Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. METHODS: We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. RESULTS: A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. CONCLUSION: Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Identification and immunological role of cuproptosis in osteoporosis.

Background: osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model. Methods: we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency. Results: Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis. Conclusion: Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

Erratum: Trehalose inhibits H2O2-induced autophagic death in dopaminergic SH-SY5Y cells via mitigation of ROS-dependent endoplasmic reticulum stress and AMPK activation: Erratum.

[This corrects the article DOI: 10.7150/ijms.25656.].

Integrative multi-omics analyses unravel the immunological implication and prognostic significance of CXCL12 in breast cancer.

Background: CXCL12 is a vital factor in physiological and pathological processes, by inducing migration of multiple cells. We aimed to comprehensively detect the role of CXCL12 in breast cancer, and explore novel CXCL12-related biomarkers through integrative multi-omics analyses to build a powerful prognostic model for breast cancer patients. Methods: Immunohistochemistry analysis of the tissue microarray was performed to evaluate the correlation between CXCL12 expression levels and breast cancer patient outcomes. Combined single-nucleus and spatial transcriptomics data was used to uncover the expression distribution of CXCL12 in breast cancer microenvironment. CXCL12-related genes were identified by WGCNA analysis. Univariate Cox and LASSO regression analyses were then conducted to screen prognostic genes from above CXCL12-related genes, followed by the construction of the CXCL12-related prognostic signature, identification of risk groups, and external validation of the prognostic signature. Analyses of biological function, mutation landscape, immune checkpoint genes and immune cells, were performed to further reveal the differences between high/low-risk groups. Paired single-cell RNA-seq and bulk RNA-seq were analyzed to further disclose the association between the risk score and the complex tumor immune microenvironment. To screen potential therapeutic agents for breast cancer patients, analyses of gene-drug correlation and sensitivity to immunotherapy were conducted. Results: High expression of CXCL12 was linked with a prolonged survival in breast cancer. A total of 402 genes were identified by WGCNA analysis and 11 genes, covering VAT1L, TMEM92, SDC1, RORB, PCSK9, NRN1, NACAD, JPH3, GJA1, BMP8B and ADAMTS2, were screened as the candidate prognostic genes. Next, the prognostic signature was built and validated using these genes to predict the outcomes of breast cancers. The high-risk group patients exhibited significantly inferior prognoses. The combination of the risk score and tumor mutational burden (TMB) had remarkably improved performance in predicting patient outcomes. Besides, high-risk group patients showed higher infiltration of M2-like macrophages. Finally, several potential anticancer drugs were identified. The high-risk group patients were more sensitive to immunotherapy but resistant to docetaxel. Conclusions: CXCL12 has important immunological implication and prognostic significance in breast cancer. The CXCL12-related prognostic model could well predict the prognosis and treatment response of breast cancers.

Single-cell analysis of white adipose tissue reveals the tumor-promoting adipocyte subtypes.

BACKGROUND: The tumor-adipose microenvironment (TAME) is characterized by the enrichment of adipocytes, and is considered a special ecosystem that supports cancer progression. However, the heterogeneity and diversity of adipocytes in TAME remains poorly understood. METHODS: We conducted a single-cell RNA sequencing analysis of adipocytes in mouse and human white adipose tissue (WAT). We analyzed several adipocyte subtypes to evaluate their relationship and potential as prognostic factors for overall survival (OS). The potential drugs are screened by using bioinformatics methods. The tumor-promoting effects of a typical adipocyte subtype in breast cancer are validated by performing in vitro functional assays and immunohistochemistry (IHC) in clinical samples. RESULTS: We profiled a comprehensive single-cell atlas of adipocyte in mouse and human WAT and described their characteristics, origins, development, functions and interactions with immune cells. Several cancer-associated adipocyte subtypes, namely DPP4+ adipocytes in visceral adipose and ADIPOQ+ adipocytes in subcutaneous adipose, are identified. We found that high levels of these subtypes are associated with unfavorable outcomes in four typical adipose-associated cancers. Some potential drugs including Trametinib, Selumetinib and Ulixertinib are discovered. Emphatically, knockdown of adiponectin receptor 1 (AdipoR1) and AdipoR2 impaired the proliferation and invasion of breast cancer cells. Patients with AdipoR2-high breast cancer display significantly shorter relapse-free survival (RFS) than those with AdipoR2-low breast cancer. CONCLUSION: Our results provide a novel understanding of TAME at the single-cell level. Based on our findings, several adipocyte subtypes have negative impact on prognosis. These cancer-associated adipocytes may serve as key prognostic predictor and potential targets for treatment in the future.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

Phase retrieval via nonlocal complex-domain sparsity.

Phase retrieval is indispensable for a number of coherent imaging systems. Owing to limited exposure, it is a challenge for traditional phase retrieval algorithms to reconstruct fine details in the presence of noise. In this Letter, we report an iterative framework for noise-robust phase retrieval with high fidelity. In the framework, we investigate nonlocal structural sparsity in the complex domain by low-rank regularization, which effectively suppresses artifacts caused by measurement noise. The joint optimization of sparsity regularization and data fidelity with forward models enables satisfying detail recovery. To further improve computational efficiency, we develop an adaptive iteration strategy that automatically adjusts matching frequency. The effectiveness of the reported technique has been validated for coherent diffraction imaging and Fourier ptychography, with ≈7 dB higher peak SNR (PSNR) on average, compared with conventional alternating projection reconstruction.

Identification of a centrosome-related prognostic signature for breast cancer.

Background: As the major microtubule organizing center in animal cells, the centrosome is implicated with human breast tumor in multiple ways, such as promotion of tumor cell immune evasion. Here, we aimed to detect the expression of centrosome-related genes (CRGs) in normal and malignant breast tissues, and construct a novel centrosome-related prognostic model to discover new biomarkers and screen drugs for breast cancer. Methods: We collected CRGs from the public databases and literature. The differentially expressed CRGs between normal and malignant breast tissues were identified by the DESeq2. Univariate Cox and LASSO regression analyses were conducted to screen candidate prognostic CRGs and develop a centrosome-related signature (CRS) to score breast cancer patients. We further manipulated and visualized data from TCGA, GEO, IMvigor210, TCIA and TIMER to explore the correlation between CRS and patient outcomes, clinical manifestations, mutational landscapes, tumor immune microenvironments, and responses to diverse therapies. Single cell analyses were performed to investigate the difference of immune cell landscape between high- and low-risk group patients. In addition, we constructed a nomogram to guide clinicians in precise treatment. Results: A total of 726 CRGs were collected from the public databases and literature. PSME2, MAPK10, EIF4EBP1 were screened as the prognostic genes in breast cancer. Next, we constructed a centrosome-related prognostic signature and validated its efficacy based on the genes for predicting the survival of breast cancer patients. The high-risk group patients had poor prognoses, the area under the ROC curve for 1-, 3-, and 5-year overall survival (OS) was 0.77, 0.67, and 0.65, respectively. The predictive capacity of CRS was validated by other datasets from GEO dataset. In addition, high-risk group patients exhibited elevated level of mutational landscapes and decreased level of immune infiltration, especially T and B lymphocytes. In terms of treatment responses, patients in the high-risk group were found to be resistant to immunotherapy but sensitive to chemotherapy. Moreover, we screened a series of candidate anticancer drugs with high sensitivity in the high-risk group. Conclusion: Our work exploited a centrosome-related prognostic signature and developed a predictive nomogram capable of accurately predicting breast cancer OS. The above discoveries provide deeper insights into the vital roles of the centrosome and contribute to the development of personalized treatment for breast cancer.

The prognostic value of hedgehog signaling in bladder cancer by integrated bioinformatics.

Bladder cancer is the second most prevalent urological malignancy. It's a big contributor to cancer-related deaths throughout the globe. Researchers discovered that the hedgehog signaling (HhS) pathway contributed to the onset and spread of many different kinds of cancer. Nevertheless, the present understanding of the function of HhS in the bladder cancer molecular landscape is incomplete. Raw data were gotten from the IMvigor210, the Gene Expression Omnibus, and The Cancer Genome Atlas databases. Bioinformatics was used to examine the HhS score of each sample, and the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. The HhS prognostic signature was developed with significant assistance from the least absolute shrinkage and selection operator regression and Cox regression. An HhS-related nomogram was developed to assist in the prediction of patients' survival probability. We found that HhS was linked to poor prognosis in bladder cancer, and its activation was linked to the Basal subtype of bladder cancer. Bladder cancer with high HhS activity has higher glycolysis, nucleotide metabolism, amino acid metabolism, and other cancer-promoting metabolic activities. Furthermore, HhS mediates an immunosuppressive microenvironment in bladder cancer on the basis that HhS negatively correlates with the CD8 + T cells and correlates positively with immune checkpoints and T cell exhaustion scores. Finally, an HhS-related signature was developed for predicting the prognosis of patients with bladder cancer. Targeting HhS may be a potential therapy choice for bladder cancer.

Macrolide antibiotics activate the integrated stress response and promote tumor proliferation.

Macrolide antibiotics are widely used antibacterial agents that are associated with autophagy inhibition. This study aimed to investigate the association between macrolide antibiotics and malignant tumors, as well as the effect on autophagy, reactive oxygen species (ROS) accumulation and integrated stress response (ISR). The meta-analysis indicated a modestly higher risk of cancer in macrolide antibiotic ever-users compared to non-users. Further experiments showed that macrolides block autophagic flux by inhibiting lysosomal acidification. Additionally, azithromycin, a representative macrolide antibiotic, induced the accumulation of ROS, and stimulated the ISR and the activation of transcription factor EB (TFEB) and TFE3 in a ROS-dependent manner. Finally, animal experiments confirmed that azithromycin promoted tumor progression in vivo, which could be receded by N-acetylcysteine, an inhibitor of ROS and ISR. Overall, this study reveals the potential role of macrolide antibiotics in malignant progression and highlights the need for further investigation into their effects.

Haplotype-specific MAPK3 expression in 16p11.2 deletion contributes to variable neurodevelopment.

Recurrent proximal 16p11.2 deletion (16p11.2del) is a risk factor for diverse neurodevelopmental disorders with incomplete penetrance and variable expressivity. Although investigation with human induced pluripotent stem cell models has confirmed disruption of neuronal development in 16p11.2del neuronal cells, which genes are responsible for abnormal cellular phenotypes and what determines the penetrance of neurodevelopmental abnormalities are unknown. We performed haplotype phasing of the 16p11.2 region in a 16p11.2del neurodevelopmental disorders cohort and generated human induced pluripotent stem cells for two 16p11.2del families with distinct residual haplotypes and variable neurodevelopmental disorder phenotypes. Using transcriptomic profiles and cellular phenotypes of the human induced pluripotent stem cell-differentiated cortex neuronal cells, we revealed MAPK3 to be a contributor to dysfunction in multiple pathways related to early neuronal development, with altered soma and electrophysiological properties in mature neuronal cells. Notably, MAPK3 expression in 16p11.2del neuronal cells varied on the basis of a 132 kb 58 single nucleotide polymorphism (SNP) residual haplotype, with the version composed entirely of minor alleles associated with reduced MAPK3 expression. Ten SNPs on the residual haplotype were mapped to enhancers of MAPK3. We functionally validated six of these SNPs by luciferase assay, implicating them in the residual haplotype-specific differences in MAPK3 expression via cis-regulation. Finally, the analysis of three different cohorts of 16p11.2del subjects showed that this minor residual haplotype is associated with neurodevelopmental disorder phenotypes in 16p11.2del carriers.

KLF5 inhibition potentiates anti-PD1 efficacy by enhancing CD8+ T-cell-dependent antitumor immunity.

Background: Immune checkpoint blockers (ICBs) are revolutionized therapeutic strategies for cancer, but most patients with solid neoplasms remain resistant to ICBs, partly because of the difficulty in reversing the highly immunosuppressive tumor microenvironment (TME). Exploring the strategies for tumor immunotherapy is highly dependent on the discovery of molecular mechanisms of tumor immune escape and potential therapeutic target. Krüppel-like Factor 5 (KLF5) is a cell-intrinsic oncogene to promote tumorigenesis. However, the cell-extrinsic effects of KLF5 on suppressing the immune response to cancer remain unclear. Methods: We analyzed the immunosuppressive role of KLF5 in mice models transplanted with KLF5-deleted/overexpressing tumor cells. We performed RNA sequencing, immunohistochemistry, western blotting, real time-PCR, ELISA, luciferase assay, chromatin immunoprecipitation (ChIP), and flow cytometry to demonstrate the effects of KLF5 on CD8+ T cell infiltration and related molecular mechanism. Single-cell RNA sequencing and spatial transcriptomics analysis were applied to further decipher the association between KLF5 expression and infiltrating immune cells. The efficacy of KLF5/COX2 inhibitors combined with anti-programmed cell death protein 1 (anti-PD1) therapy were explored in pre-clinical models. Finally, a gene-expression signature depending on KLF5/COX2 axis and associated immune markers was created to predict patient survival. Results: KLF5 inactivation decelerated basal-like breast tumor growth in a CD8+ T-cell-dependent manner. Transcriptomic profiling revealed that KLF5 loss in tumors increases the number and activated function of T lymphocytes. Mechanistically, KLF5 binds to the promoter of the COX2 gene and promotes COX2 transcription; subsequently, KLF5 deficiency decreases prostaglandin E2 (PGE2) release from tumor cells by reducing COX2 expression. Inhibition of the KLF5/COX2 axis increases the number and functionality of intratumoral antitumor T cells to synergize the antitumorigenic effects of anti-PD1 therapy. Analysis of patient datasets at single-cell and spatial resolution shows that low expression of KLF5 is associated with an immune-supportive TME. Finally, we generate a KLF5/COX2-associated immune score (KC-IS) to predict patient survival. Conclusions: Our results identified a novel mechanism responsible for KLF5-mediated immunosuppression in TME, and targeting the KLF5/COX2/PGE2 axis is a critical immunotherapy sensitizer.

A Comprehensive Study on the Chemical Constituents and Pharmacokinetics of Erzhi Formula and Jiawei Erzhi Formula Based on Targeted and Untargeted LC-MS Analysis.

BACKGROUND: Erzhi formula (EZF) is a traditional Chinese medicine prescription, which has been widely used in the treatment of osteoporosis and premature ovarian failure. OBJECTIVE: To enhance curative effects, the other two herbal medicines, including Spatholobi Caulis (SC) and Achyranthes bidentata Blume (ABB), were added into the original EZF formula to obtain two new Jiawei-EZF (JW-EZF) preparations. To clarify the effect of the compatibility of herbs for original formulas, the chemical constituents and bioactive compounds in vivo were detected. METHODS: An efficient and sensitive targeted and untargeted UHPLC/ESI-Q-Orbitrap MS method, together with mass defect filter and precursor ion list, was established firstly for the profiling of different EZF formulas. Furthermore, eleven absorbed compounds (apigenin, luteoloside, luteolin, oleuropein, wedelolactone, acteoside, specnuezhenide, 11-methyloleoside, ecliptasaponin A, formononetin, and ß-ecdysone) were simultaneously quantified in rat plasma. RESULTS: A total of 124, 162, and 177 compounds were identified or tentatively identified in EZF, JW-3-EZF (EZF+SC) and JW-4-EZF (EZF+SC+ABB), respectively. 110 compounds were found to be common constituents in the three formulas. Moreover, 66 prototypes were unambiguously identified in the rats' plasma after oral administration of the three formulas using the same strategy. 11 out of the 66 absorbed components were simultaneously quantitated in the pharmacokinetic (PK) study. Compared to the original EZF, the plasma AUC(0-24h) and AUC(0-∞) of apigenin, 11-methyloleoside, luteolin, luteoloside, wedelolactone, and acteoside were found to be significantly increased after oral administration of JW-3-EZF, and plasma AUC(0-24h) and AUC(0-∞) of apigenin, wedelolactone, and acteoside, were also found to be significantly increased after JW-4-EZF administration. CONCLUSION: The combined qualitative and quantitative methods were used to provide a potential approach to the characterization and quality control of the Traditional Chinese Medicine (TCM) and its preparations.

Dietary regulation in health and disease.

Nutriments have been deemed to impact all physiopathologic processes. Recent evidences in molecular medicine and clinical trials have demonstrated that adequate nutrition treatments are the golden criterion for extending healthspan and delaying ageing in various species such as yeast, drosophila, rodent, primate and human. It emerges to develop the precision-nutrition therapeutics to slow age-related biological processes and treat diverse diseases. However, the nutritive advantages frequently diversify among individuals as well as organs and tissues, which brings challenges in this field. In this review, we summarize the different forms of dietary interventions extensively prescribed for healthspan improvement and disease treatment in pre-clinical or clinical. We discuss the nutrient-mediated mechanisms including metabolic regulators, nutritive metabolism pathways, epigenetic mechanisms and circadian clocks. Comparably, we describe diet-responsive effectors by which dietary interventions influence the endocrinic, immunological, microbial and neural states responsible for improving health and preventing multiple diseases in humans. Furthermore, we expatiate diverse patterns of dietotheroapies, including different fasting, calorie-restricted diet, ketogenic diet, high-fibre diet, plants-based diet, protein restriction diet or diet with specific reduction in amino acids or microelements, potentially affecting the health and morbid states. Altogether, we emphasize the profound nutritional therapy, and highlight the crosstalk among explored mechanisms and critical factors to develop individualized therapeutic approaches and predictors.

Lipid-associated macrophages in the tumor-adipose microenvironment facilitate breast cancer progression.

The tumor-adipose microenvironment (TAME) is a universal microecosystem, that is characterized by the dysfunction of lipid metabolism, such as excessive free fatty acids (FFAs). Macrophages are the most abundant immune cell type within TAME, although their diversity in the TAME is not clear. We first reveal that infiltration of M2-like macrophages in the TAME is associated with poor survival in breast cancer. To explore lipid-associated alterations in the TAME, we also detected the levels of FFAs transporters including fatty acid binding proteins (FABPs) and fatty acid transport protein 1 (FATP1). The results indicated that expression of fatty acid transporters in the TAME is tightly linked to the function of macrophages and predicts survival in breast cancer. To explore the impact of FFAs transporters on the function of macrophages, we performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. Consequently, we identified a special subpopulation of macrophages defined as lipid-associated macrophages (LAMs), highly expressed macrophage markers (CD163, SPP1 and C1QC), genes involved in lipid metabolism (FABP3, FABP4, FABP5, LPL and LIPA) and some lipid receptors (LGALS3 and TREM2). Functionally, LAMs were characterized by a canonical functional signature of M2-like macrophages, lipid accumulation and enhancing phagocytosis, and they were mostly distributed in tumor-adipose junctional regions. Finally, the allograft cancer mouse models confirmed that LAMs depletion in the TAME synergizes the antitumorigenic effects of anti-PD1 therapy. In summary, we defined a novel subtype of macrophages in the TAME, that has unique features and clinical outcomes.

Study on the Cooling Effect of Asphalt Pavement Blended with Composite Phase Change Materials.

To explore the cooling effect of phase change materials (PCM) on asphalt pavement, a numerical model of the coupled heat transfer process of a typical monolithic subgrade of the G7 Expressway in the eastern Tianshan mountain area was developed. Three types of paraffin materials (OP55E, OP52E, OP47E) were mixed in a 4:3:3 volume ratio and blended into the asphalt upper layer and overall asphalt layer at volume ratios of 5%, 10%, 15% and 20%. The cooling effect of different PCM addition schemes was simulated and analyzed, and the frequency and duration of asphalt pavement high temperature operation status were also measured. The results showed that: (1) Th addition of PCM in the asphalt layer can effectively reduce the frequency of pavement high temperature rutting damage. The number of days and average daily duration of high temperature on the road surface were both reduced. (2) The cooling effect was positively correlated with the PCM volume mixing ratio, and the temperature drop of the pavement also increased with the increase of the PCM blending ratio. As the PCM mixing ratio increased from 5% to 20%, the initial 75 °C pavement cooled by 1.49 °C and 4.66 °C, respectively, and the number of days and hours of pavement temperature over 70 °C decreased to 4 days and 3.3 h, respectively. (3) The cooling effect of the asphalt upper layer PCM scheme was greater at a small mixing ratio (5%), whereas the performance of the overall asphalt layer PCM blended scheme was effectively promoted by increasing the equivalent heat capacity of system under the large mixing ratio (20%).

Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here, we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct subpopulations. These cell clusters not only have diverse features, origins and functions, but also emerge to the crosstalk within subtypes. Furthermore, we find that these subclusters are mapped in distinct tissue regions, where discrepant enrichment of stromal cell types are observed. We also inferred the abundance of these tumorous subpopulations by deconvolution of large breast cancer RNA-seq cohorts, revealing differential association with patient survival and therapeutic response. Our study provides a novel insight for the cellular architecture of breast cancer and potential therapeutic strategies.

Liver volume based prediction model for patients with hepatitis B virus-related acute-on-chronic liver failure.

BACKGROUND: Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a life-threatening disease with high short-term mortality. Early and accurate prognosis is significant for clinical decisions, in which liver volume (LV) imparts important information. However, LV has not been considered in current prognostic models for HBV-ACLF. METHODS: Three hundred and twenty-three patients were recruited to the deriving cohort, while 163 were enrolled to validation cohort. The primary end-point was death within 28 days since admission. Estimated liver volume (ELV) was calculated by the formula based on healthy population. Logistic regression was used to develop a prediction model. Accuracy of models were evaluated by receiver operating characteristic (ROC) curves. RESULTS: The ratio of LV to ELV (LV/ELV%) was significantly lower in non-survivors, and LV/ELV% ≤82% indicated poor prognosis. LV/ELV%, Age, prothrombin time (PT), the grade of hepatic encephalopathy (HE), ln-transformed total bilirubin (lnTBil), and log-transformed HBV DNA (Log10 HBV DNA) were identified as independent predictors to develop an LV-based model, LEAP-HBV. The mean area under the ROC (AUC) of LEAP-HBV was 0.906 (95% CI, 0.904-0.908), higher than other non-LV-based models. CONCLUSION: Liver volume was an independent predictor, and LEAP-HBV, a prediction model based on LV, was developed for the short-term mortality in HBV-ACLF. This study was registered on ClinicalTrails (NCT03977857).

Ultrahigh-security single-pixel semantic encryption.

Single-pixel encryption is a recently developed encryption technique enabling the ciphertext amount to be decreased. It adopts modulation patterns as secret keys and uses reconstruction algorithms for image recovery in the decryption process, which are time-consuming and can easily be illegally deciphered if the patterns are exposed. We report an image-free single-pixel semantic encryption technique that significantly enhances security. The technique extracts semantic information directly from the ciphertext without image reconstruction, which significantly reduces computing resources for end-to-end real-time decoding. Moreover, we introduce a stochastic mismatch between keys and ciphertext, with random measurement shift and dropout, which effectively enhances the difficulty of illegal deciphering. Experiments on the MNIST dataset validate that 78 coupling measurements (0.1 sampling rate) with stochastic shift and random dropout achieved 97.43% semantic decryption accuracy. In the worst situation, when all the keys are illegally obtained by unauthorized attackers, only 10.80% accuracy can be achieved (39.47% in an ergodic manner).

Efficient large-scale single-pixel imaging.

The speed of single-pixel imaging (SPI) is tied to its resolution, which is positively related to the number of modulation times. Therefore, efficient large-scale SPI is a serious challenge that impedes its wide applications. In this work, we report a novel, to the best of our knowledge, sparse SPI scheme and corresponding reconstruction algorithm to image target scenes at above 1 K resolution with reduced measurements. Specifically, we first analyze the statistical importance ranking of Fourier coefficients for natural images. Then the sparse sampling with a polynomially decending probability of the ranking is performed to cover a larger range of the Fourier spectrum than non-sparse sampling. The optimal sampling strategy with suitable sparsity is summarized for the best performance. Next, a lightweight deep distribution optimization (D2O) algorithm is introduced for large-scale SPI reconstruction from sparsely sampled measurements instead of a conventional inverse Fourier transform (IFT). The D2O algorithm empowers robustly recovering sharp scenes at 1 K resolution within 2 s. A series of experiments demonstrate the technique's superior accuracy and efficiency.