Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces.

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Transcription factor CEBPB mediates intracranial aneurysm rupture by inflammatory and immune response.

INTRODUCTION: Genetic factors play a major part in mediating intracranial aneurysm (IA) rupture. However, research on the role of transcription factors (TFs) in IA rupture is rare. AIMS: Bioinformatics analysis was performed to explore the TFs and related functional pathways involved in IA rupture. RESULTS: A total of 63 differentially expressed transcription factors (DETFs) were obtained. Significantly enriched biological processes of these DETFs were related to regulation of myeloid leukocyte differentiation. The top 10 DETFs were screened based on the MCC algorithm from the protein-protein interaction network. After screening and validation, it was finally determined that CEBPB may be the hub gene for aneurysm rupture. The GSEA results of CEBPB were mainly associated with the inflammatory response, which was also verified by the experimental model of cellular inflammation in vitro. CONCLUSION: The inflammatory and immune response may be closely associated with aneurysm rupture. CEBPB may be the hub gene for aneurysm rupture and may have diagnostic value. Therefore, CEBPB may serve as the diagnostic signature for RIAs and a potential target for intervention.

Correlation Between DCAMKL-1 Protein Expression and K-ras Gene Mutation in Colorectal Cancer.

Aim: To investigate the correlation between doublecortin and CaM kinase-like-1 (DCAMKL-1) protein expression, K-ras gene mutation, and their impact on patient prognosis in colorectal cancer (CRC). Methods: Immunohistochemistry was used to detect the expression of DCAMKL-1 protein in 60 cases of colorectal adenoma, 82 cases of CRC (including 65 cases of lymph node metastasis) and paraffin-embedded paracancerous intestinal mucosal tissue. K-ras gene mutations in primary CRC lesions were detected using an amplification-refractory mutation system and fluorescent polymerase chain reaction. The relationship between DCAMKL-1 protein expression and K-ras gene mutations with the clinicopathological characteristics of patients with CRC was analyzed. Univariate Kaplan‒Meier survival analysis and multivariate Cox regression analysis were performed using follow-up data. Results: The mutation rate of the K-ras gene in 82 cases of CRC was 48.8% (40/82). The positivity rate for the presence of DCAMKL-1 protein in CRC was 70.7% (58/82), significantly higher than that for colorectal adenomas (53.3%; 32/60) and paracancerous intestinal mucosa (0%; 0/82) (P<0.05). The positive expression rate for the presence of DCAMKL-1 protein in 65 patients with lymph node metastasis was higher in the primary lesions (69.2%; 45/65) than in the lymph node metastases (52.3%; 34/65) (χ2=12.087, P=0.001). The K-ras gene mutation status was positively correlated with DCAMKL-1 protein expression (r=0.252, P=0.022). Conclusion: In this study, a potential positive correlation between K-ras gene mutation and DCAMKL-1 protein expression was identified in CRC tissues. The assessment of K-ras gene mutation status and DCAMKL-1 protein expression holds promise for augmenting early diagnosis and prognosis evaluation in CRC. This approach may improve the overall prognosis and survival outcomes for CRC patients.

Antibody-drug conjugates transform the outcome of individuals with low-HER2-expression advanced breast cancer.

Antibody-drug conjugates (ADCs)-a groundbreaking class of agents for targeted oncological therapies-consist of monoclonal antibodies with strong antigenic specificity coupled with highly active cytotoxic agents (also referred to as "payloads"). Over the past 2 decades, breast cancer research has evolved into a focal point for the research and development of ADCs, leading to several recent landmark publications. These advancements are ushering in a transformative era in breast cancer treatment and redefining conventional classifications by introducing a prospective subtype termed "HER2-low." The latest iterations of ADCs have demonstrated enhanced efficacy in disease management through the optimization of various factors, notably the incorporation of the bystander effect. These conjugates are no longer limited to the oncogenic driver human epidermal growth factor receptor 2 (HER2). Other antigens, including human epidermal growth factor receptor 3 (HER3), trophoblast cell surface antigen 2 (Trop-2), zinc transporter ZIP6 (LIV-1), and folate receptor α (FRα), have recently emerged as intriguing tumor cell surface nondriver gene targets for ADCs, each with one or more specific ADCs that showed encouraging results in the breast cancer field. This article reviews recent advances in the application of ADCs in the treatment of HER2-low breast cancer. Additionally, this review explores the underlying factors contributing to the impact of target selection on ADC efficacy to provide new insights for optimizing the clinical application of ADCs in individuals with low HER2 expression in advanced breast cancer.

Shikonin Suppresses Cell Tumorigenesis in Gastric Cancer Associated with the Inhibition of c-Myc and Yap-1.

AIM: The study aimed to study the potential roles and mechanisms of shikonin in gastric cancer by network pharmacology and biological experiments. METHODS: The key genes and targets of shikonin in gastric cancer were predicted by network pharmacology and molecular docking study. The effect of shikonin on the proliferation, migration, and invasion of gastric cancer cells was detected by the CCK8 method, and wound healing and transwell assays. The expression levels of c-Myc and Yap-1 were detected via western blotting in gastric cancer cells after shikonin intervention. RESULTS: The results of network pharmacology revealed the key target genes of shikonin on gastric cancer cells to be c-Myc, Yap-1, AKT1, etc. GO and KEGG analysis showed regulation of cell migration, proliferation, adhesion, and other biological processes, including the PI3K-Akt signaling pathway, HIF-1 signaling pathway, necroptosis, and other cancer pathways. Molecular docking showed shikonin to be most closely combined with protooncogenes c-Myc and Yap-1. In vitro experiments showed that the proliferation rate, migration, and invasion ability of the gastric cancer cell group decreased significantly after shikonin intervention for 24h. The expression levels of c-Myc and Yap-1 in gastric cancer cells were found to be significantly decreased after shikonin intervention. CONCLUSION: This study showed protooncogenes c-Myc and Yap-1 to be the core target genes of shikonin on gastric cancer cells. Shikonin may suppress gastric cancer cells by inhibiting the protooncogenes c-Myc and Yap-1. This suggests that shikonin may be a good candidate for the treatment of gastric cancer.

Artificial intelligence in ophthalmology: The path to the real-world clinic.

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.

Prospect of China's ambient air quality standards.

Ambient air quality standards are the core strategic goal of ambient air quality management. Countries worldwide have given importance to research on the development of ambient air quality standards. To understand the history of the development of China's ambient air quality standards, this study analyzed the background associated with all previous formulations and revisions of the standards, classification of functional areas, standard grading, pollutants, and evolution of the standard limits over the past 40 years. The results show that since the initial release of the "Ambient Air Quality Standard" by China in 1982, it has been supplemented once, revised twice, and modified twice. The first ambient air quality standard specified the standard limits of six pollutants commonly found in ambient air. With the development of ambient air quality management, the number of pollutants has increased to ten. Since the release of the "Ambient Air Quality Standards" in 2012, the ambient air quality in China has significantly improved. However, the proportion of cities meeting these standards is still low. At present, China is suitably positioned to make the standards associated with 24 hr SO2 concentrations more stringent such that it meets the values defined in the World Health Organization (WHO) interim target-2 and the WHO air quality guideline (AQG). We further suggested that the SO2 standard should be revised promptly. Simultaneously, regions with a relatively high proportion of cities meeting the standard are encouraged to introduce more stringent interim target limits in due course to manage the local ambient air quality.

Ten-year emission characteristics of atmospheric pollutants from incineration of sacrificial offerings in China.

The incineration of sacrificial offerings is a significant widely practiced custom that is also a kind of neglected air pollution source in China. Our results showed that the emission factors of particulate matter, SO2, CO, NOx, and VOCs emitted from the incineration of sacrificial offerings with purification systems were reduced by 95%, 19%, 9%, 82%, and 42%, respectively, compared with those without a purification system, revealing a significant effect of the flue gas purification system on reducing particulate matter and gaseous pollutants. The emission level of air pollutants from the incineration of sacrificial offerings remained stable before 2013 and then showed a remarkable decrease after the implementation of China´s Air Pollution Prevention Action Plan in 2013. The emissions of TSP (total suspended particulate), PM10, PM2.5, and NOx in 2009 were 8222, 6106, 5656 and 15,878 ton, respectively, obviously higher than 3434, 2551, 2305 and 8579 ton in 2019. Such trend was affected by both the quantity of incineration and the installation rate of purification systems after the Emission Standard of Air Pollutants for Crematory (GB 13801-2015) issued in China. Distinct spatial distribution of atmospheric pollutants from incineration of sacrificial offerings was found with higher in the east and south of China than the west and north of China, which is proportional to the regional economy and population. The maximum ground-level concentration typically occurred at 0.12-0.2 km from the pollution source, posing potential health risks to people entering and exiting funeral and burial sites and nearby residents.

Long-term characterization of roadside air pollutants in urban Beijing and associated public health implications.

Road traffic constitutes a major source of air pollutants in urban Beijing, which are responsible for substantial premature mortality. A series of policies and regulations has led to appreciable traffic emission reductions in recent decades. To shed light on long-term (2014-2020) roadside air pollution and assess the efficacy of traffic control measures and their effects on public health, this study quantitatively evaluated changes in the concentrations of six key air pollutants (PM2.5, PM10, NO2, SO2, CO and O3) measured at 5 roadside and 12 urban background monitoring stations in Beijing. We found that the annual mean concentrations of these air pollutants were remarkably reduced by 47%-71% from 2014 to 2020, while the concurrent ozone concentration increased by 17.4%. In addition, we observed reductions in the roadside increments in PM2.5, NO2, SO2 and CO of 54.8%, 29.8%, 20.6%, and 59.1%, respectively, indicating the high effectiveness of new vehicle standard (China V and VI) implementation in Beijing. The premature deaths due to traffic emissions were estimated to be 8379 and 1908 cases in 2014 and 2020, respectively. The impact of NO2 from road traffic relative to PM2.5 on premature mortality was comparable to that of traffic-related PM2.5 emissions. The public health effect of SO2 originating from traffic was markedly lower than that of PM2.5. The results indicated that a reduction in traffic-related NO2 could likely yield the greatest benefits for public health.

Artificial intelligence to detect malignant eyelid tumors from photographic images.

Malignant eyelid tumors can invade adjacent structures and pose a threat to vision and even life. Early identification of malignant eyelid tumors is crucial to avoiding substantial morbidity and mortality. However, differentiating malignant eyelid tumors from benign ones can be challenging for primary care physicians and even some ophthalmologists. Here, based on 1,417 photographic images from 851 patients across three hospitals, we developed an artificial intelligence system using a faster region-based convolutional neural network and deep learning classification networks to automatically locate eyelid tumors and then distinguish between malignant and benign eyelid tumors. The system performed well in both internal and external test sets (AUCs ranged from 0.899 to 0.955). The performance of the system is comparable to that of a senior ophthalmologist, indicating that this system has the potential to be used at the screening stage for promoting the early detection and treatment of malignant eyelid tumors.

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers.

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.

An evaluation of source apportionment of fine OC and PM2.5 by multiple methods: APHH-Beijing campaigns as a case study.

This study aims to critically evaluate the source apportionment of fine particles by multiple receptor modelling approaches, including carbon mass balance modelling of filter-based radiocarbon (14C) data, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) analysis on filter-based chemical speciation data, and PMF analysis on Aerosol Mass Spectrometer (AMS-PMF) or Aerosol Chemical Speciation Monitor (ACSM-PMF) data. These data were collected as part of the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) field observation campaigns from 10th November to 12th December in winter 2016 and from 22nd May to 24th June in summer 2017. 14C analysis revealed the predominant contribution of fossil fuel combustion to carbonaceous aerosols in winter compared with non-fossil fuel sources, which is supported by the results from other methods. An extended Gelencsér (EG) method incorporating 14C data, as well as the CMB and AMS/ACSM-PMF methods, generated a consistent source apportionment for fossil fuel related primary organic carbon. Coal combustion, traffic and biomass burning POC were comparable for CMB and AMS/ACSM-PMF. There are uncertainties in the EG method when estimating biomass burning and cooking OC. The POC from cooking estimated by different methods was poorly correlated, suggesting a large uncertainty when differentiating this source type. The PM2.5 source apportionment results varied between different methods. Through a comparison and correlation analysis of CMB, PMF and AMS/ACSM-PMF, the CMB method appears to give the most complete and representative source apportionment of Beijing aerosols. Based upon the CMB results, fine aerosols in Beijing were mainly secondary inorganic ion formation, secondary organic aerosol formation, primary coal combustion and from biomass burning emissions.

Source apportionment of fine organic carbon (OC) using receptor modelling at a rural site of Beijing: Insight into seasonal and diurnal variation of source contributions.

This study was designed to investigate the seasonal characteristics and apportion the sources of organic carbon during non-haze days (<75 µg m-3) and haze (≥75 µg m-3) events at Pinggu, a rural Beijing site. Time-resolved concentrations of carbonaceous aerosols and organic molecular tracers were measured during the winter of 2016 and summer 2017, and a Chemical Mass Balance (CMB) model was applied to estimate the average source contributions. The concentration of OC in winter is comparable with previous studies, but relatively low during the summer. The CMB model apportioned seven separate primary sources, which explained on average 73.8% on haze days and 81.2% on non-haze days of the organic carbon in winter, including vegetative detritus, biomass burning, gasoline vehicles, diesel vehicles, industrial coal combustion, residential coal combustion and cooking. A slightly lower percentage of OC was apportioned in the summer campaign with 64.5% and 78.7% accounted for. The other unapportioned OC is considered to consist of secondary organic carbon (SOC). During haze episodes in winter, coal combustion and SOC were the dominant sources of organic carbon with 23.3% and 26.2%, respectively, followed by biomass burning emissions (20%), whereas in summer, industrial coal combustion and SOC were important contributors. Diurnal contribution cycles for coal combustion and biomass burning OC showed a peak at 6-9 pm, suggesting domestic heating and cooking were the main sources of organic aerosols in this rural area. Backward trajectory analysis showed that high OC concentrations were measured when the air mass was from the south, suggesting that the organic aerosols in Pinggu were affected by both local emissions and regional transport from central Beijing and Hebei province during haze episodes. The source apportionment by CMB is compared with the results of a Positive Matrix Factorization (PMF) analysis of ACSM data for non-refractory PM1, showing generally good agreement.

Synthesis and Photocatalytic Sterilization Performance of SA/TiO2.

The photocatalyst sorbic acid (SA)/titanium dioxide (TiO2) was successfully synthesized by sol-gel method and characterized. The composite exhibited regularly spherical particles with the size of 50 nm and the specific surface area of 90.3 m2 g-1, furthermore, it showed mesoporous structure and significantly improved dispersion. SA was grafted on TiO2 surface by -COOTi and TiO2 existed as pure anatase phase in the composite. The addition of SA made the band gap of TiO2 increased from 3.03 to 3.35 eV, which indicting that the composite exhibited a strong response to the ultraviolet light. The optimum preparation parameters of the catalyst were as follows: n(Ti):n(SA) = 1:0.05, ethanol 60 mL, glacial acetic acid 40 mL, hydrothermal temperature 180 °C, hydrothermal time 12 h. The composite could reach the 4.31 log reduction of E. coli, with the optimum catalyst dosage of 0.7 g L-1, irradiated by UV light for 60 min. SA/TiO2 was an environmentally friendly, non-toxic and safe sterilized nanocomposite material appropriate for future bactericidal applications, providing a new way to effectively increase the dispersion of TiO2 particles to achieve superior photocatalytic sterilization efficiency.

GSK-3ß inhibitor 6-bromo-indirubin-3'-oxime promotes both adhesive activity and drug resistance in colorectal cancer cells.

Multi-targets inhibitor 6-bromo-indirubin-3'-oxime (BIO) has diverse biological effects on cancer cells. The key component of the ß-catenin destruction complex glycogen synthase kinase 3ß (GSK-3ß), one of the major target for BIO, polyubiquitination and degradation of the main oncoprotein ß-catenin in colorectal cancer (CRC). In the present study, we evaluated the effect of BIO on drug resistance and biological properties of CRC cells. Whole-genome transcriptional profiling revealed that differentially expressed genes were mainly centered on well-characterized signaling pathways including stem cell, cell adhesion and cell growth in BIO-treated CRC cells. BIO treatment downregulated migration and invasion abilities of CRC cells, accompanying with MMP-9 downregulated and E-cadherin upregulated CRC cells. BIO treatment decreased apoptosis induced by 5-Fu/DDP in CRC SW480 cells. In addition, BIO treatment reversed the 5-Fu-induced CD133+ cell downregulation trend in CRC SW620 cells. After incubation with BIO, the expression levels of EpCAM, TERT and DCAMKL-1 proteins were upregulated in CRC cells. BIO treatment downregulated the activity of GSK-3ß, upregulated and transported ß-catenin to the nucleus in CRC cells. Our findings reveal that BIO treatment upregulated stemness, adhesive and chemoresistance of CRC cells. GSK-3ß inhibition and WNT/ß-catenin activation by BIO, may partly result in the biological behavior alterations in CRC cells.

Comparison of inorganic chemical compositions of atmospheric TSP, PM10 and PM2.5 in northern and southern Chinese coastal cities.

To compare the inorganic chemical compositions of TSP (total suspended particulate), PM10 (particulate matter with an aerodynamic diameter less than 10µm) and PM2.5 (particulate matter with an aerodynamic diameter less than 2.5µm) in southern and northern cities in China, atmospheric particles were synchronously collected in Dalian (the northern city) and Xiamen (the southern city) in spring and autumn of 2004. The mass concentrations, twenty-three elements and nine soluble ions were assessed. The results show that in Dalian, the mass concentrations of Mg, Al, Ca, Mn and Fe in spring were 4.0-10.1, 2.6-8.0, 4.1-12, 1.2-3.6 and 2.9-7.9 times higher, respectively, than those in Xiamen. The dust storm influence is more obvious in Dalian in spring. However, in Xiamen, heavy metals accounted for 13.9%-17.9% of TSP, while heavy metals contributed to 5.5%-9.3% of TSP in Dalian. These concentrations suggest that heavy metal pollution in Xiamen was more serious. In addition, the concentrations of Na+, Cl-, Ca2+ and Mg2+ were higher in Dalian due to the influence of marine aerosol, construction activities and soil dust. The NO3-/SO42- ratios in Dalian (0.25-0.49) were lower than those in Xiamen (0.51-0.62), indicating that the contributions of vehicle emission to particles in Xiamen were higher. Coefficient of divergence values was higher than 0.40, implying that the inorganic chemical composition profiles for the particles of Dalian and Xiamen were quite different from each other.

Comparative profiling between primary colorectal carcinomas and metastases identifies heterogeneity on drug resistance.

Metastases cause recurrence and mortality for patients with colorectal carcinomas (CRC). In present study, we evaluated heterogeneity on drug resistance and its underlying mechanism between metastatic and primary CRC. Immunohistochemical results from clinical tissue microarray (TMA) suggested that the expression concordance rates of cancer stem cells (CSCs) and drug resistance relative proteins between lymph-node metastatic and primary CRC foci were low. The apoptotic and proliferation indexes in metastasis CRC specimens were decreased compared with primary. In vitro experimental results indicated that the migration and invasion abilities were upregulated in metastatic cells SW620 compared with primary cells SW480, the cellular efflux ability and WNT/ß-catenin activity were also upregulated in SW620 cells. After 5-fluorouracil (5-Fu) treatment, the reduction in the proportion of cell apoptosis, CD133 and TERT expression levels in SW620 were lower than that in SW480 cells. Bioinformatics analysis in whole-genome transcriptional profiling results between metastatic and primary CRC cells suggested that differentially expressed genes were mainly centered on well-characterized signaling pathways including WNT/ß-catenin, cell cycle and cell junction. Collectively, heterogeneity of drug resistant was present between metastatic and primary CRC specimens and cell lines, the abnormal activation of WNT/ß-catenin signaling pathway could be a potential molecular leading to drug resistant ability enhancing in metastatic CRC cells.

Executive Function and Measures of Fall Risk Among People With Obesity.

This study investigated the relationship between obesity and executive function, and between executive function and fall risk (as estimated from select gait parameters). Of the 39 young adults (age = 21.3 ± 2.6 years) recruited from the local university population via email announcement, 19 were in the obese group (based upon BMI and body fat percentage) and 20 were in the nonobese comparison group. Executive function was assessed using standardized tests including the Stroop test for selective attention; Trail Making test for divided attention, visuomotor tracking, and cognitive flexibility; the Verbal Fluency test for semantic memory; and the Digit-span test for working memory. Participants performed single- and dual-task walking (walking while talking) to evaluate fall risk during gait as measured by minimum toe clearance, required coefficient of friction, stance time, and stance-time variability. The obese group had lower scores for selective attention, semantic memory, and working memory. All participants had gait changes suggestive of a higher fall risk, for example, lower minimum toe clearance, longer stance time, and increased stance variability, during dual-task walking compared with single-task walking, and executive function scores (selective attention) were associated with gait (stance-time variability) during dual-task walking. Results indicate obesity was negatively associated with executive function among young adults and could increase fall risk.

Falls resulting from a laboratory-induced slip occur at a higher rate among individuals who are obese.

Falls due to slipping are a serious concern, with slipping estimated to cause 40-50% of all fall-related injuries. Epidemiological data indicates that older and obese adults experience more falls than young, non-obese individuals. An increasingly heavier and older U.S. population and workforce may be exacerbating the problem of slip-induced falls. The purpose of this study was to investigate the effects of obesity and age on slip severity and rate of falling resulting from laboratory-induced slips. Four groups of participants (young obese, young non-obese, older obese, older non-obese) were slipped while walking at a self-selected, slightly hurried pace. Slip severity (slip distance, slip duration, mean slip speed and peak slip speed) and slip outcome (fall or recovery) were compared between groups. Obese individuals experienced 22% faster slips than non-obese individuals in terms of mean slip speed (p=0.022). Obesity did not affect slip distance, slip duration or peak slip speed. Obese individuals also exhibited a higher rate of falls; 32% of obese individuals fell compared to 10% of non-obese (p=0.005). Obese individuals were more than eight times more likely to experience a fall than non-obese individuals when adjusting for age, gender and gait speed. No age effects were found for slip severity or slip outcome. These results, along with epidemiological data reporting higher fall rates among the obese, indicate that obesity may be a significant risk factor for experiencing slip-induced falls. Slip severity thresholds were also reported that may have value in developing controls for fall prevention.