Determination of Acetylcholinesterase Activity Based on Ratiometric Fluorescence Signal Sensing.

Acetylcholinesterase (AChE) plays an important role in the treatment of human diseases, environmental security and global food supply. In this study, the simple fluorescent indicators and MnO2 nanosheets were developed and integrated to establish a ratiometric fluorescence sensing system for the detection of AChE activity. Two fluorescence signals could be recorded independently at the same excitation wavelength, which extended the detection range and enhanced the visibility of results. Fluorescence of F-PDA was quenched by MnO2 nanosheets on account of inner filtering effect. Meanwhile, the nonfluorescent OPD was catalytically oxidized to 2,3-diaminophenazine by MnO2 nanosheets. The acetylcholine (ATCh) was catalytically hydrolyzed by AChE to enzymatic thiocholine, which decomposed MnO2 to Mn2+, recovered the fluorescence of F-PDA and reduced the emission of ox-OPD. Utilizing the fluorescence intensity ratio F468/F558 as the signal readout, the ratiometric fluorescence method was established to detect AChE activity. Under the excitation wavelength of 410 nm, the ratio F460/F558 against the AChE concentration demonstrated two linear relationships in the range 0.05 -1.0 and 1.0-50 U·L- 1 with a limit of detection (LOD) of 0.073 U·L- 1. The method was applied to the detection of AChE activity and the analysis of the inhibitor Huperzine-A. Due to the advantages of high sensitivity and favorable selectivity, the method possesses an application prospect in the activity deteceion of AChE and the screening of inhibitors.

Activation of farnesoid X receptor enhances the efficacy of normothermic machine perfusion in ameliorating liver ischemia-reperfusion injury.

BACKGROUND: The shortage of transplant organs remains a severe global issue. Normothermic machine perfusion (NMP) has the potential to increase organ availability, yet its efficacy is hampered by the inflammatory response during machine perfusion. METHODS: Mouse liver IRI models, discarded human liver models, and porcine marginal liver transplantation models were utilized to investigate whether FXR activation could mitigate inflammation-induced liver damage. FXR expression levels before and after reperfusion were measured. Gene editing and co-immunoprecipitation techniques were employed to explore the regulatory mechanism of FXR in inflammation inhibition. RESULTS: The expression of FXR correlates with the extent of liver damage after reperfusion. Activation of FXR significantly suppressed the inflammatory response triggered by IRI, diminished the release of pro-inflammatory cytokines, and improve liver function recovery during NMP, assisting discarded human livers to reach transplant standards. Mechanistically, FXR disrupts the interaction between p65 and p300, thus inhibiting modulating the nuclear factor kappa-B (NF-κB) signaling pathway, a key instigator of inflammation. CONCLUSION: Our research across multiple species confirms that activating FXR can optimize NMP by attenuating IRI-related liver damage, thereby improving the utilization of marginal livers for transplantation.

Linc-NSC affects cell differentiation, apoptosis and proliferation in mouse neural stem cells and embryonic stem cells in vitro and in vivo.

BACKGROUND: Stem cell therapy is a promising therapeutic strategy. In a previous study, we evaluated tumorigenicity by the stereotactic transplantation of neural stem cells (NSCs) and embryonic stem cells (ESCs) from experimental mice. Twenty-eight days later, there was no evidence of tumor formation or long-term engraftment in the NSCs transplantation group. In contrast, the transplantation of ESCs caused tumor formation; this was due to their high proliferative capacity. Based on transcriptome sequencing, we found that a long intergenic non-coding RNA (named linc-NSC) with unknown structure and function was expressed at 1100-fold higher levels in NSCs than in ESCs. This finding suggested that linc-NSC is negatively correlated with stem cell pluripotency and tumor development, but positively correlated with neurogenesis. In the present study, we investigated the specific role of linc-NSC in NSCs/ESCs in tumor formation and neurogenesis. METHODS: Whole transcriptome profiling by RNA sequencing and bioinformatics was used to predict lncRNAs that are widely associated with enhanced tumorigenicity. The expression of linc-NSC was assessed by quantitative real-time PCR. We also performed a number of in vitro methods, including cell proliferation assays, differentiation assays, immunofluorescence assays, flow cytometry, along with in vivo survival and immunofluorescence assays to investigate the impacts of linc-NSC on tumor formation and neurogenesis in NSCs and ESCs. RESULTS: Following the knockdown of linc-NSC in NSCs, NSCs cultured in vitro and those transplanted into the cortex of mice showed stronger survival ability (P < 0.0001), enhanced proliferation(P < 0.001), and reduced apoptosis (P < 0.05); the opposite results were observed when linc-NSC was overexpressed in ESCs. Furthermore, the overexpression of linc-NSC in ECSs induced enhanced apoptosis (P < 0.001) and differentiation (P < 0.01), inhibited tumorigenesis (P < 0.05) in vivo, and led to a reduction in tumor weight (P < 0.0001). CONCLUSIONS: Our analyses demonstrated that linc-NSC, a promising gene-edited target, may promote the differentiation of mouse NSCs and inhibit tumorigenesis in mouse ESCs. The knockdown of linc-NSC inhibited the apoptosis in NSCs both in vitro and in vivo, and prevented tumor formation, revealing a new dimension into the effect of lncRNA on low survival NSCs and providing a prospective gene manipulation target prior to transplantation. In parallel, the overexpression of linc-NSC induced apoptosis in ESCs both in vitro and in vivo and attenuated the tumorigenicity of ESCs in vivo, but did not completely prevent tumor formation.

3D hydrogel microfibers promote the differentiation of encapsulated neural stem cells and facilitate neuron protection and axon regrowth after complete transactional spinal cord injury.

Spinal cord injury (SCI) could cause permanent impairment to motor or sensory functions. Pre-cultured neural stem cell (NSC) hydrogel scaffolds were demonstrated to be a promising approach to treat SCI with anti-inflammatory effect, axon regrowth and motor function restore. Here in this study, we performed coaxial extrusion process to fabricate a core-shell hydrogel microfiber with high NSC density in the core portion. Oxidized hyaluronic acid (OHA), carboxymethyl chitosan (CMC) and Matrigel blend was used as matrix for NSC growth and to facilitate the fabrication process. During in vitro differentiation culture, it is found that NSC microfiber could differentiate into neuron and astrocyte with higher efficiency compared with NSC cultured in petri dishes. Furthermore, during in vivo transplantation, NSC microfibers were coated with poly lactic acid (PLA) nanosheet by electrospinning for reinforcement. The coated NSC nanofibers showed higher anti-inflammatory effect and lesion cavity filling rate compared with control group. Meanwhile, more neuron- and oligodendrocyte-like cells were visualized in lesion epicenter. Finally, axon regrowth across the whole lesion site was observed, demonstrating the microfiber could guide renascent axon regrowth. Experiment results indicate that the NSC microfiber is a promising bioactive treatment for complete SCI treatment with better outcomes. .

Prediction of pathological activity in Crohn's disease based on dual-energy CT enterography.

PURPOSE: To explore the feasibility of predicting the pathological activity of Crohn's disease (CD) based on dual-energy CT enterography (DECTE). METHODS: The clinical, endoscopic, imaging and pathological data of 55 patients with CD scanned by DECTE were retrospectively analyzed; the pathological results were used as a reference standard to classify the diseased bowel segments into active and inactive phases. The normalized iodine concentration (NIC), energy-spectrum curve slope K, dual energy index (DEI), fat fraction (FF) of the arterial phases and venous phases were compared. To assess the parameters' predictive ability, receiver-operating characteristic curves were used. The Delong test was used to compare the differences between the diagnostic efficiency of each parameter. RESULTS: A total of 84 intestinal segments were included in the study, including 54 active intestinal segments and 30 inactive intestinal segments. The NIC, energy-spectrum curve slope K and DEI were significantly different between active and inactive bowel segments in the arterial and venous phases (P < 0.05), while FF were not significantly different (P > 0.05). The largest area under the curve (AUC) of NIC, energy-spectrum curve slope K and DEI were higher in arterial phase than in venous phase. For identifying the intestinal activity of CD, the maximum AUC of NIC in arterial phase was 0.908, with a sensitivity of 0.833 and a specificity of 0.800, and the DEI in arterial phase had the highest sensitivity (0.944). CONCLUSION: The NIC, energy-spectrum curve slope K and DEI can effectively distinguish the active and inactive phases of the intestinal segments of CD patients and provide good assistance for determining further treatment.

Magnesium Magic: State-of-the-Art Nanocrystalline Materials Paving the Way for Hydrogen Storage.

Hydrogen has been regarded as a promising alternative to traditional fossil fuels, presenting itself as a viable and environmentally friendly energy choice. The design and fabrication of highly efficient hydrogen storage materials is crucial to the wide utilization of hydrogen-based technologies. Magnesium-based nanocrystalline materials have received significant interest in the field of hydrogen storage due to their remarkable hydrogen storage capabilities and release efficiency. This review emphasizes on the most useful techniques including vapor deposition, sol-gel synthesis, electrochemical deposition, magnetron sputtering, and template-assisted approaches used for the fabrication of Magnesium-based nanocrystalline hydrogen storage materials (Mg-NHSMs), stressing their advantages, limitations, and recent advancements. These cutting-edge techniques demonstrate their significance in offering useful insights into the performance of Mg-NHSMs. Further, this review describes various applications of Mg-NHSMs. In addition, this review highlights the conclusion and future perspectives on the improvement of magnesium based nanocrystalline materials for efficient hydrogen storage.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics.

BACKGROUND: Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes. PURPOSE: To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging. MATERIAL AND METHODS: All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves. RESULTS: In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05). CONCLUSION: It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

Comparison of three source apportionment methods based on observed and initial HCHO in Taiyuan, China.

Identifying the sources of formaldehyde (HCHO) is key to reducing the pollution of HCHO and ozone (O3) on the ground level. Using the same datasets applied to the positive matrix factorization (PMF) model by (Hua et al., 2023), the initial concentrations of HCHO were estimated using the photochemical age and the sources of observed and initial HCHO were apportioned based on multiple linear regression (MLR) and photochemical age-based parameterization (PCAP) methods. These results suggest that the source of the initial HCHO can better reflect its contribution. The secondary formation contributed to 49.3-69.1 % of initial HCHO at four sites in Taiyuan based on MLR, which was higher (7.4-36.2 %) than the contributions of secondary formation from observed HCHO. The HCHO was mainly affected by anthropogenic secondary (10.8-34.4 %) and background sources (17.4-78.7 %) based on the PCAP method. We compared the results of the HCHO sources from the MLR, PCAP, and PMF models under photochemical loss. There was good agreement among the emission ratios of acetylene-based HCHO obtained by the different methods at the four sites. The correlation analysis of different source apportionment methods illustrated that primary emissions from the PCAP and the MLR model had the greatest correlation (0.22-0.60). Secondary formations from the PMF and MLR models showed good correlations at all four sites, with R values ranging from 0.42 to 0.83. The HCHO peak of diurnal variation simulated by MLR appeared late compared to the other methods, and the difference in daily variation of HCHO from the PMF model was significantly higher than that of PCAP and MLR. The overlapping conclusions of different source apportionment methods should be considered and used to guide efforts to improve air quality.

Massively Parallel CRISPR-Cas9 Knockout Screening in Sheep Granulosa Cells for FSH Response Genes.

Follicle-stimulating hormone (FSH) regulates ovarian follicle development through specific gene expression programs. Granulosa cells (GCs) are somatic cells surrounding the oocytes, secreting gonadotropins to regulate ovulation and promote follicular development. By analyzing the effects of different doses of FSH on the proliferation of GCs, we found that adding 10 ng/mL of FSH, as the optimal concentration, could promote the growth of GCs. Furthermore, we have successfully constructed the first CRISPR-Cas9 knockout library targeting the genes on chromosomes 2 and 3 and the X chromosomes of the sheep massively parallel coding gene, as well as an ovarian GCs knockout cell library. For the first time, we have exposed the knockout cell library to a concentration of 10 ng/mL FSH to explore the underlying mechanisms. Through this screening, we have identified 836 positive-negative screening genes that are responsive to FSH, thereby revealing the regulatory mechanisms and screening the functionality of candidate genes. Next, RNA-Seq of control (0 ng/mL), low (10 ng/mL), and high (100 ng/mL) doses of FSH revealed 1708 differentially expressed genes, and combined with 836 genes, we obtained 129 FSH dose-dependent genes with extremely significant differences. This enables us to delve deeper into investigating and identifying the mechanisms by which FSH regulates GCs. More generally, we have discovered new regulatory factors and identified reproductivity-associated major effectors. These findings provide novel research directions for further studies on sheep reproduction.

Extraction of Innate Immune Genes in Dairy Cattle and the Regulation of Their Expression in Early Embryos.

As more and more of the available genomic data have been published, several databases have been developed for deciphering early mammalian embryogenesis; however, less research has been conducted on the regulation of the expression of natural immunity genes during early embryonic development in dairy cows. To this end, we explored the regulatory mechanism of innate immunity genes at the whole-genome level. Based on comparative genomics, 1473 innate immunity genes in cattle were obtained by collecting the latest reports on human innate immunity genes and updated bovine genome data for comparison, and a preliminary database of bovine innate immunity genes was constructed. In order to determine the regulatory mechanism of innate immune genes in dairy cattle early embryos, we conducted weighted co-expression network analysis of the innate immune genes at different developmental stages of dairy cattle early embryos. The results showed that specific module-related genes were significantly enriched in the MAPK signaling pathway. Protein-protein interaction (PPI) analysis showed gene interactions in each specific module, and 10 of the highest connectivity genes were chosen as potential hub genes. Finally, combined with the results for differential expressed genes (DEGs), ATF3, IL6, CD8A, CD69, CD86, HCK, ERBB3, LCK, ITGB2, LYN, and ERBB2 were identified as the key genes of innate immunity in dairy cattle early embryos. In conclusion, the bovine innate immunity gene set was determined and the co-expression network of innate immunity genes in the early embryonic stage of dairy cattle was constructed by comparing and analyzing the whole genome of bovines and humans. The findings in this study provide the basis for exploring the involvement and regulation of innate immune genes in the early embryonic development of dairy cattle.

Combined Plasma Olink Proteomics and Transcriptomics Identifies CXCL1 and TNFRSF12A as Potential Predictive and Diagnostic Inflammatory Markers for Acute Kidney Injury.

Acute kidney injury (AKI) poses a significant global public health challenge. Current methods for detecting AKI rely on monitoring changes in serum creatinine (Scr), blood urea nitrogen (BUN), urinary output and some commonly employed biomarkers. However, these indicators are usually neither specific nor sensitive to AKI, especially in cases of mild kidney injury. AKI is accompanied by severe inflammatory reactions, resulting in the upregulation of numerous inflammation-associated proteins in the plasma. Plasma biomarkers are a noninvasive method for detecting kidney injury, and to date, plasma inflammation-associated cytokines have not been adequately studied in AKI patients. The objective of our research was to identify novel inflammatory biomarkers for AKI. We utilized Olink proteomics to analyze the alterations in plasma inflammation-related proteins in the serum of healthy mice (n = 2) or mice treated with cisplatin (n = 6). Additionally, transcriptome datasets for the lipopolysaccharide (LPS), cisplatin, and ischemia‒reperfusion injury (IRI) groups were obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We calculated the intersection of differentially expressed proteins (DEPs) and genes (DEGs) from both datasets. In the Olink proteomics analysis, the AKI group had significantly greater levels of 11 DEPs than did the control group. In addition, 56 common upregulated DEGs were obtained from the transcriptome dataset. The expression of CXCL1 and TNFRSF12A overlapped across all the datasets. The transcription and protein expression levels of CXCL1 and TNFRSF12A were detected in vivo. The gene and protein levels of CXCL1 and TNFRSF12A were significantly increased in different AKI mouse models and clinical patients, suggesting that these genes and proteins could be potential specific biomarkers for the identification of AKI.

Impaired extinction of cocaine seeking in HIV-infected mice is accompanied by peripheral and central immune dysregulation.

Substance use disorders (SUDs) are highly comorbid with HIV infection, necessitating an understanding of the interactive effects of drug exposure and HIV. The relationship between HIV infection and cocaine use disorder is likely bidirectional, with cocaine use directly impacting immune function while HIV infection alters addiction-related behavior. To better characterize the neurobehavioral and immune consequences of HIV infection and cocaine exposure, this study utilizes a humanized mouse model to investigate the outcomes of HIV-1 infection on cocaine-related behaviors in a conditioned place preference (CPP) model, and the interactive effects of cocaine and HIV infection on peripheral and central nervous system inflammation. HIV infection selectively impairs cocaine CPP extinction without effecting reinstatement or cocaine seeking under conflict. Behavioral alterations are accompanied by immune changes in HIV infected mice, including increased prefrontal cortex astrocyte immunoreactivity and brain-region specific effects on microglia number and reactivity. Peripheral immune system changes are observed in human cytokines, including HIV-induced reductions in human TNFα, and cocaine and HIV interactions on GM-CSF levels. Together these data provide new insights into the unique neurobehavioral outcomes of HIV infection and cocaine exposure and how they interact to effect immune responses.

Magnetic nanoparticles immobilized thrombin ligand fishing to screen thrombin inhibitors in natural products.

In this study, thrombin was immobilized with magnetic particles modified by glutaraldehyde. The changes in secondary structures of immobilized enzyme revealed an increment in conformational rigidity and stability, which can be reflected in temperature and pH stability as well as the tolerance of organic reagents. The optimal reutilization times of magnetic particle immobilized thrombin were 7 times, and the half-life of enzyme activity preserved at room temperature was 5 days, which was 2.5 times higher than that of free enzyme. Ligusticum chuanxiong and Anemarrhenae Rhizoma with high enzyme inhibitory activity were selected for primary screening, and six potential inhibitors of thrombin were identified by HPLC/MS. The results showed that three compounds in Anemarrhenae Rhizoma had better predictive thrombin inhibitory activity. Through the in vitro thrombin activity inhibition experiment, it was also verified that mangiferin and neo-mangiferin had an ideal thrombin activity inhibition effect, which was consistent with the results of molecular docking.

Ultrastable and highly active Co-vacancies-enriched IrCo bifunctional nanoalloys for proton exchange membrane water electrolysis.

Exploring the electrocatalysts with high intrinsic activity and stability for both anode and cathode to tolerate the extremely acidic condition in proton exchange membrane water electrolyzer (PEMWE) is crucial for widespread industrial application. Herein, we constructed the bifunctional IrCox nanoalloys with abundant metal vacancies via the combination of chemical reduction and electrochemical treatment for overall water splitting. The developed IrCo0.13 exhibits ultra-low overpotentials of 238 mV for OER and 18.6 mV for HER at 10 mA cm-2 in 0.1 M HClO4, and achieves the exceptional stability of 1000 h for OER and 100 h for HER at 10 mA cm-2. Further, the cell voltage is only 1.68 V to reach a high current density of 1 A cm-2 in PEMWE with IrCo0.13 as the both cathode and anode catalytic layer, and it shows excellent corrosion resistance in acidic environment, evidenced by 415 h stable operation at 1 A cm-2. The strong electronic interactions in the Ir-Co atomic heterostructure and the in-situ generation of Co vacancies by electrochemical oxidation synergistically contribute to the enhanced activity and stability via optimizing the electronic structure of adjacent Ir active sites, enhancing the conductivity and electrochemical active surface area of the catalyst, accelerating charge transfer and kinetics. This work provides a new perspective for designing bifunctional catalysts for practical application in PEMWE.

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.

This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance (test group). The accuracy, false-positive rate, false-negative rate, and analysis time of 111 radiologists (29 senior, 32 intermediate, and 50 junior) were evaluated. A radiologist was given an initial score of 14 points for each image read, with 1 point deducted for an incorrect answer and 0 points given for a correct answer. The final score for each doctor was automatically calculated by the backend calculator. We calculated the mean scores of each radiologist in the two groups (the control group and the test group) and calculated the mean scores to evaluate the performance of the radiologists with and without AI assistance. The average score of the 111 radiologists was 597 (587-605) in the control group and 619 (612-626) in the test group (P < 0.001). The time spent by the 111 radiologists on the control and test groups was 3279 (2972-3941) and 1926 (1710-2432) s, respectively (P < 0.001). The performance of the 111 radiologists in the two groups was evaluated by the area under the receiver operating characteristic curve (AUC). The radiologists showed better performance on the test group of radiographs in terms of normal findings, pulmonary fibrosis, heart shadow enlargement, mass, pleural effusion, and pulmonary consolidation recognition, with AUCs of 1.0, 0.950, 0.991, 1.0, 0.993, and 0.982, respectively. The radiologists alone showed better performance in aortic calcification (0.993), calcification (0.933), cavity (0.963), nodule (0.923), pleural thickening (0.957), and rib fracture (0.987) recognition. This competition verified the positive effects of deep learning methods in assisting radiologists in interpreting chest X-rays. AI assistance can help to improve both the efficacy and efficiency of radiologists.

Correlation of academic emotion and hardiness personality of undergraduate nursing students.

BACKGROUND: Academic emotion is a fundamental emotional concept closely linked to academic achievement. Understanding the connection between academic emotion and the personality trait of hardiness is pivotal in maintaining a stable career orientation throughout one's educational career. Therefore, in pursuit of fostering the robust growth of nursing careers, it is imperative to delve into the academic emotions experienced by undergraduate nursing students. This study endeavors to mitigate the impact of gender differences among nursing students while investigating the intricate relationship between academic emotions and the trait of hardiness in their personalities. METHODS: This study employed a cross-sectional research design. We gathered data from a convenient sample of 292 nursing students enrolled at Shanxi University of Chinese Medicine. Each student provided demographic information and responded to a general academic mood questionnaire, as well as a Hardiness Personality Rating Scale. Subsequently, we used canonical correlation analysis to evaluate the correlation between academic emotion and tenacity personality in 292 undergraduate nursing students. RESULTS: We discovered that academic emotions among nursing students are predominantly characterized by feelings of disappointment and boredom. Furthermore, personality hardiness is primarily influenced by the dimensions of engagement and control. It is important to note that a heightened level of negative, low-arousal academic emotions can diminish the level of engagement. The first typical correlation coefficient corresponding to academic emotion and hardiness were 0.660. The linear combination of standardized variables of the first typical variable corresponding to academic emotion (X1) = -0.444*negative hyperarousal -0.443 * positive hyperarousal + 0.694 * negative hypoarousal -0.260 * positive hypoarousal. The standardized variable equation of the first typical variable corresponding to hardiness personality (η1) = 0.235* hardiness -0.433* control -0.530* investment -0.303* challenge. CONCLUSIONS: Nursing students generally believe that their input is out of proportion to the return, and this unbalanced emotional experience will seriously affect their academic emotions in China. It is suggested that paying attention to cultivating their tenacious personality traits in the teaching process may help to enhance their academic emotions and enhance the sense of belonging and identity of nursing students engaged in the nursing profession.

Graph-Based Semi-Supervised Deep Image Clustering With Adaptive Adjacency Matrix.

Image clustering is a research hotspot in machine learning and computer vision. Existing graph-based semi-supervised deep clustering methods suffer from three problems: 1) because clustering uses only high-level features, the detailed information contained in shallow-level features is ignored; 2) most feature extraction networks employ the step odd convolutional kernel, which results in an uneven distribution of receptive field intensity; and 3) because the adjacency matrix is precomputed and fixed, it cannot adapt to changes in the relationship between samples. To solve the above problems, we propose a novel graph-based semi-supervised deep clustering method for image clustering. First, the parity cross-convolutional feature extraction and fusion module is used to extract high-quality image features. Then, the clustering constraint layer is designed to improve the clustering efficiency. And, the output layer is customized to achieve unsupervised regularization training. Finally, the adjacency matrix is inferred by actual network prediction. A graph-based regularization method is adopted for unsupervised training networks. Experimental results show that our method significantly outperforms state-of-the-art methods on USPS, MNIST, street view house numbers (SVHN), and fashion MNIST (FMNIST) datasets in terms of ACC, normalized mutual information (NMI), and ARI.

The NP protein of Newcastle disease virus dictates its oncolytic activity by regulating viral mRNA translation efficiency.

Newcastle disease virus (NDV) has been extensively studied as a promising oncolytic virus for killing tumor cells in vitro and in vivo in clinical trials. However, the viral components that regulate the oncolytic activity of NDV remain incompletely understood. In this study, we systematically compared the replication ability of different NDV genotypes in various tumor cells and identified NP protein determines the oncolytic activity of NDV. On the one hand, NDV strains with phenylalanine (F) at the 450th amino acid position of the NP protein (450th-F-NP) exhibit a loss of oncolytic activity. This phenotype is predominantly associated with genotype VII NDVs. In contrast, the NP protein with a leucine amino acid at this site in other genotypes (450th-L-NP) can facilitate the loading of viral mRNA onto ribosomes more effectively than 450th-F-NP. On the other hand, the NP protein from NDV strains that exhibit strong oncogenicity interacts with eIF4A1 within its 366-489 amino acid region, leading to the inhibition of cellular mRNA translation with a complex 5' UTR structure. Our study provide mechanistic insights into how highly oncolytic NDV strains selectively promote the translation of viral mRNA and will also facilitate the screening of oncolytic strains for oncolytic therapy.

Oncologic and reproductive outcomes after fertility-sparing surgery for bilateral borderline ovarian tumors: A retrospective study.

OBJECTIVES: To investigate the oncological safety and fertility outcomes of different fertility-sparing surgery procedures for bilateral borderline ovarian tumors (BOTs) and to identify the safest and most effective approach to help patients conceive with minimal risk. STUDY DESIGN: A retrospective study of 144 patients (≤40 years) with pathologically confirmed bilateral BOTs were included in the study.The effects of surgery type on fertility outcome and recurrence were compared. Cox regression analysis was employed to determine potential prognostic factors. Survival analysis utilized the Kaplan-Meier method. RESULTS: Three therapeutic modalities were applied in our study, including bilateral ovarian cystectomy (BOC; n = 29), unilateral adnexectomy + contralateral cystectomy (UAC; n = 4) and radical surgery (n = 61). Totally 33 cases (22.9 %) relapsed during the follow-up period. In 37 % of cases administered conservative surgery, relapses were diagnosed in the first 2 years. Only conservative surgery and adjuvant chemotherapy were risk factors for recurrence. Meanwhile, a pregnancy rate of 55.4 % was obtained in patients with bilateral BOTs. The pregnancy rate was slightly higher but no significant (P = 0.539) difference in patients treated with BOC (n = 17, 63 %) compared with UAC (n = 29, 55.8 %) group. GnRHa treatment significantly improved the clinical pregnancy rate in this study(P = 0.029). CONCLUSIONS: Satisfactory pregnancy rate can be achieved after conservative surgery in patients with bilateral BOTs. BOC is worth recommending for bilateral borderline ovarian tumors and a critical factor in fertility is the preservation of maximum healthy ovarian tissue. Patients should make a pregnancy plan in 2 years after the first surgery. GnRHa increase the rate of successful clinical pregnancies.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma.

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.