A facile and on-site sensing strategy for phenolic compounds based on a novel nanozyme with high polyphenol oxidase-like activity.

Phenolic compounds (PCs) are diverse in nature and undergo complex migration and transformations in the environment, making it challenging to use techniques such as chromatography and other traditional methods to determine the concentration of PCs by separation, individual monitoring and subsequent addition. To address this issue, a facile and on-site strategy was developed to measure the concentration of PCs using a novel nanozyme with polyphenol oxidase-like activity. First, the nanozyme was designed by coordinating the asymmetric ligand nicotinic acid with copper to mimic the structure of mononuclear and trinuclear copper clusters of natural laccases. Subsequently, by introducing 2-mercaptonicotinic acid to regulate the valence state of copper, the composite nanozyme CuNA10S was obtained with significantly enhanced activity. Interestingly, CuNA10S was shown to have a broad substrate spectrum capable of catalyzing common PCs. Building upon the superior performance of this nanozyme, a method was developed to determine the concentration of PCs. To enable rapid on-site sensing, we designed and prepared CuNA10S-based test strips and developed a tailored smartphone sensing platform. Using paper strip sensors combined with a smartphone sensing platform with RGB streamlined the sensing process, facilitating rapid on-site analysis of PCs within a range of 0-100 µM. Our method offers a solution for the quick screening of phenolic wastewater at contaminated sites, allowing sensitive and quick monitoring of PCs without the need for standard samples. This significantly simplifies the monitoring procedure compared to more cumbersome large-scale instrumental methods.

Assessment of secular trends of three major gynecologic cancers burden and attributable risk factors from 1990 to 2019: an age period cohort analysis.

BACKGROUND: This study aims to assess the long-term trends in the burden of three major gynecologic cancers(GCs) stratified by social-demographic status across the world from 1990 to 2019. To assess the trends of risk factor attributed mortality, and to examine the specific effects of age, period, cohort behind them in different regions. METHODS: We extracted data on the mortality, disability-adjusted life years(DALYs), and age-standardized rates(ASRs) of cervical cancer(CC), uterine cancer(UC), and ovarian cancer(OC) related to risks from 1990 to 2019, as GCs burden measures. Age-period-cohort analysis was used to analyze trends in attributable mortality rates. RESULTS: The number of deaths and DALYs for CC, UC and OC increased since 1990 worldwide, while the ASDRs decreased. Regionally, the ASDR of CC was the highest in low SDI region at 15.05(11.92, 18.46) per 100,000 in 2019, while the ASDRs of UC and OC were highest in high SDI region at 2.52(2.32,2.64), and 5.67(5.16,6.09). The risk of CC death caused by unsafe sex increased with age and then gradually stabilized, with regional differences. The period effect of CC death attributed to smoking showed a downward trend. The cohort effect of UC death attributed to high BMI decreased in each region, especially in the early period in middle, low-middle and low SDI areas. CONCLUSIONS: Global secular trends of attributed mortality for the three GCs and their age, period, and cohort effects may reflect the diagnosis and treatment progress, rapid socioeconomic transitions, concomitant changes in lifestyle and behavioral patterns in different developing regions. Prevention and controllable measures should be carried out according to the epidemic status in different countries, raising awareness of risk factors to reduce future burden.

Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.

BACKGROUND: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient. METHOD: In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning. RESULTS: Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms. CONCLUSION: To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.

Transcriptomic Insights into Host Metabolism and Immunity Changes after Parasitization by Leptopilina myrica.

Parasitoids commonly manipulate their host's metabolism and immunity to facilitate their offspring survival, but the mechanisms remain poorly understood. Here, we deconstructed the manipulation strategy of a newly discovered parasitoid wasp, L. myrica, which parasitizes D. melanogaster. Using RNA-seq, we analyzed transcriptomes of L. myrica-parasitized and non-parasitized Drosophila host larvae. A total of 22.29 Gb and 23.85 Gb of clean reads were obtained from the two samples, respectively, and differential expression analysis identified 445 DEGs. Of them, 304 genes were upregulated and 141 genes were downregulated in parasitized hosts compared with non-parasitized larvae. Based on the functional annotations in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, we found that the genes involved in host nutrition metabolism were significantly upregulated, particularly in carbohydrate, amino acid, and lipid metabolism. We also identified 30 other metabolism-related DEGs, including hexokinase, fatty acid synthase, and UDP-glycosyltransferase (Ugt) genes. We observed that five Bomanin genes (Boms) and six antimicrobial peptides (AMPs) were upregulated. Moreover, a qRT-PCR analysis of 12 randomly selected DEGs confirmed the reproducibility and accuracy of the RNA-seq data. Our results provide a comprehensive transcriptomic analysis of how L. myrica manipulates its host, laying a solid foundation for studies on the regulatory mechanisms employed by parasitoid wasps in their hosts.

A membrane associated tandem kinase from wild emmer wheat confers broad-spectrum resistance to powdery mildew.

Crop wild relatives offer natural variations of disease resistance for crop improvement. Here, we report the isolation of broad-spectrum powdery mildew resistance gene Pm36, originated from wild emmer wheat, that encodes a tandem kinase with a transmembrane domain (WTK7-TM) through the combination of map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, and transformation. Mutagenesis assay reveals that the two kinase domains and the transmembrane domain of WTK7-TM are critical for the powdery mildew resistance function. Consistently, in vitro phosphorylation assay shows that two kinase domains are indispensable for the kinase activity of WTK7-TM. Haplotype analysis uncovers that Pm36 is an orphan gene only present in a few wild emmer wheat, indicating its single ancient origin and potential contribution to the current wheat gene pool. Overall, our findings not only provide a powdery mildew resistance gene with great potential in wheat breeding but also sheds light into the mechanism underlying broad-spectrum resistance.

High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion.

The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system. By introducing the surface normal vector information obtained from polarization measurement and integrating it with the light-field 3D point cloud data, 3D reconstruction results accuracy is highly improved in axial direction. Experimental results with a Fourier light-field 3D imaging microscope demonstrated a substantial enhancement of vertical resolution with a depth resolution to depth of field ratio of 0.19%. This represented approximately 44 times the improvement compared to the theoretical ratio before data fusion, enabling the system to access more detailed information with finer measurement accuracy for test samples. This work not only provides a feasible solution for breaking the limitations imposed by traditional light-field microscope hardware configurations but also offers superior 3D measurement approach in a more cost-effective and practical manner.

KAT8/SIRT7-mediated Fascin-K41 acetylation/deacetylation regulates tumor metastasis in esophageal squamous cell carcinoma.

Fascin actin-bundling protein 1 (Fascin) is highly expressed in a variety of cancers, including esophageal squamous cell carcinoma (ESCC), working as an important oncogenic protein and promoting the migration and invasion of cancer cells by bundling F-actin to facilitate the formation of filopodia and invadopodia. However, it is not clear how exactly the function of Fascin is regulated by acetylation in cancer cells. Here, in ESCC cells, the histone acetyltransferase KAT8 catalyzed Fascin lysine 41 (K41) acetylation, to inhibit Fascin-mediated F-actin bundling and the formation of filopodia and invadopodia. Furthermore, NAD-dependent protein deacetylase sirtuin (SIRT) 7-mediated deacetylation of Fascin-K41 enhances the formation of filopodia and invadopodia, which promotes the migration and invasion of ESCC cells. Clinically, the analysis of cancer and adjacent tissue samples from patients with ESCC showed that Fascin-K41 acetylation was lower in the cancer tissue of patients with lymph node metastasis than in that of patients without lymph node metastasis, and low levels of Fascin-K41 acetylation were associated with a poorer prognosis in patients with ESCC. Importantly, K41 acetylation significantly blocked NP-G2-044, one of the Fascin inhibitors currently being clinically evaluated, suggesting that NP-G2-044 may be more suitable for patients with low levels of Fascin-K41 acetylation, but not suitable for patients with high levels of Fascin-K41 acetylation. © 2024 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence.

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.

Light field measurement of specular surfaces by multi-polarization and hybrid modulated illumination.

Specular highlights present a challenge in light field microscopy imaging fields, leading to loss of target information and incorrect observation results. Existing highlight elimination methods suffer from computational complexity, false information and applicability. To address these issues, an adaptive multi-polarization illumination scheme is proposed to effectively eliminate highlight reflections and ensure uniform illumination without complex optical setup or mechanical rotation. Using a multi-polarized light source with hybrid modulated illumination, the system achieved combined multi-polarized illumination and physical elimination of specular highlights. This was achieved by exploiting the different light contributions at different polarization angles and by using optimal solution algorithms and precise electronic control. Experimental results show that the proposed adaptive illumination system can efficiently compute control parameters and precisely adjust the light source output in real time, resulting in a significant reduction of specular highlight pixels to less than 0.001% of the original image. In addition, the system ensures uniform illumination of the target area under different illumination configurations, further improving the overall image quality. This study presents a multi-polarization-based adaptive de-highlighting system with potential applications in miniaturization, biological imaging and materials analysis.

Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study.

Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis. To eliminate the need for human efforts in conventional evaluation methods, our approach executes poisoning attacks during model retraining by generating both static and dynamic triggers. We then propose a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, covering a wide range of correctness, completeness, consistency, and complexity. In addition, we include an extensive case study to showcase the proposed evaluation strategy by applying widely-used XAI methods on COVID-19 X-ray imaging classification tasks, as well as a thorough review of existing XAI methods in medical imaging analysis with evaluation availability. The proposed patch perturbation-based workflow offers model developers an automated and generalizable evaluation strategy to identify potential pitfalls and optimize their proposed explainable solutions, while also aiding end-users in comparing and selecting appropriate XAI methods that meet specific clinical needs in real-world clinical research and practice.

Early and fair COVID-19 outcome risk assessment using robust feature selection.

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.

Fine mapping of two recessive powdery mildew resistance genes from Aegilops tauschii accession CIae8.

KEY MESSAGE: Two recessive powdery mildew resistance loci pmAeCIae8_2DS and pmAeCIae8_7DS from Aegilops tauschii were mapped and two synthesized hexaploid wheat lines were developed by distant hybridization. Wheat powdery mildew (Pm), one of the worldwide destructive fungal diseases, causes significant yield loss up to 30%. The identification of new Pm resistance genes will enrich the genetic diversity of wheat breeding for Pm resistance. Aegilops tauschii is the ancestor donor of sub-genome D of hexaploid wheat. It provides beneficial genes that can be easily transferred into wheat by producing synthetic hexaploid wheat followed by genetic recombination. We assessed the Pm resistance level of 35 Ae. tauschii accessions from different origins. Accession CIae8 exhibited high Pm resistance. Inheritance analysis and gene mapping were performed using F2 and F2:3 populations derived from the cross between CIae8 and a Pm susceptible accession PI574467. The Pm resistance of CIae8 was controlled by two independent recessive genes. Bulked segregate analysis using a 55 K SNP array revealed the SNPs were mainly enriched into genome regions, i.e. 2DS (13.5-20 Mb) and 7DS (4.0-15.5 Mb). The Pm resistance loci were named as pmAeCIae8_2DS and pmAeCIae8_7DS, respectively. By recombinant screening, we narrowed the pmAeCIae8_2DS into a 370-kb interval flanked by markers CINAU-AE7800 (14.89 Mb) and CINAU-AE20 (15.26 Mb), and narrowed the pmAeCIae8_7DS into a 260-kb interval flanked by markers CINAU-AE58 (4.72 Mb) and CINAU-AE25 (4.98 Mb). The molecular markers closely linked with the resistance loci were developed, and two synthesized hexaploid wheat (SHW) lines were produced. These laid the foundation for cloning of the two resistance loci and for transferring the resistance into common wheat.

Risk assessment of dietary factors in global pattern of ischemic heart disease mortality and disability-adjusted life years over 30 years.

Objectives: The aim of this study was to investigate differences in the burden of ischemic heart disease (IHD)-related mortality and disability-adjusted life years (DALYs) caused by dietary factors, as well as the influencing factors with age, period, and cohort effects, in regions with different social-demographic status from 1990 to 2019. Methods: We extracted data on IHD mortality, DALYs, and age-standardized rates (ASRs) related to dietary risks from 1990 to 2019 as IHD burden measures. Hierarchical age-period-cohort analysis was used to analyze age- and time-related trends and the interaction between different dietary factors on the risk of IHD mortality and DALYs. Results: Globally, there were 9.2 million IHD deaths and 182 million DALYs in 2019. Both the ASRs of death and DALYs declined from 1990 to 2019 (percentage change: -30.8% and -28.6%, respectively), particularly in high and high-middle socio-demographic index (SDI) areas. Low-whole-grain, low-legume, and high-sodium diets were the three main dietary factors that increased the risk of IHD burden. Advanced age [RR (95%CI): 1.33 (1.27, 1.39)] and being male [1.11 (1.06, 1.16)] were independent risk factors for IHD mortality worldwide and in all SDI regions. After controlling for age effects, IHD risk showed a negative period effect overall. Poor diets were positively associated with increased risk of death but were not yet statistically significant. Interactions between dietary factors and advanced age were observed in all regions after adjusting for related variables. In people aged 55 and above, low intake of whole grains was associated with an increased risk of IHD death [1.28 (1.20, 1.36)]. DALY risks showed a similar but more obvious trend. Conclusion: IHD burden remains high, with significant regional variations. The high IHD burden could be attributed to advanced age, sex (male), and dietary risk factors. Dietary habits in different SDI regions may have varying effects on the global burden of IHD. In areas with lower SDI, it is recommended to pay more attention to dietary problems, particularly in the elderly, and to consider how to improve dietary patterns in order to reduce modifiable risk factors.

Selective sensing of catechol based on a fluorescent nanozyme with catechol oxidase activity.

Nanozymes, an unusual category of nanomaterials possessing enzymatic properties, and have generated considerable interest regarding their application feasibilities on several important fronts. In the present work, an innovative sensing device for catechol was established ground on a fluorescent nanozyme (Cu-BDC-NH2) that exhibited catechol oxidase activity. The fluorescent nanozyme combines both functions of catechol recognition and response signal output, and can realize the sensing of catechol without the addition of other chromogenic agents. In the existence of Cu-BDC-NH2, catechol can be oxidized efficiently to produce quinones or polymers with strong electron absorption capacity, which immediately results in efficient fluorescence quenching of Cu-BDC-NH2. However, other common phenolic compounds, such as phenol, the other two diphenols (hydroquinone and resorcinol), phloroglucinol, and chlorophenol, do not result in efficient fluorescence quenching of Cu-BDC-NH2. The method shows a nice linear relationship between catechol concentration prep the fluorescence intensity of Cu-BDC-NH2 in the scope of 0-10 µM, with a detection limit of 0.997 µM. The detection of catechol in actual water samples has also achieved the satisfactory consequences, which provides a new strategy for the convenient and selective detection of catechol.

Metabolomics Provides New Insights into Host Manipulation Strategies by Asobara japonica (Hymenoptera: Braconidae), a Fruit Fly Parasitoid.

Asobara japonica (Hymenoptera: Braconidae) is an endoparasitoid wasp that can successfully parasitize a wide range of host species across the Drosophila genus, including the invasive crop pest Drosophila suzukii. Parasitoids are capable of regulating the host metabolism to produce the nutritional metabolites for the survival of their offspring. Here, we intend to investigate the metabolic changes in D. melanogaster hosts after parasitization by A. japonica, using the non-targeted LC-MS (liquid chromatography-mass spectrometry) metabolomics analysis. In total, 3043 metabolites were identified, most of which were not affected by A. japonica parasitization. About 205 metabolites were significantly affected in parasitized hosts in comparison to non-parasitized hosts. The changed metabolites were divided into 10 distinct biochemical groups. Among them, most of the lipid metabolic substances were significantly decreased in parasitized hosts. On the contrary, most of metabolites associated with the metabolism of amino acids and sugars showed a higher abundance of parasitized hosts, and were enriched for a wide range of pathways. In addition, eight neuromodulatory-related substances were upregulated in hosts post A. japonica parasitization. Our results reveal that the metabolites are greatly changed in parasitized hosts, which might help uncover the underlying mechanisms of host manipulation that will advance our understanding of host-parasitoid coevolution.

Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

Targeting USP10 induces degradation of oncogenic ANLN in esophageal squamous cell carcinoma.

Anillin (ANLN) is a mitosis-related protein that promotes contractile ring formation and cytokinesis, but its cell cycle-dependent degradation mechanisms in cancer cells remain unclear. Here, we show that high expression of ANLN promotes cytokinesis and proliferation in esophageal squamous cell carcinoma (ESCC) cells and is associated with poor prognosis in ESCC patients. Furthermore, the findings of the study showed that the deubiquitinating enzyme USP10 interacts with ANLN and positively regulates ANLN protein levels. USP10 removes the K11- and K63-linked ubiquitin chains of ANLN through its deubiquitinase activity and prevents ANLN ubiquitin-mediated degradation. Importantly, USP10 promotes contractile ring assembly at the cytokinetic furrow as well as cytokinesis by stabilizing ANLN. Interestingly, USP10 and the E3 ubiquitin ligase APC/C co-activator Cdh1 formed a functional complex with ANLN in a non-competitive manner to balance ANLN protein levels. In addition, the macrolide compound FW-04-806 (F806), a natural compound with potential for treating ESCC, inhibited the mitosis of ESCC cells by targeting USP10 and promoting ANLN degradation. F806 selectively targeted USP10 and inhibited its catalytic activity but did not affect the binding of Cdh1 to ANLN and alters the balance of the USP10-Cdh1-ANLN complex. Additionally, USP10 expression was positively correlated with ANLN level and poor prognosis of ESCC patients. Overall, targeting the USP10-ANLN axis can effectively inhibit ESCC cell-cycle progression.

The odorant receptor co-receptor gene contributes to mating and host-searching behaviors in parasitoid wasps.

BACKGROUND: Biological control of pest insects by parasitoid wasps is an effective and environmentally friendly strategy compared with the use of synthetic pesticides. Successful courtship and host-search behaviors of parasitoid wasps are important for biological control efficiency and are often mediated by chemical odorant cues. The odorant receptor co-receptor (Orco) gene has an essential role in the perception of odors in insects. However, the function of Orco in the mating and host-searching behaviors of parasitoid wasps remains underexplored. RESULTS: We identified the full-length Orco genes of four Drosophila parasitoid species in the genus Leptopilina, namely L. heterotoma, L. boulardi, L. syphax and L. drosophilae. Sequence alignment and membrane-topology analysis showed that Leptopilina Orcos had similar amino acid sequences and topology structures. Phylogenetic analysis revealed that Leptopilina Orcos were highly conserved. Furthermore, the results of quantitative real-time polymerase chain reactions showed that all four Orco genes had a typical antennae-biased tissue expression pattern. After knockdown of Orco in these different parasitoid species, we found that Orco-deficient male parasitoid wasps, but not females, lost their courtship ability. Moreover, Orco-deficient female parasitoid wasps presented impaired host-searching performance and decreased oviposition rates. CONCLUSION: Our study demonstrates that Orcos are essential in the mating and host-searching behaviors of parasitoid wasps. To our knowledge, this is the first time that the functions of Orco genes have been characterized in parasitoid wasps, which broadens our understanding of the chemoreception basis of parasitoid wasps and contributes to developing advanced pest management strategies. © 2022 Society of Chemical Industry.

Accelerating Multi-site Health Informatics with Streamlined Data Infrastructure using OMOP-on-FHIR.

Shriners Children's (SHC) is a hospital system whose mission is to advance the treatment and research of pediatric diseases. SHC success has generated a wealth of clinical data. Unfortunately, barriers to healthcare data access often limit data-driven clinical research. We decreased this burden by allowing access to clinical data via the standardized data access standard called FHIR (Fast Healthcare Interoperability Resources). Specifically, we converted existing data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard into FHIR data elements using a technology called OMOP-on-FHIR. In addition, we developed two applications leveraging the FHIR data elements to facilitate patient cohort curation to advance research into pediatric musculoskeletal diseases. Our work enables clinicians and clinical researchers to use hundreds of currently available open-sourced FHIR applications. Our successful implementation of OMOP-on-FHIR within a large hospital system will accelerate advancements in pediatric disease treatment and research.