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Abstract To further investigate the weakening effect of pore water pressure on intact rock mechanics properties and characteristics of fracture surface after failure, direct shear tests of sandstone were conducted under different pore pressure. A 3D scanner was employed to digitize the morphology of the post-shear fracture surface. The variogram function was applied to quantify the anisotropic characteristics of post-shear fracture surface. The relationship between deformation during shear failure of intact rock and quantitative parameters of fracture surface after shear failure was initially established. It can be found that amplitudes of the sinusoidal surface determine the maximum value of variogram, and period affect lag distance that reach the maximum value of variogram. Test results revealed that the increase of pore pressure has obvious weakening effect on shear strength and deformation of rock. Moreover, the increase of pore pressure makes the shear fracture surface flatter. It can be obtained that both Sill max and Range max are positively related to shear strain, but negatively related to normal strain.

Abstract Background Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. Methods Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. Results By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P

Background & Aims: The machinery that prevents colorectal cancer liver metastasis (CRLM) in the context of liver regeneration (LR) remains elusive. Ceramide (CER) is a potent anti-cancer lipid involved in intercellular interaction. Here, we investigated the role of CER metabolism in mediating the interaction between hepatocytes and metastatic colorectal cancer (CRC) cells to regulate CRLM in the context of LR. Methods: Mice were intrasplenically injected with CRC cells. LR was induced by 2/3 partial hepatectomy (PH) to mimic the CRLM in the context of LR. The alteration of corresponding CER-metabolizing genes was examined. The biological roles of CER metabolism in vitro and in vivo were examined by performing a series of functional experiments. Results: Induction of LR augmented apoptosis but promoted matrix metalloproteinase 2 (MMP2) expression and epithelial-mesenchymal transition (EMT) to increase the invasiveness of metastatic CRC cells, resulting in aggressive CRLM. Up-regulation of sphingomyelin phosphodiesterase 3 (SMPD3) was determined in the regenerating hepatocytes after LR induction and persisted in the CRLM-adjacent hepatocytes after CRLM formation. Hepatic Smpd3 knockdown was found to further promote CRLM in the context of LR by abolishing mitochondrial apoptosis and augmenting the invasiveness in metastatic CRC cells by up-regulating MMP2 and EMT through promoting the nuclear translocation of β-catenin. Mechanistically, we found that hepatic SMPD3 controlled the generation of exosomal CER in the regenerating hepatocytes and the CRLM-adjacent hepatocytes. The SMPD3-produced exosomal CER critically conducted the intercellular transfer of CER from the hepatocytes to metastatic CRC cells and impeded CRLM by inducing mitochondrial apoptosis and restricting the invasiveness in metastatic CRC cells. The administration of nanoliposomal CER was found to suppress CRLM in the context of LR substantially. Conclusions: SMPD3-produced exosomal CER constitutes a critical anti-CRLM mechanism in LR to impede CRLM, offering the promise of using CER as a therapeutic agent to prevent the recurrence of CRLM after PH.

This work is dedicated to exploring the globally exponential stability of anti-periodic solutions in inertial CGNNs that incorporate time delays. This is based on a strategic variable substitution to transform the complex system into a first-order differential equation. By leveraging the Lyapunov functional and demonstrating uniformly converging properties, we establish sufficient conditions that guarantee the existence and global exponential stability of anti-periodic solutions for the system. Finally, examples are presented to illustrate the effectiveness of the obtained theoretical results. This work contributes significantly to enhancing our understanding of the stability dynamics in neural networks with time delays and provides valuable insights for applications across various fields.

ObjectivesThe Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.MethodsA total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan−Meier method.ResultsThe collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P

The current tumour-node-metastasis staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer. Herein, the authors propose a pathomics signature that was derived from multiple features of haematoxylin and eosin-stained slides.

Abstract The current tumor‐node‐metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II‐III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50‐layer residual network model for predicting disease‐free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P

Summary: The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CSTME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of “TNM stage + CSTME” with that of TNM stage alone. Results indicated that the CSTME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180–3.962, p

A capture systematic evolution of ligands by exponential enrichment (Capture-SELEX) was described to discover novel aptamers specific for 5-hydroxymethylfurfural (5-HMF), and a biosensor based on molecular beacon was constructed to detect 5-HMF. The ssDNA library was immobilized to streptavidin (SA) resin to select the specific aptamer. The selection progress was monitored using real-time quantitative PCR (Q-PCR), and the enriched library was sequenced by high-throughput sequencing (HTS). Candidate and mutant aptamers were selected and identified by Isothermal Titration Calorimetry (ITC). The FAM-aptamer and BHQ1-cDNA were designed as the quenching biosensor to detect 5-HMF in milk matrix. After the 18th round selection, the Ct value decreased from 9.09 to 8.79, indicating that the library was enriched. The HTS results indicated that the total sequence numbers for 9th, 13th, 16th, and 18th were 417054, 407987, 307666, and 259867, but the number of sequences for the top 300 sequences was gradually increased from 9th to 18th, and the ClustalX2 analysis showed that there were four families with high homology rate. ITC results indicated that the Kd values of H1 and its mutants H1-8, H1-12, H1-14, and H1-21 were 2.5 μM, 1.8 μM, 1.2 μM, 6.5 μM, and 4.7 μM. The linear range of the quenching biosensor was from 0 μM to 75 μM, and it had a similar linear range in the 0.1% milk matrix. This is the first report to select a novel aptamer specific for 5-HMF and develop quenching biosensor for the rapid detection of 5-HMF in milk matrix.

According to the sixth edition of China’s “New Coronavirus Diagnosis and Treatment Plan (NCDTP),” ReDuNing injection (RDN) was firstly introduced to treat severe and critical COVID-19, whereas its combination with broad-spectrum antibiotics was suggested to take with extreme caution and full reasons. Therefore, we aim to describe the pharmacokinetics of seven active phytochemicals and semiquantification of nine relevant metabolites in ReDuNing injection (RDN) after combining with cefuroxime sodium (CNa) for injection in rat plasma. Male Sprague–Dawley rats were randomly assigned to six groups, and they were intravenously administered, respectively, with different prescriptions of RDN (2 mL/kg) and CNa (225 mg/kg). At different time points (0.03, 0.08, 0.17, 0.24, 0.33, 0.50, 0.67, 1, and 6 h) after administration, the drug concentrations of iridoids glycosides, organic acids, and metabolites in rat plasma were determined using ultrahigh-pressure liquid chromatography coupled with linear ion rap-orbitrap tandem mass spectrometry (UHPLC–LTQ–Orbitrap–MS), and main pharmacokinetic parameters were estimated by noncompartment model. The results showed that there were differences in pharmacokinetic parameters, AUC(0-t), T1/2, Cmax, CL of iridoids glycosides, and organic acids, after the intravenous administration of the different combinations of RDN and CNa. Moreover, different combinations of the injections also resulted in different curves of relative changes of each metabolite. The obtained results suggested that RDN and CNa existed pharmacokinetic drug–herb interactions in rats. The findings not only lay the foundation for evaluating the safety of RDN injection combined with CNa but also make contributions to clinically applying RDN injection combined with CNa, which works potentially against severe forms of COVID-19.

Inactive ovaries (IO) and ovarian (follicular or luteal) cysts (FC or LC) are two common ovarian diseases leading to infertility in dairy cattle. Both disorders are associated with altered metabolites and hormones. There are currently no known effective biomarkers that can be used for early diagnosis of ovarian diseases. The purpose of this study was to identify the plasma biomarkers of ovarian diseases in Holstein dairy cows that facilitate an early diagnosis of the diseases and control its progression. The experiment was performed from 3 weeks postpartum and last for 7 weeks. Seventy-six multiparous Holstein cows (mean age, 4.36 years; weight, 635.63 kg) were divided into healthy control group (HC, n = 22), FC group (n = 18), LC group (n = 18) and IO group (n = 18) by rectal palpation or ultrasonography during the last 2 weeks before trial end. Blood was collected via tail vein for measurement of plasma energy metabolites, liver function indicators, minerals, and hormones at 3 and 8 weeks postpartum. Data were analyzed by Mann-Whitney U, Kruskal-Wallis, Spearman correlation, binary logistic regression analysis and receiver operating characteristic analysis, where applicable. At 8 weeks postpartum, FC cows had a more severe body condition score loss and these had greater levels of non-esterified fatty acids (NEFA) and estradiol, and lesser levels of alanine aminotransferase (ALT), progesterone and insulin-like growth factor 1 (IGF-1) levels than HC cows (P < 0.05). LC cows had a lower milk yield, higher NEFA and progesterone levels, and lower calcium, phosphorus and magnesium levels than HC cows (P < 0.05). IO cows had a lower body condition score, higher NEFA levels, and lower ALT, calcium, phosphorus, magnesium, estradiol, progesterone and IGF-1 levels than HC cows (P < 0.05). At 3 weeks postpartum, cows with ovarian diseases had greater (P < 0.05) concentrations of NEFA, and lesser concentrations of ALT, calcium, phosphorus and IGF-1 than HC cows. Early warning values for ovarian diseases were plasma NEFA concentrations >0.50 mmol/L, or calcium concentrations

This study investigated the relationship between death anxiety and experienced meaning in life. Six hundred and forty-eight Chinese college students were surveyed using the Death Anxiety Scale, the Prosocial Behavior Scale, and the Meaning in Life Scale. The results showed that death anxiety predicted experienced meaning through three pathways: the first one was through search for meaning singly; the second one was through prosocial behavior singly; and the third one was through search for meaning and prosocial behavior serially, which accounted for the highest proportion of the total effect. This study highlights the positive side of death anxiety.

Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.