PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants.

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework.

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods.

Chemoenzymatic Installation of Site-Specific Chemical Groups on DNA Enhances the Catalytic Activity.

Functional DNAs are valuable molecular tools in chemical biology and analytical chemistry but suffer from low activities due to their limited chemical functionalities. Here, we present a chemoenzymatic method for site-specific installation of diverse functional groups on DNA, and showcase the application of this method to enhance the catalytic activity of a DNA catalyst. Through chemoenzymatic introduction of distinct chemical groups, such as hydroxyl, carboxyl, and benzyl, at specific positions, we achieve significant enhancements in the catalytic activity of the RNA-cleaving deoxyribozyme 10-23. A single carboxyl modification results in a 100-fold increase, while dual modifications (carboxyl and benzyl) yield an approximately 700-fold increase in activity when an RNA cleavage reaction is catalyzed on a DNA-RNA chimeric substrate. The resulting dually modified DNA catalyst, CaBn, exhibits a kobs of 3.76 min-1 in the presence of 1 mM Mg2+ and can be employed for fluorescent imaging of intracellular magnesium ions. Molecular dynamics simulations reveal the superior capability of CaBn to recruit magnesium ions to metal-ion-binding site 2 and adopt a catalytically competent conformation. Our work provides a broadly accessible strategy for DNA functionalization with diverse chemical modifications, and CaBn offers a highly active DNA catalyst with immense potential in chemistry and biotechnology.

Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of myasthenia gravis.

The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.

Dual Function of Naphthalenediimide Supramolecular Photocatalyst with Giant Internal Electric Field for Efficient Hydrogen and Oxygen Evolution.

Organic supramolecular photocatalysts have garnered widespread attention due to their adjustable structure and exceptional photocatalytic activity. Herein, a novel bis-dicarboxyphenyl-substituent naphthalenediimide self-assembly supramolecular photocatalyst (SA-NDI-BCOOH) with efficient dual-functional photocatalytic performance is successfully constructed. The large molecular dipole moment and short-range ordered stacking structure of SA-NDI-BCOOH synergistically create a giant internal electric field (IEF), resulting in a remarkable 6.7-fold increase in its charge separation efficiency. Additionally, the tetracarboxylic structure of SA-NDI-BCOOH greatly enhances its hydrophilicity. Thus, SA-NDI-BCOOH demonstrates efficient dual-functional activity for photocatalytic hydrogen and oxygen evolution, with rates of 372.8 and 3.8 µmol h-1, respectively. Meanwhile, a notable apparent quantum efficiency of 10.86% at 400 nm for hydrogen evolution is achieved, prominently surpassing many reported supramolecular photocatalysts. More importantly, with the help of dual co-catalysts, it exhibits photocatalytic overall water splitting activity with H2 and O2 evolution rates of 3.2 and 1.6 µmol h-1. Briefly, this work sheds light on enhancing the IEF by controlling the molecular polarity and stacking structure to dramatically improve the photocatalytic performance of supramolecular materials.

Validation of the PowerPlex®35GY System: a novel eight-dye STR multiplex kit on the Spectrum Compact CE System.

The PowerPlex® 35GY System (Promega, USA) is an advanced eight-dye multiplex STR kit, incorporating twenty-three autosomal STR loci, eleven Y chromosome STR loci, one sex determining marker Amelogenin, and two quality indicators. This multiplex system includes 20 CODIS loci and up to 15 mini-STR loci with sizing values less than 250 bases. In this study, validation for PowerPlex® 35GY System was conducted following the guidelines of SWGDAM, encompassing sensitivity, precision, accuracy, concordance, species specificity, stutter, mixture, stability, and degraded DNA. The results from experiments demonstrated that the PowerPlex® 35GY System could effectively amplify DNA samples, with complete allele detection achieved at 125 pg. Moreover, over 90% of alleles from minor contributors were detected at a mixed ratio of 1:4. Additionally, the system was found to yield full profiles even in the presence of hematin, humic acid, and indigo. The PowerPlex® 35GY System demonstrated superior performance in the sensitivity and degraded DNA studies compared to a six-dye STR kit. Hence, it is evident that the PowerPlex® 35GY System is well-suited for forensic practice, whether in casework or for database samples. These findings provide strong support for the efficacy and reliability of the PowerPlex® 35GY System in forensic applications.

LOXL1 promotes tumor cell malignancy and restricts CD8 + T cell infiltration in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer mortality globally. Lymph node metastasis and immunosuppression are main factors of poor prognosis in CRC patients. Lysyl oxidase like 1 (LOXL1), part of the lysyl oxidase (LOX) family, plays a yet unclear role in CRC. This study aimed to identify effective biomarkers predictive of prognosis and efficacy of immunotherapy in CRC patients, and to elucidate the prognostic value, clinical relevance, functional and molecular features, and immunotherapy predictive role of LOXL1 in CRC and pan-cancer. METHODS: Weighted gene co-expression network analysis (WGCNA) was employed to explore gene modules related to tumor metastasis and CD8 + T cell infiltration. LOXL1 emerged as a hub gene through differential gene expression and survival analysis. The molecular signatures, functional roles, and immunological characteristics affected by LOXL1 were analyzed in multiple CRC cohorts, cell lines and clinical specimens. Additionally, LOXL1's potential as an immunotherapy response indicator was assessed, along with its role in pan-cancer. RESULTS: Turquoise module in WGCNA analysis was identified as the hub module associated with lymph node metastasis and CD8 + T cell infiltration. Aberrant elevated LOXL1 expression was observed in CRC and correlated with poorer differentiation status and prognosis. Molecular and immunological characterization found that LOXL1 might mediate epithelial-mesenchymal transition (EMT) process and immunosuppressive phenotypes of CRC. Functional study found that LOXL1 enhanced tumor cell proliferation, migration and invasion. Moreover, high LOXL1 levels corresponded to reduced CD8 + T cell infiltration and predicted poor clinical outcomes of immunotherapy. Similar trends were also observed at the pan-cancer level. CONCLUSIONS: Our findings underscore the critical role of LOXL1 in modulating both malignancy and immunosuppression in CRC. This positions LOXL1 as a promising biomarker for predicting prognosis and the response to immunotherapy in CRC patients.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Prevalence and factors associated with antenatal depressive symptoms across trimesters: a study of 110,584 pregnant women covered by a mobile app-based screening programme in Shenzhen, China.

BACKGROUND: Antenatal depression is a significant public health issue affecting pregnant women both globally and in China. Using data from a mobile app-based screening programme, this study explored the prevalence and factors associated with antenatal depressive symptoms across different trimesters in Shenzhen. METHODS: A retrospective cross-sectional study was conducted on pregnant women who gave birth in any hospital in Shenzhen between July 2021 and May 2022 and underwent depression screening using an official maternal and infant health mobile app at least once during pregnancy. Depressive symptoms were evaluated using the 9-item Patient Health Questionnaire (PHQ-9), with cut-off scores of 5 and 10 for mild and high level of symptoms, respectively. The prevalence for each trimester was determined by calculating the proportion of women scoring 5 or higher. A variety of sociodemographic, obstetric, psychological, and lifestyle factors were assessed for their association with depressive symptoms. Chi-square test and multivariate logistic regression were performed to identify significant predictors. RESULTS: A total of 110,584 pregnant women were included in the study, with an overall prevalence of depressive symptoms of 18.0% and a prevalence of high-level symptoms of 4.2%. Depressive symptoms were most prevalent in the first trimester (10.9%) and decreased in the second (6.2%) and third trimesters (6.3%). Only a small proportion (0.4%) of women showed persistent depressive symptoms across all trimesters. Anxiety symptoms in early pregnancy emerged as the most significant predictor of depressive symptoms. Other factors linked to an increased risk throughout pregnancy include lower marital satisfaction, living with parents-in-law, experience of negative life events, as well as drinking before and during pregnancy. Factors associated with a reduced risk throughout pregnancy include multiparity and daily physical activity. CONCLUSIONS: This large-scale study provides valuable insights into the prevalence and factors associated with antenatal depressive symptoms in Shenzhen. The findings underscore the need for targeted interventions for high-risk groups and the integration of mental health care into routine antenatal services. Continuous, dynamic monitoring of depressive symptoms for pregnant women and ensuring at-risk women receive comprehensive follow-up and appropriate psychological or psychiatric care are crucial for effectively addressing antenatal depression and improving maternal and infant health outcomes.

APOE2 protects against Aß pathology by improving neuronal mitochondrial function through ERRα signaling.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease and apolipoprotein E (APOE) genotypes (APOE2, APOE3, and APOE4) show different AD susceptibility. Previous studies indicated that individuals carrying the APOE2 allele reduce the risk of developing AD, which may be attributed to the potential neuroprotective role of APOE2. However, the mechanisms underlying the protective effects of APOE2 is still unclear. METHODS: We analyzed single-nucleus RNA sequencing and bulk RNA sequencing data of APOE2 and APOE3 carriers from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort. We validated the findings in SH-SY5Y cells and AD model mice by evaluating mitochondrial functions and cognitive behaviors respectively. RESULTS: The pathway analysis of six major cell types revealed a strong association between APOE2 and cellular stress and energy metabolism, particularly in excitatory and inhibitory neurons, which was found to be more pronounced in the presence of beta-amyloid (Aß). Moreover, APOE2 overexpression alleviates Aß1-42-induced mitochondrial dysfunction and reduces the generation of reactive oxygen species in SH-SY5Y cells. These protective effects may be due to ApoE2 interacting with estrogen-related receptor alpha (ERRα). ERRα overexpression by plasmids or activation by agonist was also found to show similar mitochondrial protective effects in Aß1-42-stimulated SH-SY5Y cells. Additionally, ERRα agonist treatment improve the cognitive performance of Aß injected mice in both Y maze and novel object recognition tests. ERRα agonist treatment increased PSD95 expression in the cortex of agonist-treated-AD mice. CONCLUSIONS: APOE2 appears to enhance neural mitochondrial function via the activation of ERRα signaling, which may be the protective effect of APOE2 to treat AD.

[Clinical and genetic characteristics of a child with Developmental and epileptic encephalopathy 104 due to variant of ATP6V0A1 gene].

OBJECTIVE: To explore the clinical phenotype and genetic etiology of a child with Developmental epileptic encephalopathy type 104 (DEE 104). METHODS: A child who had presented at the Children's Medical Center of the Affiliated Hospital of Guangdong Medical University in February 2021 for recurrent seizures over 1 month was selected as the study subject. Clinical data of the child was collected. Peripheral blood samples of the child and his parents were collected and subjected to whole exome sequencing (WES). Candidate variant was verified by Sanger sequencing. RESULTS: The child, a five-month-old male, had presented with frequent focal seizures with severe developmental retardation from infancy. Physical examination showed emaciation, microcephaly, oblique palpebral fissures, Stahl's ears, and hypotonia in the limbs. Electroencephalogram revealed multi-focal sharp waves, slow waves and slow spinal waves. Cranial magnetic resonance imaging revealed enlargement of bilateral lateral ventricles and the third ventricle, along with widening of brain sulci, fissure and cisterna. WES revealed that he had harbored a heterozygous c.2401C>T (p.His801Tyr) missense variant of the ATP6V0A1 gene. Sanger sequencing showed that both of his parents were of the wild type. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was predicted to be likely pathogenic (PS2+PM2_Supporting+PP3). The proband was diagnosed with DEE 104. Early treatment with sodium valproate has failed, but the child had become seizure free after the addition of levetiracetam and topiramate. He still had abnormal EEG discharges and severe psychomotor retardation. Combining our case and a review of literature, DEE104 is mainly caused by de novo heterozygous variants of the ATP6V0A1 gene with an autosomal dominant inheritance. The patients may show refractory epilepsy and severe global developmental delay from infancy. CONCLUSION: The c.2401C>T (p.His801Tyr) variant probably underlay the DEE104 in this child.

Functional co-delivery nanoliposomes based on improving hypoxia for increasing photoimmunotherapy efficacy of cold tumors.

Cold tumors lack T cells infiltration and have low immunogenicity, resulting insufficient immunotherapy response. Therefore, how to realize the transformation from cold tumor to hot tumor is an urgent problem to be solved. Photodynamic therapy can induce immunogenic death of tumor cells (ICD) and activate T lymphocytes to produce tumor immune response. However, hypoxia in the cold tumor microenvironment limits the effectiveness of photodynamic therapy. So in this article, MET-HMME/CAT-HMME@Nlip as a functional co-delivery nanoliposomes was constructed based on overcoming the above problems. Firstly, the oxygen-deficient state could be improved by the following two ways, one is catalase loaded in CAT-HMME@Nlip can decompose high concentration hydrogen peroxide to produce oxygen, and the other is metformin loaded in MET-HMME@Nlip can decrease oxygen consumption by inhibiting of mitochondrial respiration. And then with the increase of substrate oxygen concentration, the sensitivity of photodynamic therapy can be greatly improved and the anti-tumor immune response by PDT-induced ICD can also be enhanced obviously. In addition, metformin could act as a small molecule immune checkpoint inhibitor to reduce the expression of PD-L1 on the surface of tumor cells, thereby effectively improving the specific killing ability of cytotoxic T cells to tumor cells which could not only erasing the primary tumor, but also inhibiting the growth of simulated distant tumors through the immune memory function. This study provides a new idea for improving the clinical treatment effect of hypoxic cold tumors, especially for tumors that could not benefit from immunotherapy due to low or no expression of PD-L1 protein on the surface of tumor cells.

Genetic association of the gut microbiota with epigenetic clocks mediated by inflammatory cytokines: a Mendelian randomization analysis.

Background: A new aging biomarker epigenetic clock has been developed. There exists a close link between aging and gut microbiota, which may be mediated by inflammatory cytokines. However, the relationship between the epigenetic clock, gut microbiota, and the mediating substances is unclear. Methods: Two large genome-wide association meta-analyses were analyzed by two-sample Mendelian randomization. The results between gut microbiota and epigenetic clock were investigated using the four methods (Inverse variance weighted, MR-Egger, weighted median, MR-PRESSO). Genetic correlation was measured by Linked disequilibrium score regression (LDSC). The correctness of the study direction was checked by the Steiger test. Cochran's Q statistic and MR-Egger intercept were used as sensitivity analyses of the study. The two-step method was used to examine the mediating role of inflammatory cytokines. We use the Benjamini-Hochberg correction method to correct the P value. Results: After FDR correction, multiple bacterial genera were significantly or suggestively associated with four epigenetic clocks (GrimAge, HannumAge, IEAA, PhenoAge). And we detected several inflammatory factors acting as mediators of gut microbiota and epigenetic clocks. Conclusion: This study provides genetic evidence for a positive and negative link between gut microbiota and aging risk. We hope that by elucidating the genetic relationship and potential mechanisms between aging and gut microbiota, we will provide new avenues for continuing aging-related research and treatment.

Arctigenin from Fructus arctii Exhibits Antiaging Effects via Autophagy Induction, Antioxidative Stress, and Increase in Telomerase Activity in Yeast.

Aging is often accompanied by irreversible decline in body function, which causes a large number of age-related diseases and brings a huge economic burden to society and families. Many traditional Chinese medicines have been known to extend lifespan, but it has still been a challenge to isolate a single active molecule from them and verify the mechanism of anti-aging action. Drugs that inhibit senescence-associated secretory phenotypes (SASPs) are called "senomorphics". In this study, arctigenin (ATG), a senomorphic, was screened from the Chinese medicine Fructus arctii using K6001 yeast replicative lifespan. Autophagy, oxidative stress, and telomerase activity are key mechanisms related to aging. We found that ATG may act through multiple mechanisms to become an effective anti-aging molecule. In exploring the effect of ATG on autophagy, it was clearly observed that ATG significantly enhanced autophagy in yeast. We further verified that ATG can enhance autophagy by targeting protein phosphatase 2A (PP2A), leading to an increased lifespan. Meanwhile, we evaluated the antioxidant capacity of ATG and found that ATG increased the activities of the antioxidant enzymes, thereby reducing reactive oxygen species (ROS) and malondialdehyde (MDA) levels to improve the survival of yeast under oxidative stress. In addition, ATG was able to increase telomerase activity by enhancing the expression of EST1, EST2, and EST3 genes in yeast. In conclusion, ATG exerts anti-aging effects through induction of autophagy, antioxidative stress, and enhancement of telomerase activity in yeast, which is recognized as a potential molecule with promising anti-aging effects, deserving in-depth research in the future.

Molecular phylogenetics of the Ophiocordyceps sinensis-species complex lineage (Ascomycota, Hypocreales), with the discovery of new species and predictions of species distribution.

Ophiocordyceps sinensis is a famous traditional Chinese medicine adapted to the alpine environment of the Qinghai-Tibet Plateau and adjacent regions. Clarification of the species diversity of Ophiocordyceps sinensis and its relatives could expand the traditional medicinal resources and provide insights into the speciation and adaptation. The study is prompted by the discovery of a new species, O. megala, described here from a biodiversity hotspot in the Hengduan Mountains, China. Combined morphological, ecological, and phylogenetic evidence supports its distinctiveness from O. sinensis, O. xuefengensis, and O. macroacicularis. Additionally, based on the phylogenetic construction of Ophiocordyceps, a special clade was focused phylogenetically on the more closely related O. sinensis complex, which was defined as the O. sinensis- species complex lineage. A total of 10 species were currently confirmed in this lineage. We made a comprehensive comparison of the sexual/asexual morphological structures among this species complex, distinguishing their common and distinctive features. Furthermore, using the method of species distribution modelling, we studied the species ocurrences in relation to climatic, edaphic, and altitudinal variables for the eight species in the O. sinensis-species complex, and determined that their potential distribution could extend from the southeastern Qinghai-Tibet Plateau to the Xuefeng Mountains without isolating barrier. Thus, the biodiversity corridor hypothesis was proposed around the O. sinensis-species complex. Our study highlights the phylogeny, species diversity, and suitable distribution of the O. sinensis-species complex lineage, which should have a positive implication for the resource discovery and adaptive evolution of this unique and valuable group.

Effect of Online Clinic on Follow-Up Compliance and Survival Outcomes in Nasopharyngeal Carcinoma: Real-World Cohort Study from Endemic Area.

Nasopharyngeal carcinoma (NPC) requires regular follow-up to detect recurrence as early as possible. However, many patients are unable to regularly follow up due to the inconvenience of the conventional approach. Therefore, this study was designed to investigate the impact of the online clinic on follow-up compliance and prognosis in NPC patients. Patients who were first diagnosed with NPC between April 2019 and November 2019 were enrolled. Good follow-up compliance was defined as having at least one follow-up visit every 6 months within 2 years after treatment completion. Sensitivity analyses were performed using a propensity score matching model. A total of 539 (42%) patients used online follow-up while 731 (58%) used traditional follow-up. The median age of patients in the online cohort was lower than that in the traditional cohort (44 vs. 47, p < 0.001). Compared with the traditional cohort, the online cohort had significantly better follow-up compliance (57.3% vs. 17.1%, p < 0.001) and a higher 2-year PFS rate (98.1% vs. 94.4%, p = 0.003). Survival analysis showed that online follow-up was an independent factor for better survival prognosis (HR 0.39, 95%CI 0.20-0.74, p = 0.004). Sensitivity analysis further confirmed these results. Our study found that the online clinic increased follow-up compliance and improved prognosis in NPC patients.

Role of nuclear protein Akirin in the modulation of female reproduction in Nilaparvata lugens (Hemiptera: Delphacidae).

Introduction: Akirin as a highly conserved transcription factor, exerts a profound influence on the growth, development, immune response, and reproductive processes in animals. The brown planthopper (BPH), Nilaparvata lugens, a major pest in rice production in Asia, possesses high reproductive capacity, a critical factor contributing to reduced rice yields. The aims of this study were to demonstrate the regulatory role of Akirin in the reproduction of BPH. Methods: In this study, quantitative PCR (qPCR) was used to detect the mRNA expression of genes. RNA interference (RNAi) was used to downregulate the expression of Akirin gene, and RNA sequencing (RNA-seq) was used to screen for differentially expressed genes caused by Akirin downregulation. Hormone contents were measured with the enzyme linked immunosorbent assay (ELISA), and protein content was evaluated with the bicinchoninic acid (BCA) method. Results: Using BPH genome data, we screened for an Akirin gene (NlAkirin). An analysis of tissue-specific expressions showed that NlAkirin was expressed in all tissues tested in female BPH, but its expression level was highest in the ovary. After inhibiting the mRNA expression of NlAkirin in BPH females, the number of eggs laid, hatching rate, and number of ovarioles decreased. Transcriptome sequencing was performed, following a NlAkirin double-stranded RNA treatment. Compared with the genes of the control, which was injected with GFP double-stranded RNA, there were 438 upregulated genes and 1012 downregulated genes; the expression of vitellogenin (Vg) and vitellogenin receptor (VgR) genes as well as the mRNA expression of genes related to the target of rapamycin (TOR), juvenile hormone (JH), and insulin pathways involved in Vg synthesis was significantly downregulated. As a result of NlAkirin knockdown, the titers of JH III and Ecdysone (Ecd) were downregulated in unmated females but returned to normal levels in mated females. The ovarian protein contents in both unmated and mated females were downregulated. Discussion and conclusion: Our results suggest that NlAkirin affects female BPH reproduction by regulating the mRNA expression of genes related to the Vg, VgR, TOR, JH, and insulin signaling pathways, in addition to the titers of JH III and Ecd. The findings of this research provide novel insights into the regulatory role of Akirin in insect reproductive capacity.

Prognosis and immunotherapy response prediction based on M2 macrophage-related genes in colon cancer.

BACKGROUND: M2 macrophage were revealed to play a crucial role in immune evasion and immunotherapies. This study aims to explore the potential significance of M2 macrophage-related genes in colon adenocarcinoma (COAD) by analysizing the transcriptome data in a comprehensive way. METHODS: We collected RNA-sequencing (RNA-seq) data of COAD from The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO) databases. We calculated the immune infiltration scores of every sample using CIBERSORT algorithm. Through weighted gene co-expression network analysis (WGCNA), we picked out M2 macrophage-related genes. With these genes we screened out prognosis related genes which were utilized to construct a signature to assess the prognosis of patients. To extend the potential application of the signature, we also calculated the correlations with immune infiltration. Finally, we applied techniques such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunoblotting (Western Blotting) to validate the RNF32 gene in cellular in vitro assays. RESULTS: Seven M2 macrophage-related genes signature was constructed, which was an excellent prognostic predictor in two independent groups. The high-risk group showed lower immune infiltration and poorer response to immunotherapies than those of the low-risk group. The cell vitro experiments showed that the expression level of RNF32 was upregulated in colon cancer cell lines compared with normal cell lines. Moreover, we found that RNF32 may promote the proliferation, migration and invasion of cancer cells in vitro by inhibiting apoptosis. CONCLUSION: A novel M2 macrophage-related gene signature affects the prognosis and immune characteristics of colon cancer.

Thymol improves the growth performance of blue foxes by regulating the gut microbiota.

Introduction: The drawbacks of using antibiotics as feed additives for blue foxes have gradually become apparent; moreover, thymol has wide-spectrum antimicrobial activity and has the potential to replace antibiotics in various animals. However, there are few reports on the effects of thymol on blue foxes. Methods: This study aimed to investigate the effects of different concentrations of thymol on the growth performance, apparent nutrient digestibility, serum biochemical indicators, intestinal morphology, and gut microbiota of blue foxes. Twenty-four male blue foxes (120 ± 5 d) of similar weight (6.05 ± 0.16 kg) were randomly divided into 4 groups. 0, 100, 200, and 300 mg/kg thymol were added to the basal diets of groups C, L, M, and H, respectively. Results: Compared with those in the C group, the addition of 100 mg/kg thymol to the diet significantly increased organic matter (OM) digestibility, crude protein (CP) digestibility, immunoglobulin (Ig) A, IgM, the VH of the duodenum, the CD of the jejunum, the VH of the ileum, and the VH/CD of the ileum (P < 0.05) and strongly significantly increased IgG (P < 0.01). The addition of 200 mg/kg thymol to the diet increased the VH/CD of the duodenum (P < 0.05). The addition of 300 mg/kg thymol to the diet significantly increased the VH and CD of the jejunum (P < 0.05). The addition of 200 mg/kg and 300 mg/kg thymol to the diets increased the final weight (FW) (P < 0.05). Adding 100 mg/kg thymol significantly increased the levels of interleukin-4 (IL-4) and catalase (CAT) compared with those in the other groups (P < 0.05). 16S rRNA gene detection revealed that thymol can change the abundances of Bifidobacterium, Fusobacterium, Allobaculum, Streptococcus, Megasphaera, and Lactobacillus in the gut. Conclusion: The addition of thymol to diets can increase the abundance of Bifidobacterium, Fusobacterium, and Allobaculum, which may contribute to improving the growth performance of blue foxes.