Dissecting gastric cancer heterogeneity and exploring therapeutic strategies using bulk and single-cell transcriptomic analysis and experimental validation of tumor microenvironment and metabolic interplay.

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets.

Prognoses and risk stratification of thrombus-associated events in heart failure patients without atrial fibrillation.

AIMS: We aim to assess the risk of thrombus-associated events (TAE) in patients with heart failure (HF) without atrial fibrillation (AF) and develop an effective scoring system for a risk stratification model. METHODS AND RESULTS: This retrospective study included 450 patients (median age 64.0 years, interquartile range [55.0, 75.0]; 31.6% women) hospitalized for HF without AF and atrial flutter, but with a left ventricular ejection fraction (LVEF) ≤ 55% and New York Heart Association (NYHA) functional class of III-IV. A median follow-up of 47 months was conducted. In the present study, TAE during follow-up was independently associated with both all-cause death [hazard ratio (HR) 1.756, 95% confidence interval (CI) 1.324-2.328, P < 0.001] and readmission for HF (HR 1.574, 95% CI 1.122-2.208, P = 0.009) after adjustment for covariates. Hypertension (HR 1.573, 95% CI 1.018-2.429, P = 0.041), atrial arrhythmia excluding AF (AAexAF) (HR 2.041, 95% CI 1.066-3.908, P = 0.031), previous ischaemic stroke (HR 2.469, 95% CI 1.576-3.869, P < 0.001), and vascular disease (HR 1.658, 95% CI 1.074-2.562, P = 0.023) were independently associated with TAE. Age (HR 1.021, 95% CI 1.008-1.033, P = 0.001), previous ischaemic stroke (HR 1.685, 95% CI 1.248-2.274, P = 0.001), LVEF ([10, 25] vs. [40, 55]) HR 1.925, 95% CI 1.311-2.826, P = 0.001; (25, 40] vs. (40, 55] HR 1.084, 95% CI 0.825-1.424, P = 0.563), and creatinine clearance rate (Ccr) (HR 0.991, 95% CI 0.986-0.996, P = 0.001) were independently associated with composite events of TAE and death (TAE-D). CHA2DS2VASc modestly predicted 5-year TAE [area under the receiver operating characteristic curves (AUC) 0.660, P < 0.001 compared with 0.5] and TAE-D (AUC 0.639, P < 0.001 compared with 0.5). (C)ACE, formed by incorporating AAexAF, LVEF, and Ccr into CHA2DS2VASc, had higher AUC for predicting 5-year TAE (0.694 vs. 0.660, P = 0.018) and TAE-D (0.708 vs. 0.639, P < 0.001) compared with CHA2DS2VASc. In patients with HF with reduced ejection fraction (HFrEF), (C)ACE and (C)ACEN [formed by incorporating NYHA into (C)ACE] had higher AUC compared with CHA2DS2VASc in predicting 5-year TAE (0.700 and 0.707 vs. 0.649, P = 0.013 and 0.030, respectively) and TAE-D (0.712 and 0.713 vs. 0.622, P < 0.001 and <0.001, respectively). The AUC did not improve statistically from (C)ACE to (C)ACEN (0.700 vs. 0.707, P = 0.600 for TAE; 0.712 vs. 0.713, P = 0.917 for TAE-D). CONCLUSIONS: In HF without AF, TAE during follow-up was associated with adverse prognoses. The independent risk factors of TAE or TAE-D improved CHA2DS2-VASc predictive ability, especially in patients with HFrEF. Our findings provide new evidence for TAE risk stratification in HF without AF, potentially guiding prophylactic anticoagulation.

Synthesis of two Fluorescent Complexes and Their use as Multifunctional Nanomedicine Carriers for Rhabdomyosarcoma Treatment.

This study focuses on the design and synthesis of two novel coordination polymers (CPs), named 1 and 2, with excellent fluorescent properties. Their structures were characterized by X-ray single-crystal diffraction, revealing that both materials exhibit promising fluorescence performance, indicating their potential as fluorescent detection tools. Additionally, 1 was chosen to be combined with chitosan (CS), resulting in the successful fabrication of a biodegradable and non-toxic efficient drug carrier, termed CS-1@Cisplatin. This carrier possesses a large surface area and good solubility, enabling sustained drug release to target cells. Given that CXC motif chemokine receptor type 4 (CXCR4) is a key marker gene highly expressed in Rhabdomyosarcoma (RMS) cells and tissues, RMS was chosen as the biological model for testing. The results demonstrated that CS-1@Cisplatin effectively inhibited the invasiveness of RMS cells by significantly suppressing CXCR4 expression. Therefore, the system shows great potential for applications in RMS treatment, biometrics, and drug delivery, particularly in its unique advantage of targeting RMS by inhibiting the key marker gene CXCR4.

Retraction: inhibitory effect of lovastatin on human lung cancer cell proliferation by regulating the ERK1/2 and COX-2 pathways.

[This retracts the article DOI: 10.21037/tcr-22-346.].

Efficient Parameterization of Density Functional Tight-Binding for 5f-Elements: A Th-O Case Study.

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO2. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

Multi-omics and single cell characterization of cancer immunosenescence landscape.

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called "senescence signature", and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model ( https://bio-pub.shinyapps.io/cs_nomo/ ).

A Patent Analysis on Nano Drug Delivery Systems.

BACKGROUND: A nano drug delivery system is an effective tool for drug delivery and controlled release, which is used for a variety of medical applications. In recent decades, nano drug delivery systems have been significantly developed with the emergence of new nanomaterials and nanotechnologies. OBJECTIVE: This article aimed to provide insight into the technological development of nano drug delivery systems through patent analysis. METHODS: 3708 patent documents were used for patent analysis after retrieval from the Incopat patent database. RESULTS: The number of patents on nano drug delivery systems has shown a rapid growth trend in the past two decades. At present, China and the United States have obvious contributions to the number of patents. According to the patent data, the nanomaterials used in nano drug delivery system are mainly inorganic nanomaterials, lipid-based nanomaterials, and macromolecules. In recent years, the highly cited patents (≥14) for nano drug delivery systems mainly involve lipid-based nanomaterials, indicating that their technology is mature and widely used. The inorganic nanomaterials in drug delivery have received increasing attention, and the number of related patents has increased significantly after 2016. The number of highly cited patents in the United States is 250, which is much higher than in other countries. CONCLUSION: Even after decades of development, nano drug delivery systems remain a hot topic for researchers. The significant increase in patents since 2016 can be attributed to the large number of new patents from China. However, according to the proportion of highly cited patents in total, China's patented technologies in nano drug delivery systems are not advanced enough compared to developed countries, including the United States, Canada, Germany, and France. In the future, research on emerging nanomaterials for nano drug delivery systems, such as inorganic nanomaterials, may focus on developing new materials and optimising their properties. The lipid-based and polymer- based nanomaterials can be continuously improved for the development of new nanomedicines.

The Effects and Mechanisms of AM1241 in Alleviating Cerebral Ischemia-Reperfusion Injury.

OBJECTIVE: Research has shown that cerebral ischemia-reperfusion injury (CIRI) involves a series of physiological and pathological mechanisms, including inflammation, oxidative stress, and cell apoptosis. The cannabinoid receptor 2 agonist AM1241 has been found to have anti-inflammatory and anti-oxidative stress effects. However, it is unclear whether AM1241 has a protective effect against brain ischemia-reperfusion injury, and its underlying mechanisms are not yet known. METHODS: In this study, we investigated the anti-inflammatory, anti-oxidative stress, and anti-apoptotic effects of AM1241 and its mechanisms in BV2 cells stimulated with H2O2 and in a C57BL/6 mouse model of CIRI in vitro and in vivo, respectively. RESULTS: In vitro, AM1241 significantly inhibited the release of pro-inflammatory cytokines TNF-α and IL-6, reactive oxygen species (ROS), and the increase in Toll-like receptor 4/myeloid differentiation protein 2 (MD2/TLR4) complex induced by H2O2. Under H2O2 stimulation, MD2 overexpression resulted in increased levels of MD2/TLR4 complex, TNF-α, IL-6, NOX2, BAX, and Cleaved-Caspase3 (C-Caspase3), as well as the activation of the MAPK pathway and NF-κB, which were reversed by AM1241. In addition, molecular docking experiments showed that AM1241 directly interacted with MD2. Surface Plasmon Resonance (SPR) experiments further confirmed the binding of AM1241 to MD2. In vivo, AM1241 significantly attenuated neurofunctional impairment, brain edema, increased infarct volume, oxidative stress levels, and neuronal apoptosis in CIRI mice overexpressing MD2. CONCLUSION: Our study demonstrates for the first time that AM1241 alleviates mouse CIRI by inhibiting the MD2/TLR4 complex, exerting anti-inflammatory, anti-oxidative stress and anti-apoptotic effects.

Wheel-Running Exercise Alleviates Anxiety-Like Behavior via Down-Regulating S-Nitrosylation of Gephyrin in the Basolateral Amygdala of Male Rats.

Physical exercise has beneficial effect on anxiety disorders, but the underlying molecular mechanism remains largely unknown. Here, it is demonstrated that physical exercise can downregulate the S-nitrosylation of gephyrin (SNO-gephyrin) in the basolateral amygdala (BLA) to exert anxiolytic effects. It is found that the level of SNO-gephyrin is significantly increased in the BLA of high-anxiety rats and a downregulation of SNO-gephyrin at cysteines 212 and 284 produced anxiolytic effect. Mechanistically, inhibition of SNO-gephyrin by either Cys212 or Cys284 mutations increased the surface expression of GABAAR γ2 and the subsequent GABAergic neurotransmission, exerting anxiolytic effect in male rats. On the other side, overexpression of neuronal nitric oxide synthase in the BLA abolished the anxiolytic-like effects of physical exercise. This study reveals a key role of downregulating SNO-gephyrin in the anxiolytic effects of physical exercise, providing a new explanation for protein post-translational modifications in the brain after exercise.

Bromodomain Protein Inhibition Protects ß-Cells from Cytokine-Induced Death and Dysfunction via Antagonism of NF-κB Pathway.

Cytokine-induced ß-cell apoptosis is a major pathogenic mechanism in type 1 diabetes (T1D). Despite significant advances in understanding its underlying mechanisms, few drugs have been translated to protect ß-cells in T1D. Epigenetic modulators such as bromodomain-containing BET (bromo- and extra-terminal) proteins are important regulators of immune responses. Pre-clinical studies have demonstrated a protective effect of BET inhibitors in an NOD (non-obese diabetes) mouse model of T1D. However, the effect of BET protein inhibition on ß-cell function in response to cytokines is unknown. Here, we demonstrate that I-BET, a BET protein inhibitor, protected ß-cells from cytokine-induced dysfunction and death. In vivo administration of I-BET to mice exposed to low-dose STZ (streptozotocin), a model of T1D, significantly reduced ß-cell apoptosis, suggesting a cytoprotective function. Mechanistically, I-BET treatment inhibited cytokine-induced NF-kB signaling and enhanced FOXO1-mediated anti-oxidant response in ß-cells. RNA-Seq analysis revealed that I-BET treatment also suppressed pathways involved in apoptosis while maintaining the expression of genes critical for ß-cell function, such as Pdx1 and Ins1. Taken together, this study demonstrates that I-BET is effective in protecting ß-cells from cytokine-induced dysfunction and apoptosis, and targeting BET proteins could have potential therapeutic value in preserving ß-cell functional mass in T1D.

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Marine aquaculture can deliver 40% lower carbon footprints than freshwater aquaculture based on feed, energy and biogeochemical cycles.

Freshwater aquaculture is an increasingly important source of blue foods but produces substantial methane and nitrous oxide emissions. Marine aquaculture, also known as mariculture, is a smaller sector with a large growth potential, but its climate impacts are challenging to accurately quantify. Here we assess the greenhouse gas emissions from mariculture's aquatic environment in global potentially suitable areas at 10 km resolution on the basis of marine biogeochemical cycles, greenhouse gas measurements from research cruises and satellite-observed net primary productivity. Mariculture's aquatic emissions intensities are estimated to be 1-6 g CH4 kg-1 carcass weight and 0.05-0.2 g N2O kg-1 carcass weight, >98% and >80% lower than freshwater systems. Using a life-cycle assessment approach, we show that mariculture's carbon footprints are ~40% lower than those of freshwater aquaculture based on feed, energy use and the aquatic environment emissions. Adoption of mariculture alongside freshwater aquaculture production could offer considerable climate benefits to meet future dietary protein and nutritional needs.

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Optimal debulking surgery in ovarian cancer patients: MRI may predict the necessity of rectosigmoid resection.

OBJECTIVES: To determine whether MRI can predict the necessity of rectosigmoid resection (RR) for optimal debulking surgery (ODS) in ovarian cancer (OC) patients and to compare the predictive accuracy of pre- and post-neoadjuvant chemotherapy (NACT) MRI. METHODS: The MRI of 82 OC were retrospectively analyzed, including six bowel signs (length, transverse axis, thickness, circumference, muscularis involvement, and submucosal edema) and four para-intestinal signs (vaginal, parametrial, ureteral, and sacro-recto-genital septum involvement). The parameters reflecting the degree of muscularis involvement were measured. Patients were divided into non-RR and RR groups based on the operation and postoperative outcomes. The independent predictors of the need for RR were identified by multivariate logistic regression analysis. RESULTS: Imaging for 82 patients was evaluated (67 without and 15 with NACT). Submucosal edema and muscularis involvement (OR 13.33 and 8.40, respectively) were independent predictors of the need for RR, with sensitivities of 83.3% and 94.4% and specificities of 93.9% and 81.6%, respectively. Among the parameters reflecting the degree of muscularis involvement, circumference ≥ 3/12 had the highest prediction accuracy, increasing the specificity from 81.6% for muscularis involvement only to 98.0%, with only a slight decrease in sensitivity (from 94.4% to 88.9%). The predictive sensitivities of pre-NACT and post-NACT MRI were 100.0% and 12.5%, respectively, and the specificities were 85.7% and 100.0%, respectively. CONCLUSIONS: MRI analysis of rectosigmoid muscularis involvement and its circumference can help predict the necessity of RR in OC patients, and pre-NACT MRI may be more suitable for evaluation. CRITICAL RELEVANCE STATEMENT: We analyzed preoperative pelvic MRI in OC patients. Our findings suggest that MRI has predictive potential for identifying patients who require RR to achieve ODS. KEY POINTS: The need for RR must be determined to optimize treatment for OC patients. Muscularis involvement circumference ≥ 3/12 could help predict RR. Pre-NACT MRI may be superior to post-NACT MRI in predicting RR.

The relationship between prognosis and temporal muscle thickness in 102 patients with glioblastoma.

Temporal muscle thickness measured on 3D MRI has recently been linked to prognosis in glioblastoma patients and may serve as an independent prognostic indicator. This single-center study looked at temporal muscle thickness and prognosis in patients with primary glioblastoma. Overall survival was the major study outcome. For a retrospective analysis from 2010 to 2020, clinical data from 102 patients with glioblastoma at the Department of Oncology Radiotherapy of the First Affiliated Hospital of Dalian Medical University were gathered. Fifty-five cases from 2016 to 2020 contained glioblastoma molecular typing data, of which 45 were IDH wild-type glioblastomas and were analysed separately. TMT was measured on enhanced T1-weighted magnetic resonance images in patients with newly diagnosed glioblastoma.Overall patient survival (OS) was calculated by the Kaplan-Meier method and survival curves were plotted using the log-rank-sum test to determine differences between groups, and multifactorial analyses were performed using a Cox proportional-risk model.The median TMT for 102 patients was 6.775 mm (range: 4.95-10.45 mm). Patients were grouped according to median TMT, and the median overall survival (23.0 months) was significantly longer in the TMT > median group than in the TMT median group (P 0.001; Log-rank test). Analysing 45 patients with IDH wild type alone, the median overall survival (12 months) of patients in the TMT > median group was significantly longer than that of patients in the TMT ≤ median group (8 months) (P < 0.001; Log-rank test).TMT can serve as an independent prognostic factor for glioblastoma.

Differentiating Benign From Malignant Ovarian Masses With Solid Components: Diagnostic Performance of CEUS Combined With IOTA Simple Rules and O-RADS.

OBJECTIVE: This study aimed to apply the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR), the Ovarian-Adnexal Reporting and Data System (O-RADS) and contrast-enhanced ultrasound (CEUS) in an identical cohort of Chinese patients and to analyze their performance in discrimination of ovarian masses with solid components. METHODS: This was a two-center retrospective study that included a total of 94 ovarian lesions in 86 women enrolled from January 2018 to February 2023. The lesions were classified by using the IOTA terminology and CEUS was performed for the lesions exhibiting solid components on ultrasonography, IOTA SR and O-RADS were applied, and CEUS images were analyzed retrospectively. We assessed the time to wash-in, time to peak intensity (PI), PI compared to myometrium, and time to wash-out, and observed statistically significant differences between benign and malignant lesions in the first three parameters. CEUS characteristics were employed to determine CEUS scores for benign (score 0) and malignant (score 3) lesions. Subsequently, the lesions were reassessed based on the IOTA SR and O-RADS classifications and CEUS scores. The sensitivity, specificity, and area under the receiver-operating-characteristics curve (AUC) of the different models were also determined. RESULTS: Among the 94 ovarian lesions, 46 (48.9%) were benign and 48 (51.1%) were malignant. It was found that in the 60 lesions to which the SR could be applied, the sensitivity, specificity, and AUC was 0.900, 0.667, and 0.783, respectively. The sensitivity, specificity, and AUC of O-RADS was observed to be 1.000, 0.283 and 0.641, respectively. When SR and O-RADS were combined with CEUS, their sensitivity, specificity, and AUC values were increased to 0.917, 0.891, 0.904, and 0.958, 0.783, 0.871, respectively. CONCLUSION: IOTA SR and O-RADS exhibited relatively low specificity in differentiating malignant from benign ovarian lesions with the solid components, and their diagnostic performance can be significantly improved when combined with CEUS.

Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. PURPOSE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891). CONCLUSION: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

Citrobacter rodentium infection impairs dopamine metabolism and exacerbates the pathology of Parkinson's disease in mice.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with indistinct etiology and ill-defined pathophysiology. Intestinal inflammation involved in the pathogenesis of PD, but the underlying mechanism is not fully understood. Citrobacter rodentium (C.R) is a gram-negative bacterium that can be used to induce human inflammatory bowel disease in mice. Here, we investigated whether the proinflammatory effects caused by C.R infection initiate PD-like injury and/or exacerbate PD pathology and extensively studied the underlying mechanism. Mice were gavaged once with C.R and monitored for several pathological features at 9 days post infection. The results showed that C.R delivery in mice induced IBD-like symptoms, including significant weight loss, increased fecal water content, an impaired intestinal barrier, intestinal hyperpermeability and inflammation, and intestinal microbiota disturbances. Notably, C.R infection modified dopamine (DA) metabolism in the brains of both male and female mice. Subsequently, a single high dose of MPTP or normal saline was administered at 6 days post infection. At 3 days after MPTP administration, the feces were collected for 16 S rRNA analysis, and PD-like phenotypes and mechanisms were systemically analyzed. Compared with C.R or MPTP injection alone, the injection of C.R and MPTP combined worsened behavioral performance. Moreover, such combination triggered more severe dopaminergic degeneration and glial cell overactivation in the nigrostriatal pathway of mice. Mechanistically, the combination of C.R and MPTP increased the expression of TLR4 and NF-κB p65 in the colon and striatum and upregulated proinflammatory cytokine expression. Therefore, C.R infection-induced intestinal inflammation can impair dopamine metabolism and exacerbate PD pathological processes.

The first-principles study of interfacial bonding strength and segregation behavior of alloyed elements at the η(MgZn2)/Al interface.

For precipitation-strengthened Al alloys, the interfacial segregation behavior of alloying elements plays an important role in controlling the effectiveness of precipitation strengthening. In this work, the adhesion work (Wad) and interfacial energy (γ) of the η(0001)/Al(111) interface were studied to gain an insight into the interface properties between the precipitate η and the Al matrix. Additionally, we examined the impact of the segregation behavior of alloyed elements on the bonding strength of the interface. The computed values for Wad and interfacial energies indicated that the T6S3 terminated configuration represents the interfacial structure with the highest stability across all models analyzed. Focusing on the T6S3 interface, the assessed segregated energies (Eseg) disclose that the segregation ability of elements from strong to weak exhibits the order of Ti > Sc > Zr > Y > Ta > Nb > Lu > Hf > Mo > V > W, while Cr and Mn elements are not easy to segregate at the interface. Sc, Ti, V, Cr, Mn, Zr, Nb, Mo, Hf, and Ta preferentially occupy Al atoms, whereas Y and Lu predominantly inhabit Mg atoms. Relative to the clean interface, the electron cloud enrichment at the interface after alloying element X (Zr, Sc, Ti, W, Hf, Mn, Y, Lu and V) doping is weakened, and the ion interaction among interface atoms is enhanced. After doping alloying element X (Nb, Mo, Ta, and Cr), the degree of electron cloud enrichment at the interface is obviously enhanced, and the covalent interaction among interface atoms is enhanced. This suggests that the introduction of alloyed elements through doping can augment the bond strength at the interface between the precipitated phase and matrix, thereby reinforcing the strength and toughness of 7xxx series alloys.

PDIA3 orchestrates effector T cell program by serving as a chaperone to facilitate the non-canonical nuclear import of STAT1 and PKM2.

Dysregulated T cell activation underpins the immunopathology of rheumatoid arthritis (RA), yet the machineries that orchestrate T cell effector program remain incompletely understood. Herein, we leveraged bulk and single-cell RNA sequencing data from RA patients and validated protein disulfide isomerase family A member 3 (PDIA3) as a potential therapeutic target. PDIA3 is remarkably upregulated in pathogenic CD4 T cells derived from RA patients and positively correlates with C-reactive protein level and disease activity score 28. Pharmacological inhibition or genetic ablation of PDIA3 alleviates RA-associated articular pathology and autoimmune responses. Mechanistically, T cell receptor signaling triggers intracellular calcium flux to activate NFAT1, a process that is further potentiated by Wnt5a under RA settings. Activated NFAT1 then directly binds to the Pdia3 promoter to enhance the expression of PDIA3, which complexes with STAT1 or PKM2 to facilitate their nuclear import for transcribing T helper 1 (Th1) and Th17 lineage-related genes, respectively. This non-canonical regulatory mechanism likely occurs under pathological conditions, as PDIA3 could only be highly induced following aberrant external stimuli. Together, our data support that targeting PDIA3 is a vital strategy to mitigate autoimmune diseases, such as RA, in clinical settings.