Coiled Carbon Nanotube Fibers Sheathed by a Reinforced Liquid Crystal Elastomer for Strong and Programmable Artificial Muscles.

Fibers of liquid crystal elastomers (LCEs) as promising artificial muscle show ultralarge and reversible contractile strokes. However, the contractile force is limited by the poor mechanical properties of the LCE fibers. Herein, we report high-strength LCE fibers by introducing a secondary network into the single-network LCE. The double-network LCE (DNLCE) shows considerable improvements in tensile strength (313.9%) and maximum actuation stress (342.8%) compared to pristine LCE. To facilitate the controllability and application, a coiled artificial muscle fiber consisting of DNLCE-coated carbon nanotube (CNT) fiber is prepared. When electrothermally driven, the artificial muscle fiber outputs a high actuation performance and programmable actuation. Furthermore, by knitting the artificial muscle fibers into origami structures, an intelligent gripper and crawling inchworm robot have been demonstrated. These demonstrations provide promising application scenarios for advanced intelligent systems in the future.

Implicating the cholecystokinin B receptor in liver stem cell oncogenesis.

Hepatocellular carcinoma (HCC) is the fastest-growing cause of cancer-related deaths worldwide. Chronic inflammation and fibrosis are the greatest risk factors for the development of HCC. Although the cell of origin for HCC is uncertain, many theories believe this cancer may arise from liver progenitor cells or stem cells. Here, we describe the activation of hepatic stem cells that overexpress the cholecystokinin-B receptor (CCK-BR) after liver injury with either a DDC diet (0.1% 3, 5-diethoxy-carbonyl 1,4-dihydrocollidine) or a NASH-inducing CDE diet (choline-deficient ethionine) in murine models. Pharmacologic blockade of the CCK-BR with a receptor antagonist proglumide or knockout of the CCK-BR in genetically engineered mice during the injury diet reduces the expression of hepatic stem cells and prevents the formation of three-dimensional tumorspheres in culture. RNA sequencing of livers from DDC-fed mice treated with proglumide or DDC-fed CCK-BR knockout mice showed downregulation of differentially expressed genes involved in cell proliferation and oncogenesis and upregulation of tumor suppressor genes compared with controls. Inhibition of the CCK-BR decreases hepatic transaminases, fibrosis, cytokine expression, and alters the hepatic immune cell signature rendering the liver microenvironment less oncogenic. Furthermore, proglumide hastened recovery after liver injury by reversing fibrosis and improving markers of synthetic function. Proglumide is an older drug that is orally bioavailable and being repurposed for liver conditions. These findings support a promising therapeutic intervention applicable to patients to prevent the development of HCC and decrease hepatic fibrosis.NEW & NOTEWORTHY This investigation identified a novel pathway involving the activation of hepatic stem cells and liver oncogenesis. Receptor blockade or genetic disruption of the cholecystokinin-B receptor (CCK-BR) signaling pathway decreased the activation and proliferation of hepatic stem cells after liver injury without eliminating the regenerative capacity of healthy hepatocytes.

Design and modeling for drug combination experiments with order effects.

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Loss of ANCO1 Expression Regulates Chromatin Accessibility and Drives Progression of Early-Stage Triple-Negative Breast Cancer.

Mutations in the gene ankyrin repeat domain containing 11 (ANKRD11/ANCO1) play a role in neurodegenerative disorders, and its loss of heterozygosity and low expression are seen in some cancers. Here, we show that low ANCO1 mRNA and protein expression levels are prognostic markers for poor clinical outcomes in breast cancer and that loss of nuclear ANCO1 protein expression predicts lower overall survival of patients with triple-negative breast cancer (TNBC). Knockdown of ANCO1 in early-stage TNBC cells led to aneuploidy, cellular senescence, and enhanced invasion in a 3D matrix. The presence of a subpopulation of ANCO1-depleted cells enabled invasion of the overall cell population in vitro and they converted more rapidly to invasive lesions in a xenograft mouse model. In ANCO1-depleted cells, ChIP-seq analysis showed a global increase in H3K27Ac signals that were enriched for AP-1, TEAD, STAT3, and NFκB motifs. ANCO1-regulated H3K27Ac peaks had a significantly higher overlap with known breast cancer enhancers compared to ANCO1-independent ones. H3K27Ac engagement was associated with transcriptional activation of genes in the PI3K-AKT, epithelial-mesenchymal transition (EMT), and senescence pathways. In conclusion, ANCO1 has hallmarks of a tumor suppressor whose loss of expression activates breast-cancer-specific enhancers and oncogenic pathways that can accelerate the early-stage progression of breast cancer.

Five new sesquiterpenoids from agarwood of Aquilaria sinensis.

Five new eudesmane-type sesquiterpenoids (aquisinenoids F-J (1-5)) and five known compounds (6-10) were isolated from the agarwood of Aquilaria sinensis. Their structures, including absolute configurations, were identified by comprehensive spectroscopic analyses and computational methods. Inspired by our previous study on the same kinds of skeletons, we speculated that the new compounds have anticancer and anti-inflammatory activities. The results did not show any activity, but they revealed the structure-activity relationships (SAR).

Correction: Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities.

[This corrects the article DOI: 10.3762/bjoc.19.59.].

Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities.

Six new compounds, including a tetralone 1, two xanthones 2 and 3, a flavan derivative 4, and two nor-diterpenoids 7 and 8, accompanied by two known flavan derivatives 5 and 6 and a known olefine acid (9) were isolated from whole bodies of Kronopolites svenhedini (Verhoeff). The structures of the new compounds were determined by 1D and 2D nuclear magnetic resonance (NMR) and other spectroscopic methods, as well as computational methods. Selected compounds were evaluated for their biological properties against a mouse pancreatic cancer cell line and inhibitory effects on iNOS and COX-2 in RAW264.7 cells.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Experimental design for multi-drug combination studies using signaling networks.

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules.

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

Nonlinear response surface in the study of interaction analysis of three combination drugs.

Few articles have been written on analyzing three-way interactions between drugs. It may seem to be quite straightforward to extend a statistical method from two-drugs to three-drugs. However, there may exist more complex nonlinear response surface of the interaction index (II) with more complex local synergy and/or local antagonism interspersed in different regions of drug combinations in a three-drug study, compared in a two-drug study. In addition, it is not possible to obtain a four-dimensional (4D) response surface plot for a three-drug study. We propose an analysis procedure to construct the dose combination regions of interest (say, the synergistic areas with II≤0.9). First, use the model robust regression method (MRR), a semiparametric method, to fit the entire response surface of the II, which allows to fit a complex response surface with local synergy/antagonism. Second, we run a modified genetic algorithm (MGA), a stochastic optimization method, many times with different random seeds, to allow to collect as many feasible points as possible that satisfy the estimated values of II≤0.9. Last, all these feasible points are used to construct the approximate dose regions of interest in a 3D. A case study with three anti-cancer drugs in an in vitro experiment is employed to illustrate how to find the dose regions of interest.

Small non-coding RNA biomarkers in sputum for lung cancer diagnosis.

The early detection of lung cancer can reduce the mortality. However, there is no effective means in clinical settings for noninvasively detecting lung cancer. We previously developed 3 sputum miRNA biomarkers and 2 sputum small nucleolar RNA (snoRNA) biomarkers that can potentially be used for noninvasively diagnosing lung cancer. Here we evaluate the individual and combined applications of the two types of biomarkers in different sets of lung cancer patients and controls. Combined analysis of the miRNAs and snoRNAs has a synergistic effect with 89 % sensitivity and 89 % specificity, and may provide a useful tool for lung cancer early detection.

Complications of Robotic Gynecologic Surgery in the Severely Morbidly Obese.

BACKGROUND: Surgeons are increasingly faced with the challenge of caring for obese patients. Advanced laparoscopic procedures have been shown to be safe in women with high BMI, but conversion rates remain high. Because robotics holds many potential advantages over traditional laparoscopic surgery, we sought to evaluate the outcome of robotic-assisted gynecologic surgery in obese patients. METHODS: A retrospective chart review of obese female patients undergoing robotic gynecologic surgery between January 2008 and August 2010 was done. Patients were divided into three groups based on BMI (group I, BMI 30-34.9 kg/m2; group II, BMI 35-39.9 kg/m2; and group III, BMI >40 kg/m2). Patients were assessed for baseline characteristics, comorbid conditions, and surgical outcomes and complications. RESULTS: A total of 128 obese patients were identified with 43 in group I, 30 in group II, and 55 in group III. There was an increased prevalence of diabetes and asthma in group III and younger age in group I. Of the 128, 117 (91 %) underwent hysterectomy and/or staging with the majority of patients having surgery for either endometrial cancer or fibroids. Conversion to laparotomy was more common in groups 2 and 3 with a positive correlation between increasing BMI and conversion. There was no difference in complications between the groups and only 1 major postoperative complication in the entire cohort. CONCLUSIONS: Robotic-assisted gynecologic surgery can be safely performed in severely morbidly obese patients. Although conversion rates are higher with increasing obesity, a majority of procedures can still be completed minimally invasively.

Single center experience with total body irradiation and melphalan (TBI-MEL) myeloablative conditioning regimen for allogeneic stem cell transplantation (SCT) in patients with refractory hematologic malignancies.

We retrospectively evaluated the tolerability and efficacy of fractionated total body irradiation (TBI) (1,200 cGy) and melphalan (MEL) (100-110 mg/m(2)) myeloablative conditioning in 48 patients with nonremission AML (n = 14), ALL (n = 10), NHL (n = 18), and other refractory hematologic malignancies (n = 6) who received allogeneic stem cell transplantation (SCT) between 2002 and 2011. Median age was 48 years (22 to 68); 14 out of 26 leukemia patients (54 %) had circulating blasts at transplant, 20 (50 %) evaluable patients had poor-risk cytogenetics, 12 (25 %) had prior SCT, and 10 (21 %) received stem cells from a mismatch donor. All patients received tacrolimus with or without methotrexate for GVHD prophylaxis. At the time of analysis, 13 patients (27 %) were alive and disease free. Engraftment was complete in all patients. The median time to ANC recovery (>500) was 12 days (range, 6-28). The most common grade III and IV toxicities were mucositis and infections. Eighteen patients (43 %) developed grade II-IV acute GVHD, and eight (26 %) had extensive chronic GVHD. Of 44 evaluable patients for response, 28 (64 %) achieved a complete remission (CR), and seven (15 %) had a partial remission after the transplant. With a median follow-up of 30 months (4 to 124 months) for surviving patients, the cumulative incidence of relapse was 45 % at 1 year, and the probability of overall survival (OS) at 5 years was 22.5 %. Multivariate analysis showed that platelet count (<80,000/mL) and lactic dehydrogenase (>500 IU/L) at SCT were associated with relapse. Age less than 53 years and CR after SCT were associated with better OS. Our data suggest that TBI-MEL can result in CR in two thirds, durable remission in one third, and 5-year survival in about one quarter of patients with nonremission hematologic malignancies. Further studies with TBI-MEL in standard risk transplant patients are warranted.

Joint longitudinal and survival-cure models in tumour xenograft experiments.

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival-cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival-cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors.

Modeling sustained treatment effects in tumor xenograft experiments.

In cancer drug development, demonstrated efficacy in tumor xenograft models is an important step toward bringing a promising compound to human use. A key outcome variable is tumor volume measured over a period of time, while mice are treated with certain treatment regimens. A constrained parametric model has been proposed to account for special features, such as intrinsic tumor growth, or tumor volume truncations due to tumor size being either too large or too small to detect. However, since the drug concentration in the blood of a mouse or its tissues may be stabilized at a certain level and maintained during a period of time, the treatment may have sustained effects. This article extends the constrained parametric model to account for the sustained drug effects. The ECM algorithm for incomplete data is applied to estimating the dose-response relationship in the proposed model. The model selection based on likelihood functions is given and a simulation study is conducted to investigate the performance of the proposed estimator. A real xenograft study on the antitumor agent temozolomide combined with irinotecan against the rhabdomyosarcoma is analyzed using the proposed methods.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Origami-Enhanced Mechanical Properties for Worm-Like Robot.

In recent years, the exploration of worm-like robots has garnered much attention for their adaptability in confined environments. However, current designs face challenges in fully utilizing the mechanical properties of structures/materials to replicate the superior performance of real worms. In this article, we propose an approach to address this limitation based on the stacked Miura origami structure, achieving the seamless integration of structural design, mechanical properties, and robotic functionalities, that is, the mechanical properties originate from the geometric design of the origami structure and at the same time serve the locomotion capability of the robot. Three major advantages of our design are: the implementation of origami technology facilitates a more accessible and convenient fabrication process for segmented robotic skin with periodicity and flexibility, as well as robotic bristles with anchoring effect; the utilization of the Poisson's ratio effect for deformation amplification; and the incorporation of localized folding motion for continuous peristaltic locomotion. Utilizing the high geometric designability inherent in origami, our robot demonstrates customizable morphing and quantifiable mechanical properties. Based on the origami worm-like robot prototype, we experimentally verified the effectiveness of the proposed design in realizing the deformation amplification effect and localized folding motion. By comparing this to a conventional worm-like robot with discontinuous deformation, we highlight the merits of these mechanical properties in enhancing the robot's mobility. To sum up, this article showcases a bottom-up approach to robot development, including geometric design, mechanical characterization, and functionality realization, presenting a unique perspective for advancing the development of bioinspired soft robots.

Environmental Evaluation on Toxicity, Toxic Mechanism, and Hydrolysis Behavior of Potential Acethydrazide Fungicide Candidates.

The evaluation of toxicity and environmental behavior of bioactive lead molecules is helpful in providing theoretical support for the development of agrochemicals, in line with the sustainable development of the ecological environment. In previous work, some acethydrazide structures have been demonstrated to exhibit excellent and broad-spectrum fungicidal activity; however, its environmental compatibility needs to be further elucidated if it is to be identified as a potential fungicide. In this project, the toxicity of fungicidal acethydrazide lead compounds F51, F58, F72, and F75 to zebrafish was determined at 10 µg mL-1 and 1 µg mL-1. Subsequently, the toxic mechanism of compound F58 was preliminarily explored by histologic section and TEM observations, which revealed that the gallbladder volume of common carp treated with compound F58 increased, accompanied by a deepened bile color, damaged plasma membrane, and atrophied mitochondria in gallbladder cells. Approximately, F58-treated hepatocytes exhibited cytoplasmic heterogeneity, with partial cellular vacuolation and mitochondrial membrane rupture. Metabolomics analysis further indicated that differential metabolites were enriched in the bile formation-associated steroid biosynthesis, primary bile acid biosynthesis, and taurine and hypotaurine metabolism pathways, as well as in the membrane function-related glycerophospholipid metabolism, linolenic acid metabolism, α-linolenic acid metabolism, and arachidonic acid metabolism pathways, suggesting that the acethydrazide F58 may have acute liver toxicity to common carp. Finally, the hydrolysis dynamics of F58 was investigated, with the obtained half-life of 5.82 days. The above results provide important guiding significance for the development of new green fungicides.

Dimeric diarylheptanoids with anti-inflammatory activity from Zingiber officinale.

Two previously undescribed chain diarylheptanoid derivatives (2-3), five previously undescribed dimeric diarylheptanoids (4-8), together with one known cyclic diarylheptanoid (1) were isolated from Zingiber officinale. Their structures were elucidated by extensive spectroscopic analyses (HR-ESI-MS, IR, UV, 1D and 2D NMR) and ECD calculations. Biological evaluation of compounds 1-8 revealed that compounds 2, 3 and 4 could inhibit nitrite oxide and IL-6 production in lipopolysaccharide induced RAW264.7 cells in a dose-dependent manner.