HNetGO: protein function prediction via heterogeneous network transformer.

Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of protein function prediction models. However, the heavy reliance on complex feature engineering and model integration methods limits the development of existing methods. Besides, models based on deep learning only use labeled data in a certain dataset to extract sequence features, thus ignoring a large amount of existing unlabeled sequence data. Here, we propose an end-to-end protein function annotation model named HNetGO, which innovatively uses heterogeneous network to integrate protein sequence similarity and protein-protein interaction network information and combines the pretraining model to extract the semantic features of the protein sequence. In addition, we design an attention-based graph neural network model, which can effectively extract node-level features from heterogeneous networks and predict protein function by measuring the similarity between protein nodes and gene ontology term nodes. Comparative experiments on the human dataset show that HNetGO achieves state-of-the-art performance on cellular component and molecular function branches.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

MOTIVATION: Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS: MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION: https://github.com/1160300611/MCDHGN.

Genome-wide identification and analysis of the cotton ALDH gene family.

BACKGROUND: Aldehyde dehydrogenases (ALDHs) are a family of enzymes that catalyze the oxidation of aldehyde molecules into the corresponding carboxylic acid, regulate the balance of aldehydes and protect plants from the poisoning caused by excessive accumulation of aldehydes; however, this gene family has rarely been studied in cotton. RESULTS: In the present study, genome-wide identification was performed, and a total of 114 ALDH family members were found in three cotton species, Gossypium hirsutum, Gossypium arboreum and Gossypium raimondii. The ALDH genes were divided into six subgroups by evolutionary analysis. ALDH genes in the same subgroup showed similar gene structures and conserved motifs, but some genes showed significant differences, which may result in functional differences. Chromosomal location analysis and selective pressure analysis revealed that the ALDH gene family had experienced many fragment duplication events. Cis-acting element analysis revealed that this gene family may be involved in the response to various biotic and abiotic stresses. The RT‒qPCR results showed that the expression levels of some members of this gene family were significantly increased under salt stress conditions. Gohir.A11G040800 and Gohir.D06G046200 were subjected to virus-induced gene silencing (VIGS) experiments, and the sensitivity of the silenced plants to salt stress was significantly greater than that of the negative control plants, suggesting that Gohir.A11G040800 and Gohir.D06G046200 may be involved in the response of cotton to salt stress. CONCLUSIONS: In total, 114 ALDH genes were identified in three Gossypium species by a series of bioinformatics analysis. Gene silencing of the ALDH genes of G. hirsutum revealed that ALDH plays an important role in the response of cotton to salt stress.

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network.

MOTIVATION: Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed. RESULTS: In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. AVAILABILITY AND IMPLEMENTATION: SLGNN is freely available at https://github.com/zy972014452/SLGNN.

Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.

MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein-protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein-protein interaction network, such models cannot make effective predictions. To address this, this article proposes the Struct2GO model, which combines protein structure and sequence data to enhance the precision of protein function prediction and the generality of the model. RESULTS: We obtain amino acid residue embeddings in protein structure through graph representation learning, utilize the graph pooling algorithm based on a self-attention mechanism to obtain the whole graph structure features, and fuse them with sequence features obtained from the protein language model. The results demonstrate that compared with the traditional protein sequence-based function prediction model, the Struct2GO model achieves better results. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available at https://github.com/lyjps/Struct2GO.

NY-ESO-1-specific T cell receptor-engineered T cells and Tranilast, a TRPV2 antagonist bivalent treatment enhances the killing of esophageal cancer: a dual-targeted cancer therapeutic route.

BACKGROUND: Esophageal cancer (EC) is a global canker notorious for causing high mortality due to its relentless incidence rate, convoluted with unyielding recurrence and metastasis. However, these intricacies of EC are associated with an immoderate expression of NY-ESO-1 antigen, presenting a lifeline for adoptive T cell therapy. We hypothesized that naturally isolated higher-affinity T cell receptors (TCRs) that bind to NY-ESO-1 would allow T lymphocytes to target EC with a pronounced antitumor response efficacy. Also, targeting TRPV2, which is associated with tumorigenesis in EC, creates an avenue for dual-targeted therapy. We exploited the dual-targeting antitumor efficacy against EC. METHODS: We isolated antigen-specific TCRs (asTCRs) from a naive library constructed with TCRs obtained from enriched cytotoxic T lymphocytes. The robustness of our asTCRs and their TCR-T cell derivatives, Tranilast (TRPV2 inhibitor), and their bivalent treatment were evaluated with prospective cross-reactive human-peptide variants and tumor cells. RESULTS: Our study demonstrated that our naive unenhanced asTCRs and their TCR-Ts perpetuated their cognate HLA-A*02:01/NY-ESO-1(157-165) specificity, killing varying EC cells with higher cytotoxicity compared to the known affinity-enhanced TCR (TCRe) and its wild-type (TCR0) which targets the same NY-ESO-1 antigen. Furthermore, the TCR-Ts and Tranilast bivalent treatment showed superior EC killing compared to any of their monovalent treatments of either TCR-T or Tranilast. CONCLUSION: Our findings suggest that dual-targeted immunotherapy may have a superior antitumor effect. Our study presents a technique to evolve novel, robust, timely therapeutic strategies and interventions for EC and other malignancies.

N6 methyladenosine eraser FTO suppresses Staphylococcus aureus-induced ferroptosis of bone marrow mesenchymal stem cells to ameliorate osteomyelitis through regulating the MDM2/TLR4/SLC7A11 signaling pathway.

Osteomyelitis is a bone destructive inflammatory disease caused by infection. Ferroptosis is closely related to multiple inflammatory diseases, but the role of ferroptosis in Staphylococcus aureus (SA)-induced osteomyelitis remains unknown. In the present study, we found that SA treatment promoted the accumulation of iron, Fe2+ , lipid peroxide, and malondialdehyde, increased TFRC and reduced FTH1 and GPX4 to trigger ferroptosis in rat bone marrow mesenchymal stem cells (BMSCs). Interestingly, increased level of N6 methyl adenosine (m6A) modification along with decreased expression level of m6A eraser FTO were observed in SA-induced BMSCs, while upregulating FTO alleviated SA-triggered ferroptosis and protected cell viability in BMSCs. Mechanistically, MDM2 was identified as a target of FTO-mediated m6A demethylation, and FTO upregulation promoted MDM2 instability to downregulated TLR4 signal and elevate the expression of SLC7A11 and GPX4 in SA-induced BMSCs. Functional recovery experiments verified that overexpressing MDM2 or TLR4 reversed the inhibiting effect of FTO upregulation on ferroptosis in SA-treated BMSCs. Additionally, FTO upregulation restrained ferroptosis and pathological damage to bone tissue in SA-induced osteomyelitis model rats. Altogether, m6A eraser FTO alleviates SA-induced ferroptosis in osteomyelitis models partly through inhibiting the MDM2-TLR4 axis.

FTO-mediated ZNF687 accelerates tumor growth, metastasis, and angiogenesis in colorectal cancer through the Wnt/ß-catenin pathway.

Colorectal cancer (CRC) is a common and lethal cancer. ZNF687 has been disclosed to take part in diversified cancers' progression by serving as a facilitator. However, the detailed regulatory functions of ZNF687 in the CRC have not been investigated. This work is planned to probe the impacts of ZNF687 on CRC progression. The IHC, RT-qPCR, and western blot assays were used to examine mRNA and protein gene expressions. The cell proliferation measurement was accompanied by a CCK-8 assay. The Transwell assay was performed to evaluate cell invasion and migration. The angiogenesis ability was evaluated by a tube formation experiment. The m6A level was evaluated through MeRIP and m6A dot blot assays. The binding ability between ZNF687 and FTO (fat mass and obesity associated protein) was tested through an RIP assay. The ß-catenin nuclear translocation was assessed through an immunofluorescence assay. The tumor growth was evaluated through an in vivo assay. ZNF687 exhibited higher expression in CRC cells and resulted in a poor prognosis. Additionally, ZNF687 inhibition suppressed CRC cell proliferation, invasion, migration, and angiogenesis. Furthermore, the suppression of ZNF687 retarded the Wnt pathway. Through rescue assays, the reduced cell migration, proliferation, invasion, and angiogenesis mediated by ZNF687 knockdown could be reversed after BML-284 (the activator of the Wnt pathway) treatment. Finally, it was explained that ZNF687 knockdown inhibited in vivo tumor growth. This study manifested that FTO-mediated ZNF687 aggravated tumor growth, metastasis, and angiogenesis of CRC through Wnt/ß-catenin pathway. This finding may provide a hopeful molecular target for CRC treatment.

Dynamic pressure surface deformation measurement based on a chromatic confocal sensor: erratum.

This erratum corrects errors in Fig. 4 of the original paper, Appl. Opt.62, 1467 (2023)APOPAI0003-693510.1364/AO.482808.

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome.

LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

Using machine learning to develop a five-item short form of the children's depression inventory.

BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. METHODS: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. RESULTS: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77). CONCLUSIONS: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.

Target Cell Extraction and Spectrum-Effect Relationship Coupled with BP Neural Network Classification for Screening Potential Bioactive Components in Ginseng Extract with a Protective Effect against Myocardial Damage.

Cardiovascular disease has become a common ailment that endangers human health, having garnered widespread attention due to its high prevalence, recurrence rate, and sudden death risk. Ginseng possesses functions such as invigorating vital energy, enhancing vein recovery, promoting body fluid and blood nourishment, calming the nerves, and improving cognitive function. It is widely utilized in the treatment of various heart conditions, including palpitations, chest pain, heart failure, and other ailments. Although numerous research reports have investigated the cardiovascular activity of single ginsenoside, there remains a lack of systematic research on the specific components group that predominantly contribute to cardiovascular efficacy in ginseng medicinal materials. In this research, the spectrum-effect relationship, target cell extraction, and BP neural network classification were used to establish a rapid screening system for potential active substances. The results show that red ginseng extract (RGE) can improve the decrease in cell viability and ATP content and inhibit the increase in ROS production and LDH release in OGD-induced H9c2 cells. A total of 70 ginsenosides were identified in RGE using HPLC-Q-TOF-MS/MS analysis. Chromatographic fingerprints were established for 12 batches of RGE by high-performance liquid chromatography (HPLC). A total of 36 common ingredients were found in 12 batches of RGE. The cell viability, ATP, ROS, and LDH of 12 batches RGE were tested to establish gray relationship analysis (GRA) and partial least squares discrimination analysis (PLS-DA). BP neural network classification and target cell extraction were used to narrow down the scope of Spectral efficiency analysis and screen the potential active components. According to the cell experiments, RGE can improve the cell viability and ATP content and reduce the oxidative damage. Then, seven active ingredients, namely, Ginsenoside Rg1, Rg2, Rg3, Rb1, Rd, Re, and Ro, were screened out, and their cardiovascular activity was confirmed in the OGD model. The seven ginsenosides were the main active substances of red ginseng in treating myocardial injury. This study offers a reference for quality control in red ginseng and preparations containing red ginseng for the management of cardiovascular diseases. It also provides ideas for screening active ingredients of the same type of multi-pharmacologically active traditional Chinese medicines.

Effect of comprehensive nursing intervention on wound pain and wound complications in patients with tonsillectomy: A meta-analysis.

To systematically evaluate the effects of comprehensive nursing interventions on wound pain and complications in patients after tonsillectomy, with a view to providing a reference basis for future post-tonsillectomy care. According to the developed literature search strategy, PubMed, Web of Science, Cochrane Library, Embase, Wanfang, China Biomedical Literature Database and China National Knowledge Infrastructure database were systematically searched, from database inception to October 2023, for randomised controlled trials (RCTs) of the application of comprehensive nursing interventions in patients undergoing tonsillectomy. Two researchers independently screened the literature, evaluated the risk of bias of the included studies and extracted data in strict accordance with the inclusion and exclusion criteria. RevMan 5.4 software was applied for data analysis. Overall, 18 RCTs involving 1954 patients were included, including 967 in the comprehensive nursing group and 987 in the conventional nursing group. The analyses revealed that compared with conventional nursing, patients who received comprehensive nursing interventions had lower postoperative wound pain scores (standardised mean difference [SMD]: -2.30, 95% confidence interval [CI]: -2.19 to -1.70, p < 0.00001), shorter hospital stays (SMD: -1.95, 95% CI: -2.39 to -1.51, p < 0.00001), incidence of postoperative haemorrhage (1.60% vs. 6.41%, odds ratio [OR]: 0.29, 95% CI: 0.12-0.70, p = 0.006) and complication rates (4.21% vs. 19.01%, OR: 0.19, 95% CI: 0.11-0.32, p < 0.00001) was lower. This study concludes as follows: comprehensive nursing intervention applied to tonsillectomy can significantly reduce patients' postoperative wound pain, shorten hospital stay, reduce postoperative bleeding and postoperative complications, which is worthy of being promoted and applied in the clinic.

The auxin response factor (ARF) gene family in Cyclocarya paliurus: genome-wide identification and their expression profiling under heat and drought stresses.

Auxin response factors (ARFs), as the main components of auxin signaling, play a crucial role in various processes of plant growth and development, as well as in stress response. So far, there have been no reports on the genome-wide identification of the ARF transcription factor family in Cyclocarya paliurus, a deciduous tree plant in the family Juglaceae. In this study, a total of 34 CpARF genes were identified based on whole genome sequence, and they were unevenly distributed on 16 chromosomes, with the highest distribution on chromosome 6. Domain analysis of CpARF proteins displayed that 31 out of 34 CpARF proteins contain a typical B3 domain (DBD domain), except CpARF12/ CpARF14/CpARF31, which all belong to Class VI. And 20 CpARFs (58.8%) contain an auxin_IAA binding domain, and are mainly distributed in classes I, and VI. Phylogenetic analysis showed that CpARF was divided into six classes (I-VI), each containing 4, 4, 1, 8, 4, and 13 members, respectively. Gene duplication analysis showed that there are 14 segmental duplications and zero tandem repeats were identified in the CpARF gene family of the C. paliurus genome. The Ka/Ks ratio of duplicate gene pairs indicates that CpARF genes are subjected to strong purification selection pressure. Synteny analysis showed that C. paliurus shared the highest homology in 74 ARF gene pairs with Juglans regia, followed by 73, 51, 25, and 11 homologous gene pairs with Populus trichocarpa, Juglans cathayensis, Arabidopsis, and rice, respectively. Promoter analysis revealed that 34 CpARF genes had cis-elements related to hormones, stress, light, and growth and development except for CpARF12. The expression profile analysis showed that almost all CpARF genes were differentially expressed in at least one tissue, and several CpARF genes displayed tissue-specific expression. Furthermore, 24 out of the 34 CpARF genes have significantly response to drought stress (P < 0.05), and most of them (16) being significantly down-regulated under moderate drought treatment. Meanwhile, the majority of CpARF genes (28) have significantly response to drought stress (P < 0.05), and most of them (26) are significantly down-regulated under severe drought treatment. Furthermore, 32 out of the 34 CpARF genes have significantly response to high, middle, and low salt stress under salt treatment (P < 0.05). Additionally, subcellular localization analysis confirmed that CpARF16 and CpARF32 were all localized to nucleus. Thus, our findings expand the understanding of the function of CpARF genes and provide a basis for further functional studies on CpARF genes in C. paliurus. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-024-01474-1.

Unusual Thermal Quenching of Photoluminescence from an Organic-Inorganic Hybrid [MnBr4 ]2- -based Halide Mediated by Crystalline-Crystalline Phase Transition.

The ability to generate and manipulate photoluminescence (PL) behavior has been of primary importance for applications in information security. Excavating novel optical effects to create more possibilities for information encoding has become a continuous challenge. Herein, we present an unprecedented PL temporary quenching that highly couples with thermodynamic phase transition in a hybrid crystal (DMML)2 MnBr4 (DMML=N,N-dimethylmorpholinium). Such unusual PL behavior originates from the anomalous variation of [MnBr4 ]2- tetrahedrons that leads to non-radiation recombination near the phase transition temperature of 340 K. Remarkably, the suitable detectable temperature, narrow response window, high sensitivity, and good cyclability of this PL temporary quenching will endow encryption applications with high concealment, operational flexibility, durability, and commercial popularization. Profited from these attributes, a fire-new optical encryption model is devised to demonstrate high confidential information security. This unprecedented optical effect would provide new insights and paradigms for the development of luminescent materials to enlighten future information encryption.

Electrooxidative Rhodium(III)/Chiral Carboxylic Acid-Catalyzed Enantioselective C-H Annulation of Sulfoximines with Alkynes.

The combination of achiral Cp*Rh(III) with chiral carboxylic acids (CCAs) represents an efficient catalytic system in transition metal-catalyzed enantioselective C-H activation. However, this hybrid catalysis is limited to redox-neutral C-H activation reactions and the adopt to oxidative enantioselective C-H activation remains elusive and pose a significant challenge. Herein, we describe the development of an electrochemical Cp*Rh(III)-catalyzed enantioselective C-H annulation of sulfoximines with alkynes enabled by chiral carboxylic acid (CCA) in an operationally friendly undivided cell at room temperature. A broad range of enantioenriched 1,2-benzothiazines are obtained in high yields with excellent enantioselectivities (up to 99 % yield and 98 : 2 er). The practicality of this method is demonstrated by scale-up reaction in a batch reactor with external circulation. A crucial chiral Cp*Rh(III) intermediate is isolated, characterized, and transformed, providing rational support for a Rh(III)/Rh(I) electrocatalytic cycle.

NLRP3 Plays a Key Role in Antihelminth Immunity in the Enteral and Parenteral Stages of Trichinella spiralis-Infected Mice.

Trichinellosis is an important foodborne zoonosis, and no effective treatments are yet available. Nod-like receptor (NLR) plays a critical role in the host response against nematodes. Therefore, we aimed to explore the role of the NLRP3 inflammasome (NLRP3) during the adult, migrating, and encysted stages of Trichinella spiralis infection. The mice were treated with the specific NLRP3 inhibitor MCC950 after inoculation with T. spiralis. Then, the role that NLRP3 plays during T. spiralis infection of mice was evaluated using enzyme-linked immunosorbent assay (ELISA), Western blotting, flow cytometry, histopathological evaluation, bone marrow-derived macrophage (BMDM) stimulation, and immunofluorescence. The in vivo results showed that NLRP3 enhanced the Th1 immune response in the adult and migrating stages and weakened the Th2 immune response in the encysted stage. NLRP3 promoted the release of proinflammatory factors (interferon gamma [IFN-γ]) and suppressed the release of anti-inflammatory factors (interleukin 4 [IL-4]). Pathological changes were also improved in the absence of NLRP3 in mice during T. spiralis infection. Importantly, a significant reduction in adult worm burden and muscle larvae burden at 7 and 35 days postinfection was observed in mice treated with the specific NLRP3 inhibitor MCC950. In vitro, we first demonstrated that NLRP3 in macrophages can be activated by T. spiralis proteins and promotes IL-1ß and IL-18 release. This study revealed that NLRP3 is involved in the host response to T. spiralis infection and that targeted inhibition of NLRP3 enhanced the Th2 response and accelerated T. spiralis expulsion. These findings may help in the development of protocols for controlling trichinellosis.

Single-cell RNA-seq reveals a microenvironment and an exhaustion state of T/NK cells in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous blood cancer. Effective immunotherapies for AML are hindered by a lack of understanding of the tumor microenvironment (TME). Here, we retrieved published single-cell RNA sequencing data for 128,688 cells derived from 29 bone marrow aspirates, including 21 AML patients and eight healthy donors. We established a global tumor ecosystem including nine main cell types. Myeloid, T, and NK cells were further re-clustered and annotated. Developmental trajectory analysis indicated that exhausted CD8+ T cells might develop via tissue residual memory T cells (TRM) in the AML TME. Significantly higher expression levels of exhaustion molecules in AML TRM cells suggested that these cells were influenced by the TME and entered an exhausted state. Meanwhile, the upregulation of checkpoint molecules and downregulation of granzyme were also observed in AML NK cells, suggesting an exhaustion state. In conclusion, our comprehensive profiling of T/NK subpopulations provides deeper insights into the AML immunosuppressive ecosystem, which is critical for immunotherapies.

Toxicity Characterization of Environment-Related Pollutants Using a Biospectroscopy-Bioreporter-Coupling Approach: Potential for Real-World Toxicity Determination and Source Apportionment of Multiple Pollutants.

Exposure to environmental pollutants occurs ubiquitously and poses many risks to human health and the ecosystem. Although many analytical methods have been developed to assess such jeopardies, the circumstances applying these means are restricted to linking the toxicities to compositions in the pollutant mixtures. The present study proposes a novel analytical approach, namely, biospectroscopy-bioreporter-coupling (BBC), to quantify and apportion the toxicities of metal ions and organic pollutants. Using a toxicity bioreporter ADPWH_recA and Raman spectroscopy, both bioluminescent signals and spectral alterations had similar dosage- and time-response behavior to the toxic compounds, validating the possibility of coupling these two methods from practical aspects. Raman spectral alterations successfully distinguished the biomarkers for different toxicity mechanisms of individual pollutants, such as ring breathing mode of DNA/RNA bases (1373 cm-1) by Cr, reactive oxygen species-induced peaks of proteins (1243 cm-1), collagen (813 cm-1), and lipids (1255 cm-1) by most metal ions, and indicative fingerprints of organic toxins. The support vector machine model had a satisfactory performance in distinguishing and apportioning toxicities of individual toxins from all input data, achieving a sensitivity of 88.54% and a specificity of 97.80%. This work set a preliminary database for Raman spectral alterations of whole-cell bioreporter response to multiple pollutants. It proved the state-of-the-art concept that the BBC approach is feasible to rapidly quantify and precisely apportion toxicities of numerous pollutant mixtures.

Facile Control of Ferroelectricity Driven by Ingenious Interaction Engineering.

Construction of ferroelectric and optimization of macroscopic polarization has attracted tremendous attention for next generation light weight and flexible devices, which brings fundamental vitality for molecular ferroelectrics. However, effective molecular tailoring toward cations makes ferroelectric synthesis and modification relatively elaborate. Here, the study proposes a facile method to realize triggering and optimization of ferroelectricity. The experimental and theoretical investigation reveals that orientation and alignment of polar cations, dominated factors in molecular ferroelectrics, can be controlled by easily processed anionic modification. In one respect, ferroelectricity is induced by strengthened intermolecular interaction. Moreover, ≈50% of microscopic polarization enhancement (from 8.07 to 11.68 µC cm-2 ) and doubling of equivalent polarization direction (from 4 to 8) are realized in resultant ferroelectric FEtQ2ZnBrI3 (FEQZBI, FEtQ = N-fluoroethyl-quinuclidine). The work offers a totally novel platform for control of ferroelectricity in organic-inorganic hybrid ferroelectrics and a deep insight of structure-property correlations.