSIONS: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses.

Hyperparathyroidism (HPT) manifests as a complex condition with a substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying disease progression remain lacking. Identifying putative genetic drivers and exploring novel drug targets that can impede HPT progression remain critical unmet needs. A Mendelian randomization (MR) analysis was performed to uncover putative therapeutic targets implicated in hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving as genetic instrumental variables were obtained from the eQTLGen Consortium and Genotype-Tissue Expression (GTEx) portal. Hyperparathyroidism summary statistics for single nucleotide polymorphism (SNP) associations were sourced from the FinnGen study (5590 cases; 361,988 controls). Colocalization analysis was performed to determine the probability of shared causal variants underlying SNP-hyperparathyroidism and SNP-eQTL links. Five drug targets (CMKLR1, FSTL1, IGSF11, PIK3C3 and SLC40A1) showed significant causation with hyperparathyroidism in both eQTLGen and GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and solute carrier family 40 member 1 (SLC40A1) showed strong evidence of colocalization with HPT. Multivariable MR and Phenome-Wide Association Study analyses indicated these two targets were not associated with other traits. Additionally, drug prediction analysis implies the potential of these two targets for future clinical applications. This study identifies PIK3C3 and SLC40A1 as potential genetically proxied druggable genes and promising therapeutic targets for hyperparathyroidism. Targeting PIK3C3 and SLC40A1 may offer effective novel pharmacotherapies for impeding hyperparathyroidism progression and reducing disease risk. These findings provide preliminary genetic insight into underlying drivers amenable to therapeutic manipulation, though further investigation is imperative to validate translational potential from preclinical models through clinical applications.

Minimally Invasive Surgical Therapies for Ureteral Polyps: A Systematic Review.

Background: Ureteral polyps are rare benign ureteral tumor. No guideline recommends that open or minimally invasive surgery is best for treating ureteral polyps. This article aims to provide a comprehensive review of the minimally invasive techniques currently available for treating ureteral polyps. Materials and Methods: We performed a comprehensive search of articles published in PubMed, using the keywords "ureteral" and "polyp," or "polyps." Results: A total of 275 studies were obtained from the literature search but 96 articles were excluded. Conclusions: Several minimally invasive approaches were developed with the advancement of medical technology, including endoscopic, laparoscopic, and robotic approaches; however, the best surgical technique was yet to be decided. Due to the advantages and disadvantages of these approaches, the best surgical approach should be tailored to each patient's needs and the surgeon's preferences and experience.

Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.

Prostate cancer (PCa) patients with lymph node involvement (LNI) constitute a single-risk group with varied prognoses. Existing studies on this group have focused solely on those who underwent prostatectomy (RP), using statistical models to predict prognosis. This study aimed to develop an easily accessible individual survival prediction tool based on multiple machine learning (ML) algorithms to predict survival probability for PCa patients with LNI. A total of 3280 PCa patients with LNI were identified from the Surveillance, Epidemiology, and End Results (SEER) database, covering the years 2000-2019. The primary endpoint was overall survival (OS). Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. Discrimination was evaluated using the time-dependent areas under the receiver operating characteristic curve (time-dependent AUC) and the concordance index (c-index). Calibration was assessed using the time-dependent Brier score (time-dependent BS) and the integrated Brier score (IBS). Moreover, the beeswarm summary plot in SHAP (SHapley Additive exPlanations) was used to display the contribution of variables to the results. The 3280 patients were randomly split into a training cohort (n = 2624) and a validation cohort (n = 656). Nine variables including age at diagnosis, race, marital status, clinical T stage, prostate-specific antigen (PSA) level at diagnosis, Gleason Score (GS), number of positive lymph nodes, radical prostatectomy (RP), and radiotherapy (RT) were used to develop models. The mean time-dependent AUC for GBSA, RSF, and EST was 0.782 (95% confidence interval [CI] 0.779-0.783), 0.779 (95% CI 0.776-0.780), and 0.781 (95% CI 0.778-0.782), respectively, which were higher than the Cox regression model of 0.770 (95% CI 0.769-0.773). Additionally, all models demonstrated almost similar calibration, with low IBS. A web-based prediction tool was developed using the best-performing GBSA, which is accessible at https://pengzihexjtu-pca-n1.streamlit.app/ . ML algorithms showed better performance compared with Cox regression and we developed a web-based tool, which may help to guide patient treatment and follow-up.

Transmembrane nuclease NUMEN/ENDOD1 regulates DNA repair pathway choice at the nuclear periphery.

Proper repair of DNA damage lesions is essential to maintaining genome integrity and preventing the development of human diseases, including cancer. Increasing evidence suggests the importance of the nuclear envelope in the spatial regulation of DNA repair, although the mechanisms of such regulatory processes remain poorly defined. Through a genome-wide synthetic viability screen for PARP-inhibitor resistance using an inducible CRISPR-Cas9 platform and BRCA1-deficient breast cancer cells, we identified a transmembrane nuclease (renamed NUMEN) that could facilitate compartmentalized and non-homologous end joining-dependent repair of double-stranded DNA breaks at the nuclear periphery. Collectively, our data demonstrate that NUMEN generates short 5' overhangs through its endonuclease and 3'→5' exonuclease activities, promotes the repair of DNA lesions-including heterochromatic lamina-associated domain breaks as well as deprotected telomeres-and functions as a downstream effector of DNA-dependent protein kinase catalytic subunit. These findings underline the role of NUMEN as a key player in DNA repair pathway choice and genome-stability maintenance, and have implications for ongoing research into the development and treatment of genome instability disorders.

Training Compact CNNs for Image Classification Using Dynamic-Coded Filter Fusion.

The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is first given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.

Prioritized Subnet Sampling for Resource-Adaptive Supernet Training.

A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource-adaptive supernet, termed PSS-Net. We maintain multiple subnet pools, each of which stores the information of substantial subnets with similar resource consumption. Considering a resource constraint, subnets conditioned on this resource constraint are sampled from a pre-defined subnet structure space and high-quality ones will be inserted into the corresponding subnet pool. Then, the sampling will gradually be prone to sampling subnets from the subnet pools. Moreover, the one with a better performance metric is assigned with higher priority to train our PSS-Net, if sampling is from a subnet pool. At the end of training, our PSS-Net retains the best subnet in each pool to entitle a fast switch of high-quality subnets for inference when the available resources vary. Experiments on ImageNet using MobileNet-V1/V2 and ResNet-50 show that our PSS-Net can well outperform state-of-the-art resource-adaptive supernets. Our project is publicly available at https://github.com/chenbong/PSS-Net.

Distinct dosage compensations of ploidy-sensitive and -insensitive X chromosome genes during development and in diseases.

The active X chromosome in mammals is upregulated to balance its dosage to autosomes during evolution. However, it is elusive why the known dosage compensation machinery showed uneven and small influence on X genes. Here, based on >20,000 transcriptomes, we identified two X gene groups (ploidy-sensitive [PSX] and ploidy-insensitive [PIX]), showing distinct but evolutionarily conserved dosage compensations (termed XAR). We demonstrated that XAR-PIX was downregulated whereas XAR-PSX upregulated at both RNA and protein levels across cancer types, in contrast with their trends during stem cell differentiation. XAR-PIX, but not XAR-PSX, was lower and correlated with autoantibodies and inflammation in patients of lupus, suggesting that insufficient dosage of PIX genes contribute to lupus pathogenesis. We further identified and experimentally validated two XAR regulators, TP53 and ATRX. Collectively, we provided insights into X dosage compensation in mammals and demonstrated different regulation of PSX and PIX and their pathophysiological roles in human diseases.

1xN Pattern for Pruning Convolutional Neural Networks.

Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as well as achieve significant speedups on general CPUs. In this paper, we propose a novel 1×N pruning pattern to break this limitation. In particular, consecutive N output kernels with the same input channel index are grouped into one block, which serves as a basic pruning granularity of our pruning pattern. Our 1×N pattern prunes these blocks considered unimportant. We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations. Moreover, the output computation after our 1×N pruning can be realized via a parallelized block-wise vectorized operation, leading to significant speedups on general CPUs. The efficacy of our pruning pattern is proved with experiments on ILSVRC-2012. For example, given the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our project is made available at https://github.com/lmbxmu/1xN.

A simple method for restoring the femoral head center in hip arthroplasty: a 3-dimensional analysis in the Chinese population.

BACKGROUND: Various authors have successfully demonstrated that the distance from the greater trochanter to the femoral head center (GTFHC) and the distance from the lesser trochanter to the femoral head center (LTFHC) can be used as parameters to determine the recovery of the femoral head center (FHC) during hip arthroplasty. It is necessary to undertake an anatomical study concerning the correlations between the greater trochanter (GT), the lesser trochanter (LT), and the FHC using data obtained from the 3D-CT reconstruction method. METHODS: The study comprised 293 patients (151 males and 142 females), with an average age of 65.06 years. The femoral head diameter(FHD), the linear distance from FHC to GT (GTFHC), and the linear distance from FHC to LT(LTFHC) were all measured and recorded data. The correlation between FHD with LTFHC and GTFHC was assessed using Pearson correlation coefficients, and the ratio of LTFHC and GTFHC to FHD was calculated from this ratio. All measured parameters were compared between the left and right sides and the sexes of the participants. RESULTS: The average ratios of GTFHC/FHD and LTFHC/FHD were 0.99 and 0.95, respectively .96% of the LTFHC had absolute lateral differences of < 4 mm . 92% of the GTFHC had absolute lateral differences of < 4 mm. CONCLUSION: LTFHC and GTFHC are reliable reference parameters for preoperative planning and reconstruction of FHC of hip arthroplasty. The ratio displayed in this research may yield insight into a practical and straightforward method for orthopedic surgeons to perform hip arthroplasty in patients with femoral neck fractures. Ratios from studies based on the same race may be desirable for future work.

Identification and Analysis of Small Molecule Inhibitors of CRISPR-Cas9 in Human Cells.

Genome editing tools based on CRISPR-Cas systems can repair genetic mutations in situ; however, off-target effects and DNA damage lesions that result from genome editing remain major roadblocks to its full clinical implementation. Protein and chemical inhibitors of CRISPR-Cas systems may reduce off-target effects and DNA damage. Here we describe the identification of several lead chemical inhibitors that could specifically inhibit the activity of Streptococcus pyogenes Cas9 (SpCas9). In addition, we obtained derivatives of lead inhibitors that could penetrate the cell membrane and inhibit SpCas9 in cellulo. Two of these compounds, SP2 and SP24, were able to improve the specificity of SpCas9 in cellulo at low-micromolar concentration. Furthermore, microscale thermophoresis (MST) assays showed that SP24 might inhibit SpCas9 activity by interacting with both the SpCas9 protein and the SpCas9-gRNA ribonucleoprotein complex. Taken together, SP24 is a novel chemical inhibitor of SpCas9 which has the potential to enhance therapies that utilize SpCas9.

Green synthesis of submicron copper powder with narrow particle size distribution via a simple methanol thermal reduction.

In this work, submicron copper powder with narrow particle size distribution was synthesized via a simple methanol thermal reduction method without using any surfactants. Smaller copper powder with narrower particle size distribution could be realized by increasing the reaction temperature. Submicron copper powder with an average particle size of 206.6 nm and a particle size distribution of 100-300 nm could be obtained when the reaction temperature was 200 °C. Methyl formate was the only organic product found in the reaction. No organic products could be realized when the reaction temperature increased to 180 °C and above, which was environmentally friendly and was conducive to the subsequent copper powder extraction and washing. The synthesis of submicron copper powder via methanol thermal reduction was found to be a top-down process, which was beneficial for ultrafine copper powder production via wet chemical approaches.

Bioinformatics analysis of differentially expressed proteins in alcoholic fatty liver disease treated with recombinant human cytoglobin.

Cytoglobin (Cygb) is a globin molecule that is ubiquitously expressed in all tissues and has a protective role under oxidative stress. It has also been demonstrated to be effective in the treatment of alcoholic fatty liver disease (AFLD). In order to study the molecular mechanisms underlying its beneficial effects for the treatment of alcoholic liver, two­dimensional electrophoresis and mass spectrometric analysis were performed on serum and liver tissues from an in vivo rat model of AFLD. A total of 26 differentially expressed proteins were identified in the serum and 20 differentially expressed proteins were identified in liver specimens. Using online bioinformatics tools, it was indicated that these differentially expressed proteins were primarily associated with pathways including binding and uptake of ligands by scavenger receptors, response to corticosteroid, plasma lipoprotein remodeling, regulation of complement cascade, hydrogen peroxide catabolic process, as well as response to nutrient and monosaccharide. The present results suggested that recombinant human Cygb exerts its role in the treatment of AFLD primarily through affecting nutrient metabolism, monocarboxylic acid biosynthesis, regulation of glutathione expression, plasma lipoprotein remodeling and removal of metabolic waste from the blood.

Effective Decolorization of Rhodamine B by a Ti Foam-Based Photocatalytic Membrane Reactor.

In this study, a Ti foam-based photocatalytic membrane reactor (PMR) was constructed for rhodamine B (RhB) wastewater decolorization. Ti foam was chosen as a membrane and visible-light-driven C3N4 was chosen as a photocatalyst. The results showed that the decolorization efficiency could be up to 100% with a flow rate of 6.93 mL/min when the PMR was applied in the treatment of a 30 mg/L RhB solution and the concentration of C3N4 was 1 g/L. pH played an important role in the decolorization performance of the PMR. Alkalinity was not conducive to the decolorization of RhB in the PMR, which was mainly due to the property that RhB was difficult to dissolve in an alkaline solution. Membrane fouling was mainly caused by a C3N4 photocatalyst, while the effect of RhB on membrane fouling was negligible. Membrane fouling could be retarded when the PMR was applied for RhB treatment under visible-light irradiation, which was because of photoinduced hydrophilicity of C3N4. The membrane flux could be restored to its initial values after simple ultrasonic cleaning and backwashing. This was due to the large difference between the pore size of Ti foam and the particle size of C3N4 and the super hydrophilicity of Ti foam and C3N4.

Live cell imaging and proteomic profiling of endogenous NEAT1 lncRNA by CRISPR/Cas9-mediated knock-in.

In mammalian cells, long noncoding RNAs (lncRNAs) form complexes with proteins to execute various biological functions such as gene transcription, RNA processing and other signaling activities. However, methods to track endogenous lncRNA dynamics in live cells and screen for lncRNA interacting proteins are limited. Here, we report the development of CERTIS (CRISPR-mediated Endogenous lncRNA Tracking and Immunoprecipitation System) to visualize and isolate endogenous lncRNA, by precisely inserting a 24-repeat MS2 tag into the distal end of lncRNA locus through the CRISPR/Cas9 technology. In this study, we show that CERTIS effectively labeled the paraspeckle lncRNA NEAT1 without disturbing its physiological properties and could monitor the endogenous expression variation of NEAT1. In addition, CERTIS displayed superior performance on both short- and long-term tracking of NEAT1 dynamics in live cells. We found that NEAT1 and paraspeckles were sensitive to topoisomerase I specific inhibitors. Moreover, RNA Immunoprecipitation (RIP) of the MS2-tagged NEAT1 lncRNA successfully revealed several new protein components of paraspeckle. Our results support CERTIS as a tool suitable to track both spatial and temporal lncRNA regulation in live cells as well as study the lncRNA-protein interactomes.