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.

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.

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.

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.

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.

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.

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.

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.

The Identification and Evaluation of Interleukin-7 as a Myokine Biomarker for Peripheral Artery Disease Prognosis.

Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12-1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22-1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes.

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.

Purpurin ameliorated neuropathic allodynia and hyperalgesia by modulating neuronal mitochondrial bioenergetics and redox status 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. Purpurin, a natural anthraquinone compound from Rubia tinctorum L., 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 purpurin 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 the pain-like behaviors, shown as heat hyperalgesia and mechanical allodynia respectively. Chronic treatment of diabetic mice with purpurin 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 purpurin 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 in sciatic nerves. Notably, the analgesic actions of purpurin were modified by pharmacologically manipulating redox status and mitochondrial bioenergetics. These findings unveil the analgesic activity of purpurin, an effect that is causally associated with its bioenergetics-enhancing and antioxidant effects, in mice with type 1 diabetes.

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.

TFEB/LAMP2 contributes to PM0.2-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.

Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's diseases (PD) by promoting the alpha-synuclein (α-syn) pathology in the brain. However, the molecular mechanisms of astrocytes involvement in α-syn pathology underlying the process remain unclear. This study investigated PM with particle size <200 nm (PM0.2) exposure-induced α-syn pathology in ICR mice and primary astrocytes, then assessed the effects of mammalian target of rapamycin inhibitor (PP242) in vitro studies. We observed the α-syn pathology in the brains of exposed mice. Meanwhile, PM0.2-exposed mice also exhibited the activation of glial cell and the inhibition of autophagy. In vitro study, PM0.2 (3, 10 and 30 µg/mL) induced inflammatory response and the disorders of α-syn degradation in primary astrocytes, and lysosomal-associated membrane protein 2 (LAMP2)-mediated autophagy underlies α-syn pathology. The abnormal function of autophagy-lysosome was specifically manifested as the expression of microtubule-associated protein light chain 3 (LC3II), cathepsin B (CTSB) and lysosomal abundance increased first and then decreased, which might both be a compensatory mechanism to toxic α-syn accumulation induced by PM0.2. Moreover, with the transcription factor EB (TFEB) subcellular localization and the increase in LC3II, LAMP2, CTSB, and cathepsin D proteins were identified, leading to the restoration of the degradation of α-syn after the intervention of PP242. Our results identified that PM0.2 exposure could promote the α-syn pathological dysregulation in astrocytes, providing mechanistic insights into how PM0.2 increases the risk of developing PD and highlighting TFEB/LAMP2 as a promising therapeutic target for antagonizing PM0.2 toxicity.

Effect of Raltitrexed on ECA109 Cellular Radiosensitivity and its Mechanism in Esophageal Cancer.

BACKGROUND: To investigate the effect of raltitrexed + X-ray irradiation on esophageal cancer ECA109 cells and analyze the potential action mechanism. METHODS: The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to analyze the inhibitory effect of raltitrexed on cell proliferation. The effect of raltitrexed on radiosensitivity was studied through a clone-forming experiment. The scratch assay and invasion test were performed to understand the cell migration and invasion abilities. The apoptosis rate change was measured using a flow cytometer, and Western Blotting was used to determine the expression of B cell lymphoma-2 (Bcl-2) and Bcl2-associated X protein (Bax) in each group. RESULTS: Raltitrexed significantly inhibited ECA109 proliferation in a time-dose-dependent manner; there were significant differences among different concentrations and times of action. The results of the clone-forming experiment showed a sensitization enhancement ratio of 1.65, and this demonstrated a radiosensitization effect. After the combination of raltitrexed with X-ray, the cell migration distance was shortened, and the number of cells penetrating the membrane was reduced. CONCLUSION: Raltitrexed can inhibit the growth of esophageal cancer ECA109 cells and has a radiosensitization effect.

Research on the Effect Mechanism of Re on Interface Dislocation Networks of Ni-Based Single Crystal Alloys.

The effect of interface dislocation networks on the mechanical properties of new Ni-based single crystal alloys containing Rhenium (Re) is very large. Because the interface dislocations are microscopic in the nano-scale range, this has not been investigated, and it is very difficult to prepare new Ni-based single crystal alloys containing Re. Therefore, six kinds of new Ni-based single crystal alloys containing Re were prepared, and the hardness tests and nonlinear ultrasonic lamb wave tests were performed on the samples. It was found that the density of interface dislocation networks increases with the increase in the content of Re, which improves the blocking ability of matrix phase dislocation cutting into precipitated phase and enhances the inhibition of dislocation movement. The nonlinear ultrasonic lamb wave tests showed that the materials exhibit better mechanical properties when the density of the interface dislocation networks increases. Meanwhile, a new molecular dynamics model which is closer to the real state of an Ni-based single crystal alloy was constructed to reveal the evolution mechanism of interface dislocation networks. The results showed that the potential energy of Re atoms at the interface is the lowest, which affects the reduction of the potential energy of other atoms at the interface, and thus the stability of the model is improved. In addition, according to the change in the total length of dislocation loops in the model system, with the increase in the content of Re atoms, the inhibition of dislocation movement by dislocation networks at the interface is strengthened.

CMOS Scaling for the 5 nm Node and Beyond: Device, Process and Technology.

After more than five decades, Moore's Law for transistors is approaching the end of the international technology roadmap of semiconductors (ITRS). The fate of complementary metal oxide semiconductor (CMOS) architecture has become increasingly unknown. In this era, 3D transistors in the form of gate-all-around (GAA) transistors are being considered as an excellent solution to scaling down beyond the 5 nm technology node, which solves the difficulties of carrier transport in the channel region which are mainly rooted in short channel effects (SCEs). In parallel to Moore, during the last two decades, transistors with a fully depleted SOI (FDSOI) design have also been processed for low-power electronics. Among all the possible designs, there are also tunneling field-effect transistors (TFETs), which offer very low power consumption and decent electrical characteristics. This review article presents new transistor designs, along with the integration of electronics and photonics, simulation methods, and continuation of CMOS process technology to the 5 nm technology node and beyond. The content highlights the innovative methods, challenges, and difficulties in device processing and design, as well as how to apply suitable metrology techniques as a tool to find out the imperfections and lattice distortions, strain status, and composition in the device structures.

Modelling the dynamic gas/particle partitioning process of semi-volatile organic compounds emitted from point sources: Quantitative analysis and impact assessment.

The deleterious impact of pollution point sources on the surrounding environment and human has long been a focal point of environmental research. When considering the local atmospheric dispersion of semi-volatile organic compounds (SVOCs) around the emission sites, it is essential to account the dynamic process for the gas/particle (G/P) partitioning, which involves the transition from an initial state to a steady state. In this study, we have developed a model that enables the prediction of the dynamic process for G/P partitioning of SVOCs, particularly considering the influence from emission. It is important to note that the dynamic processes of the concentrations of SVOCs in particle phase (CP) and in gas phase (CG) differ significantly. These differences arise due to the influence of two critical factors: particulate proportion of SVOCs in the emissions (ϕ0) and octanol-air partitioning coefficient (KOA). The validity of our model was assessed by comparing its predictions of the extremum value of the G/P partitioning quotient (KP) with the results obtained from the steady-state model. Remarkably, the characteristic time (tC), used to evaluate the timescale required for SVOCs to reach steady state, demonstrated different variations with KOA for CP and CG. Additionally, the values of tC were quite different for CP and CG, which were markedly influenced by ϕ0. For some SVOCs with high KOA values, it took approximately 35 h to reach steady state. Furthermore, it was found that the time to achieve 95 % of steady state (t95 ≈ 3tC) could reach approximately 105 h. This duration is sufficient for chemicals to disperse from their emission site to the surrounding areas. Therefore, it is crucial to consider the dynamic process of G/P partitioning in local atmospheric transport studies. Moreover, the influence of ϕ0 should be incorporated into future investigations examining the dynamic process of G/P partitioning.