SEM Electron-Beam-Induced Ultrathin Carbon Deposition Layer on Cu Substrate: Improved Dry Oxidation Protection Performance than CVD Single Layer Graphene.

An amorphous carbon deposition layer (CDL) with nanoscale thickness induced by scanning electron microscope (SEM) electron beam is studied as a carbon-based protective layer on copper (Cu). CDL is prepared by inducing the deposition of pollutants or hydrocarbons in the cavity of SEM through electron beam irradiation (EBI). Wrinkles and cracks will not form and the interfacial spacing of CDL/Cu is smaller than Graphene/Cu (Gr/Cu). The thickness and coverage of the interfacial oxide layer of CDL/Cu are all smaller than that of the Gr/Cu after the same oxidation conditions. Characterization of Raman mapping also demonstrates that CDL shows better oxidation inhibition effects than graphene. The structure of CDL is determined to be C = C and C = O, CH3- and C-O can be loaded vertically on CDL. Density functional theory (DFT) is employed for demonstrating the smaller interfacial gap of CDL/Cu, less wrinkles and cracks and larger adsorbing energy of water/oxygen compared with Gr/Cu. Molecular dynamic (MD) simulation also indicates that the diffusion of water or oxygen into CDL/Cu is more difficult and the oxidation of Cu covered by CDL is well suppressed. This work provides a new approach for the study of carbon-based antioxidant materials on Cu.

A new intestinal supplement 'Synbiotics' therapeutically regulates gut microbiota and activates PPARs pathway to inhibit Alzheimer's disease progression in mouse models.

We aimed to investigate the role of Synbiotic preparations on the interaction of gut microbiota with AD development. APP/PS1 mice were randomized into APP/PS1 and Synbiotics groups, and C57BL/6J mice were used as wild type (WT) control group. The mice in the Synbiotics group and the APP/PS1 group were given Synbiotics and xylo-oligosaccharides for 3 months, respectively. The mice in the WT group were given the same amount of normal saline. Cognitive function was measured. Positron emission computed tomography/magnetic resonance imaging (PET/MRI) was used to detect fasting blood glucose level. Immunohistochemical assay, ELISA, western blot and qRT-PCR were carried out to detect inflammatory factors. DNA extraction of fecal sample was performed to carry out sequencing. Bioinformatics analysis, metabolites sample preparation and Liquid Chromatograph Mass Spectrometer (LC/MS) analysis were also performed. Synbiotics treatment can significantly ameliorate learning and memory competence by inhibiting Aß protein deposition. Different bacteria in the intestine were significantly improved and changes in gut microbiota can affect the intestinal metabolism to affect multiple potential pathways after Synbiotics treatment. Synbiotics treatment can activate peroxisome proliferator activated receptor (PPARs) signaling pathway and significantly reduce neuroinflammation in APP/PS1 mice brains. Synbiotics treatment can effectively reduce neuro-inflammatory response through the regulation of intestinal microflora to delay AD development.

Antimicrobial limonoids from the seeds of Cipadessa cinerascensa.

Phytochemical investigation on the 90% EtOH extract of the seeds of Cipadessa cinerascensa led to the isolation of three new limonoids, cinerascenoids A-C (1-3). Structural elucidation of all the compounds were performed by spectral methods such as 1 D and 2 D (1H-1H COSY, HMQC, and HMBC) NMR spectroscopy. All the limonoids were in vitro evaluated for their antimicrobial activities against six pathogenic microorganisms. Limonoids 1 and 2 exhibited some activities against three Gram negative bacteria with MIC values less than 60 µg/ml.

[A multi-label fusion based level set method for multiple sclerosis lesion segmentation].

A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.

Cytotoxic lanostane triterpenoids from the stems of Schisandra glaucescens.

Phytochemical investigation on the stems of Schisandra glaucescens resulted into the isolation of three new lanostane triterpenoids, 12-hydroxyschiglausin B (1), 12-hydroxykadsuphilactone B (2), and 20R-hydroxyschinalactone C (3). Structural elucidation of all the compounds was accomplished by spectral methods. The isolated compounds were tested in vitro for cytotoxic activities. As a result, triterpenoids 1 and 2 showed cytotoxic activities for all six tested tumor cell lines with IC50 values less than 15 µM.

Effect of autologous bone marrow cell transplantation combined with off-pump coronary artery bypass grafting on cardiac function in patients with chronic myocardial infarction.

OBJECTIVES: This study aimed to investigate the feasibility and effects of intramuscular injections of autologous bone marrow cells (BMC) combined with off-pump coronary artery bypass grafts (OPCAB) on improving cardiac function in chronic myocardial infarction patients. METHODS: Ninety patients with chronic myocardial infarction were prospectively enrolled and randomized to an OPCAB with saline or an OPCAB with BMC-treatment group. After finishing CABG, patients received injections of BMC or saline into the marginal area of the infarct. The primary endpoint was incidence of emergent adverse events within 6 months. RESULTS: There were no differences between the control and BMC-treated groups in baseline ejection fractions (EF) or wall motion score indices (WMSI) in the affected segments. At the 6-month follow-up, the ejection fraction was significantly increased in the BMC-treated group compared to controls (47.58 ± 6.34 vs. 40.11 ± 7.42; p < 0.05), whereas the WMSI were significantly decreased (1.25 ± 0.32 vs. 1.54 ± 0.53; p < 0.05), with no occurrences of life-threatening arrhythmias or death. The addition of BMC injections to OPCAB treatment increased regional perfusion to the marginal infarct area. CONCLUSION: These results demonstrate that BMC transplant is beneficial to the cardiac function with no adverse effects, and therefore a safe and feasible adjunct therapy providing beneficial effects in clinical practice.

125I seed implantation for lymph node metastasis from radioactive iodine-refractory differentiated thyroid carcinoma: a study on short-term efficacy and dosimetry.

Objective: To investigate the feasibility and evaluate the safety and effectiveness of Computed Tomography (CT) guided125I radioactive particle implantation for treating lymph node metastases in radioiodine-refractory differentiated thyroid cancer (RAIR-DTC). To verify the accuracy of the computerized three-dimensional treatment planning system (TPS) in treating lymph node metastasis using125I particle implantation at the dosimetric level. Methods: A retrospective analysis was conducted on 42 patients with RAIR-DTC and lymph node metastases who were admitted to the General Hospital of the Northern Theater Command between December 2016 and January 2019. During this analysis, physicians utilized preoperative CT images to design an intraoperative plan using TPS. The dosimetric parameters of the postoperative plan were then compared to the preoperative plan. Additionally, this study examined the changes in tumor size and tumor-related marker Thyroglobulin (Tg) values in patients at 2, 6, and 12 months after the operation. Results: The number of125I radioactive particles implanted in 42 patients was 226, with an average of 14.5 (range 2.0-30.0) particles implanted per lesion. The local remission rates were 97.62% (41/42), 88.10% (37/42), and 85.71% (36/42) at 2, 6, and 12 months postoperatively, respectively. The volume of the lesions was (4.44 ± 1.57) cm3, (4.20 ± 1.70) cm3, and (4.23 ± 1.77) cm3at 2, 6, and 12 months after treatment, respectively, which significantly decreased from the preoperative baseline level of (6.87 ± 1.67) cm3(t-values: 9.466, 9.923, 7.566, all P<0.05). The Tg levels were 15.95 (5.45, 73.93) µg/L, 8.90 (2.20, 39.21) µg/L, and 6.00 (1.93, 14.18) µg/L at 2, 6, and 12 months after treatment, respectively, which were significantly lower than the preoperative baseline levels of 53.50 (20.94, 222.92) µg/L (Z values: -5.258, -5.009, -4.987, all P < 0.001). Postoperatively, Delivered to 90% of the GTV(D90) was slightly lower than the prescribed dose in 95.23% (40/42) of patients, but the difference was not statistically significant [(12,378.8 ± 3,182.0), (12,497.8 ± 1,686.4) cGy; t=0.251, P>0.05], and postoperative dose parameters delivered to 100% of the gross tumor volume (GTV)(D100) (6,881.5 ± 1,381.8) cGy, the volume percentages of GTV receiving 150% of the prescribed dose(V150) (58.5 ± 18.40)%) were lower than the preoperative plan D100 (8,085.8 ± 2,330.0) cGy, V150 (66.5 ± 17.70)%; t-value=8.913 and 3.032, both P<0.05; the remaining indicators were not significantly different from the preoperative plan (the differences in the number of implanted particles, Planning Target Volume(PTV), the volume percentages of GTV receiving 100% of the prescribed dose(V100), Homogeneity Index(HI)were not statistically significant (t/Z = -0.593, -1.604, 1.493, -0.663, all P>0.05). Conclusion: Referring to the TPS preoperative plan, the125I particle implantation therapy for RAIR-DTC lymph node metastasis can achieve the expected dose distribution, ensuring precise short-term local tumor control efficacy.

Predictive Value of Serum microRNA-29b-3p in Recurrence of Atrial Fibrillation After Radiofrequency Catheter Ablation.

Objective: Atrial fibrillation (AF) is a common arrhythmia. This study explored serum miR-29b-3p expression in AF patients and its value in predicting AF recurrence after radiofrequency catheter ablation (RFCA). Methods: Totally 100 AF patients who underwent RFCA were enrolled, with 100 individuals without AF as controls. Serum miR-29b-3p expression in participants was determined using RT-qPCR. The correlation between miR-29b-3p and atrial fibrosis markers (FGF-21/FGF-23) was assessed by Pearson analysis. The diagnostic efficacy of serum miR-29b-3p and FGF-21/FGF-23 in predicting AF recurrence after RFCA was analyzed by the receiver operating characteristic (ROC) curves. The Kaplan-Meier method was adopted to evaluate the effect of miR-29b-3p expression on the incidence of AF recurrence after RFCA. The independent risk factors for AF recurrence after RFCA were analyzed by logistic regression analysis. Results: Serum miR-29b-3p was poorly expressed in AF patients. After RFCA, AF patients showed elevated serum miR-29b-3p expression. Serum miR-29b-3p expression in AF patients negatively correlated with serum FGF-21 and FGF-23 concentrations. The cut-off values of serum miR-29b-3p, FGF-21, and FGF-23 in identifying AF recurrence were 0.860 (sensitivity: 100.00%, specificity: 39.71%), 222.2 pg/mL (sensitivity: 96.88%, specificity: 32.35%) and 216.3 ng/mL (sensitivity: 53.13%, specificity: 70.59%), respectively. Patients with low miR-29b-3p expression had a significantly higher incidence of AF recurrence than patients with high miR-29b-3p expression. Serum miR-29b-3p expression was one of the independent risk factors for AF recurrence after RFCA. Conclusion: Low miR-29b-3p expression in AF patients has certain predictive values and is one of the independent risk factors for AF recurrence after RFCA.

Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.

Objectives: This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours. Methods: Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours. Results: In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886-0.996], and 0.956 [0.873-1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887-1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887-1.000], and 1.000 [0.999-1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867-1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990-1.000] and 1.000 [0.999-1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999-1.000] under the early fusion strategy. Conclusion: This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Optimization of a Ge2Sb2Te5-Based Electrically Tunable Phase-Change Thermal Emitter for Dynamic Thermal Camouflage.

Controlling infrared thermal radiations can significantly improve the environmental adaptability of targets and has attracted increasing attention in the field of thermal camouflage. Thermal emitters based on Ge2Sb2Te5 (GST) can flexibly change their radiation energy by controlling the reversible phase transition of GST, which possesses fast switching speed and low power consumption. However, the feasibility of the dynamic regulation of GST emitters lacks experimental and simulation verification. In this paper, we propose an electrically tunable thermal emitter consisting of a metal-insulator-metal plasmonic metasurface based on GST. Both optical and thermal simulations are conducted to optimize the structural parameters of the GST emitter. The results indicate that this emitter possesses large emissivity tunability, wide incident angle, polarization insensitivity, phase-transition feasibility, and dynamic thermal camouflage capability. Therefore, this work proposes a reliable optimization method to design viable GST-based thermal emitters. Moreover, it provides theoretical support for the practical application of phase-change materials in dynamic infrared thermal camouflage technology.

Analyzing the value of delayed 18 F-FDG PET/CT images in diagnosing small colorectal cancer liver metastases in patients with hypothyroidism based on diagnostic accuracy and image standardized uptake value.

PURPOSE: The objective of this study was to investigate the value of delayed 18F fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) images in patients with small colorectal cancer liver metastases (CRLMs) with hypothyroidism. METHOD: We performed a retrospective analysis of 66 small-CRLM patients with hypothyroidism and 66 small-CRLM patients with euthyroidism, all of whom underwent dual-time-point 18 F-FDG PET/CT imaging. First, the diagnostic accuracy of PET/CT early imaging and PET/CT delayed imaging on lesions was analyzed. Next, the correlation of metabolic parameters between PET/CT early imaging and PET/CT delayed imaging was analyzed according to the grouping of all lesions. Finally, PET/CT parameters were analyzed for correlation with thyroid hormones. RESULTS: The diagnostic accuracy of delayed imaging in small-CRLM patients with hypothyroidism is not as good as that in small-CRLM patients with euthyroidism; PET/CT metabolic parameters are also unfavorable for the diagnosis of small-CRLM. For small-CRLM patients with hypothyroidism, the greater the thyroid-stimulating hormone level, the greater the uptake of 18 F-FDG in normal liver tissue, and the smaller the ratio of tumor lesion uptake to normal liver tissue uptake. CONCLUSION: PET/CT-delayed imaging has better performance than early imaging in small-CRLM patients with euthyroidism. However, the more severe the hypothyroidism, the worse the diagnostic delayed imaging performance. The scan time can be extended appropriately to optimize the imaging efficacy.

Meta-analysis of the clinical efficacy and safety of single versus dual plate in the treatment of comminuted distal femur fractures.

Objective: Through meta-analysis, this study aims to comprehensively evaluate the efficacy of single-plating and double-plating in the treatment of comminuted fractures of the distal femur. Methods: Computer searches of PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), VIP, and Wanfang digital journals were performed, and the timeframe for the searches was from the establishment of each database to July 2023 for each of the databases. Meta-analysis was performed using RevMan 5.4 software provided by the Cochrane Library, and the review process was registered in the PROSPERO database. Results: A total of ten studies were included for statistical analysis. One randomised controlled study and nine retrospective cohort studies with a total of 563 patients were included. The double-plate group was superior to the single-plate group in terms of knee mobility at 6 months postoperatively, overall postoperative complications, and the rate of healing of knee deformity. However, it increased the operation time and intraoperative bleeding, and the difference between the two groups was statistically significant (P < 0.05). There was no significant difference between the two groups in terms of excellent knee function rate, fracture healing time, plate fracture, postoperative infection, delayed fracture healing, and non-union (P ≥ 0.05). Conclusion: Double plate fixation for comminuted fractures of the distal femur can improve knee mobility at 6 months postoperatively, reduce overall postoperative complications, and decrease the incidence of malunion healing. However, it increases operative time and bleeding. Randomised studies are needed to provide strong evidence in the future.

High-Safety Electrolytes with an Anion-Rich Solvation Structure Tuned by Difluorinated Cations for High-Voltage Lithium Metal Batteries.

As next-generation energy storage devices, lithium metal batteries (LMBs) must offer high safety, high-voltage resistance, and a long life span. Electrolyte engineering is a facile strategy to tailor the interfacial chemistry of LMBs. In particular, the solvation structure and derived solid electrolyte interphase (SEI) are crucial for a satisfactory battery performance. Herein, a novel middle-concentrated ionic liquid electrolyte (MCILE) with an anion-rich solvation structure tuned by difluorinated cations is demonstrated to achieve ultrahigh safety, high-voltage stability, and excellent ternary-cathode compatibility. Novel gem-difluorinated cations first synthesized for prestoring fluorine on positively charged species, not only preferentially adsorb in the inner-Helmholtz layers, but also participate in regulating the Li+ solvation structure, resulting in a robust interphase. Moreover, these weak interactions in the Li+ solvation structure including anion-solvent and ionic liquid (IL) cation-solvent pairs are first revealed, which are beneficial for promoting an anion-dominated solvation structure and the desolvation process. Benefiting from the unique anion-rich solvation structure, a stable hetero-SEI structure is obtained. The designed MCILE exhibits compatibility with Li metal anode and the high-voltage ternary cathode at high temperatures (60 °C). This work provides a new approach for regulating the solvation structure and electrode interphase chemistry of LMBs via difluorinated IL cations.

DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

N4-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Traditional laboratory methods for detecting 4mC DNA methylation have limitations, but the rise of artificial intelligence has introduced efficient computational strategies for 4mC site prediction. Despite this progress, challenges persist in terms of model performance and interpretability. To tackle these challenges, we propose DeepSF-4mC, a deep learning model specifically designed for predicting DNA cytosine 4mC methylation sites by leveraging sequence features. Our approach incorporates multiple encoding techniques to enhance prediction accuracy, increase model stability, and reduce the computational resources needed. Leveraging transfer learning, we harness existing models to enhance performance through learned representations or fine-tuning. Ensemble learning techniques combine predictions from multiple models, boosting robustness and accuracy. This research contributes to DNA methylation analysis and lays the groundwork for understanding 4mC's multifaceted role in biological processes. The web server for DeepSF-4mC is accessible at: http://deepsf-4mc.top/and the original code can be found at: https://github.com/754131799/DeepSF-4mC.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

Tubeimuside I improves the efficacy of a therapeutic Fusobacterium nucleatum dendritic cell-based vaccine against colorectal cancer.

Introduction: Fusobacterium nucleatum (F. nucleatum) infection has been confirmed to be associated with the development, chemoresistance, and immune evasion of colorectal cancer (CRC). The complex relationship between the microorganism, host cells, and the immune system throughout all stages of CRC progression, which makes the development of new therapeutic methods difficult. Methods: We developed a new dendritic cell (DC) vaccine to investigate the antitumor efficacy of CRC immunotherapy strategies. By mediating a specific mode of interaction between the bacteria, tumor, and host, we found a new plant-derived adjuvant, tubeimuside I (TBI), which simultaneously improved the DC vaccine efficacy and inhibited the F. nucleatum infection. Encapsulating TBI in a nanoemulsion greatly improved the drug efficacy and reduced the drug dosage and administration times. Results: The nanoemulsion encapsulated TBI DC vaccine exhibited an excellent antibacterial and antitumor effect and improved the survival rate of CRC mice by inhibiting tumor development and progression. Discussion: In this study, we provide a effective strategy for developing a DC-based vaccine against CRC and underlies the importance of further understanding the mechanism of CRC processes caused by F. nucleatum.

Analysis of independent risk factors for aneurysm rupture based on carotid tortuosity index and morphological parameters of single intracranial aneurysms in anterior circulation.

PURPOSE: Our study focused on the risk factors associated with anterior circulation intracranial aneurysm (IA) rupture by examining the carotid artery (CA) tortuosity index (TI) and anterior circulation IA morphological parameters. METHOD: This study conducted a retrospective analysis of clinical and imaging data from 163 patients with anterior circulation IA diagnosed by head and neck computed tomography angiography (CTA). The patients were categorized into two groups: the ruptured group (57 cases) and the unruptured group (106 cases). CA was categorized based on its location into three segments: the extracranial segment of the internal carotid artery (EICA) TI, the angle of the internal carotid artery (ICA) and the common carotid artery (CCA) TI. Measure the morphological parameters of all IA: IA length neck (L), IA height (H), aneurysm diameter width (D), the ratio of L to the mean diameter of the IA-bearing artery (SR), the ratio of H to D (AR), the angle of flow inflow (FA) and IA angle (AA). The study conducted five types of analysis to determine the risk factors for anterior circulation IA rupture. The first was an univariate analysis of the risk factors. The second was an analysis of the correlation between CA TI and IA morphological parameters. The third used multivariate logistic stepwise regression analysis to analyse independent risk factors for IA rupture. The fourth was to plot ROC curves to build a predictive model for IA rupture and calculate diagnostic thresholds. Finally, a data set from another hospital (78 cases) was used as a validation set to validate the multivariate model. RESULT: Univariate analysis revealed that there were statistically significant differences (P < 0.05) in gender, EICA TI, location of IA and IA morphological parameters (FA, H, AR, L, SR), which acted as risk factors for anterior circulation IA rupture. The results of Spearman correlation analysis indicate that CCA TI is significantly correlated with SR, H and L (P < 0.05), while EICA TI is significantly correlated with FA and L (P < 0.05). The results of multivariate logistic analysis showed that FA (OR = 1.072, 95%CI = 1.04-1.10, P < 0.001), SR (OR = 4.949, 95%CI = 1.96-12.53, P = 0.001), EICA TI (OR = 1.037, 95%CI = 1.01-1.07, P = 0.003) were independent risk factors for IA rupture. The ROC curve plotting results suggest that the area under the curve (AUC) of FA is 0.860 with a diagnostic threshold of 110.1°; the AUC of SR is 0.786 with a diagnostic threshold of 1.67; the AUC of EICA TI is 0.723 with a diagnostic threshold of 28.845; the AUC of the three combined is 0.903 with a threshold of 0.480. The combined factor diagnostic model is validated according to the validation set, and the results show that the AUC (0.866) of the validation set is not much different from the AUC (0.903) of the multivariate model, and the multivariate model has a better diagnostic effect. CONCLUSION: In clinical practice, it is important to consider the evaluation of aneurysm rupture in combination with imaging, as FA, SR and ECIA TI are independent risk factors for IA rupture in the anterior circulation. Unlike the IA morphological parameters, EICA TI is an often overlooked extracranial parameter, but is equally important in its power to predict IA rupture. When the EICA TI exceeds 28.845, the IA has the possibility of rupture. Finally, multivariate diagnostic model are of interest when considering rupture of the anterior circulation IA.

Clinical efficacy analysis of radioactive 125I particle implantation for the treatment of pelvic local recurrence of cervical cancer after radiotherapy.

Purpose: To explore the efficacy of CT-guided iodine-125 (125I) radioactive particle implantation for the treatment of pelvic local recurrence of cervical cancer after radiotherapy. Material and methods: To analyze clinical data of 46 patients with pelvic local recurrence of cervical cancer after radiotherapy, who underwent 125I particle implantation, post-operative follow-up using Kaplan-Meier method to calculate the rate of local control, and observation of complications. Univariate and multi-factorial analyses were performed with log-rank test and Cox regression statistical method to observe changes in serum SCC-Ag and CEA levels. Results: A total of 57 lesions in 46 patients had a short-term local control rate of 87.72% (50/57) and effective rate of 68.42% (39/57). As of the end of follow-up, the median local progression-free survival time of 46 patients was 12.2 (range, 3.5-32) months, the median overall survival time was 16.3 (range, 3.5-40) months, with 1-year and 2-year OS of 63.04% and 41.30%. Univariate and multi-factorial analyses suggested that local recurrent mass size and recurrence site were the main factors affecting survival. Loco-regional progression-free survival (LPFS) time was 15.5 and 11.25 months in cases with recurrent mass diameter < 3 cm and ≥ 3 cm, respectively (χ2 = 10.83, p = 0.001 < 0.05). In patients with pelvic wall recurrence and central recurrence, the time to LPFS was 15.80 and 10.00 months, respectively (χ2 = 8.833, p = 0.0030 < 0.05). Tumor markers serum SCC-Ag and CEA decreased to different degrees after treatment, and the difference was statistically significant. The median LPFS in the SCC-Ag ≤ 3.5 ng/ml group was significantly higher than that in the SCC-Ag > 3.5 ng/ml group (χ2 = 4.241, p = 0.036 < 0.05). No serious post-operative complications were observed. Conclusions: CT-guided 125I seed implantation has significant short-term efficacy in the treatment of pelvic local recurrence of cervical cancer after radiotherapy. It can improve the effective rate in patients with initial clinical stage II, especially in those with locally recurrent masses < 3 cm and in those with a recurrence in the pelvic wall.

3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.

BACKGROUND: Fundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature. METHODS: This study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases. RESULTS: In fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy. CONCLUSIONS: This study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.

Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images.

Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.