Clinicopathological significance of cancer stem cell marker CD44/SOX2 in esophageal squamous cell carcinoma (ESCC) patients and construction of a nomogram to predict overall survival.

Background: Esophageal squamous cell carcinoma (ESCC), a prevalent malignancy within the upper gastrointestinal system, is characterized by its unfavorable prognosis and the absence of specific indicators for outcome prediction and high-risk case identification. In our research, we examined the expression levels of cancer stem cells (CSCs), markers CD44/SOX2 in ESCC, scrutinized their association with clinicopathological parameters, and developed a predictive nomogram model. This model, which incorporates CD44/SOX2, aims to forecast the overall survival (OS) of patients afflicted with ESCC. Methods: Immunohistochemistry was utilized to detect the expression levels of CD44 and SOX2 in both cancerous and paracancerous tissues of 68 patients with ESCC. The correlation between CD44/SOX2 expression and clinicopathological parameters was subsequently analyzed. Factors impacting the prognosis of ESCC patients were assessed through univariate and multivariate Cox regression analyses. Leveraging the results of these multivariate regression analyses, a nomogram prognostic model was established to provide individualized predictions of ESCC patient survival outcomes. The predictive accuracy of the nomogram prognostic model was evaluated using the consistency index (C-index) and calibration curves. Results: The expression levels of CD44 were markedly elevated in the tumor tissues of ESCC patients. Similarly, SOX2 was significantly overexpressed in the tumor tissues of ESCC patients. The positive expression of SOX2 in ESCC demonstrated a strong correlation with both the pathological T-stage and the presence of carcinoembryonic antigen. CD44 and SOX2 co-positive expression was significantly associated with the pathological T-stage and tumor node metastasis (TNM) stage. Furthermore, ESCC patients exhibiting CD44-positive expression in their tumor tissue generally had a more adverse prognosis. The co-expression of CD44 and SOX2 resulted in a grimmer prognosis compared to patients with other combinations. Multivariate Cox regression analysis identified the co-expression of CD44 and SOX2, the pathological T-stage, and lymph node metastasis as independent prognostic indicators for ESCC patients. The three identified variables were subsequently incorporated into a nomogram for predicting OS. The C-index of the measurement model and the area under the curve of the subjects' work characteristics showed good individual prediction. This prognostic model stratified patients into low- and high-risk categories. Analysis revealed that the 5-year OS rate was significantly higher in the low-risk group compared to the high-risk group. Conclusions: Elevated CD44 levels, indicative of CSC presence, are intimately linked with the oncogenesis of ESCC and are strongly predictive of unfavorable patient outcomes. Concurrently, the SOX2 gene exhibits a heightened expression in ESCC, markedly accelerating tumor progression and fostering more extensive disease infiltration. The co-expression of CD44 and SOX2 correlates significantly with ESCC patient prognosis, serving as a reliable, independent prognostic marker. Our constructed nomogram, incorporating CD44/SOX2 expression, enhances the prediction of OS and facilitates risk stratification in ESCC patients.

Manipulating the Thermal Conductivity of the Graphene/Poly(vinyl alcohol) Composite via Surface Functionalization: A Multiscale Simulation.

The reverse non-equilibrium molecular dynamics simulation is used to investigate the influence of functional groups (FGs) on the thermal conductivity of a graphene/poly(vinyl alcohol) (PVA) composite, which considers non-polar (methyl) and polar (hydroxyl, amino, and carboxyl) groups. First, the polar groups can be more effective to improve the interfacial thermal conductivity than the non-polar group. This can be explained well by characterizing the interfacial Coulombic energy, number and lifetime of hydrogen bonds, vibrational density of states, and integrated autocorrelation of the interfacial heat power. Moreover, the hydroxyl group can improve the interfacial thermal conductivity more than the other groups, which can be rationalized by analyzing the surface roughness of graphene and the radial distribution function of FGs and the PVA chains. However, the introduction of FGs destroys the graphene structure, which consequently reduces the intrinsic thermal conductivity. Furthermore, by adopting the effective medium approximation model and finite element method, there exists a critical graphene length where the overall thermal conductivities are equal for the functionalized and pristine graphene. Finally, the distribution state of graphene is emphasized to be more vital in determining the overall thermal conductivity than the generally accepted interfacial thermal conductivity.

Correction: Effect of the content and strength of hard segment on the viscoelasticity of the polyurethane elastomer: insights from molecular dynamics simulation.

Correction for 'Effect of the content and strength of hard segment on the viscoelasticity of the polyurethane elastomer: insights from molecular dynamics simulation' by Yimin Wang et al., Soft Matter, 2022, 18, 4090-4101, https://doi.org/10.1039/D2SM00463A.

Optimizing the fracture toughness of a dual cross-linked hydrogel via molecular dynamics simulation.

In this work, a coarse-grained model is adopted to explore the fracture toughness of a dual cross-linked hydrogel which consists of a physically cross-linked network and a chemically cross-linked network. By calculating the fracture energy, the optimized fracture toughness of the hydrogel appears at the intermediate content of the chemical network. To understand it, the structure change of both the chemical network and the physical network is first characterized during the tensile process. For the chemical network, the fraction and rate of broken bonds gradually improve with increasing content of the chemical network while the strain range where the bond breakage occurs is reduced. For the physical network, the number of clusters and the interaction energy first increase and then decrease with increasing strain. This reflects the breakage and reformation of the physical network, which dissipates more energy and improves the fracture energy. Furthermore, by stress decomposition, the stress is mainly borne by the physical network at small strain and the chemical network at large strain, which proves their synergistic effect in enhancing the hydrogel. Then, the number of voids is calculated as a function of strain. It is found that the voids initiate in the weak region at small strain while in the position of the bond breakage at large strain. Moreover, the number of voids decreases with increasing content of the chemical network at small strain. Finally, the effect of the strength of the chemical network or the physical network on the fracture toughness is discussed. The optimized fracture toughness of hydrogel appears at the intermediate strength.

Effect of the content and strength of hard segment on the viscoelasticity of the polyurethane elastomer: insights from molecular dynamics simulation.

Due to the wide application, it is very crucial to understand the viscoelasticity of the polyurethane elastomer (PU, denoted by soft-hard block copolymer), which contains the soft segments (SS) and hard segments (HS). Thus, in this work, the effect of the content and strength of HS on the viscoelasticity of PU is explored in detail by adopting a coarse-grained model. First, the phase morphology of PU is characterized where both the single continuous phase and the bicontinuous phase are observed by varying the content of HS. Then, the viscoelasticity of PU is calculated by analyzing the storage modulus, the loss modulus, and the loss factor, which depends on the content and strength of HS. To further elucidate the mechanism for the storage modulus, the normalized interaction energy, the order parameter, and the formation probability of the HS or SS phase are characterized with the shear strain amplitude, which reflects the deformation of the phase structure. Then, the energy dissipation is quantified, which can rationalize the loss modulus well. A parameter is introduced, which considers the relative slippage and the content of HS or SS. It can explain the change in the loss factor with the content and strength of HS. In summary, this work can help to further understand how the content and strength of hard segments affect the viscoelasticity of the soft-hard block PU and structure evolution at the molecular level.

RNNSLAM: Reconstructing the 3D colon to visualize missing regions during a colonoscopy.

Colonoscopy is the gold standard for pre-cancerous polyps screening and treatment. The polyp detection rate is highly tied to the percentage of surveyed colonic surface. However, current colonoscopy technique cannot guarantee that all the colonic surface is well examined because of incomplete camera orientations and of occlusions. The missing regions can hardly be noticed in a continuous first-person perspective. Therefore, a useful contribution would be an automatic system that can compute missing regions from an endoscopic video in real-time and alert the endoscopists when a large missing region is detected. We present a novel method that reconstructs dense chunks of a 3D colon in real time, leaving the unsurveyed part unreconstructed. The method combines a standard SLAM system with a depth and pose prediction network to achieve much more robust tracking and less drift. It addresses the difficulties for colonoscopic images of existing simultaneous localization and mapping (SLAM) systems and end-to-end deep learning methods.

Percolation analysis of the electrical conductive network in a polymer nanocomposite by nanorod functionalization.

Chemical functionalization of nanofillers is an effective strategy to benefit the formation of the conductive network in the matrix which can enhance the electrical conductivity of polymer nanocomposites (PNCs). In this work, we adopted a coarse-grained molecular dynamics simulation to investigate the effect of the nanorod (NR) functionalization on the conductive probability of PNCs under the quiescent state or under a shear field. It is found that the direct aggregation structure of NRs is gradually broken down with increasing the NR functionalization degree λ A, which improves their dispersion state. Moreover, a local bridging structure of NRs sandwiched via one polymer layer is formed at high λ A. Corresponding to it, the percolation threshold of PNCs first quickly decreases, then increases and last slightly decreases again with the increase of λ A, which exhibits an anti N-type under the quiescent state. Meanwhile, it shows a non-monotonic dependence on the interaction between polymer and the functionalized beads which reaches the lowest value at the moderate interaction. However, the percolation threshold is nearly independent of λ A under the shear field. Compared with in the quiescent state, the decrease or the increase of the percolation threshold can be tuned by λ A under the shear field. The significant change in the percolation threshold is attributed to the orientation and the dispersion state of NRs under the shear field, which affects the conductive network. Especially, we found that the dispersion state of NRs is different for different λ A under the shear field. However, the percolation threshold is similar which indicates that the dispersion state of NRs is not completely correlated to the conductive network. In summary, this work presents some further understanding of how the NR functionalization affects the electrical conductivity of PNCs.

Isoliquiritigenin Suppresses Osteosarcoma U2OS Cell Proliferation and Invasion by Regulating the PI3K/Akt Signalling Pathway.

AIMS: Isoliquiritigenin (ISL) is a flavonoid, that has been shown to have antioxidant, vasorelaxant, anti-inflammatory, and antitumor activities. This study aimed to explore the antitumor effect of ISL on human osteosarcoma U2OS cells and investigate the mechanism of this effect. METHODS: The effect of ISL on osteosarcoma U2OS cell proliferation, invasion, migration, and apoptosis were determined by a CCK8 assay, a transwell invasion assay, a transwell migration assay, and fluorescence-activated cell sorting, respectively. In addition, the protein expression levels of Bcl2, Bax, active Caspase-3, Akt, mTOR, p70, and Cyclin D1 were detected by western blotting. RESULTS: ISL suppressed cell proliferation, inhibited invasion and migration, and promoted apoptosis in U2OS cells. After treatment with ISL, the protein expression levels of Bax and active Caspase-3 increased, while the level of Bcl-2 declined significantly. Furthermore, the phosphorylation levels of Akt and mTOR declined significantly compared with that of the control. CONCLUSION: ISL could retard proliferation and promote apoptosis of U2OS cells possibly by suppressing the PI3K/Akt signalling pathway, indicating that it might be a potential therapeutic agent for osteosarcoma treatment.

Lumen and media-adventitia border detection in IVUS images using texture enhanced deformable model.

Lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model. An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. The proposed TEDM method has been tested on 1500 images from 15 clinical IVUS datasets by comparing with the manual delineations. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.17 and 0.19 mm, respectively. Accurate segmentation results provide 2D measurements of MA/lumen areas and 3D vessel visualizations for vascular interventions.

Controlling the electrical conductive network formation in nanorod filled polymer nanocomposites by tuning nanorod stiffness.

In this work, by employing a coarse-grained molecular dynamics simulation, we have investigated the effect of the nanorod (NR) stiffness on the relationship between the NR microstructure and the conductive probability of NR filled polymer nanocomposites (PNCs) under the quiescent state and under the shear field. The conductive probability of PNCs is gradually enhanced with the increase of NR stiffness in the quiescent state; however, it first increases and then decreases under the shear field. As a result, the largest conductive probability appears at moderate NR stiffness, which results from the competition between the improved effective aspect ratio of the NR and the breakage of the conductive network. Meanwhile, compared with in the quiescent state, under the shear field the decrease or the increase of the conductive probability depends on the NR stiffness. At low NR stiffness, the increase of the effective aspect ratio of NR enhances the conductive probability, while at high NR stiffness, the breakage of the conductive network reduces the conductive probability. For flexible NRs, the conductive probability first increases and then decreases with increasing the shear rate. The maximum effective aspect ratio of NRs appears at the moderate shear rate, which is consistent with the conductive probability. In summary, this work presents some further understanding about how NR stiffness affects the electric conductive properties of PNCs under the shear field.

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images.

The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.