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.

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.

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.

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.

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.

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.

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.

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.

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.

High-performance GeSi/Ge multi-quantum well photodetector on a Ge-buffered Si substrate.

This work demonstrates a high-performance photodetector with a 4-cycle Ge0.86Si0.14/Ge multi-quantum well (MQW) structure grown by reduced pressure chemical vapor deposition techniques on a Ge-buffered Si (100) substrate. At -1 V bias, the dark current density of the fabricated PIN mesa devices is as low as 3 mA/cm2, and the optical responsivities are 0.51 and 0.17 A/W at 1310 and 1550 nm, respectively, corresponding to the cutoff wavelength of 1620 nm. At the same time, the device has good high-power performance and continuous repeatable light response. On the other hand, the temperature coefficient of resistance (TCR) of the device is as high as -5.18%/K, surpassing all commercial thermal detectors. These results indicate that the CMOS-compatible and low-cost Ge0.86Si0.14/Ge multilayer structure is promising for short-wave infrared and uncooled infrared imaging.

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.

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.

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.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm.

INTRODUCTION: Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy. MATERIALS AND METHODS: Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers. RESULTS: Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86. CONCLUSIONS: Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

In vitro simulated saliva, gastric, and intestinal digestion followed by faecal fermentation reveals a potential modulatory activity of Epimedium on human gut microbiota.

Herba Epimedii, known for its rich array of bioactive ingredients and widespread use in ethnopharmacological practices, still lacks a comprehensive understanding of its gastrointestinal biotransformation. In this study, we qualitatively explored the dynamic changes in Epimedium sagittatum components during in vitro simulated digestions, with a quantitative focus on its five major flavonoids. Notably, significant metabolism of E. sagittatum constituents occurred in the simulated small intestinal fluid and colonic fermentation stages, yielding various low molecular weight metabolites. Flavonoids like kaempferol glycosides were fully metabolized in the simulated intestinal fluid, while hyperoside digestion occurred during simulated colon digestion. Colonic fermentation led to the production of two known bioactive isoflavones, genistein, and daidzein. The content and bioaccessibility of the five major epimedium flavonoids-icariin, epimedin A, epimedin B, epimedin C, and baohuoside I-significantly increased after intestinal digestion. During colon fermentation, these components gradually decreased but remained incompletely metabolized after 72 h. Faecal samples after E. sagittatum fermentation exhibited shift towards dominance by Lactobacillus (Firmicutes), Bifidobacterium (Actinobacteria), Streptococcus (Firmicutes), and Dialister (Firmicutes). These findings enhance our comprehension of diverse stages of Herba Epimedii constituents in the gut, suggesting that the primary constituents become bioaccessible in the colon, where new bioactive compounds may emerge.