Polypharmacy side effect prediction based on semi-implicit graph variational auto-encoder.

Polypharmacy, the use of drug combinations, is an effective approach for treating complex diseases, but it increases the risk of adverse effects. To predict novel polypharmacy side effects based on known ones, many computational methods have been proposed. However, most of them generate deterministic low-dimensional embeddings when modeling the latent space of drugs, which cannot effectively capture potential side effect associations between drugs. In this study, we present SIPSE, a novel approach for predicting polypharmacy side effects. SIPSE integrates single-drug side effect information and drug-target protein data to construct novel drug feature vectors. Leveraging a semi-implicit graph variational auto-encoder, SIPSE models known polypharmacy side effects and generates flexible latent distributions for drug nodes. SIPSE infers the current node distribution by combining the distributions of neighboring nodes with embedding noise. By sampling node embeddings from these distributions, SIPSE effectively predicts polypharmacy side effects between drugs. One key innovation of SIPSE is its incorporation of uncertainty propagation through noise embedding and neighborhood sharing, enhancing its graph analysis capabilities. Extensive experiments on a benchmark dataset of polypharmacy side effects demonstrated that SIPSE significantly outperformed five state-of-the-art methods in predicting polypharmacy side effects.

Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection.

Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model's biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy.

Exploration of the pharmacological components and therapeutic mechanisms in treatment of Alzheimer's disease with Polygonati Rhizoma and its processed product using combined analysis of metabolomics, network pharmacology, and gut microbiota.

Polygonati Rhizoma (PR, Huangjing in Chinese) and its processed product (PRP), which are used in Traditional Chinese medicine (TCM) for cognitive enhancement and treatment of Alzheimer's disease (AD), have not been fully explored in terms of the different mechanisms underlying their anti-AD effects. Therefore, we used APP/PS1 mice as an AD model to assess the effects of PR and PRP on anxiety-like behaviors, cognitive function, memory performance, and pathological changes in the murine brain. UPLC-HRMS was applied to identify the components of PR and PRP that entered into the blood and brain. Network pharmacology was used to elucidate potential mechanisms underlying the improvement of AD. Differences in the intestinal flora composition between mice treated with PR and PRP were investigated using 16S rRNA sequencing, establishing a correlation between pharmacological components and distinct flora profiles. The results revealed that both PR and PRP interventions ameliorated cognitive deficits and attenuated Amyloid ß (Aß) plaque deposition in the brains of AD mice. Seven specific blood-entering components, namely glutamic acid, Phe-Phe, and uridine, etc., were associated with PR intervention, whereas ten specific blood-entering components including (2R,3S)-3-isopropylmalate, 3-methylhexahydropyrrolo[1,2-a]pyrazine-1,4-dione, and 3-methoxytyrosine were related to PRP intervention. Uridine was identified as a common brain-penetrating component in both PR and PRP interventions. Network pharmacology analysis suggested that the NOD-like receptor signaling pathway, Calcium signaling pathway and Alzheimer's disease were specific pathways targeted in AD treatment using PR intervention. Moreover, the apoptosis pathway was specifically linked to AD treatment during PRP intervention. Furthermore, the administration of both PR and PRP enhanced the abundance and diversity of the intestinal flora in APP/PS1 mice. Western blotting confirmed that PR excels in regulates inflammation, whereas PRP balances autophagy and apoptosis to alleviate the progression of AD. This study offers valuable insights and establishes a robust foundation for further comprehensive exploration of the intrinsic correlation between TCM and AD.

In vivo detection of endogenous toxic phenolic compounds of intestine.

Phenol and p-cresol are two common toxic small molecules related to various diseases. Existing reports confirmed that high L-tyrosine in the daily diet can increase the concentration of phenolic compounds in blood and urine. L-tyrosine is a common component of protein-rich foods. Some anaerobic bacteria in the gut can convert non-toxic l-tyrosine into these two toxic phenolic compounds, phenol and p-cresol. Existing methods have been constructed for measuring the concentration of phenolic compound in feces. However, there is still a lack of direct visual evidence to measure the phenolic compounds in the intestine. In this study, we aimed to construct a whole-cell biosensor for phenolic compounds detection based on the dmpR, the regulator from the phenol metabolism cluster. The commensal bacterium Citrobacter amalonaticus PS01 was selected and used as the chassis. Compared with the biosensor based on ECN1917, the biosensor PS01[dmpR] could better implant into the mouse gut through gavage and showed a higher sensitive to phenolic compound. And the concentration of phenolic compounds in the intestines could be observed with the help of in vivo imaging system using PS01[dmpR]. This paper demonstrated endogenous phenol synthesis in the gut and the strategy of using commensal bacteria to construct whole-cell biosensors for detecting small molecule compounds in the intestines.

Molecular Characterization and Classification of HER2-Positive Breast Cancer Inform Tailored Therapeutic Strategies.

HER2-positive breast cancer is an aggressive subtype that accounts for 15-20% of all breast cancers. Recent studies have suggested that HER2-positive breast cancer is a group of heterogeneous diseases with different sensitivities to standard treatment regimens. Revealing the molecular heterogeneity of HER2-positive breast cancer could potentially enable more precise treatment strategies. Here, we performed multiomics profiling on a HER2-positive breast cancer cohort and identified four transcriptome-based subtypes. The classical HER2 (HER2-CLA) subtype comprised 28.3% of the samples and displayed high ERBB2 activation and significant benefit from anti-HER2 therapy. The immunomodulatory (HER2-IM) subtype (20%) featured an immune-activated microenvironment, potentially suitable for de-escalated treatment and immunotherapy. The luminal-like (HER2-LUM) subtype (30.6%) possessed similar molecular features of hormone receptor-positive HER2-negative breast cancer, suggesting endocrine therapy and CDK4/6 inhibitors as a potential therapeutic strategy. Lastly, the basal/mesenchymal-like (HER2-BM) subtype (21.1%), had a poor response to current anti-HER2 dual-targeted therapies and could potentially benefit from tyrosine kinase inhibitors. The molecular characteristics and clinical features of the subtypes were further explored across multiple cohorts, and the feasibility of the proposed treatment strategies was validated in patient-derived organoid and patient-derived tumor fragment models. This study elucidates the molecular heterogeneity of HER2-positive breast cancer and paves the way for a more tailored treatment.

High-Performance Organic-Inorganic Hybrid Conductive Hydrogels for Stretchable Elastic All-Hydrogel Supercapacitors and Flexible Self-Powered Integrated Systems.

Conductive polymer hydrogels exhibit unique electrical, electrochemical, and mechanical properties, making them highly competitive electrode materials for stretchable high-capacity energy storage devices for cutting-edge wearable electronics. However, it remains extremely challenging to simultaneously achieve large mechanical stretchability, high electrical conductivity, and excellent electrochemical properties in conductive polymer hydrogels because introducing soft insulating networks for improving stretchability inevitably deteriorates the connectivity of rigid conductive domain and decreases the conductivity and electrochemical activity. This work proposes a distinct confinement self-assembly and multiple crosslinking strategy to develop a new type of organic-inorganic hybrid conductive hydrogels with biphase interpenetrating cross-linked networks. The hydrogels simultaneously exhibit high conductivity (2000 S m-1), large stretchability (200%), and high electrochemical activity, outperforming existing conductive hydrogels. The inherent mechanisms for the unparalleled comprehensive performances are thoroughly investigated. Elastic all-hydrogel supercapacitors are prepared based on the hydrogels, showing high specific capacitance (212.5 mF cm-2), excellent energy density (18.89 µWh cm-2), and large deformability. Moreover, flexible self-powered luminescent integrated systems are constructed based on the supercapacitors, which can spontaneously shine anytime and anywhere without extra power. This work provides new insights and feasible avenues for developing high-performance stretchable electrode materials and energy storage devices for wearable electronics.

Correction: Duhuo Jisheng Decoction regulates intracellular zinc homeostasis by enhancing autophagy via PTEN/Akt/mTOR pathway to improve knee cartilage degeneration.

[This corrects the article DOI: 10.1371/journal.pone.0290925.].

Single-cell sequencing of immune cells from the coronary sinus reveals immune mechanisms of the progression of persistent atrial fibrillation.

Identifying the atlas of immune cells from coronary sinus circulation (CSC) of patients with persistent atrial fibrillation (PerAF) may provide new insights into the role of immune cells in the progression of AF. Single-cell sequencing revealed substantial alterations in immune cells from CSCs of patients with PerAF, especially a markedly elevated abundance of T cells, after which we identified a T cell subset: FGFBP2(+)TRDC(-)CD4(-) T cells (Ftc-T cells), which can promote the proliferation of cardiac fibroblasts (CFs),and the proportion of Ftc-T had a positive linear with AF recurrence post catheter ablation (CA). Moreover, IFI27 was found to be highly enriched in Ftc-T cells and promoted CFs proliferation and collagen expression. Altogether, our findings represent a unique resource providing in-depth insights into the heterogeneity of the immune cell from CSC of patients with PerAF and highlight the potential role of Ftc-T cells and IFI27 for AF progression.

Myeloid-derived suppressor cells-induced exhaustion of CD8 + T-cell participates in rejection after liver transplantation.

Liver transplantation (LT) rejection remains the most pervasive problem associated with this procedure, while the mechanism involved is still complicated and undefined. One promising solution may involve the use of myeloid-derived suppressor cells (MDSC). However, the immunological mechanisms underlying the effects of MDSC after LT remain unclear. This study is meant to clarify the role MDSCs play after liver transplantation. In this study, we collected liver tissue and peripheral blood mononuclear cells (PBMC) from LT patients showing varying degrees of rejection, as well as liver and spleen tissue samples from mice LT models. These samples were then analyzed using flow cytometry, immunohistochemistry and multiple immunofluorescence. M-MDSCs and CD8 + T-cells extracted from C57/BL6 mice were enriched and cocultured for in vitro experiments. Results, as obtained in both LT patients and LT mice model, revealed that the proportion and frequency of M-MDSC and PD-1 + T-cells increased significantly under conditions associated with a high degree of LT rejection. Within the LT rejection group, our immunofluorescence results showed that a close spatial contiguity was present between PD-1 + T-cells and M-MDSCs in these liver tissue samples and the proportion of CD84/PD-L1 double-positive M-MDSC was greater than that of G-MDSC. There was a positive correlation between the activity of CD84 and immunosuppressive function of M-MDSCs including PD-L1 expression and reactive oxygen species (ROS) production, as demonstrated in our in vitro model. M-MDSCs treated with CD84 protein were able to induce co-cultured CD8 + T-cells to express high levels of exhaustion markers. We found that CD84 regulated M-MDSC function via expression of PD-L1 through activation of the Akt/Stat3 pathway. These results suggest that the capacity for CD84 to regulate M-MDSC induction of CD8 + T-cell exhaustion may play a key role in LT rejection. Such findings provide important, new insights into the mechanisms of tolerance induction in LT.

A large-aperture, high-power ultrawideband radiation system with beam broadening capacity.

Due to the limited maximum output power of the pulsers based on avalanche transistors, high-power ultrawideband (UWB) radiation systems usually synthesize plenty of modules simultaneously to achieve a high peak effective potential (rEp). However, this would lead to an increased aperture size as well as a narrower beam, which would limit their applications in intentional electromagnetic interference fields. In this paper, a high-power UWB radiation system with beam broadening capacity is developed. To achieve beam broadening in the time domain, a power-law time delay distribution method is proposed and studied by simulation, and then the relative excitation time delays of the modules are optimized to achieve higher rEp and avoid beam splitting in the beam broadening mode. In order to avoid false triggering of the pulser elements when implementing the beam broadening, the mutual coupling effect in the system is analyzed and suppressed by employing onboard high-pass filters, since the mutual coupling effect is much more severe in the low-frequency range. Finally, a radiation system with 36 modules is developed. Measuring results indicate that in the high-rEp mode, the developed system could achieve a maximum effective potential rEp of 313.6 kV and a maximum pulse-repetition-rate of 20 kHz. In the beam broadening mode, its half-peak-power beam width in the H-plane is broadened from the original value of 3.9° to 7.9°, with a maximum rEp of 272.9 kV. The polarization direction of the system could be flexibly adjusted by a built-in motor.

SOSTDC1 Nuclear Translocation Facilitates BTIC Maintenance and CHD1-Mediated HR Repair to Promote Tumor Progression and Olaparib Resistance in TNBC.

Breast tumor-initiating cells (BTICs) of triple-negative breast cancer (TNBC) tissues actively repair DNA and are resistant to treatments including chemotherapy, radiotherapy, and targeted therapy. Herein, it is found that a previously reported secreted protein, sclerostin domain containing 1 (SOSTDC1), is abundantly expressed in BTICs of TNBC cells and positively correlated with a poor patient prognosis. SOSTDC1 knockdown impairs homologous recombination (HR) repair, BTIC maintenance, and sensitized bulk cells and BTICs to Olaparib. Mechanistically, following Olaparib treatment, SOSTDC1 translocates to the nucleus in an importin-α dependent manner. Nuclear SOSTDC1 interacts with the N-terminus of the nucleoprotein, chromatin helicase DNA-binding factor (CHD1), to promote HR repair and BTIC maintenance. Furthermore, nuclear SOSTDC1 bound to ß-transducin repeat-containing protein (ß-TrCP) binding motifs of CHD1 is found, thereby blocking the ß-TrCP-CHD1 interaction and inhibiting ß-TrCP-mediated CHD1 ubiquitination and degradation. Collectively, these findings identify a novel nuclear SOSTDC1 pathway in regulating HR repair and BTIC maintenance, providing insight into the TNBC therapeutic strategies.

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

A potyvirus provides an efficient viral vector for gene expression and functional studies in Asteraceae plants.

Plant virus-derived vectors are rapid and cost-effective for protein expression and gene functional studies in plants, particularly for species that are difficult to genetically transform. However, few efficient viral vectors are available for functional studies in Asteraceae plants. Here, we identified a potyvirus named zinnia mild mottle virus (ZiMMV) from common zinnia (Zinnia elegans Jacq.) through next-generation sequencing. Using a yeast homologous recombination strategy, we established a full-length infectious cDNA clone of ZiMMV under the control of the cauliflower mosaic virus 35S promoter. Furthermore, we developed an efficient expression vector based on ZiMMV for the persistent and abundant expression of foreign proteins in the leaf, stem, root, and flower tissues with mild symptoms during viral infection in common zinnia. We showed that the ZiMMV-based vector can express ZeMYB9, which encodes a transcript factor inducing dark red speckles in leaves and flowers. Additionally, the expression of a gibberellic acid (GA) biosynthesis gene from the ZiMMV vector substantially accelerated plant height growth, offering a rapid and cost-effective method. In summary, our work provides a powerful tool for gene expression, functional studies, and genetic improvement of horticultural traits in Asteraceae plant hosts.

Bacterial load in meconium.

The spike-in plasmid method was utilized to perform an analysis on meconium and second-pass feces, yielding both relative and absolute quantitative results. With the absolute quantitative data, the abundance of bacteria in 17 meconium samples and 17 second-pass fecal samples were found to be 1.14 × 107 and 1.59 × 109 copies/g, respectively. The mode of delivery can significantly influence the alterations and compositions of gut bacteria in a newborn within 72 h.

Advances in targeting histone deacetylase for treatment of solid tumors.

Histone deacetylase (HDAC) serves as a critical molecular regulator in the pathobiology of various malignancies and have garnered attention as a viable target for therapeutic intervention. A variety of HDAC inhibitors (HDACis) have been developed to target HDACs. Many preclinical studies have conclusively demonstrated the antitumor effects of HDACis, whether used as monotherapy or in combination treatments. On this basis, researchers have conducted various clinical studies to evaluate the potential of selective and pan-HDACis in clinical settings. In our work, we extensively summarized and organized current clinical trials, providing a comprehensive overview of the current clinical advancements in targeting HDAC therapy. Furthermore, we engaged in discussions about several clinical trials that did not yield positive outcomes, analyzing the factors that led to their lack of anticipated therapeutic effectiveness. Apart from the experimental design factors, issues such as toxicological side effects, tumor heterogeneity, and unexpected off-target effects also contributed to these less-than-expected results. These challenges have naturally become significant barriers to the application of HDACis. Despite these challenges, we believe that advancements in HDACi research and improvements in combination therapies will pave the way or lead to a broad and hopeful future in the treatment of solid tumors.

Super-enhancer-driven IRF2BP2 enhances ALK activity and promotes neuroblastoma cell proliferation.

BACKGROUND: Super-enhancers (SEs) typically govern the expression of critical oncogenes and play a fundamental role in the initiation and progression of cancer. Focusing on genes that are abnormally regulated by SE in cancer may be a new strategy for understanding pathogenesis. In the context of this investigation, we have identified a previously unreported SE-driven gene IRF2BP2 in neuroblastoma (NB). METHODS: The expression and prognostic value of IRF2BP2 were detected in public databases and clinical samples. The effect of IRF2BP2 on NB cell growth and apoptosis was evaluated through in vivo and in vitro functional loss experiments. The molecular mechanism of IRF2BP2 was investigated by the study of chromatin regulatory regions and transcriptome sequencing. RESULTS: The sustained high expression of IRF2BP2 results from the activation of a novel SE established by NB master transcription factors MYCN, MEIS2 and HAND2, and they form a new complex that regulates the gene network associated with the proliferation of NB cell populations. We also observed a significant enrichment of the AP-1 family at the binding sites of IRF2BP2. Remarkably, within NB cells, AP-1 plays a pivotal role in shaping the chromatin accessibility landscape, thereby exposing the binding site for IRF2BP2. This orchestrated action enables AP-1 and IRF2BP2 to collaboratively stimulate the expression of the NB susceptibility gene ALK, thereby upholding the highly proliferative phenotype characteristic of NB. CONCLUSION: Our findings indicate that SE-driven IRF2BP2 can bind to AP-1 to maintain the survival of tumor cells via regulating chromatin accessibility of NB susceptibility gene ALK.

Targeting transposable elements in cancer: developments and opportunities.

Transposable elements (TEs), comprising nearly 50% of the human genome, have transitioned from being perceived as "genomic junk" to key players in cancer progression. Contemporary research links TE regulatory disruptions with cancer development, underscoring their therapeutic potential. Advances in long-read sequencing, computational analytics, single-cell sequencing, proteomics, and CRISPR-Cas9 technologies have enriched our understanding of TEs' clinical implications, notably their impact on genome architecture, gene regulation, and evolutionary processes. In cancer, TEs, including long interspersed element-1 (LINE-1), Alus, and long terminal repeat (LTR) elements, demonstrate altered patterns, influencing both tumorigenic and tumor-suppressive mechanisms. TE-derived nucleic acids and tumor antigens play critical roles in tumor immunity, bridging innate and adaptive responses. Given their central role in oncology, TE-targeted therapies, particularly through reverse transcriptase inhibitors and epigenetic modulators, represent a novel avenue in cancer treatment. Combining these TE-focused strategies with existing chemotherapy or immunotherapy regimens could enhance efficacy and offer a new dimension in cancer treatment. This review delves into recent TE detection advancements, explores their multifaceted roles in tumorigenesis and immune regulation, discusses emerging diagnostic and therapeutic approaches centered on TEs, and anticipates future directions in cancer research.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer.

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

The HLA-I landscape confers prognosis and antitumor immunity in breast cancer.

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood. In this study, we established a multiomics cohort of 1156 Chinese breast cancer patients for HLA-I investigation. We calculated four important HLA-I indicators in each individual, including HLA-I expression level, somatic HLA-I loss of heterozygosity (LOH), HLA-I evolutionary divergence (HED) and peptide-binding promiscuity (Pr). Then, we evaluated their distribution and prognostic significance in breast cancer subtypes. We found that the four breast cancer subtypes had distinct features of HLA-I indicators. Increased expression of HLA-I and LOH were enriched in triple-negative breast cancer (TNBC), while Pr was relatively higher in hot tumors within TNBCs. In particular, a higher Pr indicated a better prognosis in TNBCs by regulating the infiltration of immune cells and the expression of immune molecules. Using the matched genomic and transcriptomic data, we found that mismatch repair deficiency-related mutational signature and pathways were enriched in low-Pr TNBCs, suggesting that targeting mismatch repair deficiency for synthetic lethality might be promising therapy for these patients. In conclusion, we presented an overview of HLA-I indicators in breast cancer and provided hints for precision treatment for low-Pr TNBCs.

Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.

The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad.