Tumor Microenvironment Specific Regulation Ca-Fe-Nanospheres for Ferroptosis-Promoted Domino Synergistic Therapy and Tumor Immune Response.

Reactive oxygen species (ROS)-mediated emerging treatments exhibit unique advantages in cancer therapy in recent years. While the efficacy of ROS-involved tumor therapy is greatly restricted by complex tumor microenvironment (TME). Herein, a dual-metal CaO2@CDs-Fe (CCF) nanosphere, with TME response and regulation capabilities, are proposed to improve ROS lethal power by a multiple cascade synergistic therapeutic strategy with domino effect. In response to weak acidic TME, CCF will decompose, accompanied with intracellular Ca2+ upregulated and abundant H2O2 and O2 produced to reverse antitherapeutic TME. Then the exposed CF cores can act as both Fenton agent and sonosensitizer to generate excessive ROS in the regulated TME for enhanced synergistic CDT/SDT. In combination with calcium overloading, the augmented ROS induced oxidative stress will cause more severe mitochondrial damage and cellular apoptosis. Furthermore, CCF can also reduce GPX4 expression and enlarge the lipid peroxidation, causing ferroptosis and apoptosis in parallel. These signals of damage will finally initiate damage-associated molecular patterns to activate immune response and to realize excellent antitumor effect. This outstanding domino ROS/calcium loading synergistic effect endows CCF with excellent anticancer effect to efficiently eliminate tumor by apoptosis/ferroptosis/ICD both in vitro and in vivo.

Asymmetric Synthesis of Three Alkenyl Epoxides: Crafting the Sex Pheromones of the Elm Spanworm and the Painted Apple Moth.

A concise synthesis of the sex pheromones of elm spanworm as well as painted apple moth has been achieved. The key steps were the alkylation of acetylide ion, Sharpless asymmetric epoxidation and Brown's P2-Ni reduction. This approach provided the sex pheromone of the elm spanworm (1) in 31% total yield and those of the painted apple moth (2, 3) in 26% and 32% total yields. The ee values of three final products were up to 99%. The synthesized pheromones hold promising potential for use in the management and control of these pests.

Mycoplasma glycine cleavage system key subunit GcvH is an apoptosis inhibitor targeting host endoplasmic reticulum.

Mycoplasmas are minimal but notorious bacteria that infect humans and animals. These genome-reduced organisms have evolved strategies to overcome host apoptotic defense and establish persistent infection. Here, using Mycoplasma bovis as a model, we demonstrate that mycoplasma glycine cleavage system (GCS) H protein (GcvH) targets the endoplasmic reticulum (ER) to hijack host apoptosis facilitating bacterial infection. Mechanically, GcvH interacts with the ER-resident kinase Brsk2 and stabilizes it by blocking its autophagic degradation. Brsk2 subsequently disturbs unfolded protein response (UPR) signaling, thereby inhibiting the key apoptotic molecule CHOP expression and ER-mediated intrinsic apoptotic pathway. CHOP mediates a cross-talk between ER- and mitochondria-mediated intrinsic apoptosis. The GcvH N-terminal amino acid 31-35 region is necessary for GcvH interaction with Brsk2, as well as for GcvH to exert anti-apoptotic and potentially pro-infective functions. Notably, targeting Brsk2 to dampen apoptosis may be a conserved strategy for GCS-containing mycoplasmas. Our study reveals a novel role for the conserved metabolic route protein GcvH in Mycoplasma species. It also sheds light on how genome-reduced bacteria exploit a limited number of genomic proteins to resist host cell apoptosis thereby facilitating pathogenesis.

Tuning the End Alkyl Chain of the Ether Solvent to Stabilize the Electrode/Electrolyte Interfaces in the NCM-Li Battery.

Lithium metal batteries (LMBs) combined with a high-voltage nickel-rich cathode show great potential in meeting the growing need for high energy density. The lack of advanced electrolytes has been a major obstacle in the commercialization of high-voltage lithium metal batteries (LMBs), as these electrolytes need to effectively support both a stable lithium metal anode (LMA) and a high-voltage cathode (>4 V vs Li+/Li). In this work, by extending the two terminal methyl groups in DIGDME and TEGDME to n-butyl groups, we design a new weakly solvating electrolyte (2 M LIFSI+TEGDBE) that enables the stable cycling of NMC83 (LiNi0.83Co0.12Mn0.05O2) cathodes. The NMC83 cell exhibits a high and stable Coulombic efficiency (CE) of over 99%, as well as capacity retention of approximately 99.8% after 100 cycles at 0.3 C. X-ray photoelectron spectroscopy analysis (XPS) and high-resolution transmission electron microscope (HRTEM) images revealed that the anion species decomposed first, resulting in the formation of a cathode-electrolyte interface (CEI) film predominantly consisting of decomposition products from the anions on the positive electrode surface. This work links the functional group of solvents with the solvation structure and electrochemical performance of ether-based electrolytes, providing a distinctive sight to design advanced electrolytes for high-energy-density LMBs.

Prototype-based sample-weighted distillation unified framework adapted to missing modality sentiment analysis.

Missing modality sentiment analysis is a prevalent and challenging issue in real life. Furthermore, the heterogeneity of multimodality often leads to an imbalance in optimization when attempting to optimize the same objective across all modalities in multimodal networks. Previous works have consistently overlooked the optimization imbalance of the network in cases when modalities are absent. This paper presents a Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis (PSWD). Specifically, it fuses features with a more efficient transformer-based cross-modal hierarchical cyclic fusion module. Subsequently, we propose two strategies, namely sample-weighted distillation and prototype regularization network, to address the issues of missing modality and optimization imbalance. The sample-weighted distillation strategy assigns higher weights to samples that are located closer to class boundaries. This facilitates the obtaining of complete knowledge by the student network from the teacher's network. The prototype regularization network calculates a balanced metric for each modality, which adaptively adjusts the gradient based on the prototype cross-entropy loss. Unlike conventional approaches, PSWD not only connects the sentiment analysis study in the missing modality to the full modality, but the proposed prototype regularization network is not reliant on the network structure and can be expanded to more multimodal studies. Massive experiments conducted on IEMOCAP and MSP-IMPROV show that our method achieves the best results compared to the latest baseline methods, which demonstrates its value for application in sentiment analysis.

Genetic variants in C1GALT1 are associated with gastric cancer risk and immune infiltrations.

Core 1 synthase glycoprotein-N-acetylgalactosamine 3-ß-galactosyltransferase 1 (C1GALT1) is known to play a critical role in the development of gastric cancer, but few studies have elucidated associations between genetic variants in C1GALT1 and gastric cancer susceptibility. By using the genome-wide association study data from the database of Genotype and Phenotype (dbGAP), we evaluated these associations with a logistic regression model and identified that the rs35999583 in C1GALT1 was associated with gastric cancer risk (odd ratio, 0.83; 95% confidence interval [CI], 0.75-0.92; P = 3.95 × 10 -4]. C1GALT1 mRNA expression was significantly higher in gastric tumor tissues, and gastric cancer patients with higher C1GALT1 mRNA levels had the worse overall survival rates (hazards ratio, 1.33; 95% CI, 1.05-1.68; P log-rank = 1.90 × 10 -2). Furthermore, we found that C1GALT1 copy number variations differed in various immune cells and C1GALT1 mRNA expression was positively correlated with the infiltrating levels of CD4 + T cells and macrophages. These results highlight that genetic variants of C1GALT1 may play an important role in gastric cancer risk and provide a new insight for C1GALT1 to be a promising predictor of gastric cancer susceptibility and immune status.

Characteristics of Trypsin genes and their roles in insecticide resistance based on omics and functional analyses in the malaria vector Anopheles sinensis.

Trypsin is one of the most diverse and widely studied protease hydrolases. However, the diversity and characteristics of the Trypsin superfamily of genes have not been well understood, and their role in insecticide resistance is yet to be investigated. In this study, a total of 342 Trypsin genes were identified and classified into seven families based on homology, characteristic domains and phylogenetics in Anopheles sinensis, and the LY-Domain and CLECT-Domain families are specific to the species. Four Trypsin genes, (Astry2b, Astry43a, Astry90, Astry113c) were identified to be associated with pyrethroid resistance based on transcriptome analyses of three field resistant populations and qRT-PCR validation, and the knock-down of these genes significantly decrease the pyrethroid resistance of Anopheles sinensis based on RNAi. The activity of Astry43a can be reduced by five selected insecticides (indoxacarb, DDT, temephos, imidacloprid and deltamethrin); and however, the Astry43a could not directly metabolize these five insecticides, like the trypsin NYD-Tr did in earlier reports. This study provides the overall information frame of Trypsin genes, and proposes the role of Trypsin genes to insecticide resistance. Further researches are necessary to investigate the metabolism function of these trypsins to insecticides.

Reducing Indoor Particulate Air Pollution Improves Student Test Scores: A Randomized Double-Blind Crossover Study.

Short-term exposure to air pollution is associated with a decline in cognitive function. Standardized test scores have been employed to evaluate the effects of air pollution exposure on cognitive performance. Few studies aimed to prove whether air pollution is responsible for reduced test scores; none have implemented a "gold-standard" method for assessing the association such as a randomized, double-blind intervention. This study used a "gold-standard" method─randomized, double-blind crossover─to assess whether reducing short-term indoor particle concentrations results in improved test scores in college students in Tianjin, China. Participants (n = 162) were randomly assigned to one of two similar classrooms and completed a standardized English test on two consecutive weekends. Air purifiers with active or sham (i.e., filter removed) particle filtration were placed in each classroom. The filtration mode was switched between the two test days. Linear mixed-effect models were used to evaluate the effect of the intervention mode on the test scores. The results show that air purification (i.e., reducing PM) was significantly associated with increases in the z score for combined (0.11 [95%CI: 0.02, 0.21]) and reading (0.11 [95%CI: 0.00, 0.22]) components. In conclusion, a short-term reduction in indoor particle concentration led to improved test scores in students, suggesting an improvement in cognitive function.

Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer.

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.

Dominant Solvent-Separated Ion Pairs in Electrolytes Enable Superhigh Conductivity for Fast-Charging and Low-Temperature Lithium Ion Batteries.

The low ionic conductivities of aprotic electrolytes hinder the development of extreme fast charging technologies and applications at low temperatures for lithium-ion batteries (LIBs). Herein, we present an electrolyte with LiFSI in acetone (DMK). In DMK electrolytes, the solvation number is three, and solvent-separated ion pairs (SSIPs) are the dominant structure, which is largely different from other linear aprotic electrolytes where salts primarily exist as contact ion pairs (CIPs). With incompact solvation structures due to the weak solvation ability of DMK with Li+, the ionic conductivity reaches 45 mS/cm at room temperature. The percentage of SSIPs increases as temperatures decrease in DMK electrolytes, which is totally different from the carbonate-based electrolytes but greatly beneficial to low-temperature ionic conductivity. With the appropriate addition of VC and FEC, DMK-based electrolytes still exhibit a superhigh ionic conductivity. Even at -40 °C, the ionic conductivity is greater than 10 mS/cm. With DMK-based electrolytes, LIBs with thick LiFePO4 electrodes can be cycled at high rates and at low temperatures.

Inhibitor of P-glycoprotein-mediated drug transport by curcumin in broiler chickens.

OBJECTIVE: To investigate the role of curcumin in the regulation of P-glycoprotein (P-gp) and its influence on the pharmacokinetics of P-gp substrates. SAMPLE: 39 broiler chicken and chicken embryonic primary hepatocytes. METHODS: Chicken embryonic primary hepatocytes were treated with curcumin, after which cell viability, P-gp expression, and transport were assessed. Broiler chickens were pretreated with curcumin, after which P-gp expression and the pharmacokinetic behavior of orally administered sulfadiazine (a substrate of P-gp) were measured. RESULTS: The preliminary results showed that the viability of chicken embryonic primary hepatocytes was enhanced by pretreatment with 40, 60, and 100 µM curcumin. Curcumin inhibits the expression and transport of P-gp. In vivo experiments showed that curcumin decreased the expression of P-gp in the broiler chicken liver, kidney, and small intestine. Pretreatment with curcumin changed the pharmacokinetic behavior of orally administered sulfadiazine by increasing the area under the curve (47.36 vs 70.35 h·mg/L, P < .01) and peak concentration (10.1 vs 14.53 µg/mL, P < .01). CLINICAL RELEVANCE: Curcumin inhibited the expression and efflux of chicken P-gp, thereby improving the oral bioavailability of P-gp substrate drugs. These findings provide a rationale for exploiting herbal-drug interactions in veterinary practice to improve the absorption of drugs.

Tumor-targeted nanodrug FSGG/siGal-9 for transdermal photothermal immunotherapy of melanoma.

Photothermal therapy (PTT) is a cancer-targeted treatment approach.The occurrence of tumors may be related to microbial infections (Viruses, bacteria, fungi, etc.), which probably provokes anti-tumor immunity. However, T cells in the context of cancer become exhausted and dysfunctional. Galectin-9 (Gal-9) is highly expressed in normal tissues and associates with body immune tolerance, and was firstly evidenced with much higher expression on the primary solid tumors than CD80/86 (B7) and CD274 (PD-L1) here, which suggests that Gal-9 may be a key factor in inhibiting the anti-tumor immunity, and its receptor T cell immunoglobulin and mucin domain 3 (TIM-3) was discovered on the cytotoxic T lymphocytes (CTL) with high expression as well based on the single cell analysis. The immune checkpoint communications showed that the Gal-9/TIM-3 axis played the most vital role on negatively regulating the anti-tumor immunity of CTL for melanoma. Then, we used a novel transdermal photothermal nanosensitizer (FSGG) loading Gal-9 siRNA (FSGG/siGal-9) for knocking the tumor cells down Gal-9 to block the Gal-9/TIM-3 axis and prohibit CTL exhaustion synergizing PTT against melanoma, which evidenced good effects on inhibiting tumor growth and enhancing anti-tumor immunity, named "photothermal immunotherapy". This paper provides a new perspective for tumor prevention and treatment.

Nanomaterials-Involved Tumor-Associated Macrophages' Reprogramming for Antitumor Therapy.

Tumor-associated macrophages (TAMs) play pivotal roles in tumor development. As primary contents of tumor environment (TME), TAMs secrete inflammation-related substances to regulate tumoral occurrence and development. There are two kinds of TAMs: the tumoricidal M1-like TAMs and protumoral M2-like TAMs. Reprogramming TAMs from immunosuppressive M2 to immunocompetent M1 phenotype is considered a feasible way to improve immunotherapeutic efficiency. Notably, nanomaterials show great potential for biomedical fields due to their controllable structures and properties. There are many types of nanomaterials that exhibit great regulatory activities for TAMs' reprogramming. In this review, the recent progress of nanomaterials-involved TAMs' reprogramming is comprehensively discussed. The various nanomaterials for TAMs' reprogramming and the reprogramming strategies are summarized and introduced. Additionally, the challenges and perspectives of TAMs' reprogramming for efficient therapy are discussed, aiming to provide inspiration for TAMs' regulator design and promote the development of TAMs-mediated immunotherapy.

The metabolic profiles and body composition of non-obese metabolic associated fatty liver disease.

Background/purpose: Metabolic-associated fatty liver disease (MAFLD) is a major cause of chronic liver disease worldwide and is generally thought to be closely related to obesity and diabetes. However, it also affects non-obese individuals, particularly in Asian cultures. Methods: Healthy physical examination subjects and MAFLD patients were included in the endocrinology department of Jiangsu Provincial Hospital of Traditional Chinese Medicine. MAFLD was defined as fatty liver in imaging without virus infection, drug, alcohol, or other known causes of chronic liver disease. Non-obese MAFLD was defined as MAFLD in non-obese subjects (BMI<25 kg/m2). Results: The final analysis comprised 1047 participants in total. Of 946 MAFLD patients, 162 (17.12%) were diagnosed with non-obese MAFLD. Non-obese MAFLD patients were older, had lower alanine aminotransferase (ALT), triglyceride, and waist circumference, but had higher high density lipoprotein cholesterol (HDL-c) than obese MAFLD patients. Compared with non-obese healthy controls, non-obese MAFLD patients had higher BMI, ALT, gamma-glutamyl transferase (GGT), uric acid (UA), triglycerides (TG), and low density lipoprotein cholesterol (LDL-c). In terms of body composition, body fat mass (BFM), waist-hip ratio (WHR), percent body fat (PBF), visceral fat area (VFA), and fat mass index (FMI) were lower in non-obese healthy controls than non-obese MAFLD patients. A binary logistic regression analysis revealed that non-obese MAFLD was linked with lower GGT and higher HDL-c. Conclusion: In this study cohort, non-obese MAFLD was present at a prevalence of 13.90%. In contrast to non-obese healthy controls, non-obese MAFLD patients exhibited different metabolic profiles, but they also had different body compositions.

Gene Expression of Abcc2 and Its Regulation by Chicken Xenobiotic Receptor.

Membrane transporter multidrug resistance-associated protein 2 (MRP2/Abcc2) exhibits high pharmaco-toxicological relevance because it exports multiple cytotoxic compounds from cells. However, no detailed information about the gene expression and regulation of MRP2 in chickens is yet available. Here, we sought to investigate the expression distribution of Abcc2 in different tissues of chicken and then determine whether Abcc2 expression is induced by chicken xenobiotic receptor (CXR). The bioinformatics analyses showed that MRP2 transporters have three transmembrane structural domains (MSDs) and two highly conserved nucleotide structural domains (NBDs), and a close evolutionary relationship with turkeys. Tissue distribution analysis indicated that Abcc2 was highly expressed in the liver, kidney, duodenum, and jejunum. When exposed to metyrapone (an agonist of CXR) and ketoconazole (an antagonist of CXR), Abcc2 expression was upregulated and downregulated correspondingly. We further confirmed that Abcc2 gene regulation is dependent on CXR, by overexpressing and interfering with CXR, respectively. We also demonstrated the induction of Abcc2 expression and the activity of ivermectin, with CXR being a likely mediator. Animal experiments demonstrated that metyrapone and ivermectin induced Abcc2 in the liver, kidney, and duodenum of chickens. Together, our study identified the gene expression of Abcc2 and its regulation by CXR in chickens, which may provide novel targets for the reasonable usage of veterinary drugs.

Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction.

As the application of large language models (LLMs) has broadened into the realm of biological predictions, leveraging their capacity for self-supervised learning to create feature representations of amino acid sequences, these models have set a new benchmark in tackling downstream challenges, such as subcellular localization. However, previous studies have primarily focused on either the structural design of models or differing strategies for fine-tuning, largely overlooking investigations into the nature of the features derived from LLMs. In this research, we propose different ESM2 representation extraction strategies, considering both the character type and position within the ESM2 input sequence. Using model dimensionality reduction, predictive analysis and interpretability techniques, we have illuminated potential associations between diverse feature types and specific subcellular localizations. Particularly, the prediction of Mitochondrion and Golgi apparatus prefer segments feature closer to the N-terminal, and phosphorylation site-based features could mirror phosphorylation properties. We also evaluate the prediction performance and interpretability robustness of Random Forest and Deep Neural Networks with varied feature inputs. This work offers novel insights into maximizing LLMs' utility, understanding their mechanisms, and extracting biological domain knowledge. Furthermore, we have made the code, feature extraction API, and all relevant materials available at https://github.com/yujuan-zhang/feature-representation-for-LLMs.

Enhancing CO2/N2 and CH4/N2 separation performance by salt-modified aluminum-based metal-organic frameworks.

The energy-saving separation of CO2/N2 and CH4/N2 in the energy industry facilitates the reduction of greenhouse gas emissions and replenishes energy resources, but is a challenging separation process. The trade-off between adsorption capacity and selectivity of the adsorbents is one of the key bottlenecks in adsorption separation technologies' large-scale application in the above separation task. Herein, we introduced a series of fluoroborate or fluorosilicate salts (Cu(BF4)2, Zn(BF4)2 and ZnSiF6) into the open coordination nitrogen sites of aluminum-based metal-organic frameworks (MOF-253) to create multiple binding sites to simultaneously enhance the adsorption capacity and selectivity for the target gas. By the synergistic adsorption effect of metal ions (Cu2+ or Zn2+) and fluorinated anions (BF4- or (SiF6)2-), the single-component adsorption capacity and selectivity of salt-modified MOF-253 (MOF-253@Cu(BF4)2, MOF-253@Zn(BF4)2 and MOF-253@ZnSiF6) for CO2 and CH4 were effectively improved when compared to pristine MOF-253 at 298 K and 1 bar. In addition, the salt-modified MOF-253 has a moderate adsorption heat (<30 kJ mol-1) which could be rapidly regenerated at low energy by evacuation desorption. As confirmed by the ambient breakthrough experiments of MOF-253 and MOF-253@ZnSiF6, the real separation performance for both CO2/N2 (1/4) and CH4/N2 (1/4) was obviously improved. This work provides a feasible post-modification strategy on uncoordinated sites of the framework to improve adsorption separation performance and promote the development of ideal adsorbents with a view to realizing their application in the energy industry.

Personal Exposure to Source-Specific Particulate Polycyclic Aromatic Hydrocarbons and Systemic Inflammation: A Cross-Sectional Study of Urban-Dwelling Older Adults in China.

Environmental exposure to ambient polycyclic aromatic hydrocarbons (PAHs) can disturb the immune response. However, the evidence on adverse health effects caused by exposure to PAHs emitted from specific sources among different vulnerable subpopulations is limited. In this cross-sectional study, we aimed to evaluate whether exposure to source-specific PAHs could increase systemic inflammation in older adults. The present study included community-dwelling older adults and collected filter samples of personal exposure to PM2.5 during the winter of 2011. Blood samples were collected after the PM2.5 sample collection. We analyzed PM2.5 bound PAHs and serum inflammatory cytokines (interleukin (IL)1ß, IL6, and tumor necrosis factor alpha levels. The Positive Matrix Factorization model was used to identify PAH sources. We used a linear regression model to assess the relative effects of source-specific PM2.5 bound PAHs on the levels of measured inflammatory cytokines. After controlling for confounders, exposure to PAHs emitted from biomass burning or diesel vehicle emission was significantly associated with increased serum inflammatory cytokines and systemic inflammation. These findings highlight the importance of considering exposure sources in epidemiological studies and controlling exposures to organic materials from specific sources.

From beasts to bytes: Revolutionizing zoological research with artificial intelligence.

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.