Altered epitopes enhance macrophage-mediated anti-tumour immunity to low-immunogenic tumour mutations.

Personalized neoantigen therapy has shown long-term and stable efficacy in specific patient populations. However, not all patients have sufficient levels of neoantigens for treatment. Although somatic mutations are commonly found in tumours, a significant portion of these mutations do not trigger an immune response. Patients with low mutation burdens continue to exhibit unresponsiveness to this treatment. We propose a design paradigm for neoantigen vaccines by utilizing the highly immunogenic unnatural amino acid p-nitrophenylalanine (pNO2Phe) for sequence alteration of somatic mutations that failed to generate neoepitopes. This enhances the immunogenicity of the mutations and transforms it into a suitable candidate for immunotherapy. The nitrated altered epitope vaccines designed according to this paradigm is capable of activating circulating CD8+ T cells and inducing immune cross-reactivity against autologous mutated epitopes in different MHC backgrounds (H-2Kb, H-2Kd, and human HLA-A02:01), leading to the elimination of tumour cells carrying the mutation. After immunization with the altered epitopes, tumour growth was significantly inhibited. It is noteworthy that nitrated epitopes induce tumour-infiltrating macrophages to differentiate into the M1 phenotype, surprisingly enhancing the MHC II molecule presenting pathway of macrophages. Nitrated epitope-treated macrophages have the potential to cross-activate CD4+ and CD8+ T cells, which may explain why pNO2Phe can enhance the immunogenicity of epitopes. Meanwhile, the immunosuppressive microenvironment of the tumour is altered due to the activation of macrophages. The nitrated neoantigen vaccine strategy enables the design of vaccines targeting non-immunogenic tumour mutations, expanding the pool of potential peptides for personalized and shared novel antigen therapy. This approach provides treatment opportunities for patients previously ineligible for new antigen vaccine therapy.

Empagliflozin protects against heart failure with preserved ejection fraction partly by inhibiting the senescence-associated STAT1-STING axis.

Heart failure with preserved ejection fraction (HFpEF) is a mortal clinical syndrome without effective therapies. Empagliflozin (EMPA) improves cardiovascular outcomes in HFpEF patients, but the underlying mechanism remains elusive. Here, mice were fed a high-fat diet (HFD) supplemented with L-NAME for 12 weeks and subsequently intraperitoneally injected with EMPA for another 4 weeks. A 4D-DIA proteomic assay was performed to detect protein changes in the failing hearts. We identified 310 differentially expressed proteins (DEPs) (ctrl vs. HFpEF group) and 173 DEPs (HFpEF vs. EMPA group). The regulation of immune system processes was enriched in all groups and the interferon response genes (STAT1, Ifit1, Ifi35 and Ifi47) were upregulated in HFpEF mice but downregulated after EMPA administration. In addition, EMPA treatment suppressed the increase in the levels of aging markers (p16 and p21) in HFpEF hearts. Further bioinformatics analysis verified STAT1 as the hub transcription factor during pathological changes in HFpEF mice. We next treated H9C2 cells with IFN-γ, a primary agonist of STAT1 phosphorylation, to investigate whether EMPA plays a beneficial role by blocking STAT1 activation. Our results showed that IFN-γ treatment caused cardiomyocyte senescence and STAT1 activation, which were inhibited by EMPA administration. Notably, STAT1 inhibition significantly reduced cellular senescence possibly by regulating STING expression. Our findings revealed that EMPA mitigates cardiac inflammation and aging in HFpEF mice by inhibiting STAT1 activation. The STAT1-STING axis may act as a pivotal mechanism in the pathogenesis of HFpEF, especially under inflammatory and aging conditions.

Proprotein convertase subtilisin/kexin type 9 inhibitors protect against contrast-associated acute kidney injury in patients with atherosclerotic cardiovascular disease.

Background and aims: Contrast-associated acute kidney injury (CA-AKI) may occur in patients undergoing medical procedures involving x-rays and radiocontrast media, potentially resulting in prolonged renal impairment. However, no effective treatments are available. Therefore, this study aimed to investigate the efficacy of evolocumab, a proprotein convertase subtilisin/kexin type 9 inhibitor, in reducing CA-AKI incidence among patients with atherosclerotic cardiovascular disease (ASCVD) undergoing percutaneous coronary intervention. Methods: This retrospective cohort study included patients who underwent percutaneous coronary intervention between January 2020 and December 2021 at Tianjin Chest Hospital. The study endpoint was CA-AKI incidence, and the impact of selection bias and other potential confounding factors was mitigated using bias matching. Overall, 1,642 patients were included in this study: 821 patients received evolocumab treatment before contrast agent application, and 821 did not receive such treatment. Results: CA-AKI incidence was 6.21% and 8.04% in the evolocumab and control groups, respectively. After propensity-score matching, the incidence rate was 5.09% and 14.16% in the evolocumab and control groups, respectively. Evolocumab treatment significantly reduced CA-AKI incidence (p < 0.001). Consistent findings were obtained in the subgroups of individuals with type II diabetes mellitus, chronic heart failure, and hypertension. Evolocumab exhibited a significantly greater protective effect in the high- and extremely high-risk populations than in the low- and middle-risk populations (p < 0.001). Conclusions: Evolocumab administration significantly reduced CA-AKI incidence among patients with ASCVD. Notably, this effect was more prominent within the subset of high- and extremely high-risk individuals who were already experiencing CA-AKI.

High-temperature rotation-vibration spectrum of iminosilylene (HNSi).

Iminosilylene (HNSi) has been observed in the laboratory and is expected to occur in the envelopes of carbon-rich stars. However, the lack of spectroscopic information for HNSi has hampered its further astrophysical detection. Using robust ab initio methods, we present the first and comprehensive molecular line list for HNSi (X 1Σ+). The new line list contains almost 3.36 billion transitions between 1.57 million levels with rotational excitation up to J = 160. It is suitable for temperatures up to 3000 K and covers the wavenumber range of 0-9000 cm-1 (wavelengths λ > 1.11 µm). This new line list can be helpful for the future spectroscopic characterization and molecular detection of HNSi in the laboratory and interstellar space.

Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting.

Fish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose the deconvolution enhancement keypoint network (DEKNet), a method for fish fry counting that features a single-keypoint approach. This novel approach models the fish fry as a point located in the central part of the fish head, laying the foundation for our innovative counting strategy. To be specific, first, a fish fry feature extractor (FFE) characterized by parallel dual branches is designed for high-resolution representation. Next, two identical deconvolution modules (TDMs) are added to the generation head for a high-quality and high-resolution keypoint heatmap with the same resolution size as the input image, thus facilitating the precise counting of fish fry. Then, the local peak value of the heatmap is obtained as the keypoint of the fish fry, so the number of these keypoints with coordinate information equals the number of fry, and the coordinates of the keypoint can be used to locate the fry. Finally, FishFry-2023, a large-scale fish fry dataset, is constructed to evaluate the effectiveness of the method proposed by us. Experimental results show that an accuracy rate of 98.59% was accomplished in fish fry counting. Furthermore, DEKNet achieved a high degree of accuracy on the Penaeus dataset (98.51%) and an MAE of 13.32 on a public dataset known as Adipocyte Cells. The research outcomes reveal that DEKNet has superior comprehensive performance in counting accuracy, the number of parameters and computational effort.

Unsupervised Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering.

Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Learning (USRL). However, PLMs are sensitive to the frequency information of words from their pre-training corpora, resulting in anisotropic embedding space, where the embeddings of high-frequency words are clustered but those of low-frequency words disperse sparsely. This anisotropic phenomenon results in two problems of similarity bias and information bias, lowering the quality of sentence embeddings. To solve the problems, we fine-tune PLMs by leveraging the frequency information of words and propose a novel USRL framework, namely Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering (Slt-fai). We calculate the word frequencies over the pre-training corpora of PLMs and assign words thresholding frequency labels. With them, (1) we incorporate a similarity discriminator used to distinguish the embeddings of high-frequency and low-frequency words, and adversarially tune the PLM with it, enabling to achieve uniformly frequency-invariant embedding space; and (2) we propose a novel incomplete sentence detection task, where we incorporate an information discriminator to distinguish the embeddings of original sentences and incomplete sentences by randomly masking several low-frequency words, enabling to emphasize the more informative low-frequency words. Our Slt-fai is a flexible and plug-and-play framework, and it can be integrated with existing USRL techniques. We evaluate Slt-fai with various backbones on benchmark datasets. Empirical results indicate that Slt-fai can be superior to the existing USRL baselines.

Pivotal Impact Factors in Photocatalytic Reduction of CO2 to Value-Added C1 and C2 Products.

Over the past decades, CO2 greenhouse emission has been considerably increased, causing global warming and climate change. Indeed, converting CO2 into valuable chemicals and fuels is a desired option to resolve issues caused by its continuous emission into the atmosphere. Nevertheless, CO2 conversion has been hampered by the ultrahigh dissociation energy of C=O bonds, which makes it thermodynamically and kinetically challenging. From this prospect, photocatalytic approaches appear promising for CO2 reduction in terms of their efficiency compared to other traditional technologies. Thus, many efforts have been made in the designing of photocatalysts with asymmetric sites and oxygen vacancies, which can break the charge distribution balance of CO2 molecule, reduce hydrogenation energy barrier and accelerate CO2 conversion into chemicals and fuels. Here, we review the recent advances in CO2 hydrogenation to C1 and C2 products utilizing photocatalysis processes. We also pin down the key factors or parameters influencing the generation of C2 products during CO2 hydrogenation. In addition, the current status of CO2 reduction is summarized, projecting the future direction for CO2 conversion by photocatalysis processes.

High-temperature molecular line list of hydroboron monoxide (HBO).

Hydroboron monoxide (HBO) is expected to occur in envelopes of the asymptotic giant branch (AGB), but a lack of spectroscopic data is hampering its possible detection. Using the state-of-the-art ab initio method, we present the first, comprehensive molecular line list for HBO which is suitable for temperatures up to T = 3000 K. This new line list covers the wavenumber range of 0-9000 cm-1 (wavelengths of λ ≥ 1.11 µm), and it contains almost 75 million transitions between 435 631 energy levels with rotational excitation up to J = 120. The new line list of HBO can facilitate its future molecular detection in the laboratory and interstellar space.

Diagnostic value of serum dia in acute myocardial infarction and aortic dissection in athletic patients and those with optimal physical health

Objective: To explore the relationship between serum lipoprotein (a) levels and acute myocardial infarction (AMI) and aortic dissection in athletic patients and those with optimal physical health. Methods: This study involved 216 athletic patients admitted to a Chinese hospital for AMI who underwent Percutaneous Coronary Intervention (PCI) between 2018 and 2019. These patients, characterized by their athletic background and optimal physical health, were divided based on their serum lipoprotein (a) levels: 133 in the low-lipoprotein (a) group (<300 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression. Results: The low lipoprotein (a) group exhibited lower age, reduced Killip grades III-IV, lower LDL-C levels, and fewer diseased vessels than the high lipoprotein (a) group (P><0.05). The incidence of MACE was significantly lower in the low lipoprotein (a) group (5.3%, 7/133) compared to the high lipoprotein (a) group (27.87%, 51/183) (P><0.05). Univariate analysis identified significant differences in age, post-surgery β-blocker use, LDL-C levels, serum lipoprotein (a) levels, revascularization strategies, and the> <3 00 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression (AU)

An Improved Method for Broiler Weight Estimation Integrating Multi-Feature with Gradient Boosting Decision Tree.

Broiler weighing is essential in the broiler farming industry. Camera-based systems can economically weigh various broiler types without expensive platforms. However, existing computer vision methods for weight estimation are less mature, as they focus on young broilers. In effect, the estimation error increases with the age of the broiler. To tackle this, this paper presents a novel framework. First, it employs Mask R-CNN for instance segmentation of depth images captured by 3D cameras. Next, once the images of either a single broiler or multiple broilers are segmented, the extended artificial features and the learned features extracted by Customized Resnet50 (C-Resnet50) are fused by a feature fusion module. Finally, the fused features are adopted to estimate the body weight of each broiler employing gradient boosting decision tree (GBDT). By integrating diverse features with GBTD, the proposed framework can effectively obtain the broiler instance among many depth images of multiple broilers in the visual field despite the complex background. Experimental results show that this framework significantly boosts accuracy and robustness. With an MAE of 0.093 kg and an R2 of 0.707 in a test set of 240 63-day-old bantam chicken images, it outperforms other methods.