Active multi-focus vortex beam terahertz encoding metasurface based on Dirac semimetals.

An electromagnetic wavefront can be flexibly manipulated by discrete phase coding on the coding unit. In this paper, we designed two coding metasurfaces with 1-bit and 3-bit based on active tuning of Dirac semimetals by controlling the Fermi level (E F) with an external polarization voltage. The size and structure of the metasurface remain unchanged with this strategy. Both designs were found to be dynamically tunable. The 1-bit coding metasurface enables beam conversion, single-focus switching, and switching between single-focus and multi-focus. On the other hand, the 3-bit coding metasurface enables the switching between vortex beams and single-beam mirror reflections. These proposed structures have potential applications in terahertz (THz) communications and terahertz-focused imaging, opening up new possibilities for the dynamic modulation of THz waves.

CT Image-Based Radiomic Analysis for Detecting PD-L1 Expression Status in Bladder Cancer Patients.

RATIONALE AND OBJECTIVES: The role of Programmed death-ligand 1 (PD-L1) expression is crucial in guiding immunotherapy selection. This study aims to develop and evaluate a radiomic model, leveraging Computed Tomography (CT) imaging, with the objective of predicting PD-L1 expression status in patients afflicted with bladder cancer. MATERIALS AND METHODS: The study encompassed 183 subjects diagnosed with histologically confirmed bladder cancer, among which the PD-L1(+) cohort constituted 60.1% of the total population. Stratified random sampling was utilized at a 7:3 ratio. We employed five diverse machine learning algorithms-Decision Tree, Random Forest, Linear Support Vector Classification, Support Vector Machine, and Logistic Regression-to establish radiomic models on the training dataset. These models endeavored to predict PD-L1 expression status premised on radiomic features derived from region-of-interest segmentation. Subsequent to this, the predictive performance of these models was examined on a validation set employing the receiver operating characteristic (ROC) curve. The DeLong test was utilized to contrast ROC curves, thereby pinpointing the model with superior predictive accuracy. RESULTS: 16 features were chosen for the model construction. All five models revealed strong performance in the training set (AUC, 0.920-1) and commendable predictive ability in the validation set (AUC, 0.753-0.766). As per the DeLong test, no statistically significant disparities were observed among any of the models (P > 0.05) in the validation set. Additional verification through the calibration curve and decision curve analysis indicated that the Logistic Regression model exhibited extraordinary precision and practicality. CONCLUSION: Our machine learning model, grounded on radiomic features, demonstrated its proficiency in accurately distinguishing bladder cancer patients with high PD-L1 expression. Future research, incorporating more exhaustive datasets, could potentially augment the predictive efficiency of radiomic algorithms, thereby advancing their clinical utility.

Quantitative analysis of the impact of respiratory state on the heartbeat-induced movements of the heart and its substructures.

PURPOSE: This study seeks to examine the influence of the heartbeat on the position, volume, and shape of the heart and its substructures during various breathing states. The findings of this study will serve as a valuable reference for dose-volume evaluation of the heart and its substructures in radiotherapy for treating thoracic tumors. METHODS: Twenty-three healthy volunteers were enrolled in this study, and cine four-dimensional magnetic resonance images were acquired during periods of end-inspiration breath holding (EIBH), end-expiration breath holding (EEBH), and deep end-inspiration breath holding (DIBH). The MR images were used to delineate the heart and its substructures, including the heart, pericardium, left ventricle (LV), left ventricular myocardium, right ventricle (RV), right ventricular myocardium (RVM), ventricular septum (VS), atrial septum (AS), proximal and middle portions of the left anterior descending branch (pmLAD), and proximal portion of the left circumflex coronary branch (pLCX). The changes in each structure with heartbeat were compared among different respiratory states. RESULTS: Compared with EIBH, EEBH increased the volume of the heart and its substructures by 0.25-3.66%, while the average Dice similarity coefficient (DSC) increased by - 0.25 to 8.7%; however, the differences were not statistically significant. Conversely, the VS decreased by 0.89 mm in the left-right (LR) direction, and the displacement of the RV in the anterior-posterior (AP) direction significantly decreased by 0.76 mm (p < 0.05). Compared with EIBH and EEBH, the average volume of the heart and its substructures decreased by 3.08-17.57% and 4.09-20.43%, respectively, during DIBH. Accordingly, statistically significant differences (p < 0.05) were observed in the volume of the heart, pericardium, LV, RV, RVM, and AS. The average DSC increased by 0-37.04% and - 2.6 to 32.14%, respectively, with statistically significant differences (p < 0.05) found in the right ventricular myocardium and interatrial septum. Furthermore, the displacements under DIBH decreased in the three directions (i.e.,- 1.73 to 3.47 mm and - 0.36 to 2.51 mm). In this regard, the AP displacement of the heart, LV, RV, RVM, LR direction, LV, RV, and AS showed statistically significant differences (p < 0.05). The Hausdorff distance (HD) of the heart and its substructures under the three breathing states are all greater than 11 mm. CONCLUSION: The variations in the displacement and shape alterations of the heart and its substructures during cardiac motion under various respiratory states are significant. When assessing the dose-volume index of the heart and its substructures during radiotherapy for thoracic tumors, it is essential to account for the combined impacts of cardiac motion and respiration.

Supercritical CO2 fluid extract from Stellariae Radix ameliorates 2,4-dinitrochlorobenzene-induced atopic dermatitis by inhibit M1 macrophages polarization via AMPK activation.

Yin chai hu (Radix Stellariae) is a root medicine that is frequently used in Chinese traditional medicine to treat fever and malnutrition. In modern medicine, it has been discovered to have anti-inflammatory, anti-allergic, and anticancer properties. In a previous study, we were able to extract lipids from Stellariae Radix using supercritical CO2 extraction (SRE), and these sterol lipids accounted for up to 88.29% of the extract. However, the impact of SRE on the development of atopic dermatitis (AD) has not yet been investigated. This study investigates the inhibitory effects of SRE on AD development using a 2,4-dinitrochlorobenzene (DNCB)-induced AD mouse model. Treatment with SRE significantly reduced the dermatitis score and histopathological changes compared with the DNCB group. The study found that treatment with SRE resulted in a decrease of pro-inflammatory cytokines TNF-α, CXC-10, IL-12, and IL-1ß in skin lesions. Additionally, immunohistochemical analysis revealed that SRE effectively suppressed M1 macrophage infiltration into the AD lesion. Furthermore, the anti-inflammatory effect of SRE was evaluated in LPS + INF-γ induced bone marrow-derived macrophages (BMDMs) M1 polarization, SRE inhibited the production of TNF-α, CXC-10, IL-12, and IL-1ß and decreased the expression of NLRP3. Additionally, SRE was found to increase p-AMPKT172, but had no effect on total AMPK expression, after administration of the AMPK inhibitor Compound C, the inhibitory effect of SRE on M1 macrophages was partially reversed. The results indicate that SRE has an inhibitory effect on AD, making it a potential therapeutic agent for this atopic disorder.

Elastic properties and tensile strength of 2D Ti3C2Tx MXene monolayers.

Two-dimensional (2D) transition metal nitrides and carbides (MXenes), represented by Ti3C2Tx, have broad applications in flexible electronics, electromechanical devices, and structural membranes due to their unique physical and chemical properties. Despite the Young's modulus of 2D Ti3C2Tx has been theoretically predicted to be 0.502 TPa, which has not been experimentally confirmed so far due to the measurement is extremely restricted. Here, by optimizing the sample preparation, cutting, and transfer protocols, we perform the direct in-situ tensile tests on monolayer Ti3C2Tx nanosheets using nanomechanical push-to-pull equipment under a scanning electron microscope. The effective Young's modulus is 0.484 ± 0.013 TPa, which is much closer to the theoretical value of 0.502 TPa than the previously reported 0.33 TPa by the disputed nanoindentation method, and the measured elastic stiffness is ~948 N/m. Moreover, during the process of tensile loading, the monolayer Ti3C2Tx shows an average elastic strain of ~3.2% and a tensile strength as large as ~15.4 GPa. This work corrects the previous reports by nanoindentation method and demonstrates that the Ti3C2Tx indeed keeps immense potential for broad range of applications.

Evaluation of Prebiotic Activity of Stellariae Radix Polysaccharides and Its Effects on Gut Microbiota.

This study aims to evaluate the prebiotic potential of polysaccharides derived from Stellariae Radix (SRPs) and explore their influence on the gut microbiota composition in mice. Lactobacillus acidophilus and Bifidobacterium longum were cultivated in an MRS medium, while their growth kinetics, clumping behavior, sugar utilization, pH variation, growth density, and probiotic index were meticulously monitored. Additionally, the impact of crude Stellariae Radix polysaccharides (CSRP) on the richness and diversity of gut microbiota in mice was assessed via 16S rDNA sequencing. The results demonstrated the remarkable ability of CSRPs to stimulate the proliferation of Lactobacillus acidophilus and Bifidobacterium longum. Moreover, the oral administration of CSRPs to mice led to a noticeable increase in beneficial bacterial populations and a concurrent decrease in detrimental bacterial populations within the intestinal flora. These findings provided an initial validation of CSRPs as a promising agent in maintaining the equilibrium of gut microbiota in mice, thereby offering a substantial theoretical foundation for developing Stellariae Radix as a prebiotic ingredient in various applications, including food, healthcare products, and animal feed. Furthermore, this study presented novel insights for the exploration and utilization of Stellariae Radix resources.

Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.

BACKGROUND: The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models. METHODS: A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model's accuracy. RESULTS: The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators. CONCLUSION: The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

The Impact of Growth Years on the Medicinal Material Characteristics and Metabolites of Stellaria dichotoma L. var. lanceolata Bge. Reveals the Optimal Harvest Age.

The original plant of Chinese medicine Stellariae Radix (Yin Chai Hu) is Stellaria dichotoma L. var. lanceolata Bge (abbreviated as SDL). SDL is a perennial herbaceous plant and a characteristic crop in Ningxia. Growth years are vital factors that affect the quality of perennial medicinal materials. This study aims to investigate the impact of growth years on SDL and screen for the optimal harvest age by comparing the medicinal material characteristics of SDL with different growth years. Additionally, metabolomics analysis using UHPLC-Q-TOF MS was employed to investigate the impact of growth years on the accumulation of metabolites in SDL. The results show that the characteristics of medicinal materials and the drying rate of SDL gradually increase with the increase in growth years. The fastest development period of SDL occurred during the first 3 years, after which the development slowed down. Medicinal materials characteristics of 3-year-old SDL exhibited mature qualities with a high drying rate, methanol extract content, and the highest content of total sterols and total flavonoids. A total of 1586 metabolites were identified, which were classified into 13 major classes with more than 50 sub-classes. Multivariate statistical analysis indicated significant differences in the diversity of metabolites of SDL in different growth years, with greater differences observed in metabolites as the growth years increased. Moreover, different highly expressed metabolites in SDL at different growth years were observed: 1-2 years old was beneficial to the accumulation of more lipids, while 3-5 years old was conducive to accumulating more alkaloids, benzenoids, etc. Furthermore, 12 metabolites accumulating with growth years and 20 metabolites decreasing with growth years were screened, and 17 significantly different metabolites were noted in 3-year-old SDL. In conclusion, growth years not only influenced medicinal material characteristics, drying rate, content of methanol extract, and total sterol and flavonoid contents, but also had a considerable effect on SDL metabolites and metabolic pathways. SDL planted for 3 years presented the optimum harvest time. The screened significantly different metabolites with biological activity, such as rutin, cucurbitacin e, isorhamnetin-3-o-glucoside, etc., can be utilized as potential quality markers of SDL. This research provides references for studying the growth and development of SDL medicinal materials, the accumulation of metabolites, and the selection of optimal harvest time.

Parity-time symmetry transition and exceptional points in terahertz metal-graphene hybrid metasurface with switchable transmission and reflection characteristics.

Non-Hermitian metasurfaces provide an excellent platform for the study of parity-time (PT) symmetry transition. The exceptional point (EP) in the transition process exhibits peculiar physical phenomena and enriches the development of metasurfaces. In this study, a terahertz metal-graphene hybrid metasurface that can study PT symmetry transition and EP in transmission and reflection polarization channels is designed by using the phase transition characteristics of VO2. The tunable asymmetric loss and PT symmetry transition can be actively controlled by changing the Fermi energy of the graphene strip. Interestingly, owing to the special chirality of the structure, the original metasurface, and the mirrored metasurface degenerate into a circularly polarized state with opposite rotations at the same Fermi energy. The π-phase mutation at EP is observed following the interaction of circularly polarized waves and the metasurface and is expected to have good application prospects in environmental monitoring and gas sensing.

Metabolomic Analysis Reveals the Metabolic Diversity of Wild and Cultivated Stellaria Radix (Stellaria dichotoma L. var. lanceolata Bge.).

Stellaria Radix, called Yinchaihu in Chinese, is a traditional Chinese medicine, which is obtained from the dried roots of Stellaria dichotoma L. var. lanceolata Bge. Cultivated yinchaihu (YCH) has become a main source of production to alleviate the shortage of wild plant resources, but it is not clear whether the metabolites of YCH change with the mode of production. In this study, the contents of methanol extracts, total sterols and total flavonoids in wild and cultivated YCH are compared. The metabolites were analyzed by ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry. The content of methanol extracts of the wild and cultivated YCH all exceeded the standard content of the Chinese Pharmacopoeia. However, the contents of total sterols and total flavonoids in the wild YCH were significantly higher than those in the cultivated YCH. In total, 1586 metabolites were identified by mass spectrometry, and 97 were significantly different between the wild and cultivated sources, including ß-sitosterol, quercetin derivatives as well as many newly discovered potential active components, such as trigonelline, arctiin and loganic acid. The results confirm that there is a rich diversity of metabolites in the wild and cultivated YCH, and provide a useful theoretical guidance for the evaluation of quality in the production of YCH.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

Comparative metabolomics provides novel insights into correlation between dominant habitat factors and constituents of Stellaria Radix (Stellaria dichotoma L. var. lanceolata Bge.).

Stellaria dichotoma L. var. lanceolata Bge. (SDL) is the original plant of the traditional Chinese medicine Yinchaihu (Stellaria Radix). It is mainly distributed in the arid desert areas of northwest China, which is the genuine medicinal material and characteristic cultivated crop in Ningxia. This study aims to analyze the effects of different origins on SDL metabolites and quality, as well as to screen the dominant habitat factors affecting SDL in different origins. In this study, metabolites of SDL from nine different production areas were analyzed by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) based metabolomics. And field investigations were conducted to record thirteen habitat-related indicators. Results showed that 1586 metabolites were identified in different origins, which were classified as thirteen categories including lipids, organic acids and organic heterocyclic compounds derivatives. Multivariate statistical analysis showed that the metabonomic spectra of SDL from different origins had various characteristics. What's more, co-expression network correlation analysis revealed that three metabolites modules (MEturquoise, MEbrown and MEblue) were more closely with the habitat factors and 104 hub metabolites were further screened out as the habitat-induced metabolite indicators. Besides, soil texture, soil pH value and soil total salt content were found as the dominant habitat factors which affect SDL metabolites. In conclusion, the study showed different habitat factors had various effects on SDL's quality and established relationship between them, which provide reference for revealing SDL's genuineness formation mechanism and guiding industrial crops practical production by habitat factors selection.

Prognostic Index for Nonsmall Cell Lung Cancer Based on Immune-Related Genes Expression.

Immune system dysregulation is associated with tumor incidence and growth. Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets.

Imaging Signs for Determining Surgery Timing of Acute Intestinal Obstruction.

We aimed to analyze the computed tomography (CT) imaging signs of bowel wall ischemia in patients with acute intestinal obstruction and construct an imaging prediction model to guide clinical treatment. The CT imaging signs of patients with acute intestinal obstruction diagnosed in our center in recent 6 years were collected for retrospective analysis. The etiology of intestinal obstruction and incidence rate of bowel wall ischemia were recorded, and the specific CT findings of bowel wall ischemia, including mesenteric edema, bowel wall thickening, and fish tooth sign, were analyzed. Among the 302 patients selected, 130 surgically treated patients were eligible for analysis. Bowel wall ischemia in acute intestinal obstruction showed an incidence rate of 14.90%, and the incidence rates of bowel wall ischemia in intra-abdominal hernia, intussusception, incarcerated external abdominal hernia, and volvulus were about 92.30%, 50%, 35.71%, 33.33%, and 12.59%, respectively. The incidence rate of bowel wall ischemia in simple adhesive intestinal obstruction was about 12.59%, and that in malignancy-induced intestinal obstruction was about 6.56%. Univariate analysis revealed 5 factors with statistical significance, including bowel wall thickening, mesenteric edema, bowel wall pneumatosis, ascites, and fish tooth sign. Multivariate logistic regression analysis indicated that fish tooth sign, bowel wall thickening, and mesenteric edema were able to predict bowel wall ischemia, and the corresponding partial regression coefficients were 2.164, 1.129, and 1.173, odds ratios (ORs) were 8.707, 3.093, and 3.232, sensitivity was 0.356, 0.400, and 0.844, and specificity was 0.859, 0.835, and 0.529, respectively. Imaging signs of bowel wall thickening, mesenteric edema, and fish tooth sign are valuable in predicting bowel wall ischemia, among which bowel wall thickening and mesenteric edema have relatively high specificity and fish tooth sign has a relatively high sensitivity. Furthermore, a fish tooth sign has the most favorable predictive value for bowel wall ischemia in acute intestinal obstruction, followed by bowel wall thickening and mesenteric edema.

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images.

Background: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors-number of tumors, tumor grade, and Rad-score-the AUCs of the training group and validation group were 0.813 (95% CI 0.740-0.886) and 0.838 (95% CI 0.733-0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.

Integrated Multifunctional Graphene Discs 2D Plasmonic Optical Tweezers for Manipulating Nanoparticles.

Optical tweezers are key tools to trap and manipulate nanoparticles in a non-invasive way, and have been widely used in the biological and medical fields. We present an integrated multifunctional 2D plasmonic optical tweezer consisting of an array of graphene discs and the substrate circuit. The substrate circuit allows us to apply a bias voltage to configure the Fermi energy of graphene discs independently. Our work is based on numerical simulation of the finite element method. Numerical results show that the optical force is generated due to the localized surface plasmonic resonance (LSPR) mode of the graphene discs with Fermi Energy Ef = 0.6 eV under incident intensity I = 1 mW/µm2, which has a very low incident intensity compared to other plasmonic tweezers systems. The optical forces on the nanoparticles can be controlled by modulating the position of LSPR excitation. Controlling the position of LSPR excitation by bias voltage gates to configure the Fermi energy of graphene disks, the nanoparticles can be dynamically transported to arbitrary positions in the 2D plane. Our work is integrated and has multiple functions, which can be applied to trap, transport, sort, and fuse nanoparticles independently. It has potential applications in many fields, such as lab-on-a-chip, nano assembly, enhanced Raman sensing, etc.

Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis.

Background: To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC). Methods: By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods. Results: Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80-0.87, I 2 = 76.44%) and 0.83 (95% CI: 0.78-0.87, I 2 = 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value. Conclusion: Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice.

LncRNA-ENST00000556926 regulates the proliferation, apoptosis and mRNA transcriptome of malignant-transformed BEAS-2B cells induced by coal tar pitch.

As a byproduct of coal tar distillation, coal tar pitch (CTP) has been proven to be carcinogenic to human. However, the mechanisms of lung cancer induced by CTP are still unclear. It has been shown that long non-coding RNAs (LncRNAs) play an important role in the development of human cancers. This study aims to investigate the effect of LncRNA-ENST00000556926 on malignant-transformed human bronchial epithelial (BAES-2B) cells induced by coal tar pitch extracts (CTPE). In this study, BEAS-2B cells were treated with 2.4 µg/ml of CTPE for 72 h and then passaged; and the cells were treated 4 times in the same procedure, then passaged until passage 30 (CTPE30). Cell counting kit-8 (CCK-8) assay was used to detect cell viability, then cell cycle and apoptosis were analyzed by flow cytometry, and transcriptome sequencing analysis was used to detect differentially expressed mRNAs after interference of ENST00000556926. The results indicated that the expression of ENST00000556926 in CTPE30 group was significantly higher compared with control group. Furthermore, after interfering the expression of ENST00000556926, cell viability was inhibited, and cell cycle was arrested while apoptosis of malignant-transformed BEAS-2B cells was promoted. Moreover, a total of 159 differentially expressed mRNAs were screened out after interference of ENST00000556926, including 62 up-regulated mRNAs and 97 down-regulated mRNAs. In addition, knockdown of ENST00000556926 decreased the expression of thioredoxin domain containing 5 (TXNDC5) and FOXD1. In conclusion, LncRNA-ENST00000556926 could regulate the proliferation, apoptosis and mRNA transcriptome of malignant-transformed BEAS-2B cells induced by CTP, which may provide a novel treatment strategy for lung cancer.

Proteomics reveal biomethane production process induced by carbon nanotube.

Biomethane produced by methanogenic archaea is a main greenhouse resource of terrestrial and marine ecosystems, which strongly affects the global environment change. Conductive materials, especially nano-scale, show considerable intervention on biomethane production potential, but the mechanism is still unclear. Herein, we precisely quantified the absolute abundance of Methanosarcina spp. proteins affected by carbon nanotubes (CNTs) using tandem mass tag (TMT) proteomics technology. Among the 927 detectable proteins, more than three hundred, 304, showed differential expression. Gene Set Enrichment Analysis on KEGG pathways and GO biological processes revealed a trend of decreased protein synthesis induced by CNTs, suggesting these conductive nanomaterials may replace part of the cell structure and function. Interestingly, increased acetoclastic methanogenesis actually came at the expense of reduced protein synthesis in related pathways. CNTs stimulated biomethane production from acetate by stimulating intracellular redox activity and the -COOH oxidation process. These findings enhanced the understanding of the biomethane production process affected by conductive materials.