Porcine nasal septum cartilage-derived decellularized matrix promotes chondrogenic differentiation of human umbilical mesenchymal stem cells without exogenous growth factors

BACKGROUND

In the domain of plastic surgery, nasal cartilage regeneration is of significant importance. The extracellular matrix (ECM) from porcine nasal septum cartilage has shown potential for promoting human cartilage regeneration. Nonetheless, the specific biological inductive factors and their pathways in cartilage tissue engineering remain undefined.

METHODS

The decellularized matrix derived from porcine nasal septum cartilage (PN-DCM) was prepared using a grinding method. Human umbilical cord mesenchymal stem cells (HuMSCs) were cultured on these PN-DCM scaffolds for 4 weeks without exogenous growth factors to evaluate their chondroinductive potential. Subsequently, proteomic analysis was employed to identify potential biological inductive factors within the PN-DCM scaffolds.

RESULTS

Compared to the TGF-β3-cultured pellet model serving as a positive control, the PN-DCM scaffolds promoted significant deposition of a Safranin-O positive matrix and Type II collagen by HuMSCs. Gene expression profiling revealed upregulation of ACAN, COL2A1, and SOX9. Proteomic analysis identified potential chondroinductive factors in the PN-DCM scaffolds, including CYTL1, CTGF, MGP, ITGB1, BMP7, and GDF5, which influence HuMSC differentiation.

CONCLUSION

Our findings have demonstrated that the PN-DCM scaffolds promoted HuMSC differentiation towards a nasal chondrocyte phenotype without the supplementation of exogenous growth factors. This outcome is associated with the chondroinductive factors present within the PN-DCM scaffolds.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

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.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

[Effect of HBV DNA load on the safety and prognosis of systematic therapy in advanced hepatocellular carcinoma]

Objective: To study the effect of hepatitis B virus (HBV) infection on the occurrence of liver damage, HBV reactivation (HBVr) and the influence of HBVr on the prognosis of patients with advanced hepatocellular carcinoma (HCC) receiving systemic therapy. Methods: The clinical data of 403 patients with HBV-related HCC at the Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-Sen University et al, from July 2018 to December 2020 were collected. The incidence of liver damage and HBVr during systematic therapy, and the influence of HBVr on survival prognosis were analyzed. Results: Of the 403 patients, 89.1% were male (n=359), with a median age of 51 years (51.5±12.1). Before propensity score matching (PSM), the proportion of patients with cirrhosis, TNM and advanced BCLC stage was higher in high HBV-DNA (baseline HBV-DNA>1000 U/ml, n=147) group comparing with the low HBV-DNA (baseline HBV DNA≤1000 u/ml, n=256) group (P<0.05). There was no significant difference in baseline indexes between the two groups after PSM. In 290 patients after PSM, there was no significant difference in the incidence of liver damage and HBVr between high HBV-DNA group and low HBV-DNA group (P>0.05). Survival analysis was performed on 169 patients with survival data, the median overall survival (OS) was found to be 11.49 months (95%CI: 7.77-12.89) and 16.65 months (95%CI: 10.54-21.99, P=0.008) in the high and low HBV-DNA groups, respectively. And median progression-free survival (PFS) was 7.41 months (95%CI: 5.06-8.67) and 10.55 months (95%CI: 6.72-13.54, P=0.038), respectively, with a statistically significant difference. There were no differences in overall survival (OS) and progression-free survival (PFS) between patients with and without HBVr and those with or without liver damage (P>0.05). Conclusions: HBV-DNA levels above 1 000 U/ml before systemic therapy do not increase the risk of liver damage or HBVr during systemic therapy in patients with HBV-related hepatocellular carcinoma, and such patients can safely receive systemic therapy.

BINDTI: A bi-directional Intention network for drug-target interaction identification based on attention mechanisms

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how to precisely represent drug and protein features is a major challenge for DTI prediction. Here, we developed an end-to-end DTI identification framework called BINDTI based on bi-directional Intention network. First, drug features are encoded with graph convolutional networks based on its 2D molecular graph obtained by its SMILES string. Next, protein features are encoded based on its amino acid sequence through a mixed model called ACmix, which integrates self-attention mechanism and convolution. Third, drug and target features are fused through bi-directional Intention network, which combines Intention and multi-head attention. Finally, unknown drug-target (DT) pairs are classified through multilayer perceptron based on the fused DT features. The results demonstrate that BINDTI greatly outperformed four baseline methods (i.e., CPI-GNN, TransfomerCPI, MolTrans, and IIFDTI) on the BindingDB, BioSNAP, DrugBank, and Human datasets. More importantly, it was more appropriate to predict new DTIs than the four baseline methods on imbalanced datasets. Ablation experimental results elucidated that both bi-directional Intention and ACmix could greatly advance DTI prediction. The fused feature visualization and case studies manifested that the predicted results by BINDTI were basically consistent with the true ones. We anticipate that the proposed BINDTI framework can find new low-cost drug candidates, improve drugs' virtual screening, and further facilitate drug repositioning as well as drug discovery. BINDTI is publicly available at https://github.com/plhhnu/BINDTI.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Balancing low-carbon and eco-friendly development: coordinated development strategy for land use carbon emission efficiency and land ecological security

Correctly identifying and handling the relationship between land use carbon emission efficiency (LUCEE) and land ecological security (LES) are important to promote carbon neutrality in the overall layout of ecological civilization construction. This study takes 30 provinces in China as the research unit and measures the level of LUCEE and LES in each province in the period from 2011 to 2020 via a super-efficient slack-based measure model considering undesirable output. The coupling coordination degree (CCD) of LUCEE and LES is calculated, and its spatiotemporal evolution pattern is explored by kernel density estimation and standard deviational ellipse (SDE). The Dagum Gini coefficient is used to study spatial regional differences and the sources of differences. Results show that (1) China's LUCEE exhibited a downward and then an upward trend, as well as a spatial pattern of "high in the west and low in the east" with obvious regional differences. The LES experienced a positive transformation of "less secure → basically secure → more secure" nationwide, with no apparent regional differences. (2) The kernel density curves showed a continuous increase in CCD in general, while interprovincial differences increased, then decreased, and shifted from multipolar to bipolar differentiation. (3) The migration of SDE centers in CCD demonstrated a path of "southeast → southwest → northeast," and the ellipticity increased from 0.167 to 0.173, showing a trend of concentrated distribution. (4) The overall Gini coefficient of the national CCD indicated a decreasing trend, but imbalances remained, with the largest annual average value in the western region (0.120) and the smallest in the northeast (0.044). The main source of regional disparity was the intensity of transvariation. Accordingly, this study proposes targeted regional development strategies to promote low-carbon sustainable land use and improve the ability of land ecosystems to prevent security risks.

Circ_0026579 knockdown ameliorates lipopolysaccharide-induced human lung fibroblast cell injury by regulating CXCR1 via miR-370-3p

Pneumonia is an inflammatory disease in lower respiratory tracts and its development involves the regulation of RNAs. Circular RNAs are a class of RNA subgroups that can mediate the progression of pneumonia. However, the molecular mechanism of circ_0026579 in regulating pneumonia occurrence remains unclear. The study is designed to reveal the role of circ_0026579 in lipopolysaccharide (LPS)-induced human lung fibroblast cell injury and the underlying mechanism. The expression levels of circ_0026579, miR-370-3p and C-X-C motif chemokine receptor 1 (CXCR1) were detected by quantitative real-time polymerase chain reaction or by western blotting. The production of tumour necrosis factor-α, interleukin (IL)-1β and IL-6 was assessed by enzyme-linked immunosorbent assays. Malondialdehyde and superoxide dismutase levels were analysed using commercial kits. Cell viability, proliferation and apoptosis were analysed by cell counting kit-8 assay, 5-Ethynyl-2'-deoxyuridine assay and flow cytometry analysis, respectively. The binding relationship between miR-370-3p and circ_0026579 or CXCR1 was identified by dual-luciferase reporter assay, RNA immunoprecipitation assay and RNA pull-down assay. Circ_0026579 and CXCR1 expression were significantly upregulated, whereas miR-370-3p was downregulated in the serum of pneumonia patients. LPS treatment induced inflammatory response, oxidative stress and cell apoptosis and inhibited cell proliferation in MRC-5 cells; however, these effects were reversed after circ_0026579 depletion. In terms of the mechanism, circ_0026579 acted as a miR-370-3p sponge, and miR-370-3p combined with CXCR1. Additionally, circ_0026579 depletion ameliorated LPS-induced MRC-5 cell disorder by increasing miR-370-3p expression. CXCR1 overexpression also relieved the miR-370-3p-mediated effects in LPS-treated MRC-5 cells. Further, circ_0026579 induced CXCR1 expression by interacting with miR-370-3p. Circ_0026579 absence ameliorated MRC-5 cell dysfunction induced by LPS through the regulation of the miR-370-3p/CXCR1 axis.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

[Clinical and laboratory features of SF3B1-mutated myeloproliferative neoplasms]

Objective: To explore the clinical and laboratory characteristics of SF3B1 gene mutations in myeloproliferative neoplasms (MPN) patients. Methods: The clinical data of 273 MPN patients who were diagnosed MPN and treated in the Second Hospital of Tianjin Medical University from November 2017 to March 2023 were retrospectively analyzed. There were 133 males and 140 females, with a median age M(Q1,Q3)of 56(46, 67) years. The molecular biology and cytogenetic characteristics were detected by second-generation sequencing (NGS) and R+G banding techniques, and the clinical and laboratory characteristics of patients with SF3B1 gene mutation were analyzed. Results: SF3B1 gene mutations were found in 13 patients (4.8%, 13/273).The types of SF3B1 mutations included missense (92.3%, 12/13) and nonsense mutations (7.7%, 1/13).Compared to the non-mutant cohort, patients in SF3B1 mutant cohort had older ages [68(51, 76) vs 56(45, 66)years,P=0.025], higher proportion of splenomegaly [46.2%(6/13) vs 15.8%(41/259),P=0.014]and secondary tumor [23.1%(3/13)vs 3.8%(10/260), P=0.018]with higher proportion of bone marrow blast [0.5%(0, 1.5%) vs 0(0, 0.5%),P=0.002] and lower hemoglobin[(104±36) vs (137±40) g/L,P=0.004] and hematocrit [31%(22%, 40%) vs 41%(35%, 52%),P=0.003]. All of the 10 patients in the SF3B1 mutant cohort whose ring sideroblast (RS) could be evaluated showed no RS formation. The overall survival, thrombosis-free survival and leukemia free survival of MPN patients in SF3B1 mutant cohort were 4.0 (2.0, 6.0), 2.0 (0.5, 4.5) and 4.0 (2.0, 6.0) years, respectively, while patients in the non-mutant cohort were 6.0 (3.0, 10.0), 5.0 (1.0, 8.0), 6.0 (3.0, 10.0) years, respectively, there were no statistical significance between two groups (Z=3.69, 1.66, 2.05, all P>0.05).The secondary tumor free survival of SF3B1 mutant cohort patients was 4.0 (2.0, 6.0) years, which was lower than that of non-mutant cohort patients [5.5 (3.0, 10.0) years, Z=18.18, P<0.001). Conclusions: MPN patients with SF3B1 gene mutations are older, more prone to splenomegaly and secondary tumors. They also have a higher proportion of bone marrow blast, lower hemoglobin and hematocrit, and show no RS formation.

CellDialog: A Computational Framework for Ligand-receptor-mediated Cell-cell Communication Analysis III

Intercellular communication significantly influences tumor progression, metastasis, and therapy resistance. An intercellular communication inference method includes two main procedures: ligand-receptor interaction (LRI) curation and LRI-mediated intercellular communication strength measurement. The construction of a comprehensive, high-confident and well-organized LRI database contributes to intercellular communication inference. Here, we developed a computational framework named CellDialog to reconstruct an intercellular connectivity network based on the combined expression of ligands and receptors involved in sender and receiver cells. CellDialog first captures high-confident LRIs through LRI feature extraction, feature selection, and classification. Furthermore, CellDialog uses a three-point estimation approach to measure the LRI-mediated intercellular communication strength by combining LRI filtering and single-cell RNA sequencing data. A comparison analysis of CellDialog and the other tools was conducted, and it was found that CellDialog can efficiently decode intercellular communications. Additionally, CellDialog offers a heatmap view and network view for intercellular communication visualization. In summary, CellDialog provides a tool that allows researchers to analyze intercellular signal transduction. It is freely available at https://github.com/plhhnu/CellDialog.

Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer

Objective. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods.Approach. Unsupervised harmonization identified the batch labels by K-means clustering. Supervised harmonization regarding the image acquisition factors (center, manufacturer, scanner, filter kernel) as known/given batch labels, and Combat harmonization was then implemented separately and sequentially based on the batch labels, i.e. harmonizing features among batches determined by each factor individually or harmonizing features among batches determined by multiple factors successively. Extensive experiments were conducted to predict overall survival (OS) on public PET/CT datasets that contain 800 patients from 9 centers.Main results. In the external validation cohort, results show that compared to original models without harmonization, Combat harmonization would be beneficial in OS prediction with C-index of 0.687-0.740 versus 0.684-0.767. Supervised harmonization slightly outperformed unsupervised harmonization in all models (C-index: 0.692-0.767 versus 0.684-0.750). Separate harmonization outperformed sequential harmonization in CT_m+clinic and CT_cm+clinic models with C-index of 0.752 and 0.722, respectively, while sequential harmonization involved clinical features in PET_rs+clinic model further improving the performance and achieving the highest C-index of 0.767.Significance. Optimal batch determination especially sequential harmonization for Combat holds the potential to improve the prognostic power of radiomics model in multi-center HNC dataset with PET/CT imaging.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering

BACKGROUND

Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.

METHODS

We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets.

RESULTS

We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters.

CONCLUSION

We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Endoscopic Ultrasonography-Derived Maximum Tumor Thickness and Tumor Shrinkage Rate as Independent Prognostic Factors in Locally Advanced Esophageal Squamous Cell Carcinoma after Neoadjuvant Chemoradiotherapy

PURPOSE/OBJECTIVE(S)

Neoadjuvant chemoradiotherapy (NCRT) is increasingly used in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC). Endoscopic ultrasonography (EUS)-derived maximum tumor thickness (MTT) before and after standard NCRT for LA-ESCC indicates treatment response. However, the accuracy of predicting long-term survival remains uncertain. This study aimed to investigate the association between EUS-derived MTT pre- and post-NCRT and tumor shrinkage rate as well as long-term survival in patients with LA-ESCC receiving NCRT.

MATERIALS/METHODS

We retrospectively enrolled patients with LA-ESCC who underwent EUS examination pre- and post-NCRT from 2017 to 2021. MTT was measured using EUS. Tumor shrinkage rate was the ratio of the difference between pre- and post-MTT to pre-MTT. The most fitted cut-off value defining the EUS response was determined by the receiver operating characteristic curve. Univariate and multivariate Cox regression analyses and Kaplan-Meier (KM) curves were used to calculate overall survival (OS) and progression-free survival (PFS). Data from another center were also used for external validation testing.

RESULTS

The median follow-up period was 30.6 months.230 patients with LA-ESCC who underwent EUS pre- or post-NCRT were enrolled. Of the patients, 178 completed the first EUS pre-NCRT and obtained pre-MTT, 200 completed the re-examined EUS post-NCRT and obtained post-MTT, and 148 completed both EUS and achieved tumor shrinkage. In the whole group the 1-year and 3-year OS rates were 93.9% and 67.9%, and PFS rates were 77.7% and 54.1%, respectively. Thinner post-MTT (≤8.8 mm) and EUS-responders (tumor shrinkage rate≥52%) were independently associated with better OS. The result of EUS-respond was an independent prognostic factor could be confirmed in the external validation group. Among LA-ESCC patients with initial ultrasonic T2-3 staging and T4 staging, no statistically differences were observed between the responder and non-responder groups (P = 0.082; P = 0.190).

CONCLUSION

EUS-derived MTT and tumor shrinkage post-NCRT are independent prognostic factors for long-term survival and may be an alternative method for evaluating tumor response in patients with LA-ESCC after NCRT. Initial tumor infiltration beyond esophageal adventitial layer on ultrasound effect could not, however, predict the long-term prognosis.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Background

Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.

Methods

We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.

Results

GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.

Conclusion

The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data

BACKGROUND

Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.

METHODS

Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors.

RESULTS

The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells.

CONCLUSIONS

The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

Hippocampus shapes cortical sensory output and novelty coding through a direct feedback circuit

To extract behaviorally relevant information from our surroundings, our brains constantly integrate and compare incoming sensory information with those stored as memories. Cortico-hippocampal interactions could mediate such interplay between sensory processing and memory recall1-4 but this remains to be demonstrated. Recent work parsing entorhinal cortex-to-hippocampus circuitry show its role in episodic memory formation5-7 and spatial navigation8. However, the organization and function of the hippocampus-to-cortex back-projection circuit remains uncharted. We combined circuit mapping, physiology and behavior with optogenetic manipulations, and computational modeling to reveal how hippocampal feedback modulates cortical sensory activity and behavioral output. Here we show a new direct hippocampal projection to entorhinal cortex layer 2/3, the very layer that projects multisensory input to the hippocampus. Our finding challenges the canonical cortico-hippocampal circuit model where hippocampal feedback only reaches entorhinal cortex layer 2/3 indirectly via layer 5. This direct hippocampal input integrates with cortical sensory inputs in layer 2/3 neurons to drive their plasticity and spike output, and provides an important novelty signal during behavior for coding objects and their locations. Through the sensory-memory feedback loop, hippocampus can update real-time cortical sensory processing, efficiently and iteratively, thereby imparting the salient context for adaptive learned behaviors with new experiences.

[Effects of ppk1 deletion on the drug susceptibility of uropathogenic Escherichia coli producing ESBLs]

To investigate the effect and the mechanism of ppk1 gene deletion on the drug susceptibility of uropathogenic Escherichia coli producing extended-spectrum beta-lactamases (ESBLs-UPEC). The study was an experimental study. From March to April 2021, a strain of ESBLs-UPEC (genotype was TEM combined with CTX-M-14) named as UE210113, was isolated from urine sample of the patient with urinary tract infection in the Laboratory Department of Guangzhou Eighth People's Hospital, meanwhile its ppk1 gene knock-out strain Δpk1 and complemented strain Δpk1-C were constructed by suicide plasmid homologous recombination technique, which was used to study the effect of ppk1 gene on ESBLs-UPEC drug sensitivity and its mechanism. The drug susceptibility of UE210113, Δpk1, and Δpk1-C were measured by Vitek2 Compact System and broth microdilution method. The quantitative expression of ESBLs, outer membrane protein and multidrug efflux systems encoding genes of UE210113, Δpk1 and Δpk1-C were performed by using qRT-PCR analysis. By using two independent sample Mann-Whitney U test, the drug susceptibility results showed that, compared with UE210113 strain, the sensitivities of Δpk1 to ceftazidime, cefepime, tobramycin, minocycline and cotrimoxazole were enhanced (Z=-2.121,P<0.05;Z=-2.236,P<0.05;Z=-2.236,P<0.05;Z=-2.121,P<0.05), and the drug susceptibility of Δpk1-C restored to the same as which of UE210113 (Z=0,P>0.05). The expression levels of ESBLs-enconding genes blaTEM and blaCTX-M-14 in Δpk1 were significantly down-regulated compared with UE210113, but the expression was not restored in Δpk1-C. The expression of outer membrane protein gene omp F in Δpk1 was significantly up-regulated, while the expression of omp A and omp C were down-regulated. The results showed that the expression of multidrug efflux systems encoding genes tol C, mdt A and mdtG were down-regulated in Δpk1 compared with UE210113. The expression of all of the outer membrane protein genes and the multidrug efflux systems genes were restored in Δpk1-C. In conclusion,the lost of ppk1 gene can affect the expression of the outer membrane protein and multidrug efflux systems encoding genes of ESBLs-UPEC, which increase the sensitivity of ESBLs-UPEC to various drugs.

[Puerarin alleviates lipopolysaccharide-induced acute kidney injury in mice by modulating the SIRT1/NF-κB pathway]

OBJECTIVE

To investigate the role of the SIRT1/NF-κB pathway in mediating the effect of puerarin against lipopolysaccharide (LPS)-induced acute kidney injury (AKI).

METHODS

Fifteen BALB/C mice were randomized into control group, LPS group and puerarin treatment group, and in the latter two groups, the mice were given an intraperitoneal injection of LPS (5 mg/kg), followed by daily injection of normal saline for 3 days or injection of puerarin (25 mg/kg) given 1 h later and then on a daily basis for 3 days. On day 5 after modeling, the kidney tissues were taken for histological observation and detection of cell apoptosis. The renal function indexes including urea nitrogen (BUN), serum creatinine (Scr) and kidney injury molecule 1 (KIM-1) and the levels of tumor necrosis factor (TNF-α) and interleukin 1β (IL-1β) were measured, and the expressions of SIRT1 and NF-κB-p65(acetyl K310) in the renal tissues were detected.

RESULTS

Intraperitoneal injection of LPS caused obvious glomerular capillary dilatation, hyperemia, renal interstitial edema, and renal tubular epithelial cell swelling and deformation in the mice. The mouse models of LPS-induced AKI also showed significantly increased renal tubular injury score and renal cell apoptosis (P < 0.01) with increased serum levels of BUN, Scr, KIM-1, TNF-α and IL-1β (P < 0.01), enhanced renal expressions of TNF-α, IL-1β and NF-κB p65(acetyl K310) (P < 0.01) and lowered renal expression of SIRT1 (P < 0.05). Treatment with puerarin effectively alleviated LPS-induced renal interstitial edema and renal tubular epithelial cell shedding, lowered renal tubular injury score (P < 0.01) and renal cell apoptosis rate (P < 0.01), and decreased serum levels of BUN, Scr, KIM, TNF-α and IL-1β (P < 0.01). Puerarin treatment significantly reduced TNF-α, IL-1β and NF-κB p65 (acetyl K310) expression in the renal tissue (P < 0.05) and increased SIRT1 expression by 17% (P < 0.05) in the mouse models.

CONCLUSION

Puerarin can effectively alleviate LPS-induced AKI in mice possibly by modulating the SIRT1/NF-κB signaling pathway.

Early adjunctive diagnostic value of contrast-enhanced ultrasound-related quantitative parameter and its relationship with micro-perfusion of nontraumatic necrosis of femoral head

OBJECTIVE

The aim of this study is to explore the early diagnostic value of contrast-enhanced ultrasound (CEUS)-related quantitative parameter and its relationship with the micro-perfusion of nontraumatic necrosis of the femoral head.

PATIENTS AND METHODS

According to the random and double-blind method, the patients with non-traumatic femoral head necrosis diagnosed and treated in our hospital from July 2019 to January 2022 were selected as the subjects (the research group). According to the staging of the International Society of Bone Circulation for Femoral Head Necrosis, 89 patients with stage Ⅱ and Ⅲ A were included (39 patients with stage Ⅱ and 50 patients with stage Ⅲ A). 25 patients who conducted physical examination in our hospital during the same time were taken as the control group. Quantitative parameters of CEUS were analyzed. The content of serum vascular endothelial growth factor (VEGF) and bone morphogenetic protein-2 (BMP-2) were evaluated. The relationship among the quantitative parameters of CEUS, the expression of VEGF and BMP-2 in serum and the patient's condition, and the value for assisting the early diagnosis of nontraumatic femoral head necrosis were analyzed.

RESULTS

The body mass, body mass index (BMI), blood lipid, and cholesterol levels were much higher in the research group than in the control group (p < 0.05). The research group had a markedly higher slope of ascending branch (AS), strength enhancement index (EI), and VEGF and obviously lower decay slope (DS), mean transit time (MTT), and time to peak (TTP) than the control group (p < 0.05). In the research group, compared to stage Ⅱ, the levels of AS, EI, and VEGF in stage Ⅲ A patients were memorably higher, and the levels of DS, MTT, TTP and BMP-2 were dramatically lower (p < 0.05). Pearson's correlation test showed that AS, EI, and VEGF were positively correlated with the patients' condition, while DS, MTT, TTP and BMP-2 were negatively correlated with the patients' condition (p < 0.05). The receiver operating characteristic (ROC) curve analysis showed that the diagnostic area under the curve (AUC) of quantitative parameters of CEUS was 0.961, with sensitivity and specificity of 88.0% and 97.4%, respectively. The AUC of the combined detection of VEGF and BMP-2 was 0.945 with sensitivity and specificity of 82.3% and 87.5%, respectively, and the combined detection had a high diagnostic value (p < 0.05).

CONCLUSIONS

The quantitative parameters of CEUS were of great value in the early diagnosis of nontraumatic necrosis of the femoral head with microvascular perfusion and the patients' condition, and provided a reference for the clinical treatment of non-traumatic necrosis of the femoral head. These parameters were expected to be useful indicators for judging the efficacy before and after treatment.

SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM

Introduction

Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious.

Methods

Here, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine.

Results

The proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation.

Conclusion

We anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.

Identifying potential drug-target interactions based on ensemble deep learning

Introduction

Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.

Methods

In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest.

Results

EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation.

Discussion

We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

[High expression of MYH9 inhibits apoptosis of non-small cell lung cancer cells through activating the AKT/c-Myc pathway]

OBJECTIVE

To investigate the role of myosin heavy chain 9 (MYH9) in regulation of cell proliferation, apoptosis, and cisplatin sensitivity of non-small cell lung cancer (NSCLC).

METHODS

Six NSCLC cell lines (A549, H1299, H1975, SPCA1, H322, and H460) and a normal bronchial epithelial cell line (16HBE) were examined for MYH9 expression using Western blotting. Immunohistochemical staining was used to detect MYH9 expression in a tissue microarray containing 49 NSCLC and 43 adjacent tissue specimens. MYH9 knockout cell models were established in H1299 and H1975 cells using CRISPR/Cas9 technology, and the changes in cell proliferation cell were assessed using cell counting kit-8 (CCK8) and clone formation assays; Western blotting and flow cytometry were used to detect apoptosis of the cell models, and cisplatin sensitivity of the cells was evaluated using IC50 assay. The growth of tumor xenografts derived from NSCLC with or without MYH9 knockout was observed in nude mice.

RESULTS

MYH9 expression was significantly upregulated in NSCLC (P < 0.001), and the patients with high MYH9 expression had a significantly shorter survival time (P=0.023). In cultured NSCLC cells, MYH9 knockout obviously inhibited cell proliferation (P < 0.001), promoted cell apoptosis (P < 0.05), and increased their chemosensitivity of cisplatin. In the tumor-bearing mouse models, the NSCLC cells with MYH9 knockout showed a significantly lower growth rate (P < 0.05). Western blotting showed that MYH9 knockout inactivated the AKT/c- Myc axis (P < 0.05) to inhibit the expression of BCL2- like protein 1 (P < 0.05), promoted the expression of BH3- interacting domain death agonist and the apoptosis regulator BAX (P < 0.05), and activated apoptosis-related proteins caspase-3 and caspase-9 (P < 0.05).

CONCLUSION

High expression of MYH9 contributes to NSCLC progression by inhibiting cell apoptosis via activating the AKT/c-Myc axis.

The effects of aging and frailty on inpatient glycemic control by continuous glucose monitoring in patients with type 2 diabetes

BACKGROUND

Older adults with diabetes in the hospital are generally managed similarly to younger adults, however, it is unknown if the degree of frailty can affect glucose control among hospitalized patients.

METHODS

We examined glycemic parameters derived from continuous glucose monitoring (CGM) in older adults with type 2 diabetes and frailty who were hospitalized in non-acute settings. Data was pooled from 3 prospective studies using CGM including 97 patients wearing Libre CGM sensors and 166 patients wearing Dexcom G6 CGM. Glycemic parameters (time in range (TIR) 70-180; time below range (TBR) <70 and 54 mg/dl) by CGM were compared between 103 older adults ≥60 years and 168 younger adults <60 years. Frailty was assessed using validated laboratory and vital signs frailty index FI-LAB (n = 85), and its effect on hypoglycemia risk was studied.

RESULTS

Older adults, as compared to younger adults, had significantly lower admission HbA1c (8.76% ± 1.82 vs. 10.25% ± 2.29, p < 0.001), blood glucose (203.89 ± 88.65 vs. 247.86 ± 124.17 mg/dl, p = 0.003), mean daily BG (173.9 ± 41.3 vs. 183.6 ± 45.0 mg/dl, p = 0.07) and higher percent TIR 70-180 mg/dl (59.0 ± 25.6% vs. 51.0 ± 26.1%, p = 0.02) during hospital stay. There was no difference in hypoglycemia occurrence between older and younger adults. Higher FI-LAB score was associated with higher % CGM < 70 mg/dl (0.204) and % CGM < 54 mg/dl (0.217).

CONCLUSION

Older adults with type 2 diabetes have better glycemic control prior to admission and during hospital stay compared to younger adults. Frailty is associated with longer presence of hypoglycemia in non-acute hospital settings.

Characteristics and transfer paths of CO2 emissions embodied in trade and China's carbon reduction strategies

This study focused on CO₂ emissions embodied in trade (CEET) to better promote carbon emission reduction given that foreign trade occupied an increasing proportion of the global economy. Based on technical adjustment, CEET balance worldwide during 2006-2016 was calculated and compared to avoid false transfer. This study also explored the influencing factors of CEET balance and identified the transfer pathway of China. Results indicate that developing countries are the major exporters of CEET, and developed countries are CEET importers in general. China is the largest net exporter of CEET and bears a large amount of it for developed countries. Trade balance and trade specialization are important factors of the imbalance of CEET in China. The transfer of CEET between China and the USA, Japan, India, Germany, South Korea, and other countries is relatively active. Agriculture, mining, manufacturing, electricity, heat, gas, water production and supply, and transportation, storage, and postal services are the major sectors, where the transfer occurs in China. Reducing CO₂ emissions requires global cooperation in the context of globalization. Strategies are proposed to deal with imbalances and transfer of CEET issues in China.

Predictive value of endometrial receptivity evaluated by three-dimensional ultrasound in ectopic pregnancy after in vitro fertilization-embryo transfer

OBJECTIVE

The aim of this study was to use three-dimensional (3D) ultrasound to detect ultrasound parameters related to the uterine artery and endometrium, evaluate endometrial receptivity, and investigate the predictive value of each parameter for ectopic pregnancy (EP) after in vitro fertilization-embryo transfer (IVF-ET).

PATIENTS AND METHODS

Fifty-seven cases of pregnancy following IVF-ET in our institution were collected and split into EP and intrauterine pregnancy (IP) groups based on the location of pregnancy, with 27 cases in EP and 30 cases in IP. Endometrial thickness, type, volume, endometrial blood flow parameters, and uterine artery blood flow parameters were all measured one day before transplantation in both groups, and the differences between the two groups were examined.

RESULTS

There were differences in endometrial blood flow typing between the two groups, with type III endometrium accounting for the highest proportion in both; the uterine spiral artery pulsatility index PI was significantly higher in the EP group than in the IP group; there were no statistical differences in uterine volume, uterine artery resistance index mRI, or uterine artery resistance index S/D between the two groups; there were no statistical differences in uterine volume, or uterine artery.

CONCLUSIONS

Intracavitary 3D ultrasound can assess endometrial tolerance and may predict pregnancy outcome after IVF-ET.

Sustainable development of urban agglomeration based on material metabolism: a case study on Fujian Delta, China

As highly concentrated urbanized areas, urban agglomerations bear increasing resource depletion and environmental pressures, which threaten the regional sustainable development. Resource and environmental problems arising from the process of urbanization can be attributed to the dislocation or maladjustment of material metabolism in time or space. Conducting research on material metabolism at the level of urban agglomerations is helpful in finding the root causes of environmental problems to provide support for the reduction of regional resource consumption and pollution emissions. The material metabolism characteristics of the urban agglomeration and internal cities of the Fujian Delta Urban Agglomeration (FDUA) in China are evaluated using the material flow analysis. The following results are observed. (1) The economic development of the FDUA is still at risk of resource consumption, and a large proportion of hidden flow (HF > 80%) drags down the overall metabolic efficiency and sustainable development. (2) The discharge of various pollutants in the FDUA generally shows a downward trend. Improving metabolic efficiency, delayed MCI growth, and improved overall regional environmental quality are observed. (3) Cities that have relatively scarce land resources but are economically developed, such as Xiamen, still bear a relatively heavy ecological burden (ECdmc > 1). (4) Regional collaboration is conducive to the sustainable development of multiple regions. On the one hand, the results of this study provide decision-making basis for the sustainable development of the national ecological civilization demonstration area. On the other hand, this work guides the establishment of a comprehensive industrial linkage and cooperation mechanism for the same type of small- and medium-sized urban agglomerations.

Effects of non-invasive brain stimulation (NIBS) for executive function on subjects with ADHD: a protocol for a systematic review and meta-analysis

INTRODUCTION

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a high risk of multiple mental health and social difficulties. Executive function domains are associated with distinct ADHD symptom burdens. Non-invasive brain stimulation (NIBS) mainly includes repetitive transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), which is a promising technique, but its impact on the executive function of ADHD is uncertain. Therefore, the aim of this systematic review and meta-analysis is to derive solid and updated estimates on the effect of NIBS on executive function in children/adults with ADHD.

METHODS AND ANALYSIS

A systematic search will be performed through EMBASE, MEDLINE, PsycINFO and Web of Science databases from inception until 22 August 2022. Handsearching of grey literature and the reference lists of selected articles will also be conducted. Empirical studies assessing the effect of NIBS (TMS or tDCS) on executive function in children or adults with ADHD will be included. Two investigators will independently perform literature identification, data extraction and risk of bias assessment. Relevant data will be pooled by a fixed-effects or random-effects model according to I² statistic. Sensitivity analysis will be performed to test the robustness of the pooled estimates. Subgroup analyses will be conducted to investigate the potential heterogeneity. This protocol will generate a systematic review and meta-analysis that comprehensively synthesises the evidence on the NIBS treatment of executive function deficit of ADHD.Ethics approval is not required as this is a protocol for a systematic review of published literature. The results will be submitted to a peer-reviewed journal or a conference.

PROSPERO REGISTRATION NUMBER

CRD42022356476.

Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging

BACKGROUND AND OBJECTIVE

Accurate risk stratification is crucial for enabling personalized treatment for head and neck cancer (HNC). Current PET/CT image-based prognostic methods include radiomics analysis and convolutional neural network (CNN), while extracting radiomics or deep features in grid Euclidean space has inherent limitations for risk stratification. Here, we propose a functional-structural sub-region graph convolutional network (FSGCN) for accurate risk stratification of HNC.

METHODS

This study collected 642 patients from 8 different centers in The Cancer Imaging Archive (TCIA), 507 patients from 5 centers were used for training, and 135 patients from 3 centers were used for testing. The tumor was first clustered into multiple sub-regions by using PET and CT voxel information, and radiomics features were extracted from each sub-region to characterize its functional and structural information, a graph was then constructed to format the relationship/difference among different sub-regions in non-Euclidean space for each patient, followed by a residual gated graph convolutional network, the prognostic score was finally generated to predict the progression-free survival (PFS).

RESULTS

In the testing cohort, compared with radiomics or FSGCN or clinical model alone, the model PETCT^Fea_CT^ROI + Cli that integrates FSGCN prognostic score and clinical parameter achieved the highest C-index and AUC of 0.767 (95% CI: 0.759-0.774) and 0.781 (95% CI: 0.774-0.788), respectively for PFS prediction. Besides, it also showed good prognostic performance on the secondary endpoints OS, RFS, and MFS in the testing cohort, with C-index of 0.786 (95% CI: 0.778-0.795), 0.775 (95% CI: 0.767-0.782) and 0.781 (95% CI: 0.772-0.789), respectively.

CONCLUSIONS

The proposed FSGCN can better capture the metabolic or anatomic difference/interaction among sub-regions of the whole tumor imaged with PET/CT. Extensive multi-center experiments demonstrated its capability and generalization of prognosis prediction in HNC over conventional radiomics analysis.

Assessment of healthcare quality among village clinicians in rural China: the role of internal work motivation

INTRODUCTION

The quality of primary care is important for health outcomes among residents in China. There is evidence that internal work motivation improves the quality of healthcare provided by clinicians. However, few empirical studies have examined the relationship between internal work motivation and clinical performance among village clinicians in rural China. This study was performed to evaluate healthcare quality among village clinicians, then explore its relationships with internal work motivation among those clinicians.

METHODS

We collected survey data using a standardised patient method and a structured questionnaire. We observed 225 interactions between standardised patients and village clinicians from 21 counties in three provinces. We used logistic regression models to analyse the relationships between work motivation and healthcare quality, then conducted heterogeneity analysis.

RESULTS

Healthcare quality among village clinicians was generally low. There was a significantly positive correlation between internal work motivation and healthcare quality among village clinicians (P<0.1). Additionally, the positive effect of internal work motivation on healthcare quality was strongest among clinicians who received financial incentives and had a lighter workload (fewer patients per month) [P<0.1].

CONCLUSION

Healthcare quality among village clinicians requires urgent improvement. We recommend implementing financial incentives to stimulate internal work motivation among village clinicians, thus improving their clinical performance.

Stent pretreatment for internal carotid artery exposed to necrotic lesions in nasopharyngeal carcinoma

BACKGROUND

Post radiation nasopharyngeal necrosis (PRNN) invading the internal carotid artery (ICA) contributes to the death of 69.2-72.7% of PRNN patients. ICA occlusion is an effective treatment to avoid fatal bleeding, while some patients are intolerant. We present a novel method that allows for these patients without interrupting blood flow through the ICA.

METHODOLOGY

This study enrolled patients with PRNN-invaded ICA who were not suitable for ICA occlusion from April 2020 to November 2022. ICA stent pretreatment was performed in the 36 patients and followed the endoscopic nasopharyngectomy (ENPG) or conservative treatment for PRNN. We report the survival outcome and incidence of complications after stent implantation and compare the survival outcomes of ENPG and conservative treatment for PRNN followed by stent implantation.

RESULTS

ICA stent pretreatment was performed in the 36 enrolled patients, among which 14 underwent ENPG, and 22 received conservative treatment. 27.8% patients died after a median follow-up of 15 months. The Kaplan-Meier estimates of overall survival were higher in the ENPG group than in the conservative treatment group. Karnofsky performance status (KPS) was significantly higher in the ENPG group than in the non-ENPG group.

CONCLUSIONS

The innovative application of ICA stents is a promising treatment to improve outcomes in patients with PRNN invading the ICA who are unsuitable for ICA embolization, especially when followed by endoscopic surgery. However, methods to avoid postoperative cerebral ischemia and nasopharyngeal hemorrhage still require further study.

[Research advances of adipose stem cell matrix gel in promoting wound healing]

In recent years, with the problem of aging population in China being prominant, the number of patients with chronic wounds such as diabetic foot, pressure ulcer, and vascular ulcer is increasing. Those diseases seriously affect the life quality of patients and increase the economy and care burden of the patients' family, which have been one of the most urgent clinical problems. Many researches have confirmed that adipose stem cells can effectively promote wound healing, while exogenous protease is needed, and there are ethical and many other problems, which limit the clinical application of adipose stem cells. Adipose stem cell matrix gel is a gel-like mixture of biologically active extracellular matrix and stromal vascular fragment obtained from adipose tissue by the principle of fluid whirlpool and flocculation precipitation. It contains rich adipose stem cells, hematopoietic stem cells, endothelial progenitor cells, and macrophages, etc. The preparation method of adipose stem cell matrix gel is simple and the preparation time is short, which is convenient for clinical application. Many studies at home and abroad showed that adipose stem cell matrix gel can effectively promote wound healing by regulating inflammatory reaction, promoting microvascular reconstruction and collagen synthesis. Therefore, this paper summarized the preparation of adipose stem cell matrix gel, the mechanism and problems of the matrix gel in promoting wound repair, in order to provide new methods and ideas for the treatment of chronic refractory wounds in clinic.

Adherence to the Dietary Approaches to Stop Hypertension (DASH) Eating Pattern Reduces the Risk of Head and Neck Cancer in American Adults Aged 55 Years and Above: A Prospective Cohort Study

OBJECTIVES

Dietary Approaches to Stop Hypertension (DASH) pattern has been found to aid in the reduction of obesity, oxidative stress, and chronic inflammation, which are all strongly linked to the development of head and neck cancer (HNC). Nevertheless, no epidemiological studies have investigated the association between this dietary pattern and HNC risk. This study was conducted with the purpose of bridging this gap in knowledge.

DESIGN

A prospective cohort study involving 98,459 American adults aged 55 years and older.

SETTING AND PARTICIPANTS

Data were drawn from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Trial. In the present study, participants with dependable energy intake data who furnished baseline and dietary history information were identified as the study population.

METHODS

Diet was assessed by food frequency questionnaires and the DASH score was calculated to assess each participant's adherence to DASH eating pattern. Cox proportional hazards models were used to calculate multivariable adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the occurrence of HNC. To visualize the variation in cancer risk for HNC and its subtypes across the entire spectrum of DASH scores, restricted cubic spline plots were utilized. Additionally, a series of predefined subgroup analyses were performed to identify potential effect modifiers, and several sensitivity analyses were conducted to assess the stability of the findings.

RESULTS

During a follow-up period of 871,879.6 person-years, 268 cases of HNC were identified, comprising 161 cases pertaining to oral cavity and pharynx cancers, as well as 96 cases of larynx cancer. In the fully adjusted model, adherence to the DASH diet was associated with a remarkable 57% reduction in the risk of HNC when comparing extreme quartiles (HR quartile 4 vs 1: 0.43; 95% CI: 0.28, 0.66; P for trend < 0.001). The restricted cubic spline plots demonstrated a linear dose-response relationship between the DASH score and the risk of HNC as well as its subtypes. Subgroup analysis revealed that the protective effect of the DASH diet against HNC was particularly pronounced in individuals with lower daily energy intake. The primary association remained robust in the sensitivity analysis.

CONCLUSIONS

In American middle-aged and older population, adherence to the DASH diet may help prevent HNC, particularly for individuals with lower daily energy intake.

A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias. Furthermore, previous models have not uncovered potential proteins (or lncRNAs) interacting with a new lncRNA (or protein). Finally, the performance of these models can be improved. In this study, we develop a Deep Learning framework with Dual-net Neural architecture to find potential LPIs (LPI-DLDN). First, five LPI datasets are collected. Second, the features of lncRNAs and proteins are extracted by Pyfeat and BioTriangle, respectively. Third, these features are concatenated as a vector after dimension reduction. Finally, a deep learning model with dual-net neural architecture is designed to classify lncRNA-protein pairs. LPI-DLDN is compared with six state-of-the-art LPI prediction methods (LPI-XGBoost, LPI-HeteSim, LPI-NRLMF, PLIPCOM, LPI-CNNCP, and Capsule-LPI) under four cross validations. The results demonstrate the powerful LPI classification performance of LPI-DLDN. Case study analyses show that there may be interactions between RP11-439E19.10 and Q15717, and between RP11-196G18.22 and Q9NUL5. The novelty of LPI-DLDN remains, integrating various biological features, designing a novel deep learning-based LPI identification framework, and selecting the optimal LPI feature subset based on feature importance ranking.

LBSUN215 Evaluation Of Glycemic Control By Continuous Glucose Monitoring Among Hospitalized Older Adults With Type-2 Diabetes And Frailty

 

Recent observational and meta-analyses have reported a frailty prevalence between 10% and 25% in people with diabetes, in particular in those older than 60 years of age. The impact of frailty on hospital glycemic control and glycemic variability (GV) by continuous glucose monitoring (CGM) in insulin-treated older adults with type 2 diabetes (T2D) is not known. Accordingly, we reviewed data from 3 inpatient randomized clinical trials using CGM in insulin-treated patients with T2D. The validated laboratory-based frailty index (FI-LAB) scale was used for frailty assessment, and participants were categorized into three groups [non-frail: (0-<0.1), pre-frail (≥0.1-<0.21), and frail (≥0.21)] in 84 older adults. | There were no differences on admission clinical characteristics between the non-frail/pre-frail older adults and the frail older adults except for Creatinine (BMI: 32.4 ± 9 vs. 36.83± 13, p=0.21; HbA1c%: 9.3 ± 2 vs. 8.72± 2, p=0.18; Admission BG: 227 ± 114 vs. 194. 07± 75 mg/dl, p=0.26; Cr: 1.16 ± 1 vs. 1.65± 1. 0). There were no differences in GV by coefficient of variation (CV), amplitude of glucose excursion (MAGE), and standard deviation (SD) between the two groups. The correlation between FI-LAB score and percent time with CGM <70 was 0.204 (p=0. 064) and the correlation between FI-LAB score and percent time with percent time with CGM<54 was 0.217 (p=0. 049). Results from standard linear regression and zero-inflated Beta regression further suggest that frail old patients with higher frailty scores may be associated with larger percent time with CGM below range <70 and CGM <54 mg/dL.

Conclusion

Our results indicate that older adults with T2D with higher frailty score experience more time in hypoglycemia during their hospital stay despite having comparable mean daily blood glucose, time in range and glycemic variability compared to non-frail or pre-frail older adults. A larger prospective study is needed to confirm these findings and determine the impact of frailty on clinical outcome. Providers should be vigilant when using insulin or insulin secretagogues in hospitalized older adults with diabetes and frailty.

Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

Inflammaging: The ground for sarcopenia?

Sarcopenia is a progressive skeletal muscle disease that occurs most commonly in the elderly population, contributing to increased costs and hospitalization. Exercise and nutritional therapy have been proven to be effective for sarcopenia, and some drugs can also alleviate declines in muscle mass and function due to sarcopenia. However, there is no specific pharmacological treatment for sarcopenia at present. This review will mainly discuss the relationship between inflammaging and sarcopenia. The increased secretion of proinflammatory cytokines with aging may be because of cellular senescence, immunosenescence, alterations in adipose tissue, damage-associated molecular patterns (DAMPs), and gut microbes due to aging. These sources of inflammaging can impact the sarcopenia process through direct or indirect pathways. Conversely, sarcopenia can also aggravate the process of inflammaging, creating a vicious cycle. Targeting sources of inflammaging can influence muscle function, which could be considered a therapeutic target for sarcopenia. Moreover, not only proinflammatory cytokines but also anti-inflammatory cytokines can influence muscle and inflammation and participate in the progression of sarcopenia. This review focuses on the effects of TNF-α, IL-6, and IL-10, which can be detected in plasma. Therefore, clearing chronic inflammation by targeting proinflammatory cytokines (TNF-α, IL-1, IL-6) and the inflammatory pathway (JAK/STAT, autophagy, NF-κB) may be effective in treating sarcopenia.

Utilisation of village clinics in Southwest China: evidence from Yunnan Province

INTRODUCTION

Primary healthcare in rural China is underutilised, especially in village clinics in Southwest China. The aim of this study was to explore any relationships among the ethnicity of the healthcare provider, the clinical competence of the healthcare provider, and the utilisation of village clinics in Southwest China.

METHODS

This cross-sectional survey study involved 330 village healthcare providers from three prefectures in Yunnan Province in 2017. Multiple logistic regressions were adopted to investigate the utilisation of primary healthcare among different ethnic healthcare providers.

RESULTS

Primary healthcare utilisation was higher in village clinics where healthcare providers were Han Chinese than those where healthcare providers were ethnic minority (151 vs 101, P=0.008). The logistic regression analysis showed that clinical competence was positively associated with the utilisation of primary healthcare (odds ratio [OR]=1.49, 95% confidence interval [CI]=1.12-2.00; P=0.007) and that inadequate clinical competence of ethnic minority health workers may lead to a lag in the utilisation of primary healthcare (OR=0.45, 95% CI=0.23-0.89; P=0.022).

CONCLUSION

Our results confirm differences in the utilisation of primary healthcare in rural Yunnan Province among healthcare providers of different ethnicities. Appropriate enhancements of clinical competence could be conducive to improving the utilisation of primary healthcare, especially among ethnic minority healthcare providers.

Key mitigation regions and strategies for CO2 emission reduction in China based on STIRPAT and ARIMA models

China is facing increasing pressure to reduce CO₂ emissions from energy consumption. Given this issue, understanding the characteristics, influencing factors, and trends can provide adequate information for decision-makers to solve the CO₂ emission problem. This study analyzes the characteristics of CO₂ emissions from energy consumption in 30 regions of China from 2005 to 2018 and applies the STIRPAT model to identify the impact of the influencing factors. Combined with the CO₂ emission trend in 2030 as predicted by the ARIMA model, the key mitigation regions and strategies reduction have been determined. Results indicate that CO₂ emissions have been increasing from 2005 to 2018 in China, thus showing the characteristic of the east being larger than the west spatially. Under the baseline scenario, these emissions will continue to rise in 2030. Carbon emissions intensity is declining, and the gap between provinces with the highest and lowest per capita CO₂ emissions is widening. Although per capita GDP is significantly positively correlated with provinces, population is the key factor influencing more provinces, followed by the proportion of the secondary industry and urbanization rate. To achieve low-carbon sustainable development, Shandong, Shanxi, Inner Mongolia, Guangdong, Shaanxi, Xinjiang, and Ningxia are considered the key regions of concern for emission reduction. The heterogeneity of CO₂ emission characteristics and influencing factors among regions provides a direction for the development of targeted and differentiated regional emission reduction strategies.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.