Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications.

Background: The systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose. Methods: There were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications. Results: SII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29-3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia. Conclusion: This study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

Deep local-to-global feature learning for medical image super-resolution.

Medical images play a vital role in medical analysis by providing crucial information about patients' pathological conditions. However, the quality of these images can be compromised by many factors, such as limited resolution of the instruments, artifacts caused by movements, and the complexity of the scanned areas. As a result, low-resolution (LR) images cannot provide sufficient information for diagnosis. To address this issue, researchers have attempted to apply image super-resolution (SR) techniques to restore the high-resolution (HR) images from their LR counterparts. However, these techniques are designed for generic images, and thus suffer from many challenges unique to medical images. An obvious one is the diversity of the scanned objects; for example, the organs, tissues, and vessels typically appear in different sizes and shapes, and are thus hard to restore with standard convolution neural networks (CNNs). In this paper, we develop a dynamic-local learning framework to capture the details of these diverse areas, consisting of deformable convolutions with adjustable kernel shapes. Moreover, the global information between the tissues and organs is vital for medical diagnosis. To preserve global information, we propose pixel-pixel and patch-patch global learning using a non-local mechanism and a vision transformer (ViT), respectively. The result is a novel CNN-ViT neural network with Local-to-Global feature learning for medical image SR, referred to as LGSR, which can accurately restore both local details and global information. We evaluate our method on six public datasets and one large-scale private dataset, which include five different types of medical images (i.e., Ultrasound, OCT, Endoscope, CT, and MRI images). Experiments show that the proposed method achieves superior PSNR/SSIM and visual performance than the state of the arts with competitive computational costs, measured in network parameters, runtime, and FLOPs. What is more, the experiment conducted on OCT image segmentation for the downstream task demonstrates a significantly positive performance effect of LGSR.

Extract of Silphium perfoliatum L. improve lipid accumulation in NAFLD mice by regulating AMPK/FXR signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Globally, the incidence rate and number of patients with nonalcoholic fatty liver disease are increasing, which has become one of the greatest threats to human health. However, there is still no effective therapy and medicine so far. Silphium perfoliatum L. is a perennial herb native to North America, which is used to improve physical fitness and treat liver and spleen related diseases in the traditional medicinal herbs of Indian tribes. This herb is rich in chlorogenic acids, which have the functions of reducing blood lipids, losing weight and protecting liver. However, the effect of these compounds on nonalcoholic fatty liver disease remains unclear. AIM OF THE STUDY: Clarify the therapeutic effects and mechanism of the extract (CY-10) rich in chlorogenic acid and its analogues from Silphium perfoliatum L. on non-alcoholic fatty liver disease, and to determine the active compounds. MATERIALS AND METHODS: A free fatty acid-induced steatosis model of HepG2 cells was established to evaluate the in vitro activity of CY-10 in promoting lipid metabolism. Further, a high-fat diet-induced NAFLD model in C57BL/6 mice was established to detect the effects of CY-10 on various physiological and biochemical indexes in mice, and to elucidate the in vivo effects of the extract on regulating lipid metabolism, anti-inflammation and hepatoprotection, and nontarget lipid metabolomics was performed to analyze differential metabolites of fatty acids in the liver. Subsequently, western blotting and immunohistochemistry were used to analyze the target of the extract and elucidate its mechanism of action. Finally, the active compounds in CY-10 were elucidated through in vitro activity screening. RESULTS: The results indicated that CY-10 significantly attenuated lipid droplet deposition in HepG2 cells. The results of in vivo experiments showed that CY-10 significantly reduce HFD-induced mouse body weight and organ index, improve biochemical indexes, oxidation levels and inflammatory responses in the liver and serum, thereby protecting the liver tissue. It can promote the metabolism of unsaturated fatty acids in the liver and reduce the generation of saturated fatty acids. Furthermore, it is clarified that CY-10 can promote lipid metabolism balance by regulating AMPK/FXR/SREPB-1c/PPAR-γ signal pathway. Ultimately, the main active compound was proved to be cryptochlorogenic acid, which has a strong promoting effect on the metabolism of fatty acids in cells. Impressively, the activities of CY-10 and cryptochlorogenic acid were stronger than simvastatin in vitro and in vivo. CONCLUSION: For the first time, it is clarified that the extract rich in chlorogenic acids and its analogues in Silphium perfoliatum L. have good therapeutic effects on non-alcoholic fatty liver disease. It is confirmed that cryptochlorogenic acid is the main active compound and has good potential for medicine.

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model.

BACKGROUND: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. OBJECTIVE: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. METHODS: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. RESULTS: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. CONCLUSION: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

Inhibition of p38 MAPK/NF-κB p65 signaling pathway activity by rare ginsenosides ameliorates cyclophosphamide-induced premature ovarian failure and KGN cell injury.

ETHNOPHARMACOLOGICAL RELEVANCE: Panax ginseng C. A. Mey., one of the most used herbs in the world, shows effective treatment in reproductive injury. Recent studies have proven that the processed product, red ginseng, which is more active than ginseng itself. Therefore, it is speculated that its main functional component, rare ginsenosides (heat-transformed saponin, HTS), may be effective in treating premature ovarian failure (POF), but its efficacy has not yet been experimentally confirmed. AIM OF THE STUDY: To evaluate whether HTS could attenuate cyclophosphamide-induced inflammation and oxidative damage in POF model rats and the human granulosa-like KGN cell line and protect granulosa cell proliferation. MATERIAL AND METHODS: HTS were isolated from ginsenosides and high performance liquid chromatography (HPLC) analysis was used to analyze the HTS components. Cyclophosphamide (CP) was used to establish a POF rat model and KGN cell injury model. Reactive oxygen species (ROS) and antioxidant enzyme production was determined using specific assays, while inflammatory cytokine secretion was measured by enzyme-linked immunosorbent assay (ELISA). The proliferative function of granulosa cells was assessed using high-content screening and immunohistochemistry to determine the Ki67 protein level. Protein expression in ovarian tissues and KGN cells was analyzed by Western blotting, quantitative real-time PCR (qRT-PCR) was used to determine the transcriptional changes in ovarian tissues and KGN cells. RESULTS: In CP-treated POF model rats, HTS significantly decreased malondialdehyde (MDA), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) levels, increased glutathione oxidase (GSH) levels, and upregulated Ki67 expression in ovarian granulosa cells. In addition, HTS significantly increased cell survival and Ki67 expression levels in CP-treated cells, and superoxide dismutase (SOD) levels were significantly increased. HTS significantly downregulated IL-6, TNF-α, and interleukin-1ß (IL-1ß) mRNA expression and significantly inhibited nuclear factor kappa-B p65 (NF-κB p65) and p38 mitogen activated protein kinase (p38 MAPK) phosphorylation in POF model rats and KGN cells. Moreover, NF-κB p65 and p38 MAPK levels were significantly increased in ovarian granulosa cells. p65 and p38 protein and gene expression was significantly downregulated. CONCLUSION: HTS ameliorated CP-induced POF and human granulosa cell injury, possibly by inhibiting inflammation and oxidative damage mediated by the p38 MAPK/NF-κB p65 signaling pathway.

Endogenous cAMP elevation in Brassica napus causes changes in phytohormone levels.

In higher plants, the regulatory roles of cAMP (cyclic adenosine 3',5'-monophosphate) signaling remain elusive until now. Cellular cAMP levels are generally much lower in higher plants than in animals and transiently elevated for triggering downstream signaling events. Moreover, plant adenylate cyclase (AC) activities are found in different moonlighting multifunctional proteins, which may pose additional complications in distinguishing a specific signaling role for cAMP. Here, we have developed rapeseed (Brassica napus L.) transgenic plants that overexpress an inducible plant-origin AC activity for generating high AC levels much like that in animal cells, which served the genetic model disturbing native cAMP signaling as a whole in plants. We found that overexpression of the soluble AC activity had significant impacts on the contents of indole-3-acetic acid (IAA) and stress phytohormones, i.e. jasmonic acid (JA), abscisic acid (ABA), and salicylic acid (SA) in the transgenic plants. Acute induction of the AC activity caused IAA overaccumulation, and upregulation of TAA1 and CYP83B1 in the IAA biosynthesis pathways, but also simultaneously the hyper-induction of PR4 and KIN2 expression indicating activation of JA and ABA signaling pathways. We observed typical overgrowth phenotypes related to IAA excess in the transgenic plants, including significant increases in plant height, internode length, width of leaf blade, petiole length, root length, and fresh shoot biomass, as well as the precocious seed development, as compared to wild-type plants. In addition, we identified a set of 1465 cAMP-responsive genes (CRGs), which are most significantly enriched in plant hormone signal transduction pathway, and function mainly in relevance to hormonal, abiotic and biotic stress responses, as well as growth and development. Collectively, our results support that cAMP elevation impacts phytohormone homeostasis and signaling, and modulates plant growth and development. We proposed that cAMP signaling may be critical in configuring the coordinated regulation of growth and development in higher plants.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Synthesis of Monodisperse Mesoporous Carbon Spheres/EPDM Rubber Composites and Their Enhancement Mechanical Properties.

Monodisperse mesoporous carbon spheres (MCS) were synthesized and their potential applications in ethylene propylene diene monomer (EPDM) foam were evaluated. The obtained MCS exhibited a high specific surface area ranging from 621-to 735 m2/g along with large pore sizes. It was observed that the incorporation of MCS into EPDM foam rubber significantly enhances its mechanical properties. The prepared MCS-40 rubber composites exhibit the highest tear strength of 210 N/m and tensile strength of 132.72 kPa, surpassing those of other samples. The enhancement mechanism was further investigated by employing computer simulation technology. The pores within the MCS allowed for the infiltration of EPDM molecular chains, thereby strengthening the interaction forces between the filler and matrix. Moreover, a higher specific surface area resulted in greater adsorption of molecular chains onto the surface of these carbon spheres. This research offers novel insights for understanding the enhancement mechanism of monodisperse mesoporous particles/polymer composites (MCS/EPDM) and highlights their potential application in high-performance rubber composites.

Lung microwave ablation: Post-procedure imaging features and evolution of pulmonary ground-glass nodule-like lung cancer.

PURPOSE: To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule (GGN)-like lung cancer and its dynamic evolution over time. MATERIALS AND METHODS: From June 2020 to May 2021, 147 patients with 152 GGNs (51 pure GGNs and 101 mixed GGNs, mean size 15.0 ± 6.3 mm) were enrolled in this study. One hundred and forty-seven patients underwent MWA procedures. The imaging characteristics were evaluated at predetermined time intervals: immediately after the procedure, 24-48 h, 1, 3, 6, 12, and ≥18 months (47 GGNs). RESULTS: This study population included 147 patients with 152 GGNs, as indicated by the results: 43.5% (66/152) adenocarcinoma in situ, 41.4% (63/152) minimally invasive adenocarcinoma, and 15.1% (23/152) invasive adenocarcinoma. Immediate post-procedure tumor-level analysis revealed that the most common CT features were ground-glass opacities (93.4%, 142/152), hyperdensity within the nodule (90.7%, 138/152), and fried egg sign or reversed halo sign (46.7%, 71/152). Subsequently, 24-48 h post-procedure, ground-glass attenuations, hyperdensity, and the fried egg sign remained the most frequent CT findings, with incidence rates of 75.0% (114/152), 71.0% (108/152), and 54.0% (82/152), respectively. Cavitation, pleural thickening, and consolidation were less frequent findings. At 1 month after the procedure, consolidation of the ablation region was the most common imaging feature. From 3 to 12 months after the procedure, the most common imaging characteristics were consolidation, involutional parenchymal bands and pleural thickening. At ≥18 months after the procedure, imaging features of the ablation zone revealed three changes: involuting fibrosis (80.8%, 38/47), consolidation nodules (12.8%, 6/47), and disappearance (6.4%, 3/47). CONCLUSIONS: This study outlined the anticipated CT imaging characteristics of GGN-like lung cancer following MWA. Diagnostic and interventional radiologists should be familiar with the expected imaging characteristics and dynamic evolution post-MWA in order to interpret imaging changes with a reference image.

Inhibitory activities and rules of plant gallotannins with different numbers of galloyl moieties on sucrase, maltase and α-amylase in vitro and in vivo.

BACKGROUND: α-Glucosidase inhibitors could effectively reduce postprandial blood glucose (PBG) levels and control the occurrence of complications of diabetes. Gallotannins (GTs) in plants have attracted much attention due to their significant α-glucosidase inhibitory activities in vitro. However, there is still a lack of systematic comparative studies to further elucidate inhibitory activities in vivo and in vitro of these compounds against α-glucosidase, especially for mammalian sucrase and maltase, and analyze their structure-activity relationship. PURPOSE: Determine the in vitro and in vivo inhibitory activities of five GTs with different number of galloyl moieties (GMs) on sucrase, maltase and α-amylase, and elucidate the relationship between α-glucosidase inhibitory activities and the number and connection mode of GMs. METHODS: Molecular docking and dynamics were used to study the binding mode and binding ability of five GTs against sucrase, maltase and α-amylase. Then, the inhibitory activities and inhibitory mechanisms of these compounds on sucrase, maltase and α-amylase in vitro were studied using inhibitory assay and enzyme inhibition kinetics. Further, the hypoglycemic effects in vivo of these compounds were demonstrated by three polysaccharides tolerance experiments on diabetes model mice. RESULTS: The results of molecular docking showed that these compounds could bind to enzymes through hydrogen bonds, hydrophobic interactions, etc. In addition, the α-glucosidase inhibition comparative studies in vitro and in vivo demonstrated that the inhibitory activities of these compounds on all three sucrase, maltase and α-amylase were ranked as TA ≈ PGG > TeGG > TGG > 1GG, and their inhibitory activities increases with the increase in the number of GMs. Moreover, the hypoglycemic effects of 1,2,3,4,6-pentagalloylglucose (PGG) and tannic acid (TA) in vitro and in vivo were also confirmed to be equivalent to or even stronger than that of acarbose. CONCLUSION: α-Glucosidase inhibitory activities in vitro and in vivo of GTs were positively correlated with the number of GTs, and the more the number, the stronger the activity. However, PGG with five GTs and TA with ten GTs showed almost identical α-glucosidase inhibitory activities, possibly due to the reduced binding force with the enzyme caused by spatial hindrance.

Fluorescence immunoassay for simultaneous detection typical ß-agonists in animal derived food using blue-green upconversion nanoparticles as labels.

Common typical ß-agonists mainly include ractopamine (RAC), salbutamol (SAL), and clenbuterol (CLB). In view of the harm to human health causes by the ingestion of animal derived food containing ß-agonists, and a series of regulations have been issued to restrict the usage of ß-agonists as growth promoters. In this work, a fluorescence immunoassay is developed for the simultaneous detection of typical ß-agonists based on blue-green upconversion nanoparticles (UCNPs) combine with magnetic separation. Here, blue-green UCNPs act as a signal amplification source, and magnetic polystyrene microspheres (MPMs) act as an ideal separation medium. Based on a competitive form, capture probe competes (RAC-OVA@MPMs and SAL-OVA@MPMs) with targets to bind corresponding signal probe (anti-RAC antibody@NaYF4:Yb, Tm UCNPs and anti-SAL antibody@NaYF4:Yb, Er UCNPs). The fluorescence difference values of the competitive immune-complex obtained via magnetic separation at 483 nm and 550 nm are proportional to concentrations of RAC and SAL, respectively. The immunoassay has the wide detection linear range from 0.001 to 100 µg L-1, and the low limit of detection (LOD) is 5.04 × 10-4 µg L-1 for RAC, 1.97 × 10-4 µg L-1 for SAL, respectively. Meanwhile, use of antibody with same recognition ability for SAL and CLB makes that the fluorescence immunoassay can achieve simultaneous detection of three typical ß-agonists (RAC, SAL, and CLB). This fluorescence immunoassay has good application value and practicability for simultaneous detection of typical ß-agonists in animal derived food.

Hypoglycemic effect of Nitraria tangutorum fruit by inhibiting glycosidase and regulating IRS1/PI3K/AKT signalling pathway and its active ingredient identification by UPLC-MS.

The hypoglycemic effect of NTB-40 (40% ethanol fraction of Nitraria tangutorum fruit) in type I/II diabetic mice and its underlying mechanism and active ingredient structure were investigated. The postprandial blood glucose (PBG) lowering effect of NTB-40 treatment was confirmed by maltose, starch, and sucrose tolerance tests in alloxan-induced DM mice and sucrase and maltase inhibitory activities in vitro. More importantly, long-term dosing experiments in high-fat diet-STZ-induced diabetic mice further demonstrated that NTB-40 intervention could improve glycolipid metabolism disorder and insulin resistance (IR) by maintaining glucose homeostasis (FBG, OGTT, ITT, FINS, and HOMA-IR) and lipid homeostasis (TC, TG, HDL-C, LDL-C, and FFA), reducing inflammation (IL-6, IL-1ß, and TNF-α) and oxidative stress (SOD and MDA), ameliorating the liver's histological structural abnormalities, and modulating the IRS1/PI3K/AKT signaling pathway and downstream targets (FOXO1, GSK3ß, GLUT4) for decreasing hepatic gluconeogenesis and promoting glycogen synthesis and glucose uptake. All these results indicated that NTB-40 had an anti-diabetic effect by modulating the IRS1/PI3K/AKT signaling pathway and inhibiting α-glucosidase activity. Finally, the main chemical components of NTB-40, including phenolic acids, flavonoids, and alkaloids, were assigned by UPLC-Triple-TOF MS/MS.

Evaluation of breast cancer malignancy, prognostic factors and molecular subtypes using a continuous-time random-walk MR diffusion model.

PURPOSE: To assess the continuous-time random-walk (CTRW) model's diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors. METHOD: This retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and ß) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters. RESULTS: All parameters, ADC, Dm, α, and ß were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and ß) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower ß compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that ß produced significant correlations with ER, PR and Ki-67 expression (P < 0.05). CONCLUSIONS: The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Characteristics and source analysis of potentially toxic elements pollution in atmospheric fallout around non-ferrous metal smelting slag sites-taking southwest China as an example.

More accurate source analysis of potentially toxic elements (PTEs) in atmospheric fallout that endanger biodiversity and human health remains needed. This study determined the concentrations of seven PTEs, including Pb, Cd, As, Cu, Zn, Ni, and Cr, by inductively coupled plasma mass spectrometry (ICP-MS), and the sources of PTE pollution were quantified using multivariate statistical analysis, including principal component analysis (PCA), cluster analysis (CA), and Pearson correlation analysis, and Moran index was applied for mutual verification and supplementation. PCA and CA revealed that the same mixed sources of Pb, Cd, As, Cu, and Zn were found in the atmospheric dust fall in the study area, while Ni and Cr had the same source of pollution. Pearson correlation analysis provided that there were strong correlations between Pb-Cd, Pb-As, Pb-Cu, Cd-As, Cd-Cu, As-Cu, and Ni-Cr, indicating commonality between the two sources of heavy metal pollution. Additionally, the Moran index showed that strong spatial correlations were observed between Pb, Cd, As, Cu, and Zn, whose sources were mainly related to non-ferrous metal processing smelter smelting slag sites and an environmental company in the study area. However, no spatial correlation was found between Ni and Cr, which mainly originated from the local geological background.

End-to-end metastasis detection of breast cancer from histopathology whole slide images.

Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.

Sequence-dependent synergistic effect of aumolertinib-pemetrexed combined therapy on EGFR-mutant non-small-cell lung carcinoma with pre-clinical and clinical evidence.

BACKGROUND: Inevitably developed resistance of the third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) limited its clinical benefit on non-small cell lung cancer (NSCLC). Upfront combination therapy is promising to prevent this resistance. Compelling clinical evidence indicated the failure of third-generation EGFR TKIs combined with either immunotherapy or antiangiogenic agents. In comparison, combined treatment of third-generation EGFR TKIs and chemotherapy might be a favorable choice. Herein, we systematically analyzed and compared the effects of pemetrexed and a novel third-generation EGFR TKI aumolertinib combined in different sequences, subsequently revealed the potential mechanisms and proved the optimal combination schedule with clinical retrospective study. METHODS: Three combination schedules involving pemetrexed and aumolertinib in different sequences were developed. Their inhibition effects on cell proliferation and metastasis were firstly compared upon three human NSCLC cell lines in vitro, by cell counting kit-8, colony formation, wound healing and transwell assays respectively. Further evaluation in vivo was proceeded upon H1975 and HCC827 xenograft model. Gene and protein expression were detected by Q-PCR and western blot. Drug concentration was determined by LC-MS/MS. VEGF secretion was determined by ELISA. Tumor vessel was visualized by immunofluorescence. Lastly, a clinical retrospective study was raised with 65 patients' data. RESULTS: The combination of pemetrexed and aumolertinib exhibited a sequence-dependent and EGFR mutant-dependent synergistic effect in vitro and in vivo. Only treatment with aumolertinib following pemetrexed (P-A) exhibited synergistic effect with stronger anti-tumor growth and anti-metastasis ability than monotherapy and also other combination sequences. This synergism could exclusively be observed in H1975 and HCC827 but not A549. Pathway analysis showed that P-A significantly enhanced the suppression of EGFR pathway. In addition, our results intriguingly found an obvious reduction of VEGF secretion and the accompanying normalization of the intratumor vessel, consequently increasing intratumoral accumulation of pemetrexed in P-A group. Finally, the clinical retrospective study verified the synergistic effect of P-A combination by significantly superior tumor response than aumolertinib monotherapy. CONCLUSION: Aumolertinib-pemetrexed combined therapy is promising for EGFR mutant NSCLC but only in right administration sequence. P-A could become an advantageous combination strategy in clinical with synergistic inhibition of tumor growth and metastasis.

Enzymatic synthesis of 2-phenethyl acetate in water catalyzed by an immobilized acyltransferase from Mycobacterium smegmatis.

Although water is an ideal green solvent for organic synthesis, it is difficult for most biocatalysts to carry out transesterification reactions in water because of the reversible hydrolysis reaction. 3D structural characteristics and the microenvironment of an enzyme has an important effect on its selectivity for the transesterification reaction over the hydrolysis reaction. A novel 2-phenethyl acetate synthesis technology was developed using acyltransferase (EC 3.1.1.2) from Mycobacterium smegmatis (MsAcT) in water. Firstly, MsAcT was entrapped in a tetramethoxysilane gel network and the immobilization process of MsAcT increased its selectivity for the transesterification reaction over the hydrolysis reaction by 6.33-fold. Then, the synthesis technology of 2-phenethyl acetate using the immobilized MsAcT in water was optimized as follows: vinyl acetate was used as acyl donor, the molar ratio of vinyl acetate to 2-phenylethyl alcohol was 2 : 1, and the water content was 80% (w/w). The reaction was carried out at 40 °C for 30 min and conversion rate reached 99.17%. The immobilized MsAcT could be recycled for 10 batches. The synthesis method of 2-phenethyl acetate using MsAcT as a biocatalyst in water is a prospective green process technology.