Absolute Structures of a Mirror Pair of Infinite Na(H2O)4 + -Connected ε-Keggin-Al13 Species.

The absolute structures of a pair of infinite Na(H2O)4+-connected ε-Keggin-Al13 species (Na-ε-K-Al13) that were inversion structures and mirror images of each other were determined. Single crystals obtained by adding A2SO4 (A = Li, Na, K, Rb, or Cs) solution to NaOH-hydrolyzed AlCl3 solution were subjected to X-ray structure analyses. The statistical results for 36 single crystals showed that all the crystals had almost the same unit cell parameter, belonged to the same F4̅3m space group, and possessed the same structural formula [Na(H2O)4AlO4Al12(OH)24(H2O)12](SO4)4·10H2O. However, the crystals had two inverse absolute structures (denoted A and B), which had a crystallization ratio of 1:1. From Li+ to Cs+, with increasing volume of the cation coexisting in the mother solution, the degree of disorder of the four H2O molecules in the Na(H2O)4+ hydrated ion continuously decreased; they became ordered when the cation was Cs+. Absolute structures A and B are the first two infinite aluminum polycations connected by statistically occupied [(Na1/4)4(H2O)4]+ hydrated ions. The three-dimensional structure of the infinite Na-ε-K-Al13 species can be regarded as the assembly of finite ε-K-Al13 species linked by [(Na1/4)4(H2O)4]+ in a 1:1 ratio. In this assembly, each [(Na1/4)4(H2O)4]+ is connected to four ε-K-Al13 and each ε-K-Al13 is also connected to four [(Na1/4)4(H2O)4]+ in tetrahedral orientations to form a continuous rigid framework structure, which has an inverse spatial orientation between absolute structure A and B. This discovery clarifies that the ε-K-Al13 (or ε-K-GaAl12) species in Na[MO4Al12(OH)24(H2O)12](XO4)4·nH2O (M = Al, Ga; X = S, Se; n = 10-20) exists as discrete groups and deepens understanding of the formation and evolution process of polyaluminum species in forcibly hydrolyzed aluminum salt solution. The reason why Na+ statistically occupies the four sites was examined, and a formation and evolution mechanism of the infinite Na-ε-K-Al13 species was proposed.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Quality of life and its association with predictors in lung transplant recipients: a latent profile analysis.

Backgrounds: Improving quality of life (QOL) is one of the main aims of lung transplantation (LTx). There is a need to identify those who have poor quality of life early. However, research addressing inter individual quality of life variability among them is lacking. This study aims to identify group patterns in quality of life among lung transplant recipients and examine the predictors associated with quality of life subgroups. Methods: In total, 173 lung transplant recipients were recruited from one hospital in Guangdong Province between September 2022 and August 2023. They were assessed using the Lung Transplant Quality of Life scale (LT-QOL), Mindful Attention Awareness Scale (MAAS), Life Orientation Test-Revised scale (LOT-R), and Positive and Negative Affect Scale (PANAS). Latent profile analysis was used to identify QOL subtypes, and logistic regression analysis was used to examine the associations between latent profiles and sociodemographic and psychosocial characteristics. Results: Two distinct QOL profiles were identified: "low HRQOL" profile [N = 53 (30.94%)] and "high HRQOL" profile [N = 120 (69.06%)]. Single lung transplant recipients, and patients who reported post-transplant infection, high levels of negative emotion or low levels of mindfulness and optimism were significantly correlated with the low QOL subgroup. Conclusion: Using the domains of the LT-QOL scale, two profiles were identified among the lung transplant recipients. Our findings highlighted that targeted intervention should be developed based on the characteristics of each latent class, and timely attention must be paid to patients who have undergone single lung transplantation, have had a hospital readmission due to infection, exhibit low levels of optimism, low levels of mindfulness or high negative emotions.

Decrypting the skeletal toxicity of vertebrates caused by environmental pollutants from an evolutionary perspective: From fish to mammals.

The rapid development of modern society has led to an increasing severity in the generation of new pollutants and the significant emission of old pollutants, exerting considerable pressure on the ecological environment and posing a serious threat to both biological survival and human health. The skeletal system, as a vital supportive structure and functional unit in organisms, is pivotal in maintaining body shape, safeguarding internal organs, storing minerals, and facilitating blood cell production. Although previous studies have uncovered the toxic effects of pollutants on vertebrate skeletal systems, there is a lack of comprehensive literature reviews in this field. Hence, this paper systematically summarizes the toxic effects and mechanisms of environmental pollutants on the skeletons of vertebrates based on the evolutionary context from fish to mammals. Our findings reveal that current research mainly focuses on fish and mammals, and the identified impact mechanisms mainly involve the regulation of bone signaling pathways, oxidative stress response, endocrine system disorders, and immune system dysfunction. This study aims to provide a comprehensive and systematic understanding of research on skeletal toxicity, while also promoting further research and development in related fields.

Temperature fluctuations in mesoscopic systems.

Temperature is a fundamental concept in thermodynamics. In macroscopic thermodynamics, systems possess their own intrinsic temperature which equals the reservoir temperature when they equilibrate. In stochastic thermodynamics for simple systems at the microscopic level, thermodynamic quantities other than temperature (a deterministic parameter of the reservoir) are stochastic. To bridge the disparity in the perspectives about temperature between the micro- and macroregimes, we assign a generic mesoscopic N-body system an intrinsic fluctuating temperature T in this work. We simplify the complicated dynamics of numerous particles to one stochastic differential equation with respect to T, where the noise term accounts for finite-size effects arising from random energy transfer between the system and the reservoir. Our analysis reveals that these fluctuations make the extensive quantities (in the thermodynamic limit) deviate from being extensive. Moreover, we derive finite-size corrections, characterized by heat capacity of the system, to the Jarzynski equality. A possible violation of the principle of maximum work that scales with N^{-1} is also discussed. Additionally, we examine the impact of temperature fluctuations in a finite-size Carnot engine. We show that irreversible entropy production resulting from the temperature fluctuations of the working substance diminishes the average efficiency of the cycle as η_{C}-〈η〉∼N^{-1}, highlighting the unattainability of the Carnot efficiency η_{C} for mesoscopic heat engines even under the quasistatic limit. Our general framework paves the way for further exploration of nonequilibrium thermodynamics and the corresponding finite-size effects in a mesoscopic regime.

Association between gut microbiota dysbiosis and poor functional outcomes in acute ischemic stroke patients with COVID-19 infection.

Acute ischemic stroke (AIS) patients with active COVID-19 infection often have more severe symptoms and worse recovery. COVID-19 infection can cause gut microbiota dysbiosis, which is also a risk factor for poor outcomes in AIS patients. However, the association between gut microbiota and functional outcomes among AIS patients with COVID-19 infection has not been fully clarified yet. In this study, we performed 16S rRNA gene sequencing to characterize the gut microbial community among AIS patients with acute COVID-19 infection, AIS patients with post-acute COVID-19 infection, and AIS patients without COVID-19 infection. We found that AIS patients with acute COVID-19 experienced poorer recovery and significant gut dysbiosis, characterized by higher levels of Enterobacteriaceae and lower levels of Ruminococcaceae and Lachnospiraceae. Furthermore, a shorter time window (less than 28 days) between COVID-19 infection and stroke was identified as a risk factor for poor functional outcomes in AIS patients with COVID-19, and the enrichment of Enterobacteriaceae was indicated as a mediator in the relationship between infection time window and poor stroke outcomes. Our findings highlight the importance of early intervention after COVID-19 infection, especially by regulating the gut microbiota, which plays a role in the prognosis of AIS patients with COVID-19 infection.IMPORTANCEThe gut microbiota plays an important role in the association between respiratory system and cerebrovascular system through the gut-lung axis and gut-brain axis. However, the specific connection between gut bacteria and the functional outcomes of acute ischemic stroke (AIS) patients with COVID-19 is not fully understood yet. In our study, we observed a significant decrease in bacterial diversity and shifts in the abundance of key bacterial families in AIS patients with acute COVID-19 infection. Furthermore, we identified that the time window was a critical influence factor for stroke outcomes, and the enrichment of Enterobacteriaceae acted as a mediator in the relationship between the infection time window and poor stroke outcomes. Our research provides a new perspective on the complex interplay among AIS, COVID-19 infection, and gut microbiota dysbiosis. Moreover, recognizing Enterobacteriaceae as a potential mediator of poor stroke prognosis offers a novel avenue for future exploration and therapeutic interventions.

Effect of free and bound proanthocyanidins from Chinese quince on heterocyclic aromatic amine formation and quality in fried chicken.

The abilities of Chinese quince free proanthocyanidins (FP) and bound proanthocyanidins (BP) at different levels (0.1%, 0.15%, and 0.3%) to mitigate heterocyclic aromatic amine (HAA) formation in fried chicken patties were investigated for the first time and compared with vitamin C (Vc). FP and BP reduced HAAs in a dose-dependent manner. Significantly, high concentrations of FP (0.3%) resulted in a reduction of PhIP, harman, and norharman levels by 59.84%, 22.91%, and 38.21%, respectively, in chicken patties. The addition of proanthocyanidins significantly (p < 0.05) reduced the weight loss of fried chicken patties. Furthermore, a positive correlation was observed among pH, weight loss, and total HAA formation in all three groups (FP, BP, and Vc). Multivariate analysis showed that FP had a more pronounced effect than BP from the perspective of enhancing the quality of fried chicken patties and reducing the formation of HAAs. These results indicate that proanthocyanidins, both BP and FP, but especially FP, from Chinese quince can inhibit the formation of carcinogenic HAAs when added to protein-rich foods that are subsequently fried.

The role of circular RNAs in regulating resistance to cancer immunotherapy: mechanisms and implications.

Cancer immunotherapy has rapidly transformed cancer treatment, yet resistance remains a significant hurdle, limiting its efficacy in many patients. Circular RNAs (circRNAs), a novel class of non-coding RNAs, have emerged as pivotal regulators of gene expression and cellular processes. Increasing evidence indicates their involvement in modulating resistance to cancer immunotherapy. Notably, certain circRNAs function as miRNA sponges or interact with proteins, influencing the expression of immune-related genes, including crucial immune checkpoint molecules. This, in turn, shapes the tumor microenvironment and significantly impacts the response to immunotherapy. In this comprehensive review, we explore the evolving role of circRNAs in orchestrating resistance to cancer immunotherapy, with a specific focus on their mechanisms in influencing immune checkpoint gene expression. Additionally, we underscore the potential of circRNAs as promising therapeutic targets to augment the effectiveness of cancer immunotherapy. Understanding the role of circRNAs in cancer immunotherapy resistance could contribute to the development of new therapeutic strategies to overcome resistance and improve patient outcomes.

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

OBJECTIVE: Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. METHODS: In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. CONCLUSION: Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. SIGNIFICANCE: This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

Efficacy of endovascular therapy for cerebral vasospasm following aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

Background: Cerebral vasospasm (CV) is a common complication of aneurysmal subarachnoid hemorrhage (aSAH), leading to increased morbidity and mortality rates. Endovascular therapy, particularly intra-arterial vasodilator infusion (IAVI), has emerged as a potential alternative treatment for CV. Methods: A systematic review and meta-analysis were conducted to compare the efficacy of endovascular therapy with standard treatment in patients with CV following aSAH. The primary outcomes assessed were in-hospital mortality, discharge favorable outcome, and follow-up favorable outcome. Secondary outcomes included major infarction on CT, ICU stay duration, and total hospital stay. Results: Regarding our primary outcomes of interest, patients undergoing intervention exhibited a significantly lower in-hospital mortality compared to the standard treatment group, with the intervention group having only half the mortality risk (RR = 0.49, 95% CI [0.29, 0.83], p = 0.008). However, there were no significant differences between the two groups in terms of discharge favorable outcome (RR = 0.99, 95% CI [0.68, 1.45], p = 0.963) and follow-up favorable outcome (RR = 1.09, 95% CI [0.86, 1.39], p = 0.485). Additionally, there was no significant difference in major infarction rates (RR = 0.79, 95% CI [0.34, 1.84], p = 0.588). It is important to note that patients undergoing endovascular treatment experienced longer stays in the ICU (MD = 6.07, 95% CI [1.03, 11.12], p = 0.018) and extended hospitalization (MD = 5.6, 95% CI [3.63, 7.56], p < 0.001). Subgroup analyses based on the mode of endovascular treatment further supported the benefits of IAVI in lowering in-hospital mortality (RR = 0.5, 95% CI [0.27, 0.91], p = 0.023). Conclusion: Endovascular therapy, particularly IAVI, holds promising potential in reducing in-hospital mortality for patients with CV following aSAH. However, it did not show significant improvement in long-term prognosis and functional recovery. Further research with larger sample sizes and randomized controlled trials is necessary to validate these findings and optimize the treatment strategy for cerebral vasospasm in aSAH patients. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023451741.

A Multimode Optical Sensor for Selective and Sensitive Detection of Harmful Heavy Metal Cr(VI) in Fresh Water and Sea Water.

Water pollution originating from heavy metals has shown great impacts on the ecological environment and human health due to their extremely low biodegradability. Hexavalent chromium Cr(VI), as one harmful heavy metal with strong oxidation, high biological permeability, and high carcinogenicity, is becoming an increasingly serious threat to human health. Therefore, conveniently but accurately, monitoring the Cr(VI) level in water to maintain its normal level and ensuring the stability of the ecosystem and human health become very valuable. However, most of these heavy metal sensors reported are turn-off type single-emission sensors. In this work, a ratiometric fluorescence/colorimetry/smartphone triple-mode turn-on optical sensor for Cr(VI) was developed based on a multifunctional metal-organic framework platform. The detection limits for these three mutual verification modes were only 1.28, 4.89, and 68.4 nM, respectively. Additionally, the color changes of the detection system under sunlight can also be observed directly by the naked eye. The accuracy and practicability of this multimode sensor were further proved by the detection of Cr(VI) in actual water and seawater samples, and the recovery rate ranged from 97.308 to 104.041%.

Effects of tumor marker regression load score on long-term prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy.

BACKGROUND: The effects of the dynamics of serum tumor markers (CA72-4, CEA, CA19-9, CA125 and AFP) before and after neoadjuvant chemotherapy (NACT) on the prognosis of gastric cancer(GC) patients remain unclear. METHODS: The training set contained 334 GC patients from Fujian Medical University Union Hospital (FJMUUH) and 113 GC patients in Qinghai University Affiliated Hospital (QhUAH) were used as an external validation set. Tumor marker regression load (ΔTMRL) indicator, including ΔCA72-4, ΔCEA, ΔCA19-9, ΔCA125, and ΔAFP, is defined as [(postNACT marker- preNACT marker)/preNACT marker]. Tumor marker regression load score (TMRLS) consists of ΔCA72-4, ΔCEA and ΔCA125. The predictive performance of the nomogram-TMRLS was evaluated using the area under the receiver operating characteristic(ROC) curve(AUC), decision curve analysis(DCA), and C-index. RESULTS: Patients from FJMUUH were divided into two groups, TMRLS-low and TMRLS-high, determined by R package maxstat. Survival analysis revealed a higher 3-year overall survival(OS) in the TMRLS-low than in the TMRLS-high group. The TMRLS-high group who received postoperative adjuvant chemotherapy(AC) showed a significantly higher 3-year OS rate than those who did not. Multivariate COX regression analysis indicated that TMRLS was an independent prognostic factor for OS. A nomogram for predicting OS based on TMRLS showed a significantly higher C-index and AUC than the ypTNM stage. The above results were also found in the QhUAH external validation cohort. CONCLUSION: TMRLS is a novel independent prognostic factor for GC who underwent NACT and a radical gastrectomy. Furthermore, the TMRLS-high group, who received postoperative AC, may achieve better survival outcomes. Notably, the predictive performance of the nomogram-TMRLS significantly outperformed that of the ypTNM stage.

HMGA1 is a Prognostic Biomarker and Correlated with Glycolysis in Lung Adenocarcinoma.

Purpose: Lung cancer is one of the leading causes with high morbidity and mortality. High mobility group A1 (HMGA1) protein participates in the process of tumorigenesis. This study seeks to explore the specific role of HMGA1 in prognostic value based on The Cancer Genome Atlas (TCGA) database of Lung adenocarcinoma (LUAD) and glycolysis progression in LUAD cells. Patients and Methods: In this research, we compared HMGA1 mRNA expression between tumor tissues and normal samples and evaluated the correlations with clinical characteristics in LUAD patients based on the data of TCGA database. The survival outcome with overall survival (OS), disease-specific survival (DSS) and clinicopathologic characteristics associated were performed using the Kaplan-Meier method and Cox regression. In addition, gene-set enrichment analysis (GSEA) was carried out to explore the biological pathways that related to HMGA1. Cell experiments including cell proliferation assay and glycolysis proteins were performed with A549 and H1299 cells. Results: Our results revealed that HMGA1 mRNA expression was higher in LUAD tissues than in normal tissues. Increased HMGA1 expression in LUAD was associated with Gender (p<0.01), Pathologic stage I&II vs stage III&IV (p<0.001), T1&T2 vs T3&T4 stage (p<0.05), N0 vs N2 stage (p<0.01). Furthermore, multivariate analysis revealed that HMGA1 was an independent risk factor of OS and DSS for LUAD patients (p<0.05). HMGA1 were positively correlated with glycolysis gluconeogenesis pathway and glycolysis markers (HK2, GLUT1, PKM2, LDHA) based on GSEA and Gene Expression Profiling Interactive Analysis (GEPIA) database. At the cellular level, the results of qRT-PCR and western blot assays showed that si-HMGA1 markedly decreased the expression of glycolysis markers. HMGA1 promoted cell glycolysis progression via PI3K/AKT pathway transfected with HMGA1-plasmid and the treatment with 20 µM LY294002. Relevant animal experiments were also synchronously validated and si-HMGA1 groups down-regulated xenograft growth including the weights and size in tumor xenografts. Conclusions: In conclusion, our results suggested that HMGA1 was significantly correlated with poor survival for LUAD tissues and involved in the process of glycolysis in LUAD cells.

Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study.

BACKGROUND: Postoperative delirium is a common complication in older patients, with poor long-term outcomes. This study aimed to investigate risk factors and develop a predictive model for postoperative delirium in older patients after major abdominal surgery. METHODS: This study retrospectively recruited 7577 patients aged ≥ 65 years who underwent major abdominal surgery between January 2014 and December 2018 in a single hospital in Beijing, China. Patients were divided into a training cohort (n = 5303) and a validation cohort (n = 2224) for univariate and multivariate logistic regression analyses and to build a nomogram. Data were collected for 43 perioperative variables, including demographics, medical history, preoperative laboratory results, imaging, and anesthesia information. RESULTS: Age, chronic obstructive pulmonary disease, white blood cell count, glucose, total protein, creatinine, emergency surgery, and anesthesia time were associated with postoperative delirium in multivariate analysis. We developed a nomogram based on the above 8 variables. The nomogram achieved areas under the curve of 0.731 and 0.735 for the training and validation cohorts, respectively. The discriminatory ability of the nomogram was further assessed by dividing the cases into three risk groups (low-risk, nomogram score < 175; medium-risk, nomogram score 175~199; high-risk, nomogram score > 199; P < 0.001). Decision curve analysis revealed that the nomogram provided a good net clinical benefit. CONCLUSIONS: We developed a nomogram that could predict postoperative delirium with high accuracy and stability in older patients after major abdominal surgery.

Spatial distribution of benthic taxonomic and functional diversity in the Yellow River Basin: From ecological processes to associated determinant factors.

One of the fundamental objectives in ecology is to investigate the ecological processes and associated factors governing the abundance and spatial distribution patterns of biodiversity. However, the reaction of biological communities to environmental degradation remains relatively unknown, even for ecologically crucial communities like macroinvertebrates in aquatic ecosystems. Here, we sampled 117 locations to quantify relative contributions of geographical and environmental factors, including water quality, land use, climate, and hydrological factors, to determine the absolute and relative compositions of macroinvertebrate communities and their spatial distribution in the Yellow River Basin (YRB), the sixth-longest river system on Earth. We assessed relative roles of species sorting and dispersal in determining macroinvertebrate community structure along YRB. Our results demonstrated that alpha and beta diversity indices showed an increase from the up- to low-reaches of YRB. The middle and low-reaches exhibited elevated species diversity and both regions exhibited relatively stable community compositions. The biodiversity of macroinvertebrates was influenced by a combination of geographical factors and environmental variables, with environmental factors predominantly serving as the principal determinants. Results of multiple linear regression and variance decomposition showed that the effect of environmental factors was approximately three times greater than that of spatial factors. These findings provide support for the hypothesis that species sorting, driven by environmental gradients, plays a significant role in shaping the community structure of macroinvertebrates in running water ecosystems at the basin scales. Moreover, the factors contributing to substantial shifts in biodiversity across different segments of YRB indicate that distinct river sections have been influenced by varying stressors, with downstream areas being more susceptible to the impacts of water pollution and urbanization resulting from human activities.

Value of orthogonal axial MR images in preoperative T staging of gastric cancer.

PURPOSE: To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging. METHODS: This retrospective study enrolled 133 patients (median age, 63 [range, 24-85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy. RESULTS: Among patients with pT1-pT4 disease, MRI with OAI (accuracy: 88.7-94.7%, sensitivity: 66.7-93.0%, specificity: 91.5-100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2-88.7%, sensitivity: 46.2-83.1%, specificity: 85.5-99.1%) and CT (accuracy: 88.0-92.5%, sensitivity: 53.3-90.1%, specificity: 88.7-98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p < .001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p = .35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%). CONCLUSION: OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT.

Study on energy evolution and fractal characteristics of sandstone with different fracture dip angles under uniaxial compression.

In order to investigate the failure modes and instability mechanism of fractured rock. Uniaxial compression tests were conducted on sandstone specimens with different dip angles. Based on rock energy dissipation theory and fractal theory, the energy evolution characteristics and fragmentation fractal characteristics in the process of deformation and failure of specimens were analyzed. The results show that the peak strength and elastic modulus of fractured rock mass are lower than those of intact samples, and both show an exponential increase with the increase of fracture dip angle. The energy evolution laws of different fracture specimens are roughly similar and can be classified into four stages based on the stress-strain curve: pressure-tight, elastic, plastic, and post-destructive. The total strain energy, elastic strain energy, and dissipated strain energy of the specimen at the peak stress point increased exponentially with crack inclination, and the dissipated strain energy and compressive strength conformed to a power function growth relationship. The distribution of the fragments after the failure of the fracture sample has fractal characteristics, and the fractal dimension increases with the increase of the fracture dip angle. In addition, the higher the compressive strength of the specimen, the greater the energy dissipation, the more serious the degree of fragmentation, and the greater the fractal dimension. The data fitting further shows that there is a power function relationship between the dissipated strain energy and the fractal dimension. The research results can provide a theoretical basis for the stability of rock mass engineering and structural deformation control.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.