SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

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.

Development and external validation of a perioperative clinical model for predicting myocardial injury after major abdominal surgery: A retrospective cohort study.

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.

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.

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.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.

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.

Insights on E1-like enzyme ATG7: functional regulation and relationships with aging-related diseases.

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.

Effect of levosimendan on ventricular remodelling in patients with left ventricular systolic dysfunction: a meta-analysis.

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.

An optical mechanism-based deep learning approach for deriving water trophic state of China's lakes from Landsat images.

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.

Transcriptome-wide N6-methyladenosine (m6A) methylation profiling of long non-coding RNAs in ovarian endometriosis.

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.

Feasibility of using 8 mL of iodinated contrast media in cerebral computed tomographic angiography with a dual-layer spectral detector.

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.

The impact of extreme heat on lake warming in China.

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.

Regulated secretion of mutant p53 negatively affects T lymphocytes in the tumor microenvironment.

Several studies have demonstrated the role of the oncogenic mutant p53 in promoting tumor progression; however, there is limited information on the effects of secreted oncogenic mutant p53 on the tumor microenvironment and tumor immune escape. In this study, we found that secretion of mutant p53, determined by exosome content, is dependent on its N-terminal dileucine motif via its binding to ß-adaptin, and inhibited by the CHK2-mediated-Ser 20 phosphorylation. Moreover, we observed that the mutant p53 caused downregulation and dysfunction of CD4+ T lymphocytes in vivo and downregulated the levels and activities of rate-limiting glycolytic enzymes in vitro. Furthermore, inhibition of mutant p53 secretion by knocking down AP1B1 or mutation of dileucine motif could reverse the quantity and function of CD4+ T lymphocytes and restrain the tumor growth. Our study demonstrates that the tumor-derived exosome-mediated secretion of oncogenic mutant p53 inhibits glycolysis to alter the immune microenvironment via functional suppression of CD4+ T cells, which may be the underlying mechanism for tumor immune escape. Therefore, targeting TDE-mediated p53 secretion may serve as a potential therapeutic target for cancer treatment.

Application of medical imaging in ovarian cancer: a bibliometric analysis from 2000 to 2022.

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.

The application of radiology for dilated cardiomyopathy diagnosis, treatment, and prognosis prediction: a bibliometric analysis.

Background: Radiology plays a highly crucial role in the diagnosis, treatment, and prognosis prediction of dilated cardiomyopathy (DCM). Related research has increased rapidly over the past few years, but systematic analyses are lacking. This study thus aimed to provide a reference for further research by analyzing the knowledge field, development trends, and research hotspots of radiology in DCM using bibliometric methods. Methods: Articles on the radiology of DCM published between 2002 and 2021 in the Web of Science Core Collection database (WoSCCd) were searched and analyzed. Data were retrieved and analyzed using CiteSpace V, VOSviewer, and Scimago Graphic software, and included the name, research institution, and nationality of authors; journals of publication; and the number of citations. Results: A total of 4,257 articles were identified on radiology of DCM from WoSCCd. The number of articles published in this field has grown steadily from 2002 to 2021 and is expected to reach 392 annually by 2024. According to subfields, the number of papers published in cardiac magnetic resonance field increased steadily. The authors from the United States published the most (1,364 articles, 32.04%) articles. The author with the most articles published was Bax JJ (54 articles, 1.27%) from Leiden University Medical Center. The most cited article was titled "2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure", with 138 citations. Citation-based clustering showed that arrhythmogenic cardiomyopathy, T1 mapping, and endomyocardial biopsy are the current hots pots for research in DCM radiology. The most frequently occurring keyword was "dilated cardiomyopathy". The keyword-based clusters mainly included "late gadolinium enhancement", "congestive heart failure", "cardiovascular magnetic resonance", "sudden cardiac death", "ventricular arrhythmia", and "cardiac resynchronization therapy". Conclusions: The United States and Northern Europe are the most influential countries in research on DCM radiology, with many leading distinguished research institutions. The current research hots pots are myocardial fibrosis, risk stratification of ventricular arrhythmia, the prognosis of cardiac resynchronization therapy (CRT) treatment, and subtype classification of DCM.

Controls of thermal response of temperate lakes to atmospheric warming.

Atmospheric warming heats lakes, but the causes of variation among basins are poorly understood. Here, multi-decadal profiles of water temperatures, trophic state, and local climate from 345 temperate lakes are combined with data on lake geomorphology and watershed characteristics to identify controls of the relative rates of temperature change in water (WT) and air (AT) during summer. We show that differences in local climate (AT, wind speed, humidity, irradiance), land cover (forest, urban, agriculture), geomorphology (elevation, area/depth ratio), and water transparency explain >30% of the difference in rate of lake heating compared to that of the atmosphere. Importantly, the rate of lake heating slows as air warms (P < 0.001). Clear, cold, and deep lakes, especially at high elevation and in undisturbed catchments, are particularly responsive to changes in atmospheric temperature. We suggest that rates of surface water warming may decline relative to the atmosphere in a warmer future, particularly in sites already experiencing terrestrial development or eutrophication.

Climate change drives rapid warming and increasing heatwaves of lakes.

Climate change could seriously threaten global lake ecosystems by warming lake surface water and increasing the occurrence of lake heatwaves. Yet, there are great uncertainties in quantifying lake temperature changes globally due to a lack of accurate large-scale model simulations. Here, we integrated satellite observations and a numerical model to improve lake temperature modeling and explore the multifaceted characteristics of trends in surface temperatures and lake heatwave occurrence in Chinese lakes from 1980 to 2100. Our model-data integration approach revealed that the lake surface waters have warmed at a rate of 0.11 °C 10a-1 during the period 1980-2021, being only half of the pure model-based estimate. Moreover, our analysis suggested that an asymmetric seasonal warming rate has led to a reduced temperature seasonality in eastern plain lakes but an amplified one in alpine lakes. The durations of lake heatwaves have also increased at a rate of 7.7 d 10a-1. Under the high-greenhouse-gas-emission scenario, lake surface temperature and lake heatwave duration were projected to increase by 2.2 °C and 197 d at the end of the 21st century, respectively. Such drastic changes would worsen the environmental conditions of lakes subjected to high and increasing anthropogenic pressures, posing great threats to aquatic biodiversity and human health.

Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism.

The direction of human gaze is an important indicator of human behavior, reflecting the level of attention and cognitive state towards various visual stimuli in the environment. Convolutional neural networks have achieved good performance in gaze estimation tasks, but their global modeling capability is limited, making it difficult to further improve prediction performance. In recent years, transformer models have been introduced for gaze estimation and have achieved state-of-the-art performance. However, their slicing-and-mapping mechanism for processing local image patches can compromise local spatial information. Moreover, the single down-sampling rate and fixed-size tokens are not suitable for multiscale feature learning in gaze estimation tasks. To overcome these limitations, this study introduces a Swin Transformer for gaze estimation and designs two network architectures: a pure Swin Transformer gaze estimation model (SwinT-GE) and a hybrid gaze estimation model that combines convolutional structures with SwinT-GE (Res-Swin-GE). SwinT-GE uses the tiny version of the Swin Transformer for gaze estimation. Res-Swin-GE replaces the slicing-and-mapping mechanism of SwinT-GE with convolutional structures. Experimental results demonstrate that Res-Swin-GE significantly outperforms SwinT-GE, exhibiting strong competitiveness on the MpiiFaceGaze dataset and achieving a 7.5% performance improvement over existing state-of-the-art methods on the Eyediap dataset.