SARS-CoV-2 501Y.V2 variants lack higher infectivity but do have immune escape.

The 501Y.V2 variants of SARS-CoV-2 containing multiple mutations in spike are now dominant in South Africa and are rapidly spreading to other countries. Here, experiments with 18 pseudotyped viruses showed that the 501Y.V2 variants do not confer increased infectivity in multiple cell types except for murine ACE2-overexpressing cells, where a substantial increase in infectivity was observed. Notably, the susceptibility of the 501Y.V2 variants to 12 of 17 neutralizing monoclonal antibodies was substantially diminished, and the neutralization ability of the sera from convalescent patients and immunized mice was also reduced for these variants. The neutralization resistance was mainly caused by E484K and N501Y mutations in the receptor-binding domain of spike. The enhanced infectivity in murine ACE2-overexpressing cells suggests the possibility of spillover of the 501Y.V2 variants to mice. Moreover, the neutralization resistance we detected for the 501Y.V2 variants suggests the potential for compromised efficacy of monoclonal antibodies and vaccines.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection.

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.

Pan-genome analysis sheds light on structural variation-based dissection of agronomic traits in melon crops.

Sweetness and appearance of fresh fruits are key palatable and preference attributes for consumers and are often controlled by multiple genes. However, fine-mapping the key loci or genes of interest by single genome-based genetic analysis is challenging. Herein, we present the chromosome-level genome assembly of 1 landrace melon accession (Cucumis melo ssp. agrestis) with wild morphologic features and thus construct a melon pan-genome atlas via integrating sequenced melon genome datasets. Our comparative genomic analysis reveals a total of 3.4 million genetic variations, of which the presence/absence variations (PAVs) are mainly involved in regulating the function of genes for sucrose metabolism during melon domestication and improvement. We further resolved several loci that are accountable for sucrose contents, flesh color, rind stripe, and suture using a structural variation (SV)-based genome-wide association study. Furthermore, via bulked segregation analysis (BSA)-seq and map-based cloning, we uncovered that a single gene, (CmPIRL6), determines the edible or inedible characteristics of melon fruit exocarp. These findings provide important melon pan-genome information and provide a powerful toolkit for future pan-genome-informed cultivar breeding of melon.

A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students' grade and their perceptions of the learning environment.

BACKGROUND: Medical school learning environment (MSLE) has a holistic impact on students' psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student's grade in the perception of MSLE. METHODS: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student's grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch's ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student's grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. RESULTS: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265-1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326-1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student's low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). CONCLUSION: The student's grade was a significant influencing factor in medical students' perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students' perceptions are urgently needed.

Gender-specific lncRNA-miRNA-mRNA regulatory network to reveal potential genes for primary open-angle glaucoma.

BACKGROUND: Investigation of biomarkers may facilitate understanding the mechanisms of primary open-angle glaucoma (POAG) and developing therapeutic targets. This study aimed to identify potential genes based on competing endogenous RNA (ceRNA) network for POAG. METHODS: Based on long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) from the Gene Expression Omnibus (GEO) database, we identified differential expressed lncRNAs (DELs), differential expressed miRNAs (DEMis) and differential expressed mRNAs (DEMs) and then constructed a ceRNA network. Through weighted gene co-expression network analysis (WGCNA), we identified gender-specific genes for gender-associated ceRNA network construction, followed by the protein-protein interaction (PPI) network and functional enrichment analysis to screen hub genes and reveal their functions. The expression levels of hub genes were measured in steroid-induced ocular hypertension (SIOH) mice. RESULTS: A total of 175 DELs, 727 DEMs and 45 DEMis were screened between control and POAG samples. Seven modules were identified through WGCNA and one module was associated with gender of POAG patients. We discovered 41 gender-specific genes for gender-associated ceRNA construction and then identified 8 genes (NAV3, C1QB, RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1), which were enriched in cell cycle-related pathways and immune-related pathways. C1QB, RXRB, Top1 and ZNF207 were highly interacted with other proteins. The expression levels of NAV3 and C1QB were downregulated in SIOH, while the levels of RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1 were upregulated in SIOH. CONCLUSION: This study identifies hub genes associated with the pathogenesis of gender-specific POAG and provides potential biomarkers for POAG.

Allelic variations of ClACO gene improve nitrogen uptake via ethylene-mediated root architecture in watermelon.

KEY MESSAGE: The ClACO gene encoding 1-aminocyclopropane-1-carboxylate oxidase enabled highly efficient 15N uptake in watermelon. Nitrogen is one of the most essential nutrient elements that play a pivotal role in regulating plant growth and development for crop productivity. Elucidating the genetic basis of high nitrogen uptake is the key to improve nitrogen use efficiency for sustainable agricultural productivity. Whereas previous researches on nitrogen absorption process are mainly focused on a few model plants or crops. To date, the causal genes that determine the efficient nitrogen uptake of watermelon have not been mapped and remains largely unknown. Here, we fine-mapped the 1-aminocyclopropane-1-carboxylate oxidase (ClACO) gene associated with nitrogen uptake efficiency in watermelon via bulked segregant analysis (BSA). The variations in the ClACO gene led to the changes of gene expression levels between two watermelon accessions with different nitrogen uptake efficiencies. Intriguingly, in terms of the transcript abundance of ClACO, it was concomitant with significant differences in ethylene evolutions in roots and root architectures between the two accessions and among the different genotypic offsprings of the recombinant BC2F1(ZJU132)-18. These findings suggest that ethylene as a negative regulator altered nitrogen uptake efficiency in watermelon by controlling root development. In conclusion, our current study will provide valuable target gene for precise breeding of 'green' watermelon varieties with high-nitrogen uptake efficiencies.

A rapid and highly sensitive multiple detection of human adenovirus type 3, type 7 and respiratory syncytial virus by recombinase-aided reverse transcription PCR.

BACKGROUND: Polymerase chain reaction (PCR) has been widely used for many pathogen detection. However, PCR technology still suffers from long detection time and insufficient sensitivity. Recombinase-aided amplification (RAA) is a powerful nucleic acid detection tool with high sensitivity and amplification efficiency, but its complex probes and inability of multiplex detection hinder the further application of this technology. METHODS: In this study, we developed and validated the multiplex reverse transcription recombinase-aided PCR (multiplex RT-RAP) assay for human adenovirus 3 (HADV3), human adenovirus 7 (HADV7), and human respiratory syncytial virus (HRSV) within 1 h with Human RNaseP protein as a reference gene to monitor the whole process. RESULTS: Using recombinant plasmids, the sensitivity of multiplex RT-RAP for the detection of HADV3, HADV7, and HRSV was 18, 3, and 18 copies per reaction, respectively. The multiplex RT-RAP showed no cross-reactivity with other respiratory viruses, demonstrating its good specificity. A total of 252 clinical specimens were tested by multiplex RT-RAP and the results were found to be consistent with those of corresponding RT-qPCR assays. After testing serial dilutions of selected positive specimens, the detection sensitivity of multiplex RT-RAP was two to eightfold higher than that of corresponding RT-qPCR. CONCLUSION: We conclude the multiplex RT-RAP is a robust, rapid, highly sensitive, and specific assay with the potential to be used in the screening of clinical samples with low viral load.

Aggregation of high-frequency RBD mutations of SARS-CoV-2 with three VOCs did not cause significant antigenic drift.

Variants of SARS-CoV-2 continue to emerge, posing great challenges in outbreak prevention and control. It is important to understand in advance the impact of possible variants of concern (VOCs) on infectivity and antigenicity. Here, we constructed one or more of the 15 high-frequency naturally occurring amino acid changes in the receptor-binding domain (RBD) of Alpha, Beta, and Gamma variants. A single mutant of A520S, V367F, and S494P in the above three VOCs enhanced infectivity in ACE2-overexpressing 293T cells of different species, LLC-MK2 and Vero cells. Aggregation of multiple RBD mutations significantly reduces the infectivity of the possible three VOCs. Regarding neutralization, it is noteworthy that E484K, N501Y, K417N, and N439K predispose to monoclonal antibodies (mAbs) protection failure in the 15 high-frequency mutations. Most importantly, almost all possible VOCs (single RBD mutation or aggregation of multiple mutations) showed no more than a fourfold decrease in neutralizing activity with convalescent sera, vaccine sera, and immune sera of guinea pigs with different immunogens, and no significant antigenic drift was formed. In conclusion, our pseudovirus results could reduce the concern that the aggregation of multiple high-frequency mutations in the RBD of the spike protein of the three VOCs would lead to severe antigenic drift, and this would provide value for vaccine development strategies.

Diagnostic model based on bioinformatics and machine learning to distinguish Kawasaki disease using multiple datasets.

BACKGROUND: Kawasaki disease (KD), characterized by systemic vasculitis, is the leading cause of acquired heart disease in children. Herein, we developed a diagnostic model, with some prognosis ability, to help distinguish children with KD. METHODS: Gene expression datasets were downloaded from Gene Expression Omnibus (GEO), and gene sets with a potential pathogenic mechanism in KD were identified using differential expressed gene (DEG) screening, pathway enrichment analysis, random forest (RF) screening, and artificial neural network (ANN) construction. RESULTS: We extracted 2,017 DEGs (1,130 with upregulated and 887 with downregulated expression) from GEO. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in innate/adaptive immune response-related processes. Subsequently, the results of weighted gene co-expression network analysis and DEG screening were combined and, using RF and ANN, a model with eight genes (VPS9D1, CACNA1E, SH3GLB1, RAB32, ADM, GYG1, PGS1, and HIST2H2AC) was constructed. Classification results of the new model for KD diagnosis showed excellent performance for different datasets, including those of patients with KD, convalescents, and healthy individuals, with area under the curve values of 1, 0.945, and 0.95, respectively. CONCLUSIONS: We used machine learning methods to construct and validate a diagnostic model using multiple bioinformatic datasets, and identified molecules expected to serve as new biomarkers for or therapeutic targets in KD.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience.

OBJECTIVES: To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists. METHODS: From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed. RESULTS: After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05). CONCLUSIONS: Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

All-trans-retinoic acid shifts Th1 towards Th2 cell differentiation by targeting NFAT1 signalling to ameliorate immune-mediated aplastic anaemia.

Severe acquired aplastic anaemia (AA) is a serious disease characterised by autoreactive T cells attacking haematopoietic stem cells, leading to marrow hypoplasia and pancytopenia. Immunosuppressive therapy combined with antithymocyte globulin and ciclosporin can rescue most patients with AA. However, the relapse after ciclosporin withdrawal and the severe side effects of long-term ciclosporin administration remain unresolved. As such, new strategies should be developed to supplement current therapeutics and treat AA. In this study, the possibility of all-trans-retinoic acid (ATRA) as an alternative AA treatment was tested by using an immune-mediated mouse model of AA. Results revealed that ATRA inhibited T-cell proliferation, activation and effector function. It also restrained the Fas/Fasl pathway, shifted Th1 towards Th2 cell development, rebalanced T-cell subsets at a relatively high level and corrected the Th1/Th2 ratio by targeting NFAT1 signalling. In addition, ATRA inhibited Th17 cell differentiation and promoted regulatory T-cell development. Therefore, ATRA was an effective agent to improve AA treatment outcomes.

Bromodomains and Extra-Terminal (BET) Inhibitor JQ1 Suppresses Proliferation of Acute Lymphocytic Leukemia by Inhibiting c-Myc-Mediated Glycolysis.

BACKGROUND Acute lymphocytic leukemia (ALL) is a common blood cancer which induces high mortality in children. Bromodomains and extra-terminal (BET) protein inhibitors, such as JQ1 and ARV-825, are promising cancer therapeutic agents that can be used by targeting c-Myc. A recent work reported that JQ1 effectively attenuates ALL in vitro by suppressing cell proliferation and accelerating apoptosis. The purpose of this research was to probe into the potential mechanism of how JQ1 inhibits ALL cell proliferation in vitro. MATERIAL AND METHODS Cell viability of ALL cells were measured by CTG after treatment by JQ1. Cell cycle analysis was done by EdU and PI staining. Cell apoptosis was assessed by Annexin V/PI staining. Glycolysis was detected using Seahorse and LC-MS kits. The expression of glycolytic rate-limiting enzymes was assessed by RNA-seq, qRT-PCR, and Western blot. RESULTS JQ1 suppressed cell proliferation by arresting the cell cycle and inducing the apoptosis of acute lymphocytic leukemia cells. JQ1 inhibited cell proliferation of B-ALL cells by restraining glycolysis. Conversely, the cell cycle block of B-ALL cells induced by JQ1 was partially abolished after pretreatment with 2-Deoxy-D-glucose (2-DG), an inhibitor of glycolysis. Furthermore, JQ1 restrained the glycolysis of B-ALL cell lines by remarkably downregulating the rate-limiting enzymes of glycolysis, such as hexokinase 2, phosphofructokinase, and lactate dehydrogenase A. Moreover, the cell cycle arrest was reversed in B-ALL cells with overexpressed c-Myc treated by JQ1, which is involved in the enhancement of glycolysis. CONCLUSIONS The BET inhibitor JQ1 suppresses the proliferation of ALL by inhibiting c-Myc-mediated glycolysis, thus providing a new strategy for the treatment of ALL.

Wetting Phenomena and their Effect on the Electrochemical Performance of Surface-Tailored Lithium Metal Electrodes in Contact with Cross-linked Polymeric Electrolytes.

Li metal batteries (LMBs) containing cross-linked polymer electrolytes (PEs) are auspicious candidates for next-generation batteries. However, the wetting behavior of PEs on uneven Li metal surfaces has been neglected in most studies. Herein, it is shown that microscale defect sites with curved edges play an important role in a wettability-dependent electrodeposition. The wettability and the viscoelastic properties of PEs are correlated, and the impact of wettability on the nucleation and diffusion near the Li|PE interface is distinguished. It is found that the curvature of the edges is a key factor for the investigation of wetting phenomena. The appearance of microscale defects and phase separation are identified as main causes for erratic nucleation. It is emphasized that the implementation of stable and consistent long-term cycling performance of LMBs using PEs requires a deeper understanding of the "soft-solid"-solid contact between PEs and inherently rough Li metal surfaces.

Pharmacological Inhibition of TFF3 Enhances Sensitivity of CMS4 Colorectal Carcinoma to 5-Fluorouracil through Inhibition of p44/42 MAPK.

Increased expression of trefoil factor 3 (TFF3) has been reported in colorectal carcinoma (CRC), being correlated with distant metastasis and poor clinical outcomes. Amongst the CRC subtypes, mesenchymal (CMS4) CRC is associated with the worst survival outcome. Herein, the functional roles of TFF3 and the pharmacological inhibition of TFF3 by a novel specific small molecule TFF3 inhibitor-2-amino-4-(4-(6-fluoro-5-methylpyridin-3-yl)phenyl)-5-oxo-4H,5H-pyrano[3,2-c]chromene-3-carbonitrile (AMPC) in CMS4 CRC was explored. Forced expression of TFF3 in CMS4 CRC cells promoted cell proliferation, cell survival, foci formation, invasion, migration, cancer stem cell like behaviour and growth in 3D Matrigel. In contrast, siRNA-mediated depletion of TFF3 or AMPC inhibition of TFF3 in CMS4 CRC cells decreased oncogenic behaviour as indicated by the above cell function assays. AMPC also inhibited tumour growth in vivo. The TFF3-stimulated oncogenic behaviour of CMS4 CRC cells was dependent on TFF3 activation of the p44/42 MAPK (ERK1/2) pathway. Furthermore, the forced expression of TFF3 decreased the sensitivity of CMS4 CRC cells to 5-fluorouracil (5-FU); while depleted TFF3 expression enhanced 5-FU sensitivity in CMS4 CRC cells. 5-FU treatment induced TFF3 expression in CMS4 CRC cells. AMPC, when used in combination with 5-FU in CMS4 CRC cells exhibited a synergistic inhibitory effect. In summary, this study provides functional evidence for TFF3 as a therapeutic target in CMS4 CRC.

[Progress on in situ cell transdifferentiation in central nervous system].

Central nervous system injury leads to irreversible neuronal loss and glial scar formation, which ultimately results in persistent neurological dysfunction. Regenerative medicine suggests that replenishing missing neurons may be an ideal approach to repair the damage. Recent researches showed that many mature cells could be transdifferentiated into functional neurons by reprogramming. Therefore, reprogramming endogenous glia in situ to produce functional neurons shows great potential and unique advantage for repairing neuronal damage and treating neurodegenerative diseases. The present review summarized the current research progress on in situ transdifferentiation in the central nervous system, focusing on the cell types, characteristics and research progress of glial cells that could be transdifferentiated in situ, in order to provide theoretical basis for the development of new therapeutic strategies of neuronal injury and further clinical application.

Cytoplasmic glutamine synthetase gene expression regulates larval development in Bactrocera dorsalis (Hendel).

In insects, glutamine synthetase (GS), a key enzyme in the synthesis of glutamine, has been reported to be associated with embryonic development, heat shock response, and fecundity regulation. However, little is known about the influence of GS on postembryonic development. In this study, we demonstrate that blocking the activity of GS in the oriental fruit fly (Bactrocera dorsalis) with use of a GS-specific inhibitor (L-methionine S-sulfoximine), led to a significant delay in larval development, pupal weight loss, and inhibition of pupation. We further identify cloned and characterized two GS genes (BdGS-c and BdGS-m) from B. dorsalis. The two GS genes identified in B. dorsalis were predicted to be located in the cytosol (BdGS-c) and mitochondria (BdGS-m), and homology analysis indicated that both genes were similar to homologs from other Dipterans, such as Drosophila melanogaster and Aedes aegypti. BdGS-c was highly expressed in the larval stages, suggesting that cytosolic GS plays a predominant role in larval development. Furthermore, RNA interference experiments against BdGS-c, to specifically decrease the expression of cytosolic GS, resulted in delay in larval development as well as pupal weight loss. This study presents the prominent role played by BdGS-c in regulating larval development and suggests that the observed effect could have been modulated through ecdysteroid synthesis, agreeing with the reduced expression of the halloween gene spook. Also, the direct effects of BdGS-c silencing on B. dorsalis, such as larval lethality, delayed pupation, and late emergence, can be further exploited as novel insecticide target in the context of pest management.

Adaptive Discrete Vector Field in Sensor Networks.

Homology groups are a prime tool for measuring the connectivity of a network, and their computation in a distributed and adaptive way is mandatory for their use in sensor networks. In this paper, we propose a solution based on the construction of an adaptive discrete vector field from where, thanks to the discrete Morse theory, the generators of the homology groups are extracted. The efficiency and the adaptability of our approach are tested against two applications: the detection and the localization of the holes in the coverage, and the selection of active sensors ensuring complete coverage.

Comparative Proteomic Profiling Reveals Molecular Characteristics Associated with Oogenesis and Oocyte Maturation during Ovarian Development of Bactrocera dorsalis (Hendel).

Time-dependent expression of proteins in ovary is important to understand oogenesis in insects. Here, we profiled the proteomes of developing ovaries from Bactrocera dorsalis (Hendel) to obtain information about ovarian development with particular emphasis on differentially expressed proteins (DEPs) involved in oogenesis. A total of 4838 proteins were identified with an average peptide number of 8.15 and sequence coverage of 20.79%. Quantitative proteomic analysis showed that a total of 612 and 196 proteins were differentially expressed in developing and mature ovaries, respectively. Furthermore, 153, 196 and 59 potential target proteins were highly expressed in early, vitellogenic and mature ovaries and most tested DEPs had the similar trends consistent with the respective transcriptional profiles. These proteins were abundantly expressed in pre-vitellogenic and vitellogenic stages, including tropomyosin, vitellogenin, eukaryotic translation initiation factor, heat shock protein, importin protein, vitelline membrane protein, and chorion protein. Several hormone and signal pathway related proteins were also identified during ovarian development including piRNA, notch, insulin, juvenile, and ecdysone hormone signal pathways. This is the first report of a global ovary proteome of a tephritid fruit fly, and may contribute to understanding the complicate processes of ovarian development and exploring the potentially novel pest control targets.

[Research Progress of microRNA Influences on Colorectal Cancer].

microRNA(miRNA)is a kind of RNAs which involves in the regulation of proliferation,invasion and metastasis of tumors.It also closely connects to the colorectal cancer.This article reviews the relationship between miRNA and proliferation,invasion,metastasis,diagnosis and treatment of colorectal cancer.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

BACKGROUND: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. CONCLUSION: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.