A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer

BACKGROUND

Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs).

METHODS

A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set.

RESULTS

The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis.

CONCLUSION

Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

One-Shot Generative Prior in Hankel-k-Space for Parallel Imaging Reconstruction

Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the deep generative models, offers aggressive acceleration and better reconstruction in magnetic resonance imaging. Nevertheless, learning the data distribution as prior knowledge and reconstructing the image from limited data remains challenging. In this work, we propose a novel Hankel-k-space generative model (HKGM), which can generate samples from a training set of as little as one k-space. At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the Hankel matrix to capture the internal distribution among different patches. Extracting patches from a Hankel matrix enables the generative model to be learned from the redundant and low-rank data space. At the iterative reconstruction stage, the desired solution obeys the learned prior knowledge. The intermediate reconstruction solution is updated by taking it as the input of the generative model. The updated result is then alternatively operated by imposing low-rank penalty on its Hankel matrix and data consistency constraint on the measurement data. Experimental results confirmed that the internal statistics of patches within single k-space data carry enough information for learning a powerful generative model and providing state-of-the-art reconstruction.

Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat-water separation by field factor

PURPOSE

To reduce the ambiguity between chemical shift and field inhomogeneity with flexible TE combinations by introducing a variable (field factor).

THEORY AND METHODS

The ambiguity between chemical shift and field inhomogeneity can be eliminated directly from the multiple in-phase images acquired at different TEs; however, it is only applicable to few echo combinations. In this study, we accommodated such an implementation in flexible TE combinations by introducing a new variable (field factor). The effects of the chemical shift were removed from the field inhomogeneity in the candidate solutions, thus reducing the ambiguity problem. To validate this concept, multi-echo MRI data acquired from various anatomies with different imaging parameters were tested. The derived fat and water images were compared with those of the state-of-the-art fat-water separation algorithms.

RESULTS

Robust fat-water separation was achieved with the accurate solution of field inhomogeneity, and no apparent fat-water swap was observed. In addition to the good performance, the proposed method is applicable to various fat-water separation applications, including different sequence types and flexible TE choices.

CONCLUSION

We propose an algorithm to reduce the ambiguity of chemical shift and field inhomogeneity and achieved robust fat-water separation in various applications.

Preclinical Development of Brain Permeable ERβ Agonist for the Treatment of Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive type of adult brain tumors with low 5-year overall survival rates. Epidemiologic data suggest that estrogen may decrease brain tumor growth, and estrogen receptor beta (ERβ) has been demonstrated to exert antitumor functions in GBM. The lack of potent, selective, and brain permeable ERβ agonist to promote its antitumor action is limiting the therapeutic promise of ERβ. In this study, we discovered that Indanone and tetralone-keto or hydroxyl oximes are a new class of ERβ agonists. Because of its high activity in ERβ reporter assays, specific binding to ERβ in polar screen assays, and potent growth inhibitory activity in GBM cells, CIDD-0149897 was discovered as a possible hit by screening a library of compounds. CIDD-0149897 is more selective for ERβ than ERα (40-fold). Treatment with CIDD-0149897 markedly reduced GBM cell viability with an IC50 of ∼7 to 15 μmol/L, while having little to no effect on ERβ-KO cells and normal human astrocytes. Further, CIDD-0149897 treatment enhanced expression of known ERβ target genes and promoted apoptosis in established and patient-derived GSC models. Pharmacokinetic studies confirmed that CIDD-0149897 has systemic exposure, and good bioavailability in the brain. Mice tolerated daily intraperitoneal treatment of CIDD-0149897 (50 mg/kg) with a 7-day repeat dosage with no toxicity. In addition, CIDD-0149897 treatment significantly decreased tumor growth in U251 xenograft model and extended the survival of orthotopic GBM tumor-bearing mice. Collectively, these findings pointed to CIDD-0149897 as a new class of ERβ agonist, offering patients with GBM a potential means of improving survival.

Amphoteric composite of ZrP and N-doped porous carbon: Synthesis, characterization, and potential use for cycloaddition of CO2

Composites of amorphous ZrP and N-doped carbon were prepared in a one-step pyrolysis process instead of general post-loading technique. Owing to their mesoporous structure (6-10 nm) and Zr content (up to 41 wt%), the amphoteric materials have potential use in the cycloaddition of CO2 to epoxides, which is an acid‒base tandem process including the ring opening of epoxides and the addition of CO2. Substantial work has been done on how starting materials impact the structure and performance of composite materials. The coordination between metal and melamine has been confirmed, and it can be implanted in the melamine-polymer initiation of formation of porous metal-carbon materials. The composite catalysts exhibit amphoteric properties, present broad-spectrum adsorption, and finally produce carbonates via cycloaddition of CO2 to epoxides. It is remarkable that the multiple characteristics of porous solids are stabilized, and no significant loss of catalytic performance is observed after four cycles.

Tobacco roots increasing diameter and secondary lateral density in response to drought stress

Exploring the responses of root morphology and its physiological mechanisms under drought stress is significant for further improving water and nutrient absorption in roots. Here, we simulated drought through hydroponics combined with PEG treatments in tobacco to characterize the changes in tobacco root architecture. Our results showed the total root length, first lateral root number, and first lateral root length were significantly reduced upon increasing drought severity, but the average root diameter and secondary lateral root density increased under certain drought conditions. The change of auxin content in roots under drought stress was correlated with the root diameter and second lateral root density responses. Exogenous addition of the auxin analog (NAA) and the auxin transport inhibitor (NPA), as well as DR5:GUS staining experiments further demonstrated that auxin participated in this physiological process. Meanwhile, brassinolide (BR) exhibited a similar trend. Exogenous addition of BR (EBR) and the BR synthesis inhibitor BRZ experiments demonstrated that BR may participate upstream of auxin under drought stress. PEG treatment significantly up-regulated NtBRI1 at 9-24 h, and promoted the up-regulation of NtBSK2 and NtBSK3 at 48 h and 24 h, respectively, these genes may contribute to the change in root morphology under drought stress. This study shows that auxin and BR are involved in the changes in root morphology in tobacco exposed to drought stress. The elucidation of the molecular mechanism at play thus represents a future target for breeding drought-tolerant tobacco varieties.

WKGM: weighted k-space generative model for parallel imaging reconstruction

Deep learning based parallel imaging (PI) has made great progress in recent years to accelerate MRI. Nevertheless, it still has some limitations: for example, the robustness and flexibility of existing methods are greatly deficient. In this work, we propose a method to explore the k-space domain learning via robust generative modeling for flexible calibrationless PI reconstruction, coined the weighted k-space generative model (WKGM). Specifically, WKGM is a generalized k-space domain model, where the k-space weighting technology and high-dimensional space augmentation design are efficiently incorporated for score-based generative model training, resulting in good and robust reconstructions. In addition, WKGM is flexible and thus can be synergistically combined with various traditional k-space PI models, which can make full use of the correlation between multi-coil data and realize calibrationless PI. Even though our model was trained on only 500 images, experimental results with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results with the well learned k-space generative prior.

Development of Wushu culture industry using internet of things technology: A case study of Anhui Province, China

In recent years, as the modern economy rapidly advances, and urbanization continues to flourish, Anhui Province has emerged as a significant hub for Wushu, boasting a rich historical tradition in this martial art form. However, despite this heritage, Wushu often remains associated with traditional sports, and the growth of the Wushu cultural industry has not been as robust as desired. This paper seeks to harness the potential of the Internet of Things (IoT) and capitalize on the burgeoning trends within the cultural industry to foster the development of Chinese Wushu. The primary objective of this study is to explore strategies for advancing the Wushu cultural industry in Anhui Province, leveraging IoT technology. To achieve this goal, we employ a novel approach, combining SWOT (Strength, Weakness, Opportunity, Threat) analysis with Analytic Hierarchy Process (AHP), providing a quantitative assessment of the region's Wushu cultural industry development. This approach minimizes subjective bias and offers a sound theoretical foundation for industrial progress. Our research yields promising results, as we effectively integrate Wushu, culture, and tourism. This not only facilitates the dissemination and popularization of Wushu culture but also enhances the appeal of local tourism resources. Notably, the fifth World Wushu Championship experienced sustained popularity, and in 2014, Anhui Province successfully hosted the sixth edition of the event in Jiuhuashan, yielding a significant income of 170 million yuan. The development of the Wushu cultural industry in Anhui Province has exhibited positive growth trends and garnered considerable attention. This study underscores the importance of embracing IoT technology and aligning with broader cultural industry trends to nurture the development of Chinese Wushu. The implications of our research extend to the realms of tourism, culture, and sports, offering valuable insights into leveraging technology for the advancement of traditional practices. This study represents a novel exploration of the intersection between Wushu and IoT technology, with the potential to inform and inspire further research in this evolving field.

High resolution single-shot myocardial imaging using bSSFP with wave encoding

BACKGROUND

Single-shot balanced steady-state free precession (bSSFP) sequence is widely used in cardiac imaging. However, the limited scan time in one heartbeat greatly hinders its spatial resolution compared to the segmented acquisition mode. Therefore, a highly accelerated single-shot bSSFP imaging technology is needed for clinical use.

PURPOSE

To develop and evaluate a wave-encoded bSSFP sequence with high acceleration rates for single-shot myocardial imaging.

METHODS

The proposed Wave-bSSFP method is implemented by adding a sinusoidal wave gradient in the phase encoding direction during the readout of bSSFP sequence. Uniform undersampling is used for acceleration. Its performance was first validated via phantom studies by comparison with conventional bSSFP. Then it was evaluated in volunteer studies via anatomical imaging, T2 -prepared bSSFP, and T1 mapping in in-vivo cardiac imaging. All methods were compared with accelerated conventional bSSFP reconstructed using iterative SENSE and compressed sensing (CS), to demonstrate the advantage of wave encoding in suppressing the noise amplification and artifacts induced by acceleration.

RESULTS

The proposed Wave-bSSFP method achieved a high acceleration factor of 4 for single-shot acquisitions. The proposed method showed lower average g-factors than bSSFP, and fewer blurring artifacts than CS reconstruction. The Wave-bSSFP with R = 4 achieved higher spatial and temporal resolutions compared with the conventional bSSFP with R = 2 in several applications such as T2 -prepared bSSFP and T1 mapping, and could be applied in systolic imaging.

CONCLUSION

Wave encoding can be used to highly accelerate 2D bSSFP imaging with single-shot acquisitions. Compared with the conventional bSSFP sequence, the proposed Wave-bSSFP method can effectively reduce the g-factor and aliasing artifacts in cardiac imaging.

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

BACKGROUND

Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images.

METHODS

In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image.

RESULTS

A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min.

CONCLUSIONS

The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

Deep neural network for prediction of diet quality among doctors and nurses in North China during the COVID-19 pandemic

Objective

The COVID-19 pandemic has placed unprecedented pressure on front-line healthcare workers, leading to poor health status, especially diet quality. This study aimed to develop a diet quality prediction model and determine the predictive effects of personality traits, socioeconomic status, lifestyles, and individual and working conditions on diet quality among doctors and nurses during the COVID-19 pandemic.

Methods

A total of 5,013 doctors and nurses from thirty-nine COVID-19 designated hospitals provided valid responses in north China in 2022. Participants' data related to social-demographic characteristics, lifestyles, sleep quality, personality traits, burnout, work-related conflicts, and diet quality were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a diet quality prediction model among doctors and nurses during the COVID-19 pandemic.

Results

The mean score of diet quality was 46.14 ± 15.08; specifically, the mean scores for variety, adequacy, moderation, and overall balance were 14.33 ± 3.65, 17.99 ± 5.73, 9.41 ± 7.33, and 4.41 ± 2.98, respectively. The current study developed a DNN model with a 21-30-28-1 network framework for diet quality prediction. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE were 0.928, 0.048, 0.004, and 0.065, respectively. Among doctors and nurses in north China, the top five predictors in the diet quality prediction model were BMI, poor sleep quality, work-family conflict, negative emotional eating, and nutrition knowledge.

Conclusion

During the COVID-19 pandemic, poor diet quality is prevalent among doctors and nurses in north China. Machine learning models can provide an automated identification mechanism for the prediction of diet quality. This study suggests that integrated interventions can be a promising approach to improving diet quality among doctors and nurses, particularly weight management, sleep quality improvement, work-family balance, decreased emotional eating, and increased nutrition knowledge.

Developing mitochondrial base editors with diverse context compatibility and high fidelity via saturated spacer library

DddA-derived cytosine base editors (DdCBEs) greatly facilitated the basic and therapeutic research of mitochondrial DNA mutation diseases. Here we devise a saturated spacer library and successfully identify seven DddA homologs by performing high-throughput sequencing based screen. DddAs of Streptomyces sp. BK438 and Lachnospiraceae bacterium sunii NSJ-8 display high deaminase activity with a strong GC context preference, and DddA of Ruminococcus sp. AF17-6 is highly compatible to AC context. We also find that different split sites result in wide divergence on off-target activity and context preference of DdCBEs derived from these DddA homologs. Additionally, we demonstrate the orthogonality between DddA and DddIA, and successfully minimize the nuclear off-target editing by co-expressing corresponding nuclear-localized DddIA. The current study presents a comprehensive and unbiased strategy for screening and characterizing dsDNA cytidine deaminases, and expands the toolbox for mtDNA editing, providing additional insights for optimizing dsDNA base editors.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Photoinduced Five-Component Radical Relay Aminocarbonylation of Alkenes

Radical single carbonylation reactions with CO constitute a direct and robust strategy toward various carbonyl compounds from readily available chemicals, and have been extensively studied over the past decades. However, realizing highly selective catalytic systems for controlled radical double carbonylation reactions has remained a substantial challenge, particularly for the more advanced multicomponent variants, despite their great potential value. Herein, we report a visible-light-driven radical relay five-component radical double aminocarbonylation reaction of unactivated alkenes using CO under metal-free conditions. This protocol provides direct access to valuable γ-trifluoromethyl α-ketoamides with good yields and high chemoselectivity. Crucial was the identification of distinct dual roles of amine coupling partners, sequentially acting as electron donors for the formation of photoactive electron donor-acceptor (EDA) complexes with radical precursors and then as a CO acceptor via nitrogen radical cations to form carbamoyl radicals. Cross-coupling of carbamoyl radicals with the acyl radicals that are formed in an alkene-based relay process affords double aminocarbonylation products.

Tau-PET abnormality as a biomarker for Alzheimer's disease staging and early detection: a topological perspective

Alzheimer's disease can be detected early through biomarkers such as tau positron emission tomography (PET) imaging, which shows abnormal protein accumulations in the brain. The standardized uptake value ratio (SUVR) is often used to quantify tau-PET imaging, but topological information from multiple brain regions is also linked to tau pathology. Here a new method was developed to investigate the correlations between brain regions using subject-level tau networks. Participants with cognitive normal (74), early mild cognitive impairment (35), late mild cognitive impairment (32), and Alzheimer's disease (40) were included. The abnormality network from each scan was constructed to extract topological features, and 7 functional clusters were further analyzed for connectivity strengths. Results showed that the proposed method performed better than conventional SUVR measures for disease staging and prodromal sign detection. For example, when to differ healthy subjects with and without amyloid deposition, topological biomarker is significant with P < 0.01, SUVR is not with P > 0.05. Functionally significant clusters, i.e. medial temporal lobe, default mode network, and visual-related regions, were identified as critical hubs vulnerable to early disease conversion before mild cognitive impairment. These findings were replicated in an independent data cohort, demonstrating the potential to monitor the early sign and progression of Alzheimer's disease from a topological perspective for individual.

Changing trends of disease burden of stroke from 1990 to 2019 and its predictions among the Chinese population

Objective

This study aimed to understand the temporal trends in the disease burden of stroke and its attributable risk factors in China, along with the future trends in the next 25 years, that is important for effective prevention strategies and improvement, and to provide new insights into the age- and sex-specific incidence, prevalence, mortality, disability-adjusted life-years (DALYs) and their trends from 1990 to 2019, and the prediction in the next 25 years.

Methods

The Global Burden of Disease Study (2019) was used to extract the data on age- and sex-specific incidence, mortality, and disability-adjusted life-years (DALYs) of stroke in China, 1990-2019. We estimated the estimated annual percentage change (EAPC) to access the temporal trends of the disease burden of stroke. The R package called Nordpred was used to perform an age-period-cohort analysis to predict the prevalence of stroke.

Results

The number of incidence cases, deaths, and DALYs of stroke increased from 1990 to 2019. Overall downward trends were observed in the age-standardized incidence rate (ASIR) from 1990 to 2019. Significant temporal trends in mortality and DALYs of stroke were observed. High systolic blood pressure, smoking, and high-sodium diet were the main driving forces for stroke. The DALYs lost attributable to smoking were different for male and female patients. In the next 25 years, the number of new cases and deaths from stroke should continue to increase. The ASIR and age-standardized mortality rate (ASMR) should show a downward trend among male and female patients.

Conclusion

Despite the overall rates of stroke declined over the period from 1990 to 2019, the absolute number of people affected by stroke has substantially increased. There has been a substantial increase in the burden of stroke due to risk factors and will continue to increase in the next 25 years.

The impact of China's information infrastructure construction policy on green total factor productivity: moving towards a green world

China's economic growth over the past two decades has led to severe environmental pollution in most of its cities. To address this issue, China has developed and implemented several strategies to increase its resource utilization efficiency and move towards a green production economy, such as the information infrastructure construction policy (IICP). To assess the extent to which green production is being achieved, this study first measured the green total factor productivity (GTFP) in Chinese prefecture-level cities using a combined super slacks-based measure and the global Malmquist-Luenberger index (SBM-GML) model. Then, to assess the effectiveness of the IICP, the paths affecting the GTFP were explored using a time-varying difference-in-difference (time-varying DID) model. It was found that the IICP had significantly contributed to urban GTFP through its green technology diffusion effects and improved employment structures, with its implementation resulting in an average increase of 0.201 in the pilot cities compared to the non-pilot cities. This research attempted to explain how IICP plays a role in improving green production, and policy makers should attach importance to the power of IICP.

Real-world effectiveness of azvudine for patients infected with the SARS-CoV-2 omicron subvariant BA.5 in an intensive care unit

Background

Azvudine (FNC) has been shown to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but data regarding the Omicron BA.5.1.3 subvariant are lacking. This retrospective analysis investigated the effectiveness and safety of FNC against the SARS-CoV-2 Omicron BA.5.1.3 subvariant in a real-world setting, utilizing data from a patient cohort at our institution.

Methods

Data were retrospectively collected from patients admitted to the intensive care unit (ICU) of Sanya Central Hospital during the Sanya outbreak (August 13 to September 7, 2022). The patients, all infected with the Omicron BA.5.1.3 subvariant, were selected based on predefined inclusion and exclusion criteria. The patients were classified as the FNC (azvudine 5 mg, qd + standard supportive treatment) and non-FNC (standard supportive treatment only) groups.

Results

The study comprised 13 patients, with 6 and 7 in the FNC and non-FNC groups, respectively. Baseline data, clinical features, and imaging manifestations were generally similar between the two groups. However, patients administered FNC demonstrated significantly lower levels of inflammatory indicators at baseline. Although there was no significant difference in the duration of ICU stay between the FNC and non-FNC groups, overall ICU stay appeared to be reduced in the FNC group.

Conclusions

FNC emerges as a feasible treatment against the Omicron BA.5.1.3 subvariant. It may reduce ICU stay and demonstrate a promising safety profile without major side effects or disruption to normal physiological parameters.

Nano-seq analysis reveals different functional tendency between exosomes and microvesicles derived from hUMSC

BACKGROUND

Extracellular vesicles (EVs) from human umbilical cord mesenchymal stem cells (hUMSCs) are widely considered to be the best mediators for cell-free therapy. An understanding of their composition, especially RNA, is particularly important for the safe and precise application of EVs. Up to date, the knowledge of their RNA components is limited to NGS sequencing and cannot provide a comprehensive transcriptomic landscape, especially the long and full-length transcripts. Our study first focused on the transcriptomic profile of hUMSC-EVs based on nanopore sequencing.

METHODS

In this study, different EV subtypes (exosomes and microvesicles) derived from hUMSCs were isolated and identified by density gradient centrifugation. Subsequently, the realistic long transcriptomic profile in different subtypes of hUMSC-EVs was systematically compared by nanopore sequencing and bioinformatic analysis.

RESULTS

Abundant transcript variants were identified in EVs by nanopore sequencing, 69.34% of which transcripts were fragmented. A series of full-length and long transcripts was also observed and showed a significantly higher proportion of intact or near-complete transcripts in exosomes than that in microvesicles derived from hUMSCs. Although the composition of RNA biotypes transported by different EV subtypes was similar, the distribution of transcripts and genes revealed the inter-heterogeneity and intra-stability between exosomes and microvesicles. Further, 85 different expressed transcripts (56 genes) and 7 fusion genes were identified. Pathway enrichment analysis showed that upregulated-expressed genes in microvesicles were mainly enriched in multiple neurodegenerative diseases, while upregulated-expressed genes in exosomes were mainly enriched in neutrophil extracellular trap formation, suggesting different functional tendencies of EV subtypes.

CONCLUSIONS

This study provides a novel understanding of different types of hUMSC-EVs, which not only suggests different transcriptome sorting mechanisms between exosomes and microvesicles, but also shows that different EV subtypes from the same source have different physiological functions, suggesting distinct clinical application prospects.

Speculation on optimal numbers of examined lymph node for early-stage epithelial ovarian cancer from the perspective of stage migration

Introduction

In early-stage epithelial ovarian cancer (EOC), how to perform lymphadenectomy to avoid stage migration and achieve reliable targeted excision has not been explored in depth. This study comprehensively considered the stage migration and survival to determine appropriate numbers of examined lymph node (ELN) for early-stage EOC and high-grade serous ovarian cancer (HGSOC).

Methods

From the Surveillance, Epidemiology, and End Results database, we obtained 10372 EOC cases with stage T1M0 and ELN ≥ 2, including 2849 HGSOC cases. Generalized linear models with multivariable adjustment were used to analyze associations between ELN numbers and lymph node stage migration, survival and positive lymph node (PLN). LOESS regression characterized dynamic trends of above associations followed by Chow test to determine structural breakpoints of ELN numbers. Survival curves were plotted using Kaplan-Meier method.

Results

More ELNs were associated with more node-positive diseases, more PLNs and better prognosis. ELN structural breakpoints were different in subgroups of early-stage EOC, which for node stage migration or PLN were more than those for improving outcomes. The meaning of ELN structural breakpoint varied with its location and the morphology of LOESS curve. To avoid stage migration, the optimal ELN for early-stage EOC was 29 and the minimal ELN for HGSOC was 24. For better survival, appropriate ELN number were 13 and 8 respectively. More ELNs explained better prognosis only at a certain range.

Discussion

Neither too many nor too few numbers of ELN were ideal for early-stage EOC and HGSOC. Excision with appropriate numbers of lymph node draining the affected ovary may be more reasonable than traditional sentinel lymph node resection and systematic lymphadenectomy.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

Development of a novel UPLC-MS/MS method for the simultaneous quantification of mycophenolic mofetil, mycophenolic acid, and its major metabolites: Application to pharmacokinetic and tissue distribution study in rats

Mycophenolate mofetil (MMF) is a prodrug of mycophenolic acid (MPA) used to prevent rejection in organ transplant patients. The purpose of this study is to develop a sensitive LC-MS/MS method to simultaneously quantify MMF, MPA, and two major metabolites, mycophenolic acid-glucuronide (MPAG) and Acyl-mycophenolic acid-glucuronide (AcMPAG) and applied this method in a pharmacokinetic (PK) and tissue distribution study. A Shimadzu UHPLC system coupled to an AB Sciex QTrap 4000 mass spectrometer was used for the analysis. Protein precipitation with a mixture of methanol: acetonitrile (2:1, v:v) was used to process the plasma samples and tissue samples. Separation was achieved using an Ultra Biphenyl 5 µm column (100 × 2.1 mm) with 0.1% formic acid in water (A) and acetonitrile (B) as the mobile phases. Quantification analysis was performed under positive ionization mode using the multiple reaction monitoring (MRM) approach. The method was linear in the range of 1.22 - 1250.00 nM for all four analytes with correlation coefficient values > 0.99. The method was reproducible, with intra- and inter-day accuracy ranging from 85.0 ± 11.2-108.3 ± 6.50 for all analytes in both plasma, liver and intestine homogenates. The extraction recovery and matrix effect of plasma sample using a mixture methanol/acetonitrile (2:1, V:V) can achieve an acceptable range (<20%), but extraction recovery and matrix effect of AcMPAG decreased to 64.10 ± 15.42 in the liver and intestine homogenates. The analytes in plasma were found to be stable under bench-top, freeze-thaw, and storage conditions. The validated method was successfully applied to quantify MMF, MPA, MPAG, and AcMPAG in a rat PK study. The PK results showed MPA was the major form exposed in the plasma in rats after oral administration of MMF, but the major metabolites in the rat's tissue disposition were MPAG.

Functional proteins in breast milk and their correlation with the development of the infant gut microbiota: a study of mother-infant pairs

Introduction

Proteins in breast milk play an important role in the growth and development of infants. This study aims to explore the correlation between functional proteins in breast milk and the infant gut microbiota.

Methods

Twenty-three mothers and their infants were enrolled and breast milk samples and infant fecal samples were collected. Breast milk protein content was determined by UPLC-MS/MS, and 16S rRNA sequencing was employed to analyze the gut microbiota of infant.

Results

The results indicated that the secretory immunoglobulin A (sIgA) content in breast milk was positively correlated with the abundance of Veillonella parvula. The κ-casein content was positively correlated with the abundance of Clostridium butyricum. The osteopontin (OPN) and lactalbumin contents were positively correlated with the abundance of Parabacteroides distasonis at 42 days. Functional pathway analysis showed that the OPN and κ-casein contents in breast milk were significantly correlated with amino acid, pyruvate, propionic acid, linoleic acid, and alpha-linolenic acid metabolic pathways in early life.

Discussion

The results of this study suggest that specific proteins in breast milk can influence the abundance of certain gut microbes in infants, playing an important role in early immune and metabolic development.

Robust cooperative output regulation for heterogeneous nonlinear multi-agent systems with an unknown exosystem subject to jointly connected switching networks

This paper investigates the robust cooperative output regulation problem for heterogeneous lower triangular nonlinear multi-agent systems with an unknown exosystem over jointly connected switching networks. The problem has been studied for the exactly known exosystem over switching networks. However, the existing result for the unknown exosystem is still limited to the static networks. To ensure that all followers acquire the reference trajectory generated by the unknown exosystem through the jointly connected switching networks, by combining a set of auxiliary filtering variables and fixed-time stability theory, an adaptive distributed observer is designed. On the basis of the adaptive distributed observer and the distributed internal model approach, we propose a distributed controller under several standard assumptions to solve the problem. Compared with the similar work subject to the static networks, the controller in this paper is applicable to the more general communication network while weakening the assumptions of the controlled system.

Inhibition of TLR4 Signaling by Isorhapontigenin Targeting of the AHR Alleviates Cerebral Ischemia/Reperfusion Injury

Ischemic stroke is a major risk factor in human health, yet there are no drugs to cure cerebral ischemia/reperfusion injury (CIRI). Inflammation plays a fundamental role in the consequences of CIRI. Isorhapontigenin (ISOR) exhibits great anti-inflammatory activity; however, it is unclear whether ISOR can treat ischemic stroke through an anti-inflammation effect. Here, middle cerebral artery occlusion/reperfusion (MCAO/R) was used to investigate the effects of ISOR on CIRI. The in vitro activity was measured in BV-2 cells exposed to oxygen-glucose deprivation/reperfusion. As measured by neurological scores, brain water content, and infarction, neurological dysfunction was improved in the ISOR group. The neuronal death and microglial activation in the ipsilateral cortex were reduced by ISOR. TLR4 signaling was significantly inhibited by ISOR in vivo and in vitro. By reverse molecular docking, cellular thermal shift, and drug affinity-responsive target stability assays, an aryl hydrocarbon receptor (AHR) was found to be a target of ISOR. Furthermore, AHR knockdown blocked the effect of ISOR on TLR4 signaling, suggesting that ISOR may regulate TLR4-mediated inflammation through AHR, thereby protecting neurons from CIRI. This study demonstrated that ISOR is a promising drug candidate for the treatment of ischemic stroke and provided a theoretical basis for the development of the medicinal value of ISOR-derived foods, such as grapes.