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The 2019 novel coronavirus (SARS-CoV-2) pandemic has caused a global health emergency. The outbreak of this virus has raised a number of questions: What is SARS-CoV-2? How transmissible is SARS-CoV-2? How severely affected are patients infected with SARS-CoV-2? What are the risk factors for viral infection? What are the differences between this novel coronavirus and other coronaviruses? To answer these questions, we performed a comparative study of four pathogenic viruses that primarily attack the respiratory system and may cause death, namely, SARS-CoV-2, severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and influenza A viruses (H1N1 and H3N2 strains). This comparative study provides a critical evaluation of the origin, genomic features, transmission, and pathogenicity of these viruses. Because the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 is ongoing, this evaluation may inform public health administrators and medical experts to aid in curbing the pandemic's progression.

Background Tumor mutation burden (TMB) has been associated with cancer immunotherapeutic response and cancer prognosis. Although many explorations have revealed that high TMB may yield many neoantigens to incite antitumor immune response, a systematic exploration of the correlation between TMB and immune signatures in different cancer types is lacking. Results We classified cancer into the lower-TMB subtype and the higher-TMB subtype for each of 32 cancer types based on their somatic mutation data from the Cancer Genome Atlas (TCGA), and compared the expression levels of immune-related genes and gene-sets between both subtypes of cancers in each cancer type. In some cancer types most of the immune signatures analyzed were upregulated in the lower-TMB subtype, while in some other cancer types the immune signatures were prone to be upregulated in the higher-TMB subtype. However, the regulatory T cells, immune cell infiltrate, tumor-infiltrating lymphocytes, and cytokine signatures tended to be upregulated in the lower-TMB subtype, and the cancer-testis antigen (CTA) and pro-inflammatory signatures were inclined to be upregulated in the higher-TMB subtype. Importantly, high TMB was associated with elevated expression of PD-L1 in diverse prevailing cancers. Furthermore, we found that higher TMB was associated with better survival prognosis in numerous cancer types while was associated with worse prognosis in a few cancer types. Conclusions High TMB may inhibit immune cell infiltrations while promote CTAs expression and inflammatory response in cancer. In many common cancer types, higher TMB may respond favorably to anti-PD-1/PD-L1 immunotherapy. Our data implicate that higher-TMB patients could gain a more favorable prognosis in diverse cancer types if treated with immunotherapy, otherwise would have a poorer prognosis compared to lower-TMB patients.

Tumour-associated macrophages (TAMs) are crucial components of the tumour microenvironment and play a significant role in tumour development and drug resistance by creating an immunosuppressive microenvironment. Macrophages are essential components of both the innate and adaptive immune systems and contribute to pathogen resistance and the regulation of organism homeostasis. Macrophage function and polarization are closely linked to altered metabolism. Generally, M1 macrophages rely primarily on aerobic glycolysis, whereas M2 macrophages depend on oxidative metabolism. Metabolic studies have revealed that the metabolic signature of TAMs and metabolites in the tumour microenvironment regulate the function and polarization of TAMs. However, the precise effects of metabolic reprogramming on tumours and TAMs remain incompletely understood. In this review, we discuss the impact of metabolic pathways on macrophage function and polarization as well as potential strategies for reprogramming macrophage metabolism in cancer treatment.

Intratumor heterogeneity (ITH) is a biomarker of tumor progression, metastasis, and immune evasion. Previous studies evaluated ITH mostly based on DNA alterations. Here, we developed a new algorithm (DEPTH) for quantifying ITH based on mRNA alterations in the tumor. DEPTH scores displayed significant correlations with ITH-associated features (genomic instability, tumor advancement, unfavorable prognosis, immunosuppression, and drug response). Compared to DNA-based ITH scores (EXPANDS, PhyloWGS, MATH, and ABSOLUTE), DEPTH scores had stronger correlations with antitumor immune signatures, cell proliferation, stemness, tumor advancement, survival prognosis, and drug response. Compared to two other mRNA-based ITH scores (tITH and sITH), DEPTH scores showed stronger and more consistent associations with genomic instability, unfavorable tumor phenotypes and clinical features, and drug response. We further validated the reliability and robustness of DEPTH in 50 other datasets. In conclusion, DEPTH may provide new insights into tumor biology and potential clinical implications for cancer prognosis and treatment. Li et al develop an algorithm they call Deviating gene Expression Profiling Tumor Heterogeneity (DEPTH) to evaluate levels of intratumour heterogeneity (ITH) at the mRNA level based on the asynchrony of transcriptome alterations in tumors. Analysing TCGA and GEO data-sets, they show that this method is simpler, more effective, and more robust than other ITH scoring algorithms.

Tumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient. We analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy. Among the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes ( , , , , , , , , , ,and ) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes ( , , and ), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score's ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts. The molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management.

Primary liver cancer is one of the most common malignant tumours worldwide; it caused approximately 830,000 deaths in 2020. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for over 80% of all cases. Various methods, including surgery, chemotherapy, radiotherapy, and radiofrequency ablation, have been widely used in the treatment of HCC. With the advancement of technology, radiotherapy has become increasingly important in the comprehensive treatment of HCC. However, due to the insufficient sensitivity of tumour cells to radiation, there are still multiple limitation in clinical application of radiotherapy. In recent years, the role of immunotherapy in cancer has been increasingly revealed, and more researchers have turned their attention to the combined application of immunotherapy and radiotherapy in the hope of achieving better treatment outcomes. This article reviews the progress on radiation therapy in HCC and the current status of its combined application with immunotherapy, and discusses the prospects and value of radioimmunotherapy in HCC.

Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask APS by 3.6%, AP by 1.1% and APS by 4.6%, demonstrating its effectiveness and feasibility.

Recent studies have revealed that tumor-associated macrophages are the most abundant stromal cells in the tumor microenvironment and play an important role in tumor initiation and progression. Furthermore, the proportion of macrophages in the tumor microenvironment is associated with the prognosis of patients with cancer. Tumor-associated macrophages can polarize into anti-tumorigenic phenotype (M1) and pro-tumorigenic phenotype (M2) by the stimulation of T-helper 1 and T-helper 2 cells respectively, and then exert opposite effects on tumor progression. Besides, there also is wide communication between tumor-associated macrophages and other immune compositions, such as cytotoxic T cells, regulatory T cells, cancer-associated fibroblasts, neutrophils and so on. Furthermore, the crosstalk between tumor-associated macrophages and other immune cells greatly influences tumor development and treatment outcomes. Notably, many functional molecules and signaling pathways have been found to participate in the interactions between tumor-associated macrophages and other immune cells and can be targeted to regulate tumor progression. Therefore, regulating these interactions and CAR-M therapy are considered to be novel immunotherapeutic pathways for the treatment of malignant tumors. In this review, we summarized the interactions between tumor-associated macrophages and other immune compositions in the tumor microenvironment and the underlying molecular mechanisms and analyzed the possibility to block or eradicate cancer by regulating tumor-associated macrophage-related tumor immune microenvironment.

Heterostructure nanoparticles (NPs), constructed by two single‐component NPs with distinct nature and multifunctional properties, have attracted intensive interest in the past few years. Among them, heterostructures made of upconversion NPs (UCNPs) and metal–organic frameworks (MOFs) can not only integrate the advantageous characteristics (e.g., porosity, structural regularity) of MOFs with unique upconverted optical features of UCNPs, but also induce cooperative properties not observed either for single component due to their special optical or electronic communications. Recently, diverse UCNP‐MOF heterostructures are designed and synthesized via different strategies and have demonstrated appealing potential for applications in biosensing and imaging, drug delivery, and photodynamic therapy (PDT). In this review, the synthesis strategies of UCNP‐MOF heterostructures are first summarized, then the authors focus mainly on discussion of their biomedical applications, particularly as PDT agents for cancer treatment. Finally, the authors briefly outlook the current challenges and future perspectives of UCNP‐MOF hybrid nanocomposites. The authors believe that this review will provide comprehensive understanding and inspirations toward recent advances of UCNP‐MOF heterostructures. Heterostructures consisting of upconversion nanoparticles and metal‐organic frameworks represent powerful nanoplatforms for diverse biomedical applications. This review summarizes the key strategies for the synthesis of such structures and their potential applications in biosensing and imaging, drug delivery, and photodynamic therapy.

The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops a region-specific framework by integrating SHAP (SHapley Additive exPlanation) analysis with twelve landslide conditioning factors (LCFs) and three progressive sampling strategies, aiming to create adaptive non-landslide point selection criteria tailored to unique environmental and geological characteristics. The strategies include (1) multi-ratio random sampling (1:1 to 1:200), (2) susceptibility-based sampling adjustments derived from pre-susceptibility analysis, and (3) LCF-based correction using the NDVI threshold identified through SHAP analysis. Results show that LCF-based correction achieved the highest performance, while a 1:5 ratio proved optimal in random sampling, aligning with regional characteristics. This framework demonstrates the importance of region-specific sampling strategies in improving landslide susceptibility prediction.