Assessing Cerebral Microvascular Volumetric Pulsatility with High-Resolution 4D CBV MRI at 7T.

Arterial pulsation is crucial for promoting fluid circulation and for influencing neuronal activity. Previous studies assessed the pulsatility index based on blood flow velocity pulsatility in relatively large cerebral arteries of human. Here, we introduce a novel method to quantify the volumetric pulsatility of cerebral microvasculature across cortical layers and in white matter (WM), using high-resolution 4D vascular space occupancy (VASO) MRI with simultaneous recording of pulse signals at 7T. Microvascular volumetric pulsatility index (mvPI) and cerebral blood volume (CBV) changes across cardiac cycles are assessed through retrospective sorting of VASO signals into cardiac phases and estimating mean CBV in resting state (CBV0) by arterial spin labeling (ASL) MRI at 7T. Using data from 11 young (28.4±5.8 years) and 7 older (61.3±6.2 years) healthy participants, we investigated the aging effect on mvPI and compared microvascular pulsatility with large arterial pulsatility assessed by 4D-flow MRI. We observed the highest mvPI in the cerebrospinal fluid (CSF) on the cortical surface (0.19±0.06), which decreased towards the cortical layers as well as in larger arteries. In the deep WM, a significantly increased mvPI (p = 0.029) was observed in the older participants compared to younger ones. Additionally, mvPI in deep WM is significantly associated with the velocity pulsatility index (vePI) of large arteries (r = 0.5997, p = 0.0181). We further performed test-retest scans, non-parametric reliability test and simulations to demonstrate the reproducibility and accuracy of our method. To the best of our knowledge, our method offers the first in vivo measurement of microvascular volumetric pulsatility in human brain which has implications for cerebral microvascular health and its relationship research with glymphatic system, aging and neurodegenerative diseases.

[Distribution and health risk assessment of pesticide pollution in raw water and drinking water within key river basins in China in 2022].

OBJECTIVE: To investigate the distribution and exposure levels of pesticides in raw water and drinking water in China, as well as to assess the potential health risks associated with long-term consumption. METHODS: A total of 83 typical water plants were selected in key river basins in China to collected samples of the raw water, finished water, and tap water. The online-solid phase extraction coupled with liquid chromatography-tandem mass spectrometry method was used to determine 13 pesticides, including acetochlor, atrazine, dimethoate, malathion, carbofuran, dichlorvos, chlorpyrifos, parathion, trifluralin, isoprothiolane, simetryn, methyl parathion, and metalaxyl, as well as 6 environmental metabolites, including carbendazim, malaoxon, 3-hydroxycarbofuran, deethyl atrazine, deisopropyl atrazine and hydroxy atrazine. The carcinogenic and non-carcinogenic risks of these pesticides were assessed. RESULTS: The concentrations of total amount of pesticides in the samples ranged from 0.1 ng/L to 1299.4 ng/L, with a median value of 64.7 ng/L. The detection rates of 5 pesticides or their metabolites exceeds 80%, namely acetochlor, atrazine, hydroxyl atrazine, deethyl atrazine, and metalaxyl. More than 6 pesticides or their metabolites were detected in 77.7% of the total 498 samples. The total concentration of pesticides during the wet season ranged from 1.1 ng/L to 1299.4 ng/L, with a median of 69.2 ng/L and a median average daily dose of 2.3 ng/(kg·d). The total concentration of pesticides in the dry season samples ranged from 0.1 ng/L to 543.5 ng/L, with a median of 60.2 ng/L and a median average daily dose of 2.0 ng/(kg·d). Among the 498 samples, the maximum carcinogenic risk of dichlorvos was 2.0×10~(-7), and the maximum carcinogenic risk of trifluralin was 1.1×10~(-10). The non-carcinogenic HI of 19 pesticides and metabolites pesticides was ≤6.0×10~(-3). Among them, the maximum HI of the middle route of the South to North Water Diversion Project, the lower reaches of the Yellow River, the eastern route of the South to North Water Diversion Project, the Liaohe River, and the Songhua River basin was 2.0×10~(-3)-6.0×10~(-3), while the HI of other basins was less than 1.0×10~(-3). CONCLUSION: Different concentrations of pesticides have been detected in raw water, finished water, and tap water of key river basins in China, with the highest total concentration of detected reaching 1299.4 ng/L. The carcinogenic risk was lower than 10~(-6) and the non-carcinogenic hazard index of 19 pesticides was less than 1, and both below the acceptable level. Because of the universality and diverse occurrence of these pesticides in drinking water, long-term exposure to pesticides is still a concern.

[Clinical Characteristics and Prognostic Relevance of Co-Mutated Genes in Acute Myeloid Leukemia Patients with FLT3 Mutations].

OBJECTIVE: To investigate the clinical characteristics and influence of co-mutated gene on acute myeloid leukemia patients (AML) with FMS-like tyrosine kinase-3 (FLT3) mutations. METHODS: A total of 273 FLT3+ AML patients were enrolled, and the co-mutation gene data of the patients were collected to further analyze the prognosis of the patients. FLT3 and other common mutations were quantified by PCR amplification products direct sequencing and second-generation sequencing (NGS). RESULTS: When patients were divided into FLT3- ITD +, FLT3- TKD +, FLT3- ITD ++TKD + and FLT3- ITD -+TKD - group according to the type of FLT3 mutations, it was found that the frequencies of TET2, GATA2, NRAS and ASXL1 mutation were significantly different among the 4 groups (all P < 0.05). When patients were divided into allelic ratio (AR) ≥0.5 and <0.5 group, it was found that the frequencies of FLT3- ITD +, FLT3 -ITD - +TKD -, NPM1, NRAS and C-kit were significantly different between the two groups (all P < 0.05). When patients were divided into normal and abnormal karyotype group, it was found that the frequencies of FLT3- ITD +, FLT3- TKD +, NPM1, GATA2 and C-kit were significantly different between the two groups (all P < 0.05). The median overall survival (OS) of AML patients with FLT3 -TKD + (including FLT3- ITD ++TKD +) was longer than that of patients with FLT3- ITD + alone (P < 0.05). The OS and relapse-free survival (RFS) of AML patients with FLT3++TET2+ were both shorter than those of patients with FLT3++TET2- (both P < 0.05). CONCLUSION: The mutation frequencies of co-mutated genes are correlated with subtypes of FLT3, karyotype and AR. AML patients with FLT3 -TKD + have longer OS than patients with FLT3- ITD + alone, and patients with co-mutation of TET2 have shorter median OS and RFS.

Mature MUC5AC Expression in Resected Pancreatic Ductal Adenocarcinoma Predicts Treatment Response and Outcomes.

Neoadjuvant therapy (NAT) for early-stage pancreatic ductal adenocarcinoma (PDA) has recently gained prominence. We investigated the clinical significance of mucin 5 AC (MUC5AC), which exists in two major glycoforms, a less-glycosylated immature isoform (IM) and a heavily glycosylated mature isoform (MM), as a biomarker in resected PDA. Immunohistochemistry was performed on 100 resected PDAs to evaluate the expression of the IM and MM of MUC5AC using their respective monoclonal antibodies, CLH2 (NBP2-44455) and 45M1 (ab3649). MUC5AC localization (cytoplasmic, apical, and extra-cellular (EC)) was determined, and the H-scores were calculated. Univariate and multivariate (MVA) Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). Of 100 resected PDA patients, 43 received NAT, and 57 were treatment-naïve with upfront surgery (UpS). In the study population (n = 100), IM expression (H-scores for objective response vs. no response vs. UpS = 104 vs. 152 vs. 163, p = 0.01) and MM-MUC5AC detection rates (56% vs. 63% vs. 82%, p = 0.02) were significantly different. In the NAT group, MM-MUC5AC-negative patients had significantly better PFS according to the MVA (Hazard Ratio: 0.2, 95% CI: 0.059-0.766, p = 0.01). Similar results were noted in a FOLFIRINOX sub-group (n = 36). We established an association of MUC5AC expression with treatment response and outcomes.

Chimeric antigen receptor-T cells targeting epithelial cell adhesion molecule antigens are effective in the treatment of colorectal cancer.

OBJECTIVE: To construct chimeric antigen receptor (CAR)-T cells targeting epithelial cell adhesion molecule (EpCAM) antigen (anti-EpCAM-CAR-T). METHODS: A third-generation CAR-T cell construct used a single-chain variable fragment derived from monoclonal antibody against human EpCAM. Peripheral blood mononuclear cells were extracted from volunteers. The proportion of cluster of differentiation 8 positive (CD8+) and CD4 + T cells was measured using flow cytometry. Western blot was used to detect the expression of EpCAM-CAR. The killing efficiency was detected using the MTT assay and transwell assay, and the secretion of killer cytokines tumour necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) was detected using the ELISA. The inhibitory effect of EpCAM-CAR-T on colorectal cancer in vivo was detected using xenografts. RESULTS: It was found that T cells expanded greatly, and the proportion of CD3+, CD8 + and CD4 + T cells was more than 60%. Furthermore, EpCAM-CAR-T cells had a higher tumour inhibition rate in the EpCAM expression positive group than in the negative group (P < 0.05). The secretion of killer cytokines TNF-α and IFN-γ in the EpCAM expression positive cell group was higher than that in the negative group (P < 0.05). In the experimental group treated with EpCAM-CAR-T cells, the survival rate of nude mice was higher (P < 0.05), and the tumour was smaller than that in the blank and control groups (P < 0.05). The secretion of serum killer cytokines TNF-α and IFN-γ in tumour-bearing nude mice in the experimental group treated with EpCAM-CAR-T cells was higher than that in the blank and control groups (P < 0.05). CONCLUSION: This study successfully constructed EpCAM-CAR cells and found that they can target and recognise EpCAM-positive tumour cells, secrete killer cytokines TNF-α and IFN-γ and better inhibit the growth and metastasis of colorectal cancer in vitro and in vivo than unmodified T cells.

Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors.

Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors.

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs. SIGNIFICANCE: We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

The Tumor Microbiome Reacts to Hypoxia and Can Influence Response to Radiation Treatment in Colorectal Cancer.

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation's ability to kill cells. We hypothesized that some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. Therefore, we examined the RNA sequencing data from the Oncology Research Information Exchange Network database of patients with colorectal cancer treated with radiotherapy. We identified microbial RNAs for each tumor and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To experimentally investigate this concept, we implanted CT26 colorectal cancer cells into immune-competent BALB/c and immune-deficient athymic nude mice. After growth, in which tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed a metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-tropic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Thus, hypoxia gene expression scores seem to associate with different microbial populations and elicit an adaptive transcriptional response in intratumoral microbes, potentially influencing clinical outcomes. SIGNIFICANCE: Tumor hypoxia reduces radiotherapy efficacy. In this study, we explored whether some of the clinical effects of hypoxia could be due to interaction with the tumor microbiome. Hypoxic gene expression scores associated with certain microbes and elicited an adaptive transcriptional response in others that could contribute to poor clinical outcomes.

Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea.

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding-heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.

Mitochondria transplantation alleviates cardiomyocytes apoptosis through inhibiting AMPKα-mTOR mediated excessive autophagy.

The disruption of mitochondria homeostasis can impair the contractile function of cardiomyocytes, leading to cardiac dysfunction and an increased risk of heart failure. This study introduces a pioneering therapeutic strategy employing mitochondria derived from human umbilical cord mesenchymal stem cells (hu-MSC) (MSC-Mito) for heart failure treatment. Initially, we isolated MSC-Mito, confirming their functionality. Subsequently, we monitored the process of single mitochondria transplantation into recipient cells and observed a time-dependent uptake of mitochondria in vivo. Evidence of human-specific mitochondrial DNA (mtDNA) in murine cardiomyocytes was observed after MSC-Mito transplantation. Employing a doxorubicin (DOX)-induced heart failure model, we demonstrated that MSC-Mito transplantation could safeguard cardiac function and avert cardiomyocyte apoptosis, indicating metabolic compatibility between hu-MSC-derived mitochondria and recipient mitochondria. Finally, through RNA sequencing and validation experiments, we discovered that MSC-Mito transplantation potentially exerted cardioprotection by reinstating ATP production and curtailing AMPKα-mTOR-mediated excessive autophagy.

Impact of Preseason Climate Factors on Vegetation Photosynthetic Phenology in Mid-High Latitudes of the Northern Hemisphere.

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid-high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by -0.15 days per year (days yr-1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr-1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr-1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

Development of an alcoholic liver disease model for drug evaluation from human induced pluripotent stem cell-derived liver organoids.

Alcoholic liver disease (ALD) poses a significant health challenge, so comprehensive research efforts to improve our understanding and treatment strategies are needed. However, the development of effective treatments is hindered by the limitation of existing liver disease models. Liver organoids, characterized by their cellular complexity and three-dimensional (3D) tissue structure closely resembling the human liver, hold promise as ideal models for liver disease research. In this study, we use a meticulously designed protocol involving the differentiation of human induced pluripotent stem cells (hiPSCs) into liver organoids. This process incorporates a precise combination of cytokines and small molecule compounds within a 3D culture system to guide the differentiation process. Subsequently, these differentiated liver organoids are subject to ethanol treatment to induce ALD, thus establishing a disease model. A rigorous assessment through a series of experiments reveals that this model partially recapitulates key pathological features observed in clinical ALD, including cellular mitochondrial damage, elevated cellular reactive oxygen species (ROS) levels, fatty liver, and hepatocyte necrosis. In addition, this model offers potential use in screening drugs for ALD treatment. Overall, the liver organoid model of ALD, which is derived from hiPSC differentiation, has emerged as an invaluable platform for advancing our understanding and management of ALD in clinical settings.

Big data analytics for image processing and computer vision technologies in sports health management.

BACKGROUND: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. OBJECTIVE: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. METHODS: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. RESULTS: The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. CONCLUSION: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

Molecular subtyping and the construction of a predictive model of colorectal cancer based on ion channel genes.

PURPOSE: Colorectal cancer (CRC) is a highly heterogeneous malignancy with an unfavorable prognosis. The purpose of this study was to address the heterogeneity of CRC by categorizing it into ion channel subtypes, and to develop a predictive modeling based on ion channel genes to predict the survival and immunological states of patients with CRC. The model will provide guidance for personalized immunotherapy and drug treatment. METHODS: A consistent clustering method was used to classify 619 CRC samples based on the expression of 279 ion channel genes. Such a method was allowed to investigate the relationship between molecular subtypes, prognosis, and immune infiltration. Furthermore, a predictive modeling was constructed for ion channels to evaluate the ion channel properties of individual tumors using the least absolute shrinkage and selection operator. The expression patterns of the characteristic genes were validated through molecular biology experiments. The effect of potassium channel tetramerization domain containing 9 (KCTD9) on CRC was verified by cellular functional experiments. RESULTS: Four distinct ion channel subtypes were identified in CRC, each characterized by unique prognosis and immune infiltration patterns. Notably, Ion Cluster3 exhibited high levels of immune infiltration and a favorable prognosis, while Ion Cluster4 showed relatively lower levels of immune infiltration and a poorer prognosis. The ion channel score could predict overall survival, with lower scores correlated with longer survival. This score served as an independent prognostic factor and presented an excellent predictive efficacy in the nomogram. In addition, the score was closely related to immune infiltration, immunotherapy response, and chemotherapy sensitivity. Experimental evidence further confirmed that low expression of KCTD9 in tumor tissues was associated with an unfavorable prognosis in patients with CRC. The cellular functional experiments demonstrated that KCTD9 inhibited the proliferation, migration and invasion capabilities of LOVO cells. CONCLUSIONS: Ion channel subtyping and scoring can effectively predict the prognosis and evaluate the immune microenvironment, immunotherapy response, and drug sensitivity in patients with CRC.

Evaluation of 4D Flow MRI-Derived Relative Residence Time as a Marker for Cirrhosis Associated Portal Vein Thrombosis.

BACKGROUND: Portal vein thrombosis (PVT) is thought to arise from stagnant blood flow, yet conclusive evidence is lacking. Relative residence time (RRT) assessed using 4D Flow MRI may offer insight into portal flow stagnation. PURPOSE: To explore the relationship between RRT values and the presence of PVT in cirrhotic participants. STUDY TYPE: Prospective. POPULATION: Forty-eight participants with liver cirrhosis (27 males, median age 67 years [IQR: 57-73]) and 20 healthy control participants (12 males, median age 45 years [IQR: 40-54]). FIELD STRENGTH/SEQUENCE: 3 T/4D Flow MRI. ASSESSMENT: Laboratory (liver and kidney function test results and platelet count) and clinical data (presence of tumors and other imaging findings), and portal hemodynamics derived from 4D Flow MRI (spatiotemporally averaged RRT [RRT-mean], flow velocity, and flow rate) were analyzed. STATISTICAL TESTS: We used multivariable logistic regression, adjusted by selected covariates through the Lasso method, to explore whether RRT-mean is an independent risk factor for PVT. The area under the ROC curve (AUC) was also calculated to assess the model's discriminative ability. P < 0.05 indicated statistical significance. RESULTS: The liver cirrhosis group consisted of 16 participants with PVT and 32 without PVT. Higher RRT-mean values (odds ratio [OR] 11.4 [95% CI: 2.19, 118]) and lower platelet count (OR 0.98 per 1000 µL [95% CI: 0.96, 0.99]) were independent risk factors for PVT. The incorporation of RRT-mean (AUC, 0.77) alongside platelet count (AUC, 0.75) resulted in an AUC of 0.84. When including healthy control participants, RRT-mean had an adjusted OR of 12.4 and the AUC of the combined model (RRT-mean and platelet count) was 0.90. DATA CONCLUSION: Prolonged RRT values and low platelet count were significantly associated with the presence of PVT in cirrhotic participants. RRT values derived from 4D Flow MRI may have potential clinical relevance in the management of PVT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Exogenous silicon applied at appropriate concentrations is effective at improving tomato nutritional and flavor qualities.

Silicon can mitigate biotic and abiotic stresses in various plants; however, its effects on tomato quality under normal growth conditions are remain unclear. We used a randomized design with four Si treatments, CON (0 mmol/L), T1 (0.6 mmol/L), T2 (1.2 mmol/L), and T3 (1.8 mmol/L) on tomato fruit components Chlorogenic acid and rutin, among polyphenolic components, were increased by 56.99% and 20.31%, respectively, with T2 treatment compared to CON concentrations. T2 increased the sugar-acid ratio by 19.21%, compared to that with the CON treatment, and increased fruit Ca and Mg contents, compared to those with other treatments, improving the characteristic aroma. Furthermore, silicon application reduced the abscisic acid content by 112%, promoting ripening. Endogenous gibberellin, auxin, and salicylic acid, which retard fruit ripening and softening, were increased by 34.96%, 14.56%, and 35.21%, respectively. These findings have far-reaching implications for exogenous Si applications to enrich tomato nutritional and flavor qualities.

The improved stratified transformer for organ segmentation of Arabidopsis.

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.

Benefits of remimazolam as an anesthetic sedative for older patients: A review.

Owing to the decreased levels of receptors in the peripheral and central nervous systems, the functions of various organ systems decline in older patients. When administering anesthesia to older patients, it is necessary to consider the effects of medication on the homeostatic balance. Remimazolam, a new benzodiazepine, was recently developed as an anesthetic drug that has shown promise in clinical anesthesia application owing to its molecular structure, targets, pharmacodynamics, and pharmacokinetic characteristics. Remimazolam exhibits a rapid onset and metabolism, with minor effects on liver and kidney functions. Moreover, the drug has a specific antagonist, flumazenil. It is safer to use in older patients than other anesthetic sedatives and has been widely used since its introduction. Comparisons of the pharmacokinetics, metabolic pathways, effects on target organs, and hemodynamics of different drugs with those of commonly used anesthetic sedative drugs are useful to inform clinical practice. This article elaborates on the benefits of remimazolam compared with those of other anesthetic sedatives for sedation in older patients to demonstrate how it offers a new option for anesthetics in older patients. In cases involving older patients with increased clinical complexities or very old patients requiring anesthesia, remimazolam can be selected as the preferred anesthetic sedative, as outlined in this review.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses.

Colorectal cancer (CRC) incidence in younger adults has been steadily rising, warranting an in-depth investigation into the distinctions between early-onset CRC (EOCRC, < 50 years) and late-onset CRC (LOCRC, ≥ 50 years). Despite extensive study of clinical, pathological, and molecular traits, differentiating EOCRC from LOCRC and identifying potential biomarkers remain elusive. We analyzed plasma samples from healthy individuals, EOCRC, and LOCRC patients using liquid-chromatography mass spectrometry (LC/MS)-based metabolomics and lipidomics. Distinct polar metabolite and lipid profiles with significant metabolites altered in CRC group (e.g., choline and DG 40:4) were identified. Notably, EOCRC exhibited distinct polar metabolomic and differential lipidomic profiles compared to LOCRC, with polar metabolites like aminoadipate and uridine contributing significantly to the difference, and originating from pathways such as lysine biosynthesis and nucleotide metabolism. Furthermore, gene set enrichment analysis (GSEA) using independent TCGA gene expression data identified pathways significantly enriched in either EOCRC or LOCRC. Integrating gene expression and metabolomics data revealed numerous associations differentiating EOCRC and LOCRC. Our multi-omics integration underscores critical molecular distinctions, offers insights into the EOCRC development mechanisms and potential plasma biomarkers for diagnosis.