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Patients with device detected atrial high-rate episodes (AHRE) have an increased risk of MACE. The R2CHA2DS2-VASc, CHADS2, R2CHADS2 and CHA2DS2-VASc score have been investigated for predicting major adverse cardiovascular events (MACE) in different groups of patients. We aimed to evaluate the R2CHA2DS2-VASc score in combination with AHRE ≥ 6 min for predicting MACE in patients with dual-chamber PPM but no prior atrial fibrillation (AF). We retrospectively enrolled 376 consecutive patients undergoing dual-chamber PPM implantation and no prior AF. The primary endpoint was subsequent MACE. For all patients in the cohort, CHADS2, R2CHADS2, CHA2DS2-VASc, R2CHA2DS2-VASc scores and AHRE ≥ or 175 bpm (Medtronic) or > 200 bpm (Biotronik) lasting ≥ 30 s. Multivariate Cox regression analysis with time-dependent covariates was used to determine the independent predictors of MACE. ROC-AUC analysis was performed for CHADS2, R2CHADS2, CHA2DS2-VASc, and R2CHA2DS2-VASc scores and then adding AHRE ≥ 6 min to the four scores. The median age was 77 years, and 107 patients (28.5%) developed AHRE ≥ 6 min. After a median follow-up of 32 months, 46 (12.2%) MACE occurred. Multivariate Cox regression analysis showed that R2CHA2DS2-VASc score (HR, 1.485; 95% CI, 1.212–1.818; p p = 0.014) were independent predictors for MACE. The optimal R2CHA2DS2-VASc score cutoff value was 4.5 (set at ≥ 5), with the highest Youden index (AUC, 0.770; 95% CI, 0.709–0.831; p Z-statistic p

Research on biomarker-driven therapy and immune check-point blockade in non-small cell lung cancer (NSCLC) is rapidly evolving. The width and depth of clinical trials have also dramatically improved in an unprecedented speed. The personalized treatment paradigm evolved every year. In this review, we summarize the promising agents that have shifted the treatment paradigm for NSCLC patients across all stages, including targeted therapy and immunotherapy using checkpoint inhibitors. Based on recent evidence, we propose treatment algorithms for NSCLC and propose several unsolved clinical issues, which are being explored in ongoing clinical trials. The results of these trials are likely to impact future clinical practice.

Pancreatic β-cell compensation is a major mechanism in delaying T2DM progression. Here we report the abnormal high expression of circGlis3 in islets of male mice with obesity and serum of people with obesity. Increasing circGlis3 is regulated by Quaking (QKI)-mediated splicing circularization. circGlis3 overexpression enhances insulin secretion and inhibits obesity-induced apoptosis in vitro and in vivo. Mechanistically, circGlis3 promotes insulin secretion by up-regulating NeuroD1 and Creb1 via sponging miR-124-3p and decreases apoptosis via interacting with the pro-apoptotic factor SCOTIN. The RNA binding protein FUS recruits circGlis3 and collectively assemble abnormal stable cytoplasmic stress granules (SG) in response to cellular stress. These findings highlight a physiological role for circRNAs in β-cell compensation and indicate that modulation of circGlis3 expression may represent a potential strategy to prevent β-cell dysfunction and apoptosis after obesity.

The main characteristic of skin aging is the change in the composition of the dermis, mainly resulting from fibroblast senescence. Mesenchymal stem cells derived from fetal dermis are defined as fetal dermal mesenchymal stem cells; they reportedly exert wound healing effects on the skin and regulate keloid fibroblast proliferation. D-Galactose is widely used in animal aging models. In this study, we confirmed that D-galactose inhibits adult dermal fibroblast proliferation, and the inhibitory effect gradually increased with increasing concentration. Finally, we chose a concentration of 40 g/L D-galactose to induce adult dermal fibroblast senescence. D-Galactose increased the intensity of senescence-associated β-galactosidase staining and the levels of reactive oxygen species in adult dermal fibroblasts. Furthermore, D-galactose increased the mRNA expression of p16, p21, and p53. The fetal dermal mesenchymal stem cell-conditioned medium improved the above-mentioned effects. Overall, fetal dermal mesenchymal stem cells exerted anti-aging effects against adult dermal fibroblasts induced by D-galactose via paracrine functions.

Highly sensitive and rapid detection of chloramphenicol antibiotics (CAPs) in aquatic products via lateral flow immunoassay (LFA) strips remains challenging. This study developed a surface-enhanced Raman scattering (SERS)–based LFA strip for ultrasensitive analysis of CAPs by using noble metal nanoparticles (NPs). A kind of core–shell Au@Ag NPs were employed to prepare SERS tags considering the stability and repeatability. In order to obtain higher stability and repeatability, we designed antibody-modified gold@silver core–shell nanoparticles (Au@Ag NPs) as SERS nanotags for quantitative Raman detection of the target in the LFA strip. Results showed that SERS-based LFA strip allowed quantified analysis of chloramphenicol (CAP), thiamphenicol (TAP), and florfenicol (FFC) with the limits of detection of 0.36 ng/mL, 0.20 ng/mL, and 0.78 ng/mL, respectively. Furthermore, SERS-based LFA strip exhibited favorable sensitivity (IC50 values of CAP, TAP, and FFC were 12.94 ng/mL, 8.76 ng/mL, and 12.22 ng/mL, respectively). This SERS-based LFA strip was finally applied in the detection of CAP in chub and the recovery was 91.5 to 106.4%. This SERS-based LFA strip could be used as an effective tool for detecting CAPs in aquatic products.

Purpose Gamma knife radiosurgery (GK) is a commonly used approach for the treatment of intracranial lesions. Its radiation response is typically not immediate, but delayed. In this study, we analyzed cases from a prospectively collected database to assess the influence of COVID-19 pandemic on the decision making in patients treated by gamma knife radiosurgery. Methods From January 2019 to August 2021, 540 cases of intracranial lesions were treated by GK with 207 cases before COVID-19 pandemic as a control. During the COVID-19 pandemic, 333 cases were similarly treated on patients with or without the COVID-19 vaccination. All the GK treated parameters as well as time profile in the decision making were analyzed. The parameters included age, sex, characteristic of lesion, targeted volume, peripheral radiation dose, neurological status, Karnofsky Performance Status (KPS), time interval from MRI diagnosis to consultation, time interval from the approval to treatment, frequency of outpatient department (OPD) visit, and frequency of imaging follow-up. Results Longer time intervals from diagnosis to GK consultation and treatment were found in the pandemic group (36.8 ± 25.5/54.5 ± 27.6 days) compared with the pre-COVID control (17.1 ± 22.4/45.0 ± 28.0 days) or vaccination group (12.2 ± 7.1/29.6 ± 10.9 days) (p p p p Conclusions The decision making in patients requiring gamma knife treatment was most likely influenced by the status of the COVID-19 pandemic, while vaccination appeared to attenuate their hesitant behaviors. Patients with pre-treatment neurological deficits and high co-morbidity undergoing the gamma knife treatment were less affected by the COVID-19 pandemic.

Circular RNAs (circRNAs) are RNAs with a special closed loop structure, which play important roles in tumors and other diseases. Due to the time consumption of biological experiments, computational methods for predicting associations between circRNAs and diseases become a better choice. Taking the limited number of verified circRNA-disease associations into account, we propose a method named CDWBMS, which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease associations. CDWBMS adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and diseases. In terms of leave-one-out-cross-validation (LOOCV), 10-fold cross-validation and 5-fold cross-validation, CDWBMS yields the area under the receiver operating characteristic curve (AUC) values of 0.921 6, 0.917 2 and 0.900 5, respectively. Furthermore, case studies show that CDWBMS can predict unknow circRNA-disease associations. In conclusion, CDWBMS is an effective method for exploring disease-related circRNAs.

Hashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836–0.939) and 0.895 (0.862–0.927). HTNet exceeds radiologists’ performance on accuracy (83.2% versus 79.8%; binomial test, p p p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.

Sulfonyl fluorides are key components in the fields of chemical biology, materials science and drug discovery. In this line, the highly active SO2F radical has been employed for the construction of sulfonyl fluorides, but the utilization of gaseous ClSO2F as radical precursor is limited due to the tedious and hazardous preparation. Meanwhile, the synthesis of sulfonyl fluorides from inert SO2F2 gas through a fluorosulfonyl radical (·SO2F) process has met with inevitable difficulties due to the high homolytic bond dissociation energy of the S(VI)-F bond. Here we report a radical fluorosulfonylation strategy for the stereoselective synthesis of alkenyl sulfonyl fluorides and functional alkyl sulfonyl fluorides with an air-stable crystalline benzimidazolium fluorosulfonate cationic salt reagent. This bench-stable redox-active reagent offers a useful and operational protocol for the radical fluorosulfonylation of unsaturated hydrocarbons with good yield and high stereoselectivity, which can be further transformed into valuable functional SO2F moieties.

In this study, the interannual variations of ichthyoplankton assemblages in the Taiwan Strait (TS) during the winters of 2007–2013 were determined. The cold China Coastal Current (CCC) and Mixed China Coastal Water (MCCW) intruded into the TS and impinged with the warm Kuroshio Branch Current (KBC) with annual variations. Consequently, the ichthyoplankton community in the TS was mainly structured into two assemblages characterized by differing environmental conditions. The composition of the warm KBC assemblage was relatively stable and was characterized by Diaphus B and Bregmaceros spp. By contrast, the cold MCCW assemblage demonstrated considerable variations over the years, with demersal Gobiidae and Scorpaenidae families considered the most representative. In addition, Benthosema pterotum and Trichiurus spp. were common in both KBC and MCCW assemblages. The distribution of the KBC assemblage demonstrated sharp boundaries in the frontal zones, whereas changes in the assemblage structure between the frontal zones were gradual for the MCCW assemblage, particularly when demersal taxa were dominant. Sea surface temperature and salinity were most strongly associated with variability in the assemblage structure during the study period. Thus, this paper provides a better understanding of long-term larval fish dynamics during winter in the TS.

It is crucial to develop high-performance and cost-effective bifunctional electrocatalysts for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) toward overall water splitting. Herein, a unique heterostructure of reduced graphene oxide (rGO) and CoNiO2 nanosheets directly grown on nickel foam (NF) were successfully fabricated and applied as a kind of highly efficient bifunctional electrocatalyst. The optimum CoNiO2@rGO/NF electrode exhibits excellent electrocatalytic OER performance with an overpotential of only 272 mV to drive a current density of 100 mA·cm−2, and HER performance with an overpotential of 126 mV to achieve a current density of 10 mA·cm−2. Meanwhile, the electrodes also display outstanding long-term stability for OER and HER with negligible activity and morphology degradation after at least 40 h testing. Furthermore, when employed as both cathode and anode for overall water splitting, CoNiO2@rGO/NF electrode only requires 1.56 V at 10 mA·cm−2 and operates stably for over 40 h, which is among the best performing Co-based and Ni-based non-precious metal electrocatalysts. Detailed characterizations reveal that the extraordinary electrocatalytic performance should be attributed to the synergistic effect of the unique heterostructure of CoNiO2 nanosheets and rGO for increasing the electrode conductivity and integrity, ultrasmall primary particle size for providing larger electrode/electrolyte contact area and abundant active sites, and three-dimensional (3D) conductive networks for facilitating the electrochemical reaction.

The maintenance of information in visual working memory has been shown to bias the concurrent processing in favor of matching visual input. The present study aimed to examine whether this bias can act at an early stage of processing to enhance target feature perception in single-item displays. Participants were sequentially presented with two distinct colored stimuli as memory samples and a retro-cue indicating which of the two samples should be maintained for subsequent memory test. During the retention interval, they had to discriminate the gap orientation of a Landolt target presented through a single visual stimulus that could match one or neither of the two samples. Across two experiments, we consistently found that discrimination performance was more accurate when the Landolt target was situated within a stimulus that matched the sample being retained in visual working memory, as compared with when the target was not. This effect cannot be attributed to the mechanism of passive priming, because we failed to observe priming effects when the stimulus containing the target matched the sample that was retro-cued to be irrelevant to the working memory task, as compared to when the stimulus matched neither sample. Given the fact that target stimuli were presented in single-item displays wherein external noise was precluded, the present findings demonstrate that the working memory bias of visual attention operating in the absence of stimulus competition facilitates early perceptual processing at the attended location via signal enhancement.

The V-shaped ketone ligand, benzophenone-2,4′-dicarboxylic acid (H2bpdc), was used as a long ditopic asymmetric linker to bridge mixed cobalt(II) clusters into a three-dimensional pcu network, namely [Co7(μ3–OH)2(bpdc)6(2,2′-bpy)4]·2H2O (1) (2,2′-bpy=2,2′-bipyridine), which was synthesized by methods of hydrothermal reaction and the crystal structure was determined. The linear trinuclear cluster [Co3(COO)6] and planar tetranuclear cluster [Co4(μ3–OH)2(COO)4] simultaneously appeared in the same compound for the first time. IR, elemental analysis and XRPD confirmed the phase purity of the bulk materials. Magnetic properties of 1 in the 300–2 K have been studied, which reveal the occurrence of antiferromagnetic interactions between Co(II) ions.

We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565–6.628; p

Cloud computing is recently getting increasingly popular for supporting scientific applications and complex business processes. Clouds are highly potent for executing workflow-based tasks due to the fact that they provide elastic resource provisioning styles through which computational-intensive workflows can obtain requested resources according to their elastic demand and establish execution environment over virtual machines (VMs). However, it remains a challenge to guarantee cost-effectiveness and quality of service of workflow deployed upon clouds due to the fact that real-world cloud infrastructures are usually with fluctuating and time-varying performance. Existing researches mainly consider that cloud infrastructures are with fixed, random, or bounded quality of service (QoS). In this work, however, we consider that scientific computing processes to be supported by decentralized cloud infrastructures with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-variation-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we perform extensive experimental case analysis over real-world third-party commercial clouds and show that our method clearly beats existing approaches.

The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions. Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis. Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66–0.84) to 0.90 (95% CI 0.81–0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66–0.84) to 0.88 (95% CI 0.80–0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88–0.98) for differentiating tumor recurrence from non-recurrence. Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Napsin A is widely used in the diagnosis of lung adenocarcinoma and has also been reported to be positive in cases of thyroid carcinomas. We investigated napsin A levels through immunohistochemistry on whole sections of 210 primary thyroid tumors of various subtypes and another 41 metastatic thyroid carcinomas, and compared these with 125 primary and 25 metastatic lung adenocarcinomas. The results showed that napsin A was expressed in 23.8% thyroid tumors and 30.3% papillary thyroid carcinomas. Most cases showed a focal and weak to moderate expression. In comparison, 80.8% primary lung adenocarcinomas expressed napsin A, with mostly diffused and strong expression. For metastatic carcinomas of thyroid and lung origin, napsin A was detected in 39.0% of thyroid carcinomas in contrast to 88.0% in cases of lung adenocarcinomas. Comparisons of additional markers, TTF-1, CK7, thyroglobulin, and Pax-8 in metastatic carcinomas showed the overlapping expression of immunomarkers of TTF-1 and CK7. Thyroglobulin and Pax-8 were useful for distinguishing between metastatic carcinomas; however, Pax-8 may be a superior marker due to its higher sensitivity. The clinicopathological analysis of papillary thyroid carcinomas showed that the expression of napsin A was positively correlated with lymph node metastasis (p = 0.030). Here, we focused on the expression of napsin A in thyroid tumors and compared it with that in lung adenocarcinomas. The expression of napsin A is common in thyroid tumors and the combined expression of napsin A and TTF-1 in a metastatic thyroid carcinoma is a cause for concern due to chances of misdiagnosis as lung adenocarcinoma.

Retail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and most importantly e-commerce. Supply chain management, as a core part of retail business, has also gone through significant changes with new business scenarios and more advanced technologies in both algorithm design and computation power. In this review, we focus on several core components of supply chain management, i.e. vendor management, demand forecasting, inventory management and order fulfillment. We will discuss the key innovations from both academia and industry and highlight the current trend and future challenges.

The development of low-cost, highly active, and robust electrocatalysts for catalyzing the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) holds a pivotal role in electrocatalytic water splitting. Herein, a two-step method was presented to synthesize CoS2/CoP heterostructures grown on carbon cloth (CoS2/CoP/CC). The precursor Co(OH)F/CC synthesized through the hydrothermal process can be successfully converted into CoS2/CoP/CC catalyst via gas-phase phosphorization and sulfurization. The as-obtained CoS2/CoP/CC exhibited promising performances for HER and OER with low overpotentials of 90 and 334 mV at a current density of 10 mA cm−2 in 1.0 M KOH, respectively. The improved electrocatalytic performance of the CoS2/CoP/CC can be attributed to the strong electron interactions and synergistic efforts between CoS2 and CoP, abundant active sites of the heterostructures, and enhanced smaller charge-transfer resistance.

Correct segmentation of stroke lesions from magnetic resonance imaging (MRI) is crucial for neurologists and patients. However, manual segmentation relies on expert experience and is time-consuming. The complicated stroke evolution phase and the limited samples pose challenges for automatic segmentation. In this study, we propose a novel deep convolutional neural network (Res-CNN) to automatically segment acute ischemic stroke lesions from multi-modality MRIs. Our network draws on U-shape structure, and we embed residual unit into network. In Res-CNN, we use residual unit to alleviate the degradation problem and use multi-modality to exploit the complementary information in MRIs. Before training the model, we use data fusion and data augmentation methods to increase the number of training images. Seven neural networks are extensively evaluated on two acute ischemic stroke datasets. Res-CNN shows good performance compared with other six networks both in single modality and multi-modality. Furthermore, compared with the gold standard segmentation manually labeled by two neurologists on a local test dataset, our network achieves the best results in seven neural networks. The average Dice coefficient and Hausdorff distance of our method are 74.20% and 2.33 mm, respectively. Our proposed network may provide a useful tool for segmentation lesion of acute ischemic stroke.

Multiple animal and human studies have shown that administration of GLP-1RA can enhance β-cell recovery, reduce insulin dosage, reduce HbA1c content in the blood, reduce the risk of hypoglycemia and reduce inflammation. In the NOD mouse model, peptide VP treatment can prevent and treat type 1 diabetes through immunomodulation. Therefore, we designed a new dual-functional PGLP-1-VP, which is expected to combine the anti-inflammatory effect of PGLP-1 and the immunomodulatory effect of VP peptide. In streptozotocin-induced hyperglycemic mice model, we demonstrated that PGLP-1-VP can act as a GLP-1R agonist to improve hyperglycemia and increase insulin sensitivity. In the NOD mouse model, PGLP-1-VP treatment reduced morbidity, mortality, and pancreatic inflammation, and showed superior effect to PGLP-1 or VP treatment alone, confirming that PGLP-1-VP may act as a dual-function peptide. PGLP-1-VP provided immunomodulatory effect through increasing Th2 cell percentage and balancing the ratio of Th2/Th1 in spleen and PLN, similar to P277 and VP. Additionally, PGLP-1-VP and PGLP-1 act the anti-inflammation by increasing Treg cells and TGF-β1 content like DPP-IV inhibitor. Taken together, our data shows that the dual-functional PGLP-1-VP reduces morbidity and mortality in the NOD model, suggesting a potential role in preventing and treating type 1 diabetes.

This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

Psoriasis is a chronic inflammatory disease of the skin with highly complex pathogenesis. In this study, we identified lncRNA SPRR2C (small proline-rich protein 2C) as a hub gene with a critical effect on the pathogenesis of psoriasis and response to treatment using both weighted gene coexpression network analysis (WGCNA) and differential expression analysis. SPRR2C expression was significantly upregulated in both psoriatic lesion samples and HaCaT cell lines in response to IL-22 treatment. After SPRR2C knockdown, IL-22-induced suppression of HaCaT proliferation, changes in the KRT5/14/1/10 protein levels, and suppression of the IL-1β, IL-6, and TNF-α mRNA levels were dramatically reversed. In the coexpression network with SPRR2C based on GSE114286, miR-330 was significantly negatively correlated with SPRR2C, while STAT1 and S100A7 were positively correlated with SPRR2C. By binding to miR-330, SPRR2C competed with STAT1 and S100A7 to counteract miR-330-mediated suppression of STAT1 and S100A7. MiR-330 overexpression also reversed the IL-22-induced changes in HaCaT cell lines; in response to IL-22 treatment, miR-330 inhibition significantly attenuated the effects of SPRR2C knockdown. STAT1 and S100A7 expression was significantly upregulated in psoriatic lesion samples. The expression of miR-330 had a negative correlation with the expression of SPRR2C, while the expression of SPRR2C had a positive correlation with the expression of STAT1 and S100A7. Thus, SPRR2C modulates the IL-22-stimulated HaCaT cell phenotype through the miR-330/STAT1/S100A7 axis. WGCNA might uncover additional biological pathways that are crucial in the pathogenesis and response to the treatment of psoriasis.

Background The homologous recombination (HR) pathway is involved in DNA damage response (DDR), which is crucial to cancer cell survival after treatment with DNA damage agents. O6-methylguanine DNA methyltransferase (MGMT) is associated with cisplatin (CDDP) resistance in cancer cells; however, the underlying mechanisms remain unclear. Here, we explored the interactions between MGMT and the HR pathway in CDDP-activated DDR and their clinical implications in nasopharyngeal carcinoma (NPC). Methods Human NPC cells were assessed using loss-of-function approaches in vitro. The expression correlations between MGMT and major proteins of the HR pathway were analyzed through Western blotting, quantitative real-time PCR, and bioinformatic analysis by using a public database. The physical interactions between MGMT and HR proteins were studied using co-immunoprecipitation and immunofluorescence analyses. Cell comet tails and γ-H2AX expression levels were examined to evaluate double-strand break (DSB) formation. Established immunofluorescence and reporter analyses were conducted to measure HR activity. Xenograft and cell viability studies were used to assess the therapeutic potential of MGMT inhibition in combination with CDDP and poly(ADP-ribose) polymerase (PARP) inhibitor, respectively. Results Among major proteins of the HR pathway, MGMT suppression inhibited CDDP-induced RAD51 expression. Bioinformatic analyses showed a positive correlation between MGMT and RAD51 expression in patients with NPC. Moreover, MGMT physically interacted with BRCA1 and regulated CDDP-induced BRCA1 phosphorylation (ser 988). In functional assays, MGMT inhibition increased CDDP-induced DSB formation through attenuation of HR activity. NPC xenograft studies demonstrated that MGMT inhibition combined with CDDP treatment reduced tumor size and downregulated RAD51 expression and BRCA1 phosphorylation. Furthermore, MGMT suppression increased PARP inhibitor–induced cell death and DSB formation in NPC cells. Conclusion MGMT is crucial in the activation of the HR pathway and regulates DDR in NPC cells treated with CDDP and PARP inhibitor. Thus, MGMT is a promising therapeutic target for cancer treatments involving HR-associated DDR.

BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different‘omics’resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm calledCMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets ofMultiple tissuesandYeastfrom two different organisms (Homo SapiensandSaccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case ofMultiple Tissuesdata set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. ForYeastdata set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein–ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time. Electronic supplementary material The online version of this article (10.1007/s12539-020-00376-6) contains supplementary material, which is available to authorized users.