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Background: Although the transplantation of tissue-engineered cardiac patches with adult bone marrow-derived mesenchymal stem cells (MSCs) can enhance cardiac function after acute or chronic myocardial infarction (MI), the recovery mechanism remains controversial. This experiment aimed to investigate the outcome measurements of MSCs within a tissue-engineered cardiac patch in a rabbit chronic MI model. Methods: This experiment was divided into four groups: left anterior descending artery (LAD) sham-operation group (N = 7), sham-transplantation (control, N = 7), non-seeded patch group (N = 7), and MSCs-seeded patch group (N = 6). PKH26 and 5-Bromo-2’-deoxyuridine (BrdU) labeled MSCs-seeded or non-seeded patches were transplanted onto chronically infarct rabbit hearts. Cardiac function was evaluated by cardiac hemodynamics. H&E staining was performed to count the number of vessels in the infarcted area. Masson staining was used to observe cardiac fiber formation and to measure scar thickness. Results: Four weeks after transplantation, a remarkable improvement in cardiac functionality could be distinctly observed, which was most significant in the MSCs-seeded patch group. Moreover, labeled cells were detected in the myocardial scar, with most of them differentiated into myofibroblasts, some into smooth muscle cells, and only a few into cardiomyocytes in the MSCs-seeded patch group. We also observed significant revascularization in the infarct area implanted in either MSCs-seeded or non-seeded patches. In addition, there were significantly greater numbers of microvessels in the MSCs-seeded patch group than in the non-seeded patch group.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. The aim of the present study was to explore the expression level of tumor protein 73 (TP73) in highly malignant CRC tumors and how the long non-coding RNA tumor protein 73 antisense RNA 1 (TP73-AS1) influences that transcription. We found that TP73-AS1 was highly expressed in malignant CRC samples in The Cancer Genome Atlas (TCGA) database. We also demonstrated TP73-AS1 was expressed in thirty samples of CRC tissues collected from China Medical University patients as well as in HCT116, RKO and SW480 CRC cell lines but not in HCoEpiC or CCD-18Co normal colon cells. Only wild-type TP73-AS1, but not any of its alternate splicing isoforms, was positively correlated with tumor malignancy. TP73-AS1 transcripts were shown to be located in cell nuclei especially in close proximity to the TP73 promoter in CRC cells, but not in normal colon cells. In addition, an interaction between lysine demethylase 5A (KDM5A) and TP73-AS1 in CRC cells, but not normal colon cells, and KDM5A localization on the TP73 promoter were influenced by TP73-AS1. Interestingly, the H3K4me3 level on the TP73 promoter was reduced, but was elevated by TP73-AS1 knockdown in CRC cells. In conclusion, these results suggest a novel epigenetic role of TP73-AS1 on histone demethylation that influences TP73 transcription, and shed light on malignancy in CRC.

Purpose: We previously found that an epithelial-to-mesenchymal transition (EMT)–based gene expression signature was highly correlated with the first principal component (PC1) of 326 colorectal cancer tumors and was prognostic. This study was designed to improve these signatures for better prediction of metastasis and outcome.Experimental Design: A total of 468 colorectal cancer tumors including all stages (I–IV) and metastatic lesions were used to develop a new prognostic score (ΔPC1.EMT) by subtracting the EMT signature score from its correlated PC1 signature score. The score was validated on six other independent datasets with a total of 3,697 tumors.Results: ΔPC1.EMT was found to be far more predictive of metastasis and outcome than its parent scores. It performed well in stages I to III, among microsatellite instability subtypes, and across multiple mutation-based subclasses, demonstrating a refined capacity to predict distant metastatic potential even in tumors with a “good” prognosis. For example, in the PETACC-3 clinical trial dataset, it predicted worse overall survival in an adjusted multivariable model for stage III patients (HR standardized by interquartile range [IQR] = 1.50; 95% confidence interval, 1.25–1.81; P = 0.000016, N = 644). The improved performance of ΔPC1.EMT was related to its propensity to identify epithelial-like subpopulations as well as mesenchymal-like subpopulations. Biologically, the signature was correlated positively with RAS signaling but negatively with mitochondrial metabolism. ΔPC1.EMT was a “best of assessed” prognostic score when compared with 10 other known prognostic signatures.Conclusions: The study developed a prognostic signature score with a propensity to detect non-EMT features, including epithelial cancer stem cell–related properties, thereby improving its potential to predict metastasis and poorer outcome in stage I–III patients. Clin Cancer Res; 22(3); 734–45. ©2015 AACR.

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Energetic materials (EMs) are one of the necessities in many military and civilian applications. Measuring the thermodynamic behaviors of detonation products of EMs at high temperature and high pressure, their equations of state (EOSs) not only serve as a basis in the design of novel materials, but also provide valuable information for their practical applications. The EOS study has a long history, but keeps moving all the time. Various EMs have been developed, the EOS of detonation products provides abundant information in the thermochemistry, hydromechanics and detonation physics, which in turn feedbacks the development of novel EMs and their EOSs. With the development of experimental techniques and computer simulations, many EOSs have been proposed for various explosives in recent years. While experiments keep their fundamental roles, integrated theory-experiment study has become the main approach to the EOS establishment for novel EMs. Moreover, computer simulations based on interatomic and/or intermolecular interaction will have great potential in the future when big data and artificial intelligence are introduced into the field.

Two well-characterized EGFR inhibitor (EGFRi) therapies (cetuximab, panitumumab) have been FDA approved for metastatic colorectal cancer (CRC). Unfortunately, the extended RAS/RAF testing required in the drug labels, identifies only non-responders, and only ~50% of treated patients will respond to therapy. Thus, there is an unmet need to develop additional biomarkers to identify EGFRi sensitive patients. Using an innovative hybrid approach fusing gene expression and DNA sequencing, we recently reported that combined mutations in APC and TP53 were strongly correlated with a validated gene expression signature measuring cetuximab sensitivity in CRC human tumors (Yang et al. Cancer Epidemiol Biomarkers Prev. 2019), suggesting a potential positive biomarker role of APC + TP53 mutations. This finding prompted us to develop a DNA test designed to positively select EGFRi-responsive patients as augmentation to currently used negative predictors for CRC patients. By leveraging the TwinStrand Duplex Sequencing (DS) technology, we recently developed an ultrasensitive custom 6-gene panel (APC, TP53, KRAS, BRAF, NRAS, and HRAS) CLIA-certified assay for FFPE CRC tissues. The assay has been analytically validated using reference cell lines (n=10) which were individually sequenced to >3,000x Duplex depth. The 6-gene DS assay yielded exceptionally high assay performance: (1) accuracy (passed, r2 = 0.98 for Duplex VAF vs. target VAF for Positive Control mutations); (2) sensitivity (passed, all mutations >1/5,000 were detected in all Positive Control replicates; 1 mutation at 1/5,000 was detected in 2/3 replicates), (3) specificity (passed, 2 mutations detected in the Negative Control at 1 count in 1 replicate; VAF 1/500). Importantly, the validation analysis on fresh frozen (FF):FFPE paired tissues from 21 CRC patients shows: (1) The 6-gene DS assay performed on FFPE samples faithfully reproduces assay results on FF samples; (2) The ultrasensitive DS assay can accurately detect additional “new” mutations at low allelic frequencies compared to a standard NGS method. TwinStrand DS identified 17 mutations not identified by standard NGS; 13 of these new mutations had < 10% VAF. Furthermore, preliminary Kaplan-Meier analysis of the third-line EGFRi data shows that while the statistical significance was not achieved due to the small simple size (n=28), patients harboring combined APC and TP53 mutations(AP) (vs non-AP patients) tended to have longer progression free survival (6.65 vs 3.95 months, P=0.064) and overall survival (17.80 vs 10.65 months, P=0.14). Additional data acquisition and analysis is ongoing. Thus, there is an opportunity to change clinical practice and standards of care to ultimately improve CRC outcomes if this new, robust, sensitive test is clinically-validated. Citation Format: Mingli Yang, Michael J. Schell, Lance Pflieger, Jesse Salk, Timothy J. Yeatman. Development of an APC and TP53-based duplex sequencing assay to positively predict colorectal cancer response to EGFR inhibitors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4343.

External electric field and interlayer twist introduce diverse changes in their confined electronic states of bilayer graphene quantum dots. Using a quantum-dot model, the band gaps of twisted bilayer graphene in finite sizes of about 1.4–2.4 nm with varying twist angles are studied in the presence of an electrostatic field perpendicular to the flakes by means of first-principles calculations. The size-dependent gaps are widened by the interlayer twist, but narrowed by the applied field. Their coupling, however, results in an enhanced Stark response in the twisted structures of which the field-induced band-gap variations are about 3–4 times as large as that of the corresponding untwisted structures under the same field strength. The exceptional Stark shifts come from the field-induced asynchronous shifts in their occupied and virtual energy levels, which are further enhanced by strong interlayer coupling at specific twist angles. Moreover, the shift of band gaps with the field strength follows the quadratic Stark response with large second-order shifting coefficients. The enhanced and tunable Stark shift suggests a gateway to the band engineering of bilayer graphene quantum dots by tuning their sizes, twist angles and their coupling with applied field.

Promotion in power conversion efficiency is an appealing task for quantum dot-sensitized solar cells that have emerged as promising materials for the utilization of clean and sustainable energy. Doping of Mn atoms into quantum dots (QD) has been proven to be one of the effective approaches, although the origin of such a promotion remains controversial. While several procedures are involved in the power conversion process, electron injection from the QD to the semiconductor oxide substrate is focused on in this work using first-principles calculations. Based on the Marcus theory, the electron injection rates are evaluated for the quantum dot-sensitized solar cell models in which the pure and Mn-doped core-shell CdSe clusters are deposited on a semiconductor titanium dioxide substrate. Enhanced rates are obtained for the Mn-doped structure, which is in qualitative agreement with the experiments. A large number of dominant injection channels and strong QD-substrate coupling are responsible for the Mn-induced rate enhancement, which could be achieved by manipulating the band structure mapping between the QD and the semiconductor oxide. By addressing the role of an Mn dopant in the electron injection process, strategies for the promotion of electron injection rates are proposed for the design of quantum dot-sensitized solar cells.

Fano resonances in plasmonic nanostructures have received widespread attention due to their potential applications in chemical/biological sensing, optical switching, and filtering. However, these p...

In recent years, machine learning (ML) methods have made significant progress, and ML models have been adopted in virtually all aspects of chemistry. In this study, based on the crystal graph convolutional neural networks algorithm, an end-to-end deep learning model was developed for predicting the methane adsorption properties of metal-organic frameworks (MOFs). High-throughput grand canonical Monte Carlo calculations were carried out on the computation-ready, experimental MOF database, which contains approximately 11 000 MOFs, to construct the data set. An area under the curve of 0.930 for the test set proved the reliability of the developed deep learning model. To assess the transferability of the model, we applied it to predict the methane adsorption volume for some randomly selected covalent organic frameworks and zeolitic imidazolate framework materials. The results indicated that the model could also be suitable for other porous materials. We also applied it to the hierarchical screening of a hypothetical MOFs database (∼330 000 MOFs). Four hypothetical MOFs were demonstrated to have the highest performance in methane adsorption. A calculated maximum working capacity of 145 cm3/cm3 at 5-35 bar and 298 K indicated that the hypothetical MOF is close to the Department of Energy's 2015 target of 180 cm3/cm3. Further analyses on all screened out MOFs established correlations between some structural features with the working capacity. The successful incorporation of ML and hierarchical screening can accelerate the discovery of new materials not just for gas adsorption, but also other areas involving interactions in materials and molecules.

Zinc (Zn) has recently been recognized as a promising bone repair material due to its inherent biodegradability and favorable biocompatibility. In this work, rare earth neodymium (Nd) was introduced into a Zn-based alloy fabricated using a laser powder bed fusion (LPBF) process. Results showed that addition of Nd significantly improved the melt fluidity and reduced the evaporation of Zn, thereby achieving parts with a high densification rate of 98.71%. Significantly, the Nd alloying treatment effectively refined the grain size from 25.3 to 6.2 µm. NdZn5 eutectics precipitated and contributed to a second-phase strengthening effect. As a result, the tensile strength increased to (119.3±5.1) MPa and the Vickers hardness to (76.2±4.1). Moreover, the Zn-Nd alloy exhibited good anti-inflammatory activity, as the Nd ions released during degradation had a strong affinity with cell membrane phospholipids and consequently inhibited the release of inflammatory cytokines. It also presented favorable cytocompatibility, showing great potential as a bone repair material.

The inherent hysteresis nonlinearity of piezoelectric actuator degrades the positioning accuracy of the micro-positioning stage. Prandtl–Ishlinskii model is widely used for piezoelectric hysteresis modeling, yet it is a rate-independent model with weak generalization ability. To overcome this problem, we proposed a convolutional neural network model based on the Prandtl–Ishlinskii model, which consists of a rate-dependent Prandtl–Ishlinskii model layer and convolutional network layer. The rate-dependent Prandtl–Ishlinskii model layer extends the traditional Prandtl–Ishlinskii model to describe the rate-dependent hysteresis behavior. The convolutional network layer with deep learning ability extracts the deep features of the input signal to improve the generalization ability of the hysteresis model. The experiment results indicate that the standard error of the proposed hysteresis model to predict displacement at unmodeled frequencies has been reduced by 18.74%–36.75% in comparison with the Prandtl–Ishlinskii model, which verifies that the proposed hysteresis model has not only higher accuracy but also stronger generalization ability.

The teaching ability of College Teachers is regarded as one of the core competencies and a critical indicator for measuring comprehensive strength for a college. However, its evaluation process is a highly complex system decision-making, for there are various factors that influence on the assessment of for the College Teachers’ the teaching ability. The traditional methods have drawbacks of strong subjectivity, so they are difficult to correctly evaluate the teaching ability of College Teachers, resulting in decrease of measurement accuracy. Based on the analysis of the relevant factors, this paper presents an intelligent design based neural network model of discrete Hopfield for the measurement and analysis of College Teachers' teaching ability. Firstly, a Hopfield neural network model for the measure analysis of the teaching ability is established, and eleven measure analysis indexes are selected as input information of the Hopfield neural network model. Secondly, the College Teachers' teaching ability grades are chosen as the model output, then the input and output model based on the relationship among the self-learning abilities of neural network is established. Finally, the simulation experiment is obtained by using MATLAB. The simulation results show that the model has the characteristics of high efficiency, objectivity and fairness, which can meet the requirements of the measurement and analysis of College Teachers' teaching ability.

Aims: To explore more active fluoroquinolone anticancer candidates. Background: Cancer which can affect almost any part of the body, is most striking and deadliest disease. It is estimated that around one in five people globally develop cancer during their lifetime, and approximately 10% people eventually die from this disease, and 18.1 million new cancer cases with 9.6 million deaths occurred in 2018. The anticancer agents play an intriguingly role in fighting against cancer, and above 100 drugs have already been marketed for this purpose. However, the major drawback of current accessible anticancer agents is the low specificity which results in many side effects. Moreover, cancer cells have already generated resistance to almost all available drugs, creating an urgent need to novel anticancer agents with high specificity and great efficiency especially towards drug-resistant cancers. Quinolone and isatin derivatives were reported to possess promising anticancer activity, high specificity, and relatively few side effects. Currently, several quinolone and isatin derivatives such as Voreloxin, Quarfloxin, AT-3639, Semaxanib, Sunitinib and Nintedanib have already been introduced in clinical practice or under evaluations for the treatment of cancer including drug-resistant cancers, revealing their potential as novel anticancer agents. Hybrid molecules have the potential to increase the specificity, improve the efficiency, and overcome the drug resistance, so hybridization is a promising strategy in the drug discovery. Some of the moxifloxacin-isatin hybrids exhibited considerable activity against various cancer cells even drug-resistant cells, so it is conceivable that hybridization of quinolone and isatin moieties may provide novel anticancer candidates. The structure-activity relationships (SARs) demonstrated that the linkers between quinolone and isatin skeletons were critical for the biological activity, and 1,2,3-triazole could exert various noncovalent interactions with biological targets, so introduction of 1,2,3-triazole as the linker between the two moieties may provide more efficient anticancer candidates. Objective: To explore more active fluoroquinolone anticancer candidates and enrich the structureactivity relationships of fluoroquinolone-isatin hybrids. Methods: The synthesized moxifloxacin-isatin hybrids 5a-c, 6a-g and 13a-d were assessed for their anticancer activities against liver cancer cells HepG2, breast cancer cells MCF-7, MCF-7/DOX, prostate cancer cells DU-145 and MDR DU-145 by MTT assay. Hybrid 5b was selected for further evaluation of its tubulin polymerization inhibitory activity with combretastatin A-4 as comparison. Results: Most of the synthesized hybrids were active against the tested cancer cell lines, and the most active hybrid 5b (IC50: 31.3-76.8 μM) was more potent than vorinostat (IC50: 96.7->100 μM), demonstrating moxifloxacin-isatin hybrids are potential anticancer candidates. Conclusion: The mechanism study revealed that inhibition of tubulin polymerization is at least one of the mechanisms of action for this kind of hybrids. Other: The structure-activity relationship was summarized for further rational design of more efficient anticancer candidates.

Magnesium (Mg) degrades too fast in human body, which limits its orthopedic application. Single-phase Mg-based supersaturated solid solution is expected to possess high corrosion resistance. In this work, rare earth scandium (Sc) was used as alloying element to prepare Mg(Sc) solid solution powder by mechanical alloying (MA) and then shaped into implant using selective laser melting (SLM). MA utilizes powerful mechanical force to introduce numerous lattice defects, which promotes the dissolution of Sc in Mg matrix and forms supersaturated solid solution particles. Subsequently, SLM with fast heating and cooling rate maintains the original supersaturated solid solution structure. Immersion tests revealed that high Sc content significantly enhanced the corrosion resistance of Mg matrix because of the formation of protective corrosion product film, which was also proved by the electrochemical impedance spectroscopy measurements. Thereby, Mg(Sc) alloy showed a relatively low degradation rate of 0.61 mm/year. In addition, cell tests showed that the Mg(Sc) exhibited favorable biocompatibility and was suitable for medical application.

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The RAS pathway is activated in more than 55% of CRC and has been targeted for therapeutic intervention with MEK inhibitors. Unfortunately, many patients have de novo resistance, or can develop resistance to this new class of drugs. We have hypothesized that much of this resistance may pass through SRC as a common signal transduction node, and that inhibition of SRC may suppress MEK inhibition resistance mechanisms. CRC tumors of the Consensus Molecular Subtype (CMS) 4, enriched in stem cells, are difficult to successfully treat and have been suggested to evade traditional chemotherapy agents through resistance mechanisms. Here, we evaluate targeting two pathways simultaneously to produce an effective treatment by overcoming resistance. We show that combining Trametinib (MEKi) with Dasatinib (SRCi) provides enhanced cell death in 8 of the 16 tested CRC cell lines compared to treatment with either agent alone. To be able to select sensitive cells, we simultaneously evaluated a validated 18-gene RAS pathway activation signature score along with a 13-gene MEKi resistance signature score, which we hypothesize predict tumor sensitivity to this dual targeted therapy. We found the cell lines that were sensitive to the dual treatment were predominantly CMS4 and had both a high 18-gene and a high 13-gene score, suggesting these cell lines had potential for de novo MEKi sensitivity but were subject to the rapid development of MEKi resistance. The 13-gene score is highly correlated to a score for SRC activation, suggesting resistance is dependent on SRC. Our data show that gene expression signature scores for RAS pathway activation and for MEKi resistance may be useful in determining which CRC tumors will respond to the novel drug combination of MEKi and SRCi.

While multilayer graphene (MLG) possesses excellent intralayer electron mobility, its interlayer electrical conductance exhibits great diversity that results in exotic phenomena and various applications in electronic devices. Driven by a vertical electric field, electron flow occurs across the layers, and its current is tunable by controlling the interlayer stacking and distance, disc size and field strength. The electron rearrangement induced by the external field is appropriately described by the polarizability that measures the electronic response against the applied field. Based on the field-induced electron density variations computed with a first-principles approach, a polarizability decomposition scheme is developed in this work to isolate the inter- and intra-layer contributions from the total polarizability of twisted trilayer graphene (TTG) quantum dots. The inter- and intra-layer counterparts reflect the charge transfer (CT) and field shielding effects among the layers, respectively. Shielded by the top and bottom layers, the middle layer is particularly effective in bridging, switching and promoting the interlayer electron flow. Large CT and shielding effects occur not only in the strongly coupled Bernal stacking, but also in the structures misorientating from the full-AAA stacking by a small twist angle. Moreover, both effects vary with the twist angle and disc size, indicating a controllable conductive/dielectric conversion in the vertical direction. In light of inter- and intralayer polarizability, our study addresses the precise modulation of interlayer conductance for TTG quantum dots, which is required in the microstructure design and performance manipulation of MLG-based electronic devices.