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N6-methyladenosine (m6A) methylation is the most prevalent internal epigenetic posttranscriptional mechanism for regulating mammalian RNA. Despite recent advances in determining the biological functions of m6A methylation, its association with the pathology of ovarian endometriosis remains uncertain. Herein, we performed m6A transcriptome-wide profiling to identify key lncRNAs with m6A modification involved in ovarian endometriosis development by bioinformatics analysis. We found the total m6A level was lower in ovarian endometriosis than in normal endometrium samples, with 9663 m6A peaks associated with 8989 lncRNAs detected in ovarian endometriosis and 9902 m6A peaks associated with 9210 lncRNAs detected in normal endometrium samples. These m6A peaks were primarily enriched within AAACU motifs. Functional enrichment analysis indicated that pathways involving the regulation of adhesion and development were significantly enriched in these differentially methylated lncRNAs. The regulatory relationships among lncRNAs, microRNAs (miRNAs), and mRNAs were identified by competing endogenous RNA (ceRNA) analysis and determination of the network regulating lncRNA-mRNA expression. Several specific lncRNA, including LINC00665, LINC00937, FZD10-AS1, DIO3OS and GATA2-AS1 which were differently expressed and modified by m6A, were validated using qRT-PCR and its interaction with infiltrating immune cells was explored. Furthermore, we found LncRNA DIO3OS promotes the invasion and migration of Human endometrial stromal cells (THESCs) and ALKBH5 regulates the expression of the lncRNA DIO3OS through m6A modification in vitro. Our study firstly revealed the transcriptome-wide map of m6A modification in lncRNAs of ovarian endometriosis. These findings may enable the determination of the underlying mechanism governing the pathogenesis of ovarian endometriosis and provide theoretical basis for further deeper research on the role of m6A in the development of ovarian endometriosis.
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Endometriose , RNA Longo não Codificante , Feminino , Humanos , Animais , RNA Longo não Codificante/genética , Transcriptoma , Endometriose/genética , Adenosina , Metilação , MamíferosRESUMO
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
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Comunicação Celular , Análise de Célula Única , Humanos , Ligantes , Análise de Célula Única/métodos , Software , Biologia Computacional/métodos , Algoritmos , Máquina de Vetores de Suporte , Análise de Sequência de RNA/métodos , Melanoma/metabolismo , Melanoma/patologia , Melanoma/genética , Proteoma/metabolismo , Redes Neurais de ComputaçãoRESUMO
BACKGROUND: Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. METHODS: A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. RESULTS: Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). CONCLUSIONS: The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.
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Neoplasias Ovarianas , Radiômica , Feminino , Humanos , Algoritmos , Carcinoma Epitelial do Ovário/diagnóstico por imagem , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/cirurgia , Tomografia Computadorizada por Raios XRESUMO
Background: Virtual monoenergetic images (VMIs) at a low energy level can improve image quality when the amount of iodinated contrast media (CM) is reduced. The purpose was to evaluate the feasibility of using an extremely low CM volume and injection rate in cerebral computed tomography angiography (CTA) on a dual-layer spectral detector computed tomography (CT). Methods: Patients who were clinically suspected of intracranial aneurysm or cerebrovascular diseases were included in our study (from June to November 2022). In this prospective study, 80 patients were randomly enrolled into group A (8 mL of CM with a 1-mL/s flow rate) or group B (40 mL of CM with 4-mL/s flow rate). The VMIs at 40-70 keV in group A and polychromatic conventional images in the 2 groups were reconstructed. CT attenuation, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated via the t-test or Mann-Whitney test (2 groups), while analysis of variance or Kruskal-Wallis test (multiple groups). Subjective image quality was assessed on a 5-point scale. Results: In group A, the subjective image quality score, CT attenuation, and CNR of the internal carotid artery (ICA) and middle cerebral artery (MCA) were the highest on VMIs at 40 keV. The image noise on VMIs at 40 keV was 5.08±0.84 Hounsfield units. The subjective image quality score, CT value of the ICA, MCA, and cerebral parenchyma on VMIs at 40 keV in group A were similar to those in group B (all P values >0.05). Compared to those in group B, the VMIs at 40 keV in group A demonstrated a significantly higher mean SNR and CNR of the ICA (mean SNR: 46.22±20.18 vs. 34.32±12.40, P=0.002; CNR: 55.47±13.43 vs. 46.18±12.30, P=0.002) and MCA [SNR: 13.66 (9.78, 20.29) vs. 9.99 (7.53, 14.00), P=0.003; CNR: 47.00±12.71 vs. 39.45±10.47, P=0.005]. Conclusions: Cerebral CTA on VMIs at 40 keV with 8 mL of CM and a 1-mL/s injection rate can provide diagnostic image quality.
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Global lake ecosystems are subjected to an increased occurrence of heat extremes, yet their impact on lake warming remains poorly understood. In this study, we employed a hybrid physically-based/statistical model to assess the contribution of heat extremes to variations in surface water temperature of 2260 lakes in China from 1985 to 2022. Our study indicates that heat extremes are increasing at a rate of about 2.08 days/decade and an intensity of about 0.03 °C/ day·decade in China. The warming rate of lake surface water temperature decreases from 0.16 °C/decade to 0.13 °C/decade after removing heat extremes. Heat extremes exert a considerable influence on long-term lake surface temperature changes, contributing 36.5% of the warming trends within the studied lakes. Given the important influence of heat extremes on the mean warming of lake surface waters, it is imperative that they are adequately accounted for in climate impact studies.
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Purpose: We aimed to develop and validate a predictive model for myocardial injury in individuals undergoing major abdominal surgery. Methods: This multicenter retrospective cohort analysis included 3546 patients aged ≥45 years who underwent major abdominal surgeries at two Chinese tertiary hospitals. The primary outcome was myocardial injury after noncardiac surgery (MINS), defined as prognostically relevant myocardial injury due to ischemia that occurs during or within 30 days after noncardiac surgery. The LASSO algorithm and logistic regression were used to construct a predictive model for postoperative MINS in the development cohort, and the performance of this prediction model was validated in an external independent cohort. Results: A total of 3546 patients were included in our study. MINS manifested in 338 (9.53 %) patients after surgery. The definitive predictive model for MINS was developed by incorporating age, American Society of Anesthesiologists (ASA) classification, preoperative hemoglobin concentration, preoperative serum ALB concentration, blood loss, total infusion volume, and operation time. The area under the curve (AUC) of our model was 0.838 and 0.821 in the derivation and validation cohorts, respectively. Conclusions: Preoperative hemoglobin levels, preoperative serum ALB concentrations, infusion volume, and blood loss are independent predictors of MINS. Our predictive model can prove valuable in identifying patients at moderate-to-high risk prior to non-cardiac surgery.
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To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.
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Carcinoma Epitelial do Ovário , Nomogramas , Neoplasias Ovarianas , Tomografia Computadorizada por Raios X , Humanos , Feminino , Carcinoma Epitelial do Ovário/diagnóstico por imagem , Carcinoma Epitelial do Ovário/patologia , Pessoa de Meia-Idade , Tomografia Computadorizada por Raios X/métodos , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/patologia , Estudos Retrospectivos , Idoso , Adulto , Curva ROC , Metástase Neoplásica , Algoritmos , RadiômicaRESUMO
Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally important to monitor and assess water trophic status of lakes. However, a reliable model for accurately estimating the trophic state index (TSI) of lakes across a large-scale region is still lacking due to their high complexity. Here, we proposed an optical mechanism-based deep learning approach to remotely estimate TSI of lakes based on Landsat images. The approach consists of two steps: (1) determining the optical indicators of TSI and modeling the relationship between them, and (2) developing an approach for remotely deriving the determined optical indicator from Landsat images. With a large number of in situ datasets measured from lakes (2804 samples from 88 lakes) across China with various optical properties, we trained and validated three machine learning methods including deep neural network (DNN), k-nearest neighbors (KNN) and random forest (RF) to model TSI with the optical indicators and TSI and derive the determined optical indicator from Landsat images. The results showed that (1) the total absorption coefficients of optically active constituents at 440 nm (at-w(440)) performs best in characterizing TSI, and (2) DNN outperforms other models in the inversion of both TSI and at-w(440). Overall, our proposed optical mechanism-based deep learning approach demonstrated a robust and satisfactory performance in assessing TSI using Landsat images (root mean squared error (RMSE) = 5.95, mean absolute error (MAE) = 4.81). This highlights its merit as a nationally-adopted method in lake water TSI estimation, enabling the convenience of the acquisition of water eutrophic information in large scale, thereby assisting us in managing lake ecology. Therefore, we assessed water TSI of 961 lakes (>10 km2) across China using the proposed approach. The resulting at-w(440) and TSI ranged from 0.01 m-1 to 31.42 m-1 and from 6 to 96, respectively. Of all these studied lakes, 96 lakes (11.40 %) were oligotrophic, 338 lakes were mesotrophic (40.14 %), 360 lakes were eutrophic (42.76 %), and 48 were hypertrophic (5.70 %) in 2020.
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Aprendizado Profundo , Lagos , Humanos , Monitoramento Ambiental/métodos , Ecossistema , Eutrofização , China , ÁguaRESUMO
Heart failure is the final stage of several cardiovascular diseases, and the key to effectively treating heart failure is to reverse or delay ventricular remodelling. Levosimendan is a novel inotropic and vasodilator agent used in heart failure, whereas the impact of levosimendan on ventricular remodelling is still unclear. This study aims to investigate the impact of levosimendan on ventricular remodelling in patients with left ventricular systolic dysfunction. Electronic databases were searched to identify eligible studies. A total of 66 randomized controlled trials involving 7968 patients were included. Meta-analysis results showed that levosimendan increased left ventricular ejection fraction [mean difference (MD) = 3.62, 95% confidence interval (CI) (2.88, 4.35), P < 0.00001] and stroke volume [MD = 6.59, 95% CI (3.22, 9.96), P = 0.0001] and significantly reduced left ventricular end-systolic volume [standard mean difference (SMD) = -0.52, 95% CI (-0.67, -0.37), P < 0.00001], left ventricular end-diastolic volume index [SMD = -1.24, 95% CI (-1.61, -0.86), P < 0.00001], and left ventricular end-systolic volume index [SMD = -1.06, 95% CI (-1.43, -0.70), P < 0.00001]. In terms of biomarkers, levosimendan significantly reduced the level of brain natriuretic peptide [SMD = -1.08, 95% CI (-1.60, -0.56), P < 0.0001], N-terminal pro-brain natriuretic peptide [SMD = -0.99, 95% CI (-1.41, -0.56), P < 0.00001], and interleukin-6 [SMD = -0.61, 95% CI (-0.86, -0.35), P < 0.00001]. Meanwhile, levosimendan may increase the incidence of hypotension [risk ratio (RR) = 1.24, 95% CI (1.12, 1.39), P < 0.0001], hypokalaemia [RR = 1.57, 95% CI (1.08, 2.28), P = 0.02], headache [RR = 1.89, 95% CI (1.50, 2.39), P < 0.00001], atrial fibrillation [RR = 1.31, 95% CI (1.12, 1.52), P = 0.0005], and premature ventricular complexes [RR = 1.86, 95% CI (1.27, 2.72), P = 0.001]. In addition, levosimendan reduced all-cause mortality [RR = 0.83, 95% CI (0.74, 0.94), P = 0.002]. In conclusion, our study found that levosimendan might reverse ventricular remodelling when applied in patients with left ventricular systolic dysfunction, especially in patients undergoing cardiac surgery, decompensated heart failure, and septic shock.
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Simendana , Disfunção Ventricular Esquerda , Remodelação Ventricular , Simendana/uso terapêutico , Simendana/farmacologia , Simendana/administração & dosagem , Humanos , Disfunção Ventricular Esquerda/tratamento farmacológico , Disfunção Ventricular Esquerda/fisiopatologia , Remodelação Ventricular/efeitos dos fármacos , Função Ventricular Esquerda/fisiologia , Função Ventricular Esquerda/efeitos dos fármacos , Volume Sistólico/fisiologia , Volume Sistólico/efeitos dos fármacos , Cardiotônicos/uso terapêutico , SístoleRESUMO
Autophagy is a dynamic self-renovation biological process that maintains cell homeostasis and is responsible for the quality control of proteins, organelles, and energy metabolism. The E1-like ubiquitin-activating enzyme autophagy-related gene 7 (ATG7) is a critical factor that initiates classic autophagy reactions by promoting the formation and extension of autophagosome membranes. Recent studies have identified the key functions of ATG7 in regulating the cell cycle, apoptosis, and metabolism associated with the occurrence and development of multiple diseases. This review summarizes how ATG7 is precisely programmed by genetic, transcriptional, and epigenetic modifications in cells and the relationship between ATG7 and aging-related diseases.
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Autofagossomos , Autofagia , Proteína 7 Relacionada à Autofagia/genética , Autofagossomos/metabolismo , Autofagia/genética , Enzimas Ativadoras de Ubiquitina/metabolismo , Enzimas de Conjugação de Ubiquitina/metabolismoRESUMO
Background: Ovarian cancer (OC) is the most lethal tumor within the female reproductive system. Medical imaging plays a significant role in diagnosis and monitoring OC. This study aims to use bibliometric analysis to explore the current research hotspots and collaborative networks in the application of medical imaging in OC from 2000 to 2022. Methods: A systematica search for medical imaging in OC was conducted on the Web of Science Core Collection on August 9, 2023. All reviews and articles published from January 2000 to December 2022 were downloaded, and an analysis of countries, institutions, journals, keywords, and collaborative networks was perfomed using CiteSpace and VOSviewer. Results: A total of 5,958 publications were obtained, demonstrating a clear upward trend in annual publications over the study peroid. The USA led in productivity with 1,373 publications, and Harvard University emerged as the most prominent institution with 202 publications. Timmerman D was the most prolific contributor with 100 publications, and Gynecological Oncology led in the number of publications with 296. The top three keywords were "ovarian cancer" (1,256), "ultrasound" (725), and "diagnosis" (712). In addition, "pelvic masses" had the highest burst strength (25.5), followed by "magnetic resonance imaging (MRI)" (21.47). Recent emergent keywords such as "apoptosis", "nanoparticles", "features", "accuracy", and "human epididymal protein 4 (HE 4)" reflect research trends in this field and may become research hotspots in the future. Conclusion: This study provides a comprehensive summary of the key contributions of OC imaging to field's development over the past 23 years. Presently, primary areas of OC imaging research include MRI, targeted therapy of OC, novel biomarker (HE 4), and artificial intelligence. These areas are expected to influence future research endeavors in this field.
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To enable diverse functions and precise regulation, an RNA sequence often folds into complex yet distinct structures in different cellular states. Probing RNA in its native environment is essential to uncovering RNA structures of biological contexts. However, current methods generally require large amounts of input RNA and are challenging for physiologically relevant use. Here, we report smartSHAPE, a new RNA structure probing method that requires very low amounts of RNA input due to the largely reduced artefact of probing signals and increased efficiency of library construction. Using smartSHAPE, we showcased the profiling of the RNA structure landscape of mouse intestinal macrophages upon inflammation, and provided evidence that RNA conformational changes regulate immune responses. These results demonstrate that smartSHAPE can greatly expand the scope of RNA structure-based investigations in practical biological systems, and also provide a research paradigm for the study of post-transcriptional regulation.