Comparison of dual-energy computed tomography (DECT) polychromatic and monochromatic images with and without iterative metal artifact reduction algorithm in patients with dental implants.

Background: Dual-energy computed tomography (DECT) and iterative metal artifact reduction (iMAR) algorithms are valuable tools for reducing metal artifacts. Different parameters of these technologies and their combination can achieve different performance. This study compared various polychromatic and monochromatic images obtained via DECT with and without using iMAR algorithm to reduce artifacts in patients with dental implants. Methods: This study included 30 patients with dental implants who underwent DECT for head and neck imaging. The computed tomography (CT) image sets comprised DECT polychromatic image sets [dual-energy (DE) polychromatic] that linearly blended 100 kV and tin-filtered 140 kV images using composition ratios of -1, -0.6, -0.3, 0, and 0.6, and virtual monochromatic images (DE monochromatic) at 90, 110, 130, 150, and 170 keV. These image sets were obtained with and without using iMAR, resulting in a total of 20 image sets. For subjective analysis, metal artifacts and image quality were assessed using a 5-point Likert scale. For objective analysis, CT attenuation, standard deviation (SD), contrast-to-noise ratio (CNR) and artifact index (AI) were evaluated. In addition, subgroup analysis was performed based on implant size. Results: In the subjective evaluation, iMAR + DE polychromatic (-0.3) images exhibited the lowest metal artifact scores [median (interquartile range): 2 (2-3)]. iMAR + DE monochromatic (110 keV) images demonstrated optimal image quality scores [median (interquartile range): 2 (2-3)]. In the objective evaluation, none of the images demonstrated a significant difference in the CNR, except polychromatic images with a composition of -1 and 0.6. iMAR + DE polychromatic (0) exhibited the lowest AI [median (interquartile range): 8.7 (5.9-14.5)]. There was no significant difference between the two groups with different implant sizes for the techniques combined with iMAR (all P>0.05). Conclusion: iMAR + DE polychromatic (-0.3 and 0) and iMAR + DE monochromatic (110 keV) images exhibited better image quality and substantial metal artifact reduction (MAR) compared with the other image sets. The performance of the techniques combined with iMAR was not affected by the size of the implant.

Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus.

Objective: To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus. Materials and methods: In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model. Results: Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models. Conclusion: Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.

Synergistic Effect of Small Molecular Organic Matter and Functional Groups in Coal on Methane Adsorption.

Functional groups and small-molecule organic matter are two key parts of coal. To explore the microscopic mechanism underlying the synergistic effect of both parts on methane adsorption, the oxygen-containing (-OH, -COOH, and -C=O) and nitrogen-containing (-NH2) functional groups and two common small molecular organic matter methylbenzene and tetrahydrofuran-2-alcohol in coal are selected. The quantum chemical meta-GGA functional method is used to optimize all structures. The electrostatic potential analyses, weak interaction analyses, and theory of atoms in molecules have been used to delve further into the nature of this synergistic effect. Our results show that functional groups inhibit methane adsorption by coal molecules, and the inhibition effect is enhanced in the presence of methylbenzene. Interestingly, the synergistic effects between functional groups and small molecular organic matter are changed from inhibition to promotion after introducing tetrahydrofuran-2-alcohol, wherein -COOH has the best synergistic effect. This work not only offers a theoretical foundation for exploring the synergistic effect of small molecular organic matter and functional groups on methane adsorption by coal molecules but also lays a foundation for further research on gas prevention and extraction.

Composition analysis and health risk assessment of the hazardous compounds in cooking fumes emitted from heated soybean oils with different refining levels.

The cooking fumes generated from thermal cooking oils contains various of hazardous components and shows deleterious health effects. The edible oil refining is designed to improve the oil quality and safety. While, there remains unknown about the connections between the characteristics and health risks of the cooking fumes and oils with different refining levels. In this study, the hazardous compounds, including aldehydes, ketones, polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM) in the fumes emitted from heated soybean oils with different refining levels were characterized, and their health risks were assessed. Results demonstrated that the concentration range of aldehydes and ketones (from 328.06 ± 24.64 to 796.52 ± 29.67 µg/m3), PAHs (from 4.39 ± 0.19 to 7.86 ± 0.51 µg/m3), and PM (from 0.36 ± 0.14 to 5.08 ± 0.15 mg/m3) varied among soybean oil with different refining levels, respectively. The neutralized oil showed the highest concentration of aldehydes and ketones, whereas the refined oil showed the lowest. The highest concentration levels of PAHs and PM were observed in fumes emitted from crude oil. A highly significant (p < 0.001) positive correlation between the acid value of cooking oil and the concentrations of PM was found, suggesting that removing free fatty acids is critical for mitigating PM concentration in cooking fumes. Additionally, the incremental lifetime cancer risk (ILCR) values of PAHs and aldehydes were 5.60 × 10-4 to 8.66 × 10-5 and 5.60 × 10-4 to 8.66 × 10-5, respectively, which were substantially higher than the acceptable levels (1.0 × 10-6) established by US EPA. The present study quantifies the impact of edible oil refining on hazardous compound emissions and provides a theoretical basis for controlling the health risks of cooking fumes via precise edible oil processing.

Influences of Illumination Pretreatment on Soybean Oil Activated Clay Bleaching Effects and Soybean Oil Quality Evaluation.

Visible light has been widely studied for possible applications in food industry as being a kind of clean energy. Presently, the influences of illumination pretreatment on soybean oil quality followed by conventional activated clay bleaching, including the oil color, fatty acid composition, oxidation stability, and micronutrient content, were investigated. Results demonstrated that the illumination pretreatment increased the color differences between the non-illuminated and illuminated soybean oils, which indicated that the light exposure could improve the decoloring effects. The fatty acids composition and the peroxide value (POV) and oxidation stability index (OSI) of the soybean oils showed little changes during this process. Although the illumination pretreatment affected the content of lipid-soluble micronutrients, including phytosterols and tocopherols, no significant differences could be observed (p > 0.05). Moreover, it showed that the illumination pretreatment showed significant effects for decreasing the following activated clay bleaching temperature, indicating the energy saving potential of this novel soybean oil decoloring process. The present study might provide new insights for developing eco-friendly and efficient vegetable oil bleaching technology.

Pancreatic fat fraction in dual-energy computed tomography as a potential quantitative parameter in the detection of type 2 diabetes mellitus.

PURPOSE: To investigate the clinical value of measuring pancreatic fat fraction using dual-energy computed tomography (DECT) in association with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: This retrospective study included patients who underwent abdominal DECT between September 2021 and July 2022. The fat fractions in the head, body, and tail of the pancreas were calculated using fat maps generated from unenhanced DECT images, and CT values were measured at the same locations. The intraclass correlation coefficient (ICC) was used to analyze the reproducibility of measurements from two observers. Diagnostic performance was assessed using receiver operating characteristic curves. RESULTS: Seventy-eight patients, including 45 T2DM patients and 33 controls, were enrolled. The fat fractions of the pancreas were significantly higher in the T2DM group than in the control group (pancreatic head: 8.4 ± 6.3 % vs 5.1 ± 3.9 %; pancreatic body: 4.8 ± 4.0 % vs 2.7 ± 3.9 %; and pancreatic tail: 5.3 ± 3.2 % vs 2.7 ± 2.9 %, all p < 0.05). And the CT values of the pancreas were significantly lower in the T2DM group than in the control group (pancreatic head: 41.1 ± 8.5 HU vs 45.7 ± 4.6 HU; pancreatic body: 44.4 ± 5.0 HU vs 47.4 ± 3.7 HU; and pancreatic tail: 44.5 ± 5.0 HU vs 47.6 ± 3.2 HU, all p < 0.05). The fat fraction of the pancreatic tail was the best indicator for distinguishing T2DM patients from the controls (area under the curve: 0.716 (95 % CI: 0.601, 0.832), sensitivity: 64.4 % (95 % CI: 48.7 %, 77.7 %), and specificity: 78.8 % (95 % CI: 60.6 %, 90.4 %)). CONCLUSION: The DECT fat fractions of the pancreas could be a valuable additional parameter in the detection of T2DM.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

Astragalus polysacharin inhibits hepatocellular carcinoma-like phenotypes in a murine HCC model through repression of M2 polarization of tumour-associated macrophages.

CONTEXT: Astragalus polysaccharin (APS), an extract of Astragalus propinquus Schischk, exerts antitumor effects in hepatocellular carcinoma (HCC). OBJECTIVE: This study investigated the mechanism of action of APS in HCC. MATERIALS AND METHODS: Tumour-associated macrophages (TAMs) were treated with APS (0, 8, 16 mg/mL) for 24 h. APS (16 mg/mL)-treated TAMs were co-cultured with MHCC97H/Huh7 cells for 24 h. Finally, BALB/c nude mice were divided into PBS, APS (50 mg/kg), APS (100 mg/kg), APS (200 mg/kg) groups: mice were inoculated with Huh7 cells to construct tumour xenograft model, followed by administration of APS (50, 100, 200 mg/kg) or PBS daily for 30 days. Cell proliferation, migration, invasion, tumour growth, macrophage markers and proportions were measured. RESULTS: APS 16 mg/mL treatment enhanced the expression of M1 macrophage markers (iNOS, IL-1ß and TNF-α) and M1 macrophage proportions, while reducing the expression of M2 macrophage markers (IL-10, Arg-1) and M2 macrophage proportions in TAMs. Moreover, the APS-mediated M1 phenotype of TAMs significantly repressed cell proliferation, migration and invasion of MHCC97H and Huh7 cells. Moreover, APS (50, 100, 200 mg/kg) enhanced M1 macrophage proportions and reduced M2 macrophage proportions in the tumour tissues, and thus inhibited tumour growth of HCC. DISCUSSION AND CONCLUSIONS: APS inhibits HCC-like phenotypes in a murine HCC model through repression of M2 polarization of TAMs. This work provides a novel theoretical basis for the application of APS in the clinical treatment of HCC.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA-disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA-disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA-disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA-disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA-disease associations.

Poor awareness and attitudes to sanitation servicing can impede China's Rural Toilet Revolution: Evidence from Western China.

The ongoing Toilet Revolution in China offers an opportunity to improve sanitation in rural areas by introducing new approaches, such as urine source separation, that can contribute to achieving SDG6. However, few studies have systematically assessed the social acceptability of managing human excreta collected in new sanitation systems. Therefore, in this study we performed face-to-face interviews with 414 local residents from 13 villages across three provinces in western China, to analyze the current situation and attitudes to possible changes in the rural sanitation service chain. We found that the sanitation chain was predominantly pit latrine-based, with 86.2% of households surveyed collecting their excreta in a simple pit, 82% manually emptying their pits, and 80.2% reusing excreta in agriculture without adequate pre-treatment. A majority (72%) of the households had a generally positive attitude to production of human excreta-derived fertilizer, but only 24% agreed that urine and feces should be collected separately. Multivariate logistic regression indicated that three factors (level of education, number of permanent household residents, perceived social acceptability) significantly influenced respondents' attitudes to reuse of excreta, although only perceived social acceptability had a high strength of association. Overall, our survey revealed that rural households often misuse toilet systems, fail to comply with government-specified sanitation guidelines, have low awareness of alternative solutions, and are over-reliant on the government to fix problems in the service chain. Thus while new sanitation technologies should be developed and implemented, information campaigns that encourage rural households to manage their excreta safely are also important.

Failure of resting-state frontal-occipital connectivity in linking visual perception with reading fluency in Chinese children with developmental dyslexia.

It is widely accepted that impairment in visual perception impedes children's reading development, and further studies have demonstrated significant enhancement in reading fluency after visual perceptual training. However, the mechanism of the neural linkage between visual perception and reading is unclear. The purpose of this study was to examine the intrinsic functional relationship between visual perception (indexed by the texture discrimination task,TDT) and reading ability (character reading and reading fluency) in Chinese children with developmental dyslexia (DD) and those with typical development (TD). The resting-state functional connectivity (RSFC) between the primary visual cortex (V1, BA17) and the entire brain was analyzed. In addition, how RSFC maps are associated with TDT performance and reading ability in the DD and TD groups was examined. The results demonstrated that the strength of the RSFC between V1 and the left middle frontal gyrus (LMFG, BA9/BA46) was significantly correlated with both the threshold (SOA) of the TDT and reading fluency in TD children but not in DD children. Moreover, LMFG-V1 resting-state connectivity played a mediating role in the association of visual texture discrimination and reading fluency, but not in character reading, in TD children. In contrast, this mediation was absent in DD children, albeit their strengths of RSFC between V1 and the left middle frontal gyrus (LMFG) were comparable to those for the TD group. These findings indicate that typically developing children use the linkage of the RSFC between the V1 and LMFG for visual perception skills, which in turn promote fluent reading; in contrast, children with dyslexia, who had higher TDT thresholds than TD children, could not take advantage of their frontal-occipital connectivity to improve reading fluency abilities. These findings suggest that visual perception plays an important role in reading skills and that children with developmental dyslexia lack the ability to use their frontal-occipital connectivity to link visual perception with reading fluency.

Clinical and Imaging Analysis of Cerebral Infarction Caused by Spontaneous Cerebral Artery Dissection Based on Augmented Reality Technology.

In recent years, with the progress of population ageing, the incidence of a stroke caused by spontaneous dissection of the cerebral artery also increases with time. In order to address the health damage caused by a stroke caused by spontaneous dissection of the cerebral artery and to study its effect on human health, this article analyzes the incidence, type, electrocardiogram, and cardiovascular biomarker changes of cerebral infarction through statistical analysis and then discusses cerebral infarction. The pathogenesis and prevention measures of the disease are expected to provide better means for the treatment of cerebral infarction. Based on the case investigation of patients with cerebral infarction caused by spontaneous cerebral artery dissection, a case template was constructed, and a damage assessment matrix was created using a comprehensive quantitative and qualitative analysis method. Experimental results prove that cerebral infarction caused by spontaneous cerebral artery dissection is a great threat to human health, and the fatality rate of patients is extremely high. Enhanced imaging technology is of great help to clinical and image analysis, with a correlation coefficient of 0.87, compared with the other damage rate of cerebral infarction caused by spontaneous cerebral artery dissection which is about 15% higher than that of cerebral infarction caused by different methods. Studies have found that there are great differences in the age of people with cerebral infarction caused by spontaneous cerebral artery dissection, and the patients are generally over 45 years old. This shows that cerebral infarction caused by spontaneous cerebral artery dissection will cause great damage and affect people's health, which requires people's attention.

Attitudes of food consumers at universities towards recycling human urine as crop fertiliser: A multinational survey dataset.

We present here a data set generated from a multinational survey on opinions of university community members on the prospect of consuming food grown with human urine as fertiliser and about their urine recycling perceptions in general. The data set comprises answers from 3,763 university community members (students, faculty/researchers, and staff) from 20 universities in 16 countries and includes demographic variables (age bracket, gender, type of settlement of origin, academic discipline, and role in the university). Questions were designed based on Ajzen's theory of planned behaviour to elicit information about three components of behavioural intention-attitudes, subjective norms, and perceived behavioural control. Survey questions covered perceived risks and benefits (attitudes), perceptions of colleagues (injunctive social norm) and willingness to consume food grown with cow urine/faeces (descriptive social norm), and willingness to pay a price premium for food grown with human urine as fertiliser (perceived behavioural control). We also included a question about acceptable urine recycling and disposal options and assessed general environmental outlook via the 15-item revised New Ecological Paradigm (NEP) scale. Data were collected through a standardised survey instrument translated into the relevant languages and then administered via an online form. Invitations to the survey were sent by email to university mailing lists or to a systematic sample of the university directory. Only a few studies on attitudes towards using human urine as fertiliser have been conducted previously. The data described here, which we analysed in "Willingness among food consumers at universities to recycle human urine as crop fertiliser: Evidence from a multinational survey" [1], may be used to further understand potential barriers to acceptance of new sanitation systems based on wastewater source separation and urine recycling and can help inform the design of future sociological studies.

Occurrence of typical antibiotics, representative antibiotic-resistant bacteria, and genes in fresh and stored source-separated human urine.

Human urine is a source of fertilizer and, with proper management, it can be reused in agriculture. Determining the contamination issue of antibiotics in source-separated urine is important because the majority of antibiotics are excreted with urine. In this study, source-separated urine samples were randomly collected from a male toilet in a university building and analyzed in terms of 30 typical antibiotics (including 14 sulfonamides, 4 tetracyclines, and 12 fluoroquinolones) and tetracycline-resistant Escherichia coli, as well as its antibiotic-resistant genes to determine the contamination characteristics of antibiotic-related pollution in fresh and stored urine. Results showed that 18 out of 30 typical antibiotics were detected in fresh source-separated human urine. The dominant antibiotic was oxytetracycline with a frequency of 100%, followed by tetracycline, sparfloxacin, enrofloxacin, and ofloxacin, which demonstrated a detection frequency of 55%. Among the detected values, sulfonamides (2 antibiotics), tetracyclines (4 antibiotics), and fluoroquinolones (12 antibiotics) had a concentration range of 0.25-2.94, 0.94-41.2, and 0.06-163.16 ng/mL, respectively. Furthermore, tetracycline-resistant Escherichia coli, which was measured using plate count method, and its related gene, tet M, exhibited a maximum cell density of (200,000 ± 5000) CFU/100 mL and (2.73 ± 0.261) × 107 copies/mL, respectively. When the fresh urine was stored in an ambient environment for 30 days to simulate the real circumstances of urine management, a significant reduction in antibiotics and antibiotic-resistant bacteria was observed, while the change in antibiotic-resistant genes was insignificant. The results of this study suggest that risks associated with antibiotics and their antibiotic-resistant bacteria and genes are retained during collection and storage. Hence, these kinds of microcontaminants must be considered in further urine utilization.

Stabilization of source-separated urine by heat-activated peroxydisulfate.

Source-separated urine is an attractive fertilizer due to its high nutrient content, but the rapidly hydrolysis of urea leads to ammonia volatilization and other environmental problems. Urine stabilization, which meanly means preventing enzymatic urea hydrolysis, receives increasing attention. Accordingly, this study developed a technique to stabilize fresh urine by heat-activated peroxydisulfate (PDS). The effect of three crucial parameters, including temperature (55, 62.5, and 70 °C), heat-activated time (1, 2, and 3 h), and PDS concentration (10, 30, and 50 mM) that affect the activation of PDS in urine stabilization were investigated. Nitrogen in fresh urine treated with 50 mM PDS at 62.5 °C for 3 h existed mainly in the form of urea for more than 22 days at 25 °C. Moreover, the stabilized urine could remain stable and resist second contamination by continuous and slow pH decrease due to PDS decomposition during storage. Less than 8% of nitrogen loss in stabilized urine was detected during the experiment. The investigation of nitrogen transformation pathway demonstrated that urea was decomposed into NH4+ by heat-activated PDS and further oxidized to NO2- and NO3-. The nitrogen loss during treatment occurred via heat-driven ammonia volatilization and N2 emission produced by synproportionation of NO2- and NH4+ under acid and thermal conditions. Overall, this study investigated an efficient approach of urine stabilization to improve urine utilization in terms of nutrient recovery.

Biodiesel Production Catalyzed by a Methanol-Tolerant Lipase A from Candida antarctica in the Presence of Excess Water.

In this article, biodiesel was prepared using a novel free liquid lipase A from Candida antarctica (CALA) as a catalyst in the presence of excess water. The methanol tolerance of CALA was investigated. The effect of reaction conditions, including the molar ratio of soybean oil to methanol, water load, CALA load, reaction temperature, and reaction time, was evaluated. Reaction thermodynamics and kinetics were also analyzed. Results showed that free liquid lipase CALA showed excellent methanol tolerance in the reaction system using one-step addition of methanol and can be used to prepare biodiesel with water load of 12-14%. The influence of three transesterification variables on biodiesel yield was water load > temperature > time. The transesterification conditions were optimized by response surface methodology as follows: CALA load 5%, substrate molar ratio (soybean oil/methanol) 1:7, water load 14%, reaction time 26 h, and temperature 38 °C. The maximum biodiesel yield (92.4 ± 0.8%) was obtained under optimal conditions. The activation energy for biodiesel formation was 52.58 kJ/mol. Kinetic parameters K m ' and V max were 4.84 × 10-1 mol/L and 6.85 × 10-2 mol/(L·min), respectively. The mechanism of CALA-catalyzed transesterification was also proposed.

Erlotinib overcomes paclitaxel-resistant cancer stem cells by blocking the EGFR-CREB/GRß-IL-6 axis in MUC1-positive cervical cancer.

Cancer stem cells (CSCs) are often enriched after chemotherapy and contribute to tumor relapse. While epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for the treatment of diverse types of cancer, whether EGFR-TKIs are effective against chemoresistant CSCs in cervical cancer is largely unknown. Here, we reveal that EGFR correlates with reduced disease-free survival in cervical cancer patients with chemotherapy. Erlotinib, an EGFR-TKI, effectively impedes CSCs enrichment in paclitaxel-resistant cells through inhibiting IL-6. In this context, MUC1 induces CSCs enrichment in paclitaxel-resistant cells via activation of EGFR, which directly enhances IL-6 transcription through cAMP response element-binding protein (CREB) and glucocorticoid receptor ß (GRß). Treatment with erlotinib sensitizes CSCs to paclitaxel therapy both in vitro and in vivo. More importantly, positive correlations between the expressions of MUC1, EGFR, and IL-6 were found in 20 cervical cancer patients after chemotherapy. Mining TCGA data sets also uncovered the expressions of MUC1-EGFR-IL-6 correlates with poor disease-free survival in chemo-treated cervical cancer patients. Collectively, our work has demonstrated that the MUC1-EGFR-CREB/GRß axis stimulates IL-6 expression to induce CSCs enrichment and importantly, this effect can be abrogated by erlotinib, uncovering a novel strategy to treat paclitaxel-resistant cervical cancer.

Influence of ionic liquid on Novozym 435-catalyzed the transesterification of castor oil and ethyl caffeate.

Caffeic acid (CA), one kind of phenolic acids widely occurring in the plant kingdom, can be used as potential UV protective ingredient and antioxidant. However, the application of CA was limited because of its unsatisfactory solubility in hydrophilic and lipophilic media. In this work, BMIMPF6, one kind of ionic liquids (ILs), was developed as an environmental friendly reaction media for the enzymatic preparation of CA derivatives by the transesterification of castor oil (CO) and ethyl caffeate (EC). Different series of ILs with BF 4 - , TF 2 - , and PF 6 - were screened and compared, and the effects of transesterification variables [temperature (60-100 °C) enzyme concentration (10-90 mg/mL), substrate molar ratio (CO/EC, 1:1-5:1), water load (0-8%), and reaction pressure] were also investigated. Results showed that, in the IL system, hydrophilic and lipophilic products were formed by two competitive reactions [(i) hydrolysis + transesterification and (ii) transesterification]. The maximum hydrophilic caffeoyl lipids yield (26.10 ± 0.28%) and reaction selectivity for hydrophilic caffeoyl lipids (0.4) was achieved in BMIMPF6 system. The increases of substrate ratio (molar ratio of CO to EC, from 1:1 to 5:1), water load (from 0 to 8%), and enzyme concentration (from 10 to 90 mg/mL) were in favor of hydrophilic caffeoyl lipid formation. However, the vacuum system and high temperature (from 70 to 100 °C) are favorable for lipophilic caffeoyl lipids formation. Under the optimal reaction conditions (90 °C, 75 mg/mL enzyme concentration, substrate ratio 3:1, 60 h, and 10 mmHg vacuum pressures), the maximum EC conversion was 72.48 ± 2.67%. The activation energies of the transesterification, and the selective formations of lipophilic and hydrophilic products were calculated as 44.55, 47.65, and 54.96 kJ/mol, respectively.

Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.

BACKGROUND: Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted. RESULTS: In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced. CONCLUSIONS: >From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.

B(C6F5)3-catalyzed Markovnikov addition of indoles to aryl alkynes: an approach toward bis(indolyl)alkanes.

The first example of the metal- and solvent-free B(C6F5)3-catalyzed Markovnikov addition of indoles to aryl alkynes was disclosed. Both N-H and N-protected indoles were tolerated, leading to a wide spectrum of versatile bis(indolyl)alkanes in moderate to good yields with high regioselectivities.