Passive smoking and risk of head and neck cancer: a systematic review and meta-analysis.

Studies on the association between passive smoking and head and neck cancer (HNC) are controversial. This meta-analysis aimed to explore this association. A systematic search of the PubMed, Embase, Web of Science, and Cochrane Library databases was conducted up to July 2024 to identify relevant studies. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using the DerSimonian-Laird random-effects model. Heterogeneity among studies was assessed, and the risk of bias was evaluated. A total of 1036 records were identified, of which 17 studies were included. Passive smoking was significantly associated with an increased risk of HNC overall (OR = 1.70, 95% CI: 1.27-2.28, P < 0.001). The association was particularly strong for oral cancer (OR = 1.85, 95% CI: 1.07-3.17, P = 0.026), oropharyngeal cancer (OR = 2.78, 95% CI: 1.29-5.98, P = 0.009), laryngeal cancer (OR = 1.60, 95% CI: 1.24-2.06, P < 0.001), and hypopharyngeal cancer (OR = 2.60, 95% CI: 1.45-4.66, P = 0.001). No significant association was observed for nasopharyngeal carcinoma (OR = 1.14, 95% CI: 0.78-1.66, P = 0.498). Geographically, the risk was elevated among both Asian and European populations. Passive smoking is associated with an increased risk of HNC, particularly for subtypes such as oral, oropharyngeal, laryngeal, and hypopharyngeal cancers. These findings underscore the importance of mitigating exposure to passive smoking as a public health measure.

Post-translational modifications of pyruvate dehydrogenase complex in cardiovascular disease.

Pyruvate dehydrogenase complex (PDC) is a crucial enzyme that connects glycolysis and the tricarboxylic acid (TCA) cycle pathway. It plays an essential role in regulating glucose metabolism for energy production by catalyzing the oxidative decarboxylation of pyruvate to acetyl coenzyme A. Importantly, the activity of PDC is regulated through post-translational modifications (PTMs), phosphorylation, acetylation, and O-GlcNAcylation. These PTMs have significant effects on PDC activity under both physiological and pathophysiological conditions, making them potential targets for metabolism-related diseases. This review specifically focuses on the PTMs of PDC in cardiovascular diseases (CVDs) such as myocardial ischemia/reperfusion injury, diabetic cardiomyopathy, obesity-related cardiomyopathy, heart failure (HF), and vascular diseases. The findings from this review offer theoretical references for the diagnosis, treatment, and prognosis of CVD.

MG53 inhibits ferroptosis by targeting the p53/SLC7A11/GPX4 pathway to alleviate doxorubicin-induced cardiotoxicity.

Doxorubicin (DOX) is an anthracycline medication that is commonly used to treat solid tumors. However, DOX has limited clinical efficacy due to known cardiotoxicity. Ferroptosis is involved in DOX-induced cardiotoxicity (DIC). Although mitsugumin-53 (MG53) has cardioprotective effects and is expected to attenuate myocardial ischemic injury, its ability to inhibit DOX-induced ferroptosis has not been extensively studied. This research aims to investigate the pathophysiological impact of MG53 on DOX induced ferroptosis. Here, MG53 levels were evaluated in relation to the extent of ferroptosis by establishing in vivo and in vitro DIC mouse models. Additionally, myocardial specific MG53 overexpressing mice were used to study the effect of MG53 on cardiac function in DIC mice. The study found that the MG53 expression decreased in DOX treated mouse hearts or cardiomyocytes. However, MG53-overexpressing improved cardiac function in the DIC model and effectively reduced myocardial ferroptosis by increasing solute carrier family 7 member 11 (SLC7A11) and Glutathione peroxidase 4 (GPX4) levels, which were decreased by DOX. Mechanistically, MG53 binds to tumor suppressor 53 (p53) to regulate its ubiquitination and degradation. Ferroptosis induced by DOX was prevented by either MG53 overexpression or p53 knockdown in cardiomyocytes. The modulation of the p53/SLC7A11/GPX4 pathway by overexpression of MG53 can alleviate DOX induced ferroptosis. The study indicates that MG53 can provide protection against DIC by increasing p53 ubiquitination. These results highlight the previously unidentified role of MG53 in inhibiting ferroptosis to prevent DIC.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model.

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

Establishment and validation of nomogram model for predicting major adverse cardiac events in patients with acute ST-segment elevation myocardial infarction based on glycosylated hemoglobin A1c to apolipoprotein A1 ratio: An observational study.

The objective of the current study is to assess the usefulness of HbA1cAp ratio in predicting in-hospital major adverse cardiac events (MACEs) among acute ST-segment elevation myocardial infarction (STEMI) patients that have undergone percutaneous coronary intervention (PCI). Further, the study aims to construct a ratio nomogram for prediction with this ratio. The training cohort comprised of 511 STEMI patients who underwent emergency PCI at the Huaibei Miners' General Hospital between January 2019 and May 2023. Simultaneously, 384 patients treated with the same strategy in First People's Hospital of Hefei formed the validation cohort during the study period. LASSO regression was used to screen predictors of nonzero coefficients, multivariate logistic regression was used to analyze the independent factors of in-hospital MACE in STEMI patients after PCI, and nomogram models and validation were established. The LASSO regression analysis demonstrated that systolic blood pressure, diastolic blood pressure, D-dimer, urea, and glycosylated hemoglobin A1c (HbA1c)/apolipoprotein A1 (ApoA1) were significant predictors with nonzero coefficients. Multivariate logistic regression analysis was further conducted to identify systolic blood pressure, D-dimer, urea, and HbA1c/ApoA1 as independent factors associated with in-hospital MACE after PCI in STEMI patients. Based on these findings, a nomogram model was developed and validated, with the C-index in the training set at 0.77 (95% CI: 0.723-0.817), and the C-index in the validation set at 0.788 (95% CI: 0.734-0.841), indicating excellent discrimination accuracy. The calibration curves and clinical decision curves also demonstrated the good performance of the nomogram models. In patients with STEMI who underwent PCI, it was noted that a higher HbA1c of the ApoA1 ratio is significantly associated with in-hospital MACE. In addition, a nomogram is constructed having considered the above-mentioned risk factors to provide predictive information on in-hospital MACE occurrence in these patients. In particular, this tool is of great value to the clinical practitioners in determination of patients with a high risk.

Development and validation of a nomogram for predicting specific mortality risk: A study of competing risk model based on real endometrial cancer patients.

OBJECTIVE: This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information. METHODS: We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer-specific death, and a predictive nomogram was constructed. C-index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram. RESULTS: There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C-index = 0.883 [95% confidence interval [CI]: 0.881-0.884]), and the 1-, 3-, and 5-year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C-index = 0.883 [95%CI: 0.882-0.883]), and the 1-, 3-, and 5- AUC values were 0.908, 0.885 and 0.870, respectively. CONCLUSION: We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools.

Slimmable transformer with hybrid axial-attention for medical image segmentation.

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better; the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning.

The temporal transition zone: A gradual approach to a subjective set-point within the three-second time window.

Even though in physics "time" is considered to be continuous, how the brain and mind deal with time might be different. It has been proposed that in cognition, time windows provide logistic platforms for information processing, such as the low-frequency 3-s time window. The following series of behavioral experiments may shed light on the dynamics within such a time window. Using a duration reproduction paradigm, we first replicated a pattern of reproduced duration observed in a previous single-case study. Specifically, the reproduction increases as the pause between standard duration and reproduction increases, but only within the time window of some 3 s; when the pause goes beyond 4 s, the reproduction reaches a plateau of a subjective set-point. This increasing phase is named the "temporal transition zone." Three more experiments were performed to test the features of the transition zone as a low-frequency time window. It is also observed with different standard durations (2, 3, 4.5 s, in Experiment 2), and even when the frequency of the auditory stimuli was different in standard and reproduction (300 Hz in standard duration and 400 Hz in reproduction, in Experiment 4). The transition zone was observed only with pause durations of 2 to 3 s; when the shortest pause duration was 5 s, the transition zone was no longer observed, and the reproduction was stable at the subjective set-point (in Experiment 3). Taken together, we suggest that the temporal transition zone indicates a pre-semantic logistic platform to organize and process the information flow; in such a time window of some 3 seconds, the identity of an ongoing event is substantiated, building the "subjective present."

Identification of the HLA-A*11:463 allele in a Chinese patient and his father.

HLA-A*11:463 has one nucleotide change from HLA-A*11:01:01:01 at nucleotide 508 changing Lysine (146) to Glutamine.

Electrochemical nitrogen reduction reaction on anchored SnS2 nanosheets with TM2 dimers.

The design of efficient, high-stability nitrogen fixation catalysts remains a great challenge to achieve electrochemical nitrogen reduction reaction (NRR) under ambient conditions. Herein, the high-throughput first-principles calculations are performed to obtain potential electrochemical NRR catalysts from transition metal (TM) dimers anchored on SnS2 nanosheets. The selected W2/SnS2 behaves as a promising NRR candidate possessing -0.27 V limiting potential and 0.81 eV maximum kinetic potential, and it exhibits the adsorption advantages of *N2 over other small molecules (*H2O, *O, *OH, *H). More importantly, the moderate d orbital valence electron number and electronegativity of TM atom could obtain better NRR activity, and a new descriptor φ considering the effects of coordination environments and adsorbates is proposed to achieve the fast pre-screening among various candidates. This work presents practical insights into the fast screening of TM2/SnS2 candidates for efficient nitrogen fixation and further streamlining the design of electrochemical NRR catalysts.

Pharmacokinetics, Pharmacodynamics, and Safety of Single Dose HSK7653 Tablets in Chinese Subjects with Normal or Impaired Renal Function.

OBJECTIVE: HSK7653 is a novel, ultralong-acting dipeptidyl peptidase-4 (DPP-4) inhibitor, promising for type 2 diabetes mellitus with a dosing regimen of once every 2 weeks. This trial investigates the pharmacokinetics (PKs), pharmacodynamics (PDs),and safety of HSK7653 in outpatients with normal or impaired renal function. METHODS: This is a multicenter, open-label, nonrandomized, parallel-controlled phase I clinical study that investigates the pharmacokinetic profiles of HSK7653 after a single oral administration in 42 subjects with mild (n = 8), moderate (n = 10), severe renal impairment (n = 10), and end-stage renal disease (without dialysis, n = 5) compared with matched control subjects with normal renal function (n = 9). Safety was evaluated throughout the study, and the pharmacodynamic effects were assessed on the basis of a DPP-4 inhibition rate. RESULTS: HSK7653 exposure levels including the maximum plasma concentration (Cmax), area under the plasma concentration-time curve from zero to last time of quantifiable concentration (AUC0-t), and area under the plasma concentration-time curve from zero to infinity (AUC0-inf) showed no significant differences related to the severity of renal impairment. Renal clearance (CLR) showed a certain downtrend along with the severity of renal impairment. The CLR of the group with severe renal impairment and the group with end-stage renal disease were basically similar. The DPP-4 inhibition rate-time curve graph was similar among the renal function groups. All groups had favorable safety, and no serious adverse events occurred. CONCLUSIONS: HSK7653 is a potent oral DPP-4 inhibitor with a long plasma half-life, supporting a dosing regimen of once every 2 weeks. Impaired renal function does not appear to impact the pharmacokinetic and pharmacodynamic properties of HSK7653 after a single administration in Chinese subjects. HSK7653 is also well tolerated without an increase in adverse events with increasing renal impairment. These results indicate that dose adjustment of HSK7653 may not be required in patients with renal impairment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05497297.

Automated polyp segmentation based on a multi-distance feature dissimilarity-guided fully convolutional network.

Colorectal malignancies often arise from adenomatous polyps, which typically begin as solitary, asymptomatic growths before progressing to malignancy. Colonoscopy is widely recognized as a highly efficacious clinical polyp detection method, offering valuable visual data that facilitates precise identification and subsequent removal of these tumors. Nevertheless, accurately segmenting individual polyps poses a considerable difficulty because polyps exhibit intricate and changeable characteristics, including shape, size, color, quantity and growth context during different stages. The presence of similar contextual structures around polyps significantly hampers the performance of commonly used convolutional neural network (CNN)-based automatic detection models to accurately capture valid polyp features, and these large receptive field CNN models often overlook the details of small polyps, which leads to the occurrence of false detections and missed detections. To tackle these challenges, we introduce a novel approach for automatic polyp segmentation, known as the multi-distance feature dissimilarity-guided fully convolutional network. This approach comprises three essential components, i.e., an encoder-decoder, a multi-distance difference (MDD) module and a hybrid loss (HL) module. Specifically, the MDD module primarily employs a multi-layer feature subtraction (MLFS) strategy to propagate features from the encoder to the decoder, which focuses on extracting information differences between neighboring layers' features at short distances, and both short and long-distance feature differences across layers. Drawing inspiration from pyramids, the MDD module effectively acquires discriminative features from neighboring layers or across layers in a continuous manner, which helps to strengthen feature complementary across different layers. The HL module is responsible for supervising the feature maps extracted at each layer of the network to improve prediction accuracy. Our experimental results on four challenge datasets demonstrate that the proposed approach exhibits superior automatic polyp performance in terms of the six evaluation criteria compared to five current state-of-the-art approaches.

Exploring a frequency-domain attention-guided cascade U-Net: Towards spatially tunable segmentation of vasculature.

Developing fully automatic and highly accurate medical image segmentation methods is critically important for vascular disease diagnosis and treatment planning. Although advances in convolutional neural networks (CNNs) have spawned an array of automatic segmentation models converging to saturated high performance, none have explored whether CNNs can achieve (spatially) tunable segmentation. As a result, we propose multiple attention modules from a frequency-domain perspective to construct a unified CNN architecture for segmenting vasculature with desired (spatial) scales. The proposed CNN architecture is named frequency-domain attention-guided cascaded U-Net (FACU-Net). Specifically, FACU-Net contains two innovative components: (1) a frequency-domain-based channel attention module that adaptively tunes channel-wise feature responses and (2) a frequency-domain-based spatial attention module that enables the deep network to concentrate on foreground regions of interest (ROIs) effectively. Furthermore, we devised a novel frequency-domain-based content attention module to enhance or weaken the high (spatial) frequency information, allowing us to strengthen or eliminate vessels of interest. Extensive experiments using clinical data from patients with intracranial aneurysms (IA) and abdominal aortic aneurysms (AAA) demonstrated that the proposed FACU-Net met its design goal. In addition, we further investigated the association between varying (spatial) frequency components and the desirable vessel size/scale attributes. In summary, our preliminary findings are encouraging, and further developments may lead to deployable image segmentation models that are spatially tunable for clinical applications.

Shikonin exerts an anti-leukemia effect against FLT3-ITD mutated acute myeloid leukemia cells via targeting FLT3 tyrosine kinase and its downstream pathways.

INTRODUCTION: Acute myeloid leukemia (AML) with internal tandem duplication (ITD) mutations in Fms-like tyrosine kinase 3 (FLT3) has an unfavorable prognosis. Recently, using newly emerging inhibitors of FLT3 has led to improved outcomes of patients with FLT3-ITD mutations. However, drug resistance and relapse continue to be significant challenges in the treatment of patients with FLT3-ITD mutations. This study aimed to evaluate the anti-leukemic effects of shikonin (SHK) and its mechanisms of action against AML cells with FLT3-ITD mutations in vitro and in vivo. METHODS: The CCK-8 assay was used to analyze cell viability, and flow cytometry was used to detect cell apoptosis and differentiation. Western blotting and real-time polymerase chain reaction (RT-PCR) were used to examine the expression of certain proteins and genes. Leukemia mouse model was created to evaluate the anti-leukemia effect of SHK against FLT3-ITD mutated leukemia in vivo. RESULTS: After screening a series of leukemia cell lines, those with FLT3-ITD mutations were found to be more sensitive to SHK in terms of proliferation inhibition and apoptosis induction than those without FLT3-ITD mutations. SHK suppresses the expression and phosphorylation of FLT3 receptors and their downstream molecules. Inhibition of the NF-κB/miR-155 pathway is an important mechanism through which SHK kills FLT3-AML cells. Moreover, a low concentration of SHK promotes the differentiation of AML cells with FLT3-ITD mutations. Finally, SHK could significantly inhibit the growth of MV4-11 cells in leukemia bearing mice. CONCLUSION: The findings of this study indicate that SHK is a promising drug for the treatment of FLT3-ITD mutated AML.

S-Net: a multiple cross aggregation convolutional architecture for automatic segmentation of small/thin structures for cardiovascular applications.

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

Immune Cell Infiltration and Clinical Significance of Angiogenesis-related Genes in Lung Adenocarcinoma.

BACKGROUND/AIM: Angiogenesis is one of the hallmarks of cancer. However, the role of molecular subtypes of angiogenesis-associated genes (AAGs) in the tumor immune microenvironment (TIME) of lung adenocarcinoma (LUAD) remains unclear. MATERIALS AND METHODS: The expression of AAGs in patients with LUAD were studied. Consensus clustering was performed to identify new AAG-associated molecular subgroups. The TIME and immune status of the subgroups were analyzed. Functional enrichment analysis was performed on the differentially expression genes among the clustered subgroups to analyze their relationship with AAGs. Furthermore, a prognostic risk model and clinical nomogram associated with survival time were constructed. Risk scores of drug sensitivity, immune checkpoint molecules, tumor mutational burden, and tumor cell stemness were analyzed. Finally, a series of in vitro experiments were performed to investigate the role of dickkopf WNT signaling pathway inhibitor 1 (DKK1) in LUAD. RESULTS: Two molecular subgroups with significantly different survival rates and TIME were identified. Immune checkpoint scores were higher in the subgroup with a worse prognosis. Moreover, differentially expressed genes were enriched in cell-cycle regulation, protein metabolism, and the immune microenvironment. The risk model and clinical nomogram constructed based on AAGs accurately predicted the prognosis of patients with LUAD. Patients with high-risk scores were less sensitive to chemotherapy but more sensitive to immunotherapy. DKK1 was highly expressed in basal cells and luminal cells. In addition, the knockdown of DKK1 reduced LUAD cell proliferation, invasion, and migration. CONCLUSION: Models based on AAGs can play an important role in predicting LUAD prognosis and immunotherapy effects. We further characterized the angiogenesis of TIME and studied the AAG DKK1. Our findings provide a theoretical basis for antitumor strategies targeting angiogenesis.

Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.

Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal aortic aneurysms (AAA). However, there is a lack of well-developed quantitative assessments for disturbed aneurysmal flow. Therefore, we aimed to develop innovative metrics for quantifying disturbed aneurysm hemodynamics and evaluate their effectiveness in predicting the growth status of AAAs, specifically distinguishing between fast-growing and slowly-growing aneurysms. The growth status of aneurysms was classified as fast (≥ 5 mm/year) or slow (< 5 mm/year) based on serial imaging over time. We conducted computational fluid dynamics (CFD) simulations on 70 patients with computed tomography (CT) angiography findings. By converting hemodynamics data (wall shear stress and velocity) located on unstructured meshes into image-like data, we enabled spatial pattern analysis using Radiomics methods, referred to as "Hemodynamics-informatics" (i.e., using informatics techniques to analyze hemodynamic data). Our best model achieved an AUROC of 0.93 and an accuracy of 87.83%, correctly identifying 82.00% of fast-growing and 90.75% of slowly-growing AAAs. Compared with six classification methods, the models incorporating hemodynamics-informatics exhibited an average improvement of 8.40% in AUROC and 7.95% in total accuracy. These preliminary results indicate that hemodynamics-informatics correlates with AAAs' growth status and aids in assessing their progression.

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study.

Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation. Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models. The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ∼2 h to ∼10 min). Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.

Application of case-based learning in psychology teaching: a meta-analysis.

BACKGROUND: Case-based learning (CBL) has been found to be effective for many subjects, but there is currently a lack of evidence regarding its utility in psychology education. The present study investigated whether CBL pedagogy can improve students' academic performance in psychology courses compared to the traditional teaching methods. METHODS: A systematic review and meta-analysis were conducted to investigate the effectiveness of CBL in psychology teaching. Databases including PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), the VIP database, and Wanfang data were searched to find eligible randomized controlled trials. Pooled effect estimates were calculated using Hedges' g under the random effects model, and a subgroup analysis was carried to investigate the heterogeneity among studies. RESULTS: Fifteen studies with 2172 participants, 1086 in the CBL group and 1086 in the traditional lecture-based teaching group, were included in the meta-analysis. Students in the CBL group scored significantly higher on exams than those in the lecture-based group [Hedges' g = 0.68, 95%CI (0.49, 0.88), p < 0.00]. Relatively high heterogeneity was noted among the included studies. Publication bias was examined by the funnel plot and Egger's test, but did not significantly influence the stability of the results. A subsequent evaluation using the trim-and-fill method confirmed that no single study was skewing the overall results. A qualitative review of the included studies suggested that most students in the CBL group were satisfied with the CBL teaching mode. CONCLUSIONS: This meta-analysis indicated that the CBL pedagogy could be effective in psychology education, and might help increase students' academic scores, while encouraging a more engaging and cooperative learning environment. At present, the application of CBL in psychology education is in its initial stage. Problems related to the curriculum itself, research methodology, and challenges faced by both teachers and learners have confined its practice. Fully tapping into the strengths of CBL in psychology teaching will require additional work and advancing research.