CDKN1A promotes Cis-induced AKI by inducing cytoplasmic ROS production and ferroptosis.

BACKGROUND AND OBJECTIVE: This study focuses on investigating the role of CDKN1A in cisplatin-induced AKI (acute kidney injury, AKI) and its potential as a biomarker for early diagnosis and therapeutic intervention by integrating bioinformatics analysis, machine learning, and experimental validation. METHODS: We analyzed the GSE85957 dataset to find genes that changed between control and cisplatin-treated rats. Using bioinformatics and machine learning, we found 13 important genes related to ferroptosis and the P53 pathway. The key gene, CDKN1A, was identified using various algorithms. We then tested how reducing CDKN1A in human kidney cells affected cell health, ROS, and iron levels. We also checked how CDKN1A changes the levels of proteins linked to ferroptosis using Q-PCR and Western Blot. RESULTS: CDKN1A was found to negatively regulate the G1/S phase transition and was associated with ferroptosis in p53 signaling. Experiments in human renal tubular epithelial cells (HK-2) and rat NRK-52E cells showed that CDKN1A knockdown mitigated cisplatin-induced cell injury by reducing oxidative stress and ferroptosis. CONCLUSION: Our integrated approach identified CDKN1A as a biomarker for cisplatin-induced AKI. Its regulation could be key in AKI pathogenesis, offering new therapeutic insights and aiding in early diagnosis and intervention.

Discovery of a Potent, selective and orally bioavailable CDK9 degrader for targeting transcription regulation in Triple-Negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive, heterogeneous and invasive subtype of breast cancer with very limited effective modalities of treatment. Degrading the critical transcription regulator cyclin-dependent kinase 9 (CDK9) by proteolysis targeting chimeras (PROTACs) has shown promising potential for treating TNBC. However, to date, CDK9-targeting PROTACs for oral administration in treatment of cancers have not been reported. We herein present the design, synthesis, and extensive biological evaluation of a series of novel PROTACs as orally bioavailable, potent and selective degraders of CDK9 for targeting transcription regulation in triple-negative breast cancer. The developed compound 29 exhibited a desired potency (DC50 = 3.94 nM) with high efficacy (Dmax = 96 %) on CDK9 degradation, and effectively inhibited the proliferation of TNBC MDA-MB-231 cells. Mechanistic investigations revealed that compound 29 is a bona fide CDK9 degrader and can substantially downregulate the downstream targets c-Myc and MCL-1. Furthermore, compound 29 displayed favorable oral bioavailability in mice, and oral administration of degrader 29 significantly depleted CDK9 protein in TNBC tumor tissues and exhibited tumor growth inhibition in TNBC xenograft mice models. Collectively, our work established that degrader 29 is a highly potent and selective degraders of CDK9 with satisfactory oral bioavailability, which holds promising potential for the treatment of TNBC.

One-Step Air Spraying of Structural Coating on Cu Alloy as Superhydrophobic Surface for Enhanced Corrosion Resistance and Anti-Icing Performance.

With the development of modern technology, the construction industry, and navigation technology, the metal Cu alloy has become an important metal material in mainstream industrial applications. As an indispensable basic metal material in the field of science and technology, its problem with corrosion is still a long-term problem that scientists have been working to solve. In this paper, air spraying technology is used to prepare an Al2O3-PDMS composite coating. By adjusting the content of Al2O3, the surface of the Cu alloy can reach different wetting states. The results show that the corrosion potential of the as-prepared superhydrophobic Al2O3-PDMS coating increases by 70 mV compared with the substrate, the corrosion current density decreases by one order of magnitude, and the impedance modulus increases from 2000 to 12,000 Ω⋅cm2, indicating a significantly enhanced corrosion resistance. It also possesses excellent anti-pollution and anti-icing behaviors, thereby allowing them to work in harsh industrial conditions.

Alcohol drinking triggered decrease of oxidative balance score is associated with high all-cause and cardiovascular mortality in hypertensive individuals: findings from NHANES 1999-2014.

BACKGROUND: Oxidative stress is closely associated with hypertensive outcomes. The oxidative balance score (OBS) measures oxidative stress exposure from dietary and lifestyle elements. The objective of this study was to investigate the association between OBS and mortality in hypertensive patients. METHODS: This study included 7823 hypertensive patients from the National Health and Nutrition Examination Survey (NHANES) 1999-2014. Several models, including Cox regression, restricted cubic splines (RCS), Kaplan‒Meier survival analysis, subgroup, and sensitivity analyses, were exploited to investigate the relationship between OBS and the risk of mortality. RESULTS: Controlling for all potential confounders, a significantly inverse association was observed between elevated OBS and all-cause [hazard ratio (HR) = 0.90, 95% CI: 0.85-0.95] and cardiovascular mortality (HR = 0.85, 95% CI: 0.75-0.95). With adjustment for covariates, significant associations between lifestyle OBS and mortality risks diminished, whereas associations between dietary OBS and these mortality risks remained robust (all-cause mortality: HR = 0.91, 95% CI: 0.86-0.96; cardiovascular mortality: HR = 0.85, 95% CI: 0.76-0.96). RCS demonstrated a linear relationship between OBS and all-cause and cardiovascular mortality risk (P nonlinear = 0.088 and P nonlinear = 0.447, respectively). Kaplan‒Meier curves demonstrated that the mortality rate was lower with a high OBS (P < 0.001). The consistency of the association was demonstrated in subgroup and sensitivity analyses. RCS after stratification showed that among current drinkers, those with higher OBS had a lower risk of mortality compared with former or never drinkers. CONCLUSIONS: In hypertensive individuals, there was a negative association between OBS and all-cause and cardiovascular mortality. Encouraging hypertensive individuals, especially those currently drinking, to maintain high levels of OBS may be beneficial in improving their prognosis.

Antiallodynic and antihyperalgesic effects of decursin associated with correcting mitochondrial dysfunction and oxidative stress in type 1 diabetic mice.

A substantial proportion of diabetic patients suffer a debilitating and persistent pain state, known as peripheral painful neuropathy that necessitates improved therapy or antidote. Decursin, a major active ingredient from Angelica gigas Nakai, has been reported to possess antidepressant activity in preclinical studies. As antidepressants have been typically used as standard agents against persistent neuropathic pain, this study aimed to probe the effect of decursin on neuropathic pain associated with streptozotocin-induced type 1 diabetes in male C57BL6J mice. The Hargreaves test and the von Frey test were used to assess pain-like behaviors, shown as heat hyperalgesia and mechanical allodynia respectively. Chronic treatment of diabetic mice with decursin not only ameliorated the established symptoms of heat hyperalgesia and mechanical allodynia, but also arrested the development of these pain states given preemptively at low doses. Although decursin treatment hardly impacted on metabolic disturbance in diabetic mice, it ameliorated exacerbated oxidative stress in pain-associated tissues, improved mitochondrial bioenergetics in dorsal root ganglion neurons, and restored nerve conduction velocity and blood flow in sciatic nerves. Notably, the analgesic actions of decursin were modified by pharmacologically manipulating redox status and mitochondrial bioenergetics. These findings unveil the analgesic activity of decursin, an effect that is causally associated with its bioenergetics-enhancing and antioxidant effects, in mice with type 1 diabetes.

Sapphire-Based Optrode for Low Noise Neural Recording and Optogenetic Manipulation.

Electrophysiological recordings of neurons in deep brain regions using optogenetic stimulation are essential to understanding and regulating the role of complex neural activity in biological behavior and cognitive function. Optogenetic techniques have significantly advanced neuroscience research by enabling the optical manipulation of neural activities. Because of the significance of the technique, constant advancements in implantable optrodes that integrate optical stimulation with low-noise, large-scale electrophysiological recording are in demand to improve the spatiotemporal resolution for various experimental designs and future clinical applications. However, robust and easy-to-use neural optrodes that integrate neural recording arrays with high-intensity light emitting diodes (LEDs) are still lacking. Here, we propose a neural optrode based on Gallium Nitride (GaN) on sapphire technology, which integrates a high-intensity blue LED with a 5x2 recording array monolithically for simultaneous neural recording and optogenetic manipulation. To reduce the noise interference between the recording electrodes and the LED, which is in close physical proximity, three metal grounding interlayers were incorporated within the optrode, and their ability to reduce LED-induced artifacts during neural recording was confirmed through both electromagnetic simulations and experimental demonstrations. The capability of the sapphire optrode to record action potentials has been demonstrated by recording the firing of mitral/tuft cells in the olfactory bulbs of mice in vivo. Additionally, the elevation of action potential firing due to optogenetic stimulation observed using the sapphire probe in medial superior olive (MSO) neurons of the gerbil auditory brainstem confirms the capability of this sapphire optrode to precisely access neural activities in deep brain regions under complex experimental designs.

Comprehensive review of virtual assistants in vascular surgery.

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery.

Inflammatory Protein Panel: Exploring Diagnostic Insights for Peripheral Artery Disease Diagnosis in a Cross-Sectional Study.

Cytokine-induced neutrophil chemoattractant 1 (CINC-1), a cluster of differentiation 95 (CD95), fractalkine, and T-cell immunoglobulin and mucin domain 1 (TIM-1) are circulating proteins known to be involved in inflammation. While their roles have been studied in neurological conditions and cardiovascular diseases, their potential as peripheral artery disease (PAD) biomarkers remain unexplored. We conducted a cross-sectional diagnostic study using data from 476 recruited patients (164 without PAD and 312 with PAD). Plasma levels of CINC-1, CD95, fractalkine, and TIM-1 were measured at baseline. A PAD diagnosis was established at recruitment based on clinical exams and investigations, defined as an ankle-brachial index < 0.9 or toe-brachial index < 0.67 with absent/diminished pedal pulses. Using 10-fold cross-validation, we trained a random forest algorithm, incorporating clinical characteristics and biomarkers that showed differential expression in PAD versus non-PAD patients to predict a PAD diagnosis. Among the proteins tested, CINC-1, CD95, and fractalkine were elevated in PAD vs. non-PAD patients, forming a 3-biomarker panel. Our predictive model achieved an AUROC of 0.85 for a PAD diagnosis using clinical features and this 3-biomarker panel. By combining the clinical characteristics with these biomarkers, we developed an accurate predictive model for a PAD diagnosis. This algorithm can assist in PAD screening, risk stratification, and guiding clinical decisions regarding further vascular assessment, referrals, and medical/surgical management to potentially improve patient outcomes.

Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?

BACKGROUND AND PURPOSE: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting histologic grade and prognosis in chondrosarcoma (CS). METHODS: A multicenter 211 (training cohort/ test cohort, 127/84) CS patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting the grade. Kaplan-Meier survival analysis was used to assess the association of the model-predicted grade with recurrence-free survival (RFS). Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and the Harrell's concordance index (C-index). RESULTS: The DLRM (AUC, 0.879; 95 % confidence interval [CI], 0.802-0.956) outperformed (z = 2.773, P=0.006) the RS (AUC, 0.715;95 % CI, 0.606-0.825) in predicting grade in the test cohort. RFS showed significant differences (log-rank test, P<0.05) between low-grade and high-grade patients stratified by DLRM. The DLRM achieved a higher C-index (0.805; 95 % CI, 0.694-0.916) than the RS (0.692, 95 % CI, 0.540-0.844) did in predicting RFS for CS patients in the test cohort. CONCLUSION: The DLRM can accurately predict the histologic grade and prognosis in CS.

Investigating the Prognostic Potential of Plasma ST2 in Patients with Peripheral Artery Disease: Identification and Evaluation.

Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02-1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes.

Construction of Molecularly Dispersed Polyoxometalate-Alumina Hybrid Hollow Nanoflowers via Water-Induced Kirkendall Effect.

Hybrid nanomaterials with controllable structures and diverting components have attracted significant interest in the functional materials field. Here, we develop a solvent evaporation-induced self-assembly (EISA) strategy to synthesize nanosheet-assembled phosphomolybdic acid (H3PMo)-alumina hybrid hollow spheres. The resulting nanoflowers display a high surface area (up to 697 m2 g-1), adjustable diameter, high chemical/thermal stability, and especially molecularly dispersed H3PMo species. By employing various microscopic and spectroscopic techniques, the formation mechanism is elucidated, revealing the simultaneous control of the morphology by heteropoly acids and water through the water-induced Kirkendall effect. The versatility of the synthesis method is demonstrated by varying surfactants, heteropoly acids, and metal oxide precursors for the facile synthesis of hybrid metal oxides. Spherical hybrid alumina serves as an attractive support material for constructing metal-acid bifunctional catalysts owing to its advantageous surface area, acidity, and mesoporous microenvironment. Pt-loaded hollow flowers exhibit excellent catalytic performance and exceptional stability in the hydrodeoxygenation of vanillin with recyclability for up to 10 cycles. This research presents an innovative strategy for the controllable synthesis of hybrid metal oxide nanospheres and hollow nanoflowers, providing a multifunctional platform for diverse applications.

Surface electric field enhanced biosensor based on symmetrical U-tapered HCF structure for gastric carcinoma biomarker trace detection.

In this article, a novel U-tapered hollow-core fiber (HCF) surface plasmon resonance (SPR) biosensor coated with PtS2 for early-stage gastric carcinoma (GC) diagnosis was demonstrated. The article proposed the first investigation to detect Interleukin-10 (IL10) and Interleukin-1ß (IL1ß) which were associated with the risk of developing gastric carcinoma, using optical fiber SPR technology. Herein, the sensitivity of sensor was effectively improved through a combination of tapered and U-shaped structures. Additionally, to further enhance the detection capability, two-dimensional material PtS2 was utilized to increase the surface electric field intensity of the sensor. Simultaneously, optimization of structural parameters such as taper ratio, bending diameters, and Au film thickness was conducted. Ultimately, the designed sensor achieved a remarkable sensitivity of 13210 nm/RIU within the refractive index (RI) range of 1.33-1.37. The sensor demonstrated exceptional performance, achieving sensitivities of 3.64 nm/(ng/ml) and 7.46 nm/(ng/ml) for the detection of IL10 and IL1ß biomarkers, respectively, along with limit of detection (LOD) of 2.74 pg/ml and 1.33 pg/ml, and successfully detecting the presence of these biomarkers in the serum of gastric cancer patients. Overall, the proposed sensor exhibits significant potential in early gastric cancer detection and advances the application of optical fiber SPR sensors in trace biodetection.

Computational model for synthesizing auditory brainstem responses to assess neuronal alterations in aging and autistic animal models.

The auditory brainstem response (ABR) is a widely used objective electrophysiology measure for non-invasively assessing auditory function and neural activities in the auditory brainstem, but its ability to reflect detailed neuronal processes is limited due to the averaging nature of the electroencephalogram recordings. This study addresses this limitation by developing a computational model of the auditory brainstem which is capable of synthesizing ABR traces based on a large, population scale neural extrapolation of a spiking neuronal network of auditory brainstem neural circuitry. The model was able to recapitulate alterations in ABR waveform morphology that have been shown to be present in two medical conditions: animal models of autism and aging. Moreover, in both of these conditions, these ABR alterations are caused by known distinct changes in auditory brainstem physiology, and the model could recapitulate these changes. In the autism model, the simulation revealed myelin deficits and hyperexcitability, which caused a decreased wave III amplitude and a prolonged wave III-V interval, consistent with experimentally recorded ABRs in Fmr1-KO mice. In the aging model, the model recapitulated ABRs recorded in aged gerbils and indicated a reduction in activity in the medial nucleus of the trapezoid body (MNTB), a finding validated by confocal imaging data. These results demonstrate not only the model's accuracy but also its capability of linking features of ABR morphologies to underlying neuronal properties and suggesting follow-up physiological experiments. Significance Statement: This study presents a novel computational model of the auditory brainstem, capable of synthesizing auditory brainstem response (ABR) traces by simulating large-scale neuronal activities. Addressing limitations of traditional ABR measurements, the model links ABR waveform features to underlying neuronal properties. Validated using empirical ABRs from animal models of autism and aging, the model accurately reproduced observed ABR alterations, revealing influences of myelin deficits and hyperexcitability in Fragile X syndrome, and degraded inhibitory activity in aging. These findings, supported by experimental data, demonstrate the model's potential for predicting changes in auditory brainstem physiology and guiding further physiological investigations, thus advancing our understanding of auditory neural processes.

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS. METHODS AND RESULTS: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). CONCLUSIONS: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.

OBJECTIVES: To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC). METHODS: Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index). RESULTS: Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients. CONCLUSIONS: The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.

Predicting inferior vena cava filter complications using machine learning.

OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement. RESULTS: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups. CONCLUSIONS: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes.

Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data.

Purpose: Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data. Methods: We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers. Results: The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84. Conclusion: Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.

Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes an inflammatory protein panel to inform prognosis of PAD may improve predictive accuracy. We developed predictive models for 2-year major adverse limb events (MALE) using clinical characteristics (demographics, comorbidities, and medications) and a panel of 5 PAD-specific inflammatory biomarkers (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI) that achieved excellent performance on an independent validation cohort (AUROC 0.84). The models developed through this study may support PAD risk-stratification and targeted management strategies.

A novel protein encoded by circLARP1B promotes the proliferation and migration of vascular smooth muscle cells by suppressing cAMP signaling.

BACKGROUND AND AIMS: Circular RNA (circRNA) is closely related to atherosclerosis (AS) incidence and progression, but its regulatory mechanism in AS needs further elucidation. AS development is significantly influenced by abnormal vascular smooth muscle cells (VSMCs) growth and migration. This study explored the potential protein role of circLARP1B in VSMC proliferation and migration. METHODS: We performed whole-transcriptome sequencing in human normal arterial intima and advanced atherosclerotic plaques to screen for differentially expressed circRNAs. The sequencing results were combined with database analysis to screen for circRNAs with coding ability. Real-time quantitative polymerase chain reaction was utilized to assess circLARP1B expression levels in atherosclerotic plaque tissues and cells. circLARP1B-243aa function and pathway in VSMCs growth and migration were studied by scratch, transwell, 5-ethynyl-2'-deoxyuridine, cell counting kit-8, and Western blot experiments. RESULTS: We found that circLARP1B was downregulated in atherosclerotic plaque tissue and promoted the proliferation and migration of VSMCs. circLARP1B encodes a novel protein with a length of 243 amino acids. Through functional experiments, we confirmed the role of circLARP1B-243aa in enhancing VSMCs migration and proliferation. Mechanistically, circLARP1B-243aa promotes VSMCs migration and growth by upregulating phosphodiesterase 4C to inhibit the cyclic adenosine monophosphate signaling pathway. CONCLUSIONS: Our results suggested that circLARP1B could promote VSMCs growth and migration through the encoded protein circLARP1B-243aa. Therefore, it could be a treatment target and biomarker for AS.

Characterization of physical and chemical properties of dietary fiber from grain bran and its regulation of gut microbiota and metabolite to prevent colitis.

Grain bran dietary fiber (DF) has the effect of promoting intestinal health and is worth being studied. In the present study, the physicochemical properties and prevention effect of DF on ulcerative colitis (UC) were investigated. The results showed that the optimal extraction conditions were determined as α-amylase (350 U/g, 70 °C, pH 7.0, 2.5 h) and papain (100 U/g, 60 °C, pH 7.0, 1.5 h), resulting in a yield of 83.81% for DF. Moreover, DF exhibited unique physicochemical properties contributing to its preventive effects, as evidenced by its ability to mitigate symptoms such as hematochezia, immune inflammation, and impaired intestinal barrier in UC mice. The underlying mechanism can be attributed to the regulation of phenylalanine, tyrosine and tryptophan biosynthesis pathway and maintenance of intestinal microbial homeostasis. Therefore, our study suggests that grain bran DF holds potential for the prevention of UC, providing a basis for the development and utilization of grain bran.

Inflammatory Biomarkers to Predict Major Adverse Cardiovascular Events in Patients with Carotid Artery Stenosis.

Background and Objectives: Inflammatory proteins and their prognostic value in patients with carotid artery stenosis (CAS) have not been adequately studied. Herein, we identified CAS-specific biomarkers from a large pool of inflammatory proteins and assessed the ability of these biomarkers to predict adverse events in individuals with CAS. Materials and Methods: Samples of blood were prospectively obtained from 336 individuals (290 with CAS and 46 without CAS). Plasma concentrations of 29 inflammatory proteins were determined at recruitment, and the patients were followed for 24 months. The outcome of interest was a major adverse cardiovascular event (MACE; composite of stroke, myocardial infarction, or death). The differences in plasma protein concentrations between patients with vs. without a 2-year MACE were determined using the independent t-test or Mann-Whitney U test to identify CAS-specific prognostic biomarkers. Kaplan-Meier and Cox proportional hazards analyses with adjustment for baseline demographic and clinical characteristics were performed to assess the prognostic value of differentially expressed inflammatory proteins in predicting a 2-year MACE in patients with CAS. Results: The mean age of the cohort was 68.8 (SD 10.2) years and 39% were female. The plasma concentrations of two inflammatory proteins were significantly higher in individuals with a 2-year MACE relative to those without a 2-year MACE: IL-6 (5.07 (SD 4.66) vs. 3.36 (SD 4.04) pg/mL, p = 0.03) and CD163 (233.825 (SD 230.306) vs. 159.673 (SD 175.669) pg/mL, p = 0.033). Over a follow-up period of 2 years, individuals with elevated levels of IL-6 were more likely to develop MACE (HR 1.269 (95% CI 1.122-1.639), p = 0.042). Similarly, over a 2-year period, patients with high levels of CD163 were more likely to develop MACE (HR 1.413 (95% CI 1.022-1.954), p = 0.036). Conclusions: The plasma levels of inflammatory proteins IL-6 and CD163 are independently associated with adverse outcomes in individuals with CAS. These CAS-specific prognostic biomarkers may assist in the risk stratification of patients at an elevated risk of a MACE and subsequently guide further vascular evaluation, specialist referrals, and aggressive medical/surgical management, thereby improving outcomes for patients with CAS.