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The eco-environment is dynamic and shows a continuous process of long-term change. It is helpful for policymakers to know the status of the regional eco-environment through accurate evaluations of the history and current situation of the regional eco-environment. The remote sensing ecological index (RSEI) model of China was established in this study by using four indexes: wetness, greenness, dryness, and heat. Knowledge granulation of the RSEIs were carried out, and a method to determine the weights of the knowledge granulation entropy of the indexes based on their characteristics was proposed. This study used Moderate Resolution Image Spectroradiometer (MODIS) data from the Google Cloud Computing Platform to study and calculate the eco-environmental quality of China from 2000–2017. The overall eco-environmental quality in China tended to improve from 2000–2017, although there were large areas of ecological degradation from 2009–2014. The eco-environment of eastern China was better than that of western China. Most of the national ecological areas were third-level ecological areas, which had moderate environmental quality. Dryness was the most important factor affecting the quality of the eco-environment, followed by greenness, which reflected the increasing environmental damage caused by human activities in China in recent years.

As the first rate-limiting enzyme in fatty acid oxidation (FAO), CPT1 plays a significant role in metabolic adaptation in cancer pathogenesis. FAO provides an alternative energy supply for cancer cells and is required for cancer cell survival. Given the high proliferation rate of cancer cells, nucleotide synthesis gains prominence in rapidly proliferating cells. In the present study, we found that CPT1A is a determining factor for the abnormal activation of FAO in nasopharyngeal carcinoma (NPC) cells. CPT1A is highly expressed in NPC cells and biopsies. CPT1A dramatically affects the malignant phenotypes in NPC, including proliferation, anchorage-independent growth, and tumor formation ability in nude mice. Moreover, an increased level of CPT1A promotes core metabolic pathways to generate ATP, inducing equivalents and the main precursors for nucleotide biosynthesis. Knockdown of CPT1A markedly lowers the fraction of 13 C-palmitate-derived carbons into pyrimidine. Periodic activation of CPT1A increases the content of nucleoside metabolic intermediates promoting cell cycle progression in NPC cells. Targeting CPT1A-mediated FAO hinders the cell cycle G1/S transition. Our work verified that CPT1A links FAO to cell cycle progression in NPC cellular proliferation, which supplements additional experimental evidence for developing a therapeutic mechanism based on manipulating lipid metabolism.

Radiofrequency ablation (RFA) promotes tumor antigen-specific T cell responses and enhances the effect of immunotherapy in preclinical settings. Here we report that the existence of remnant tumor masses due to incomplete RFA (iRFA) is associated with earlier new metastases and poor survival in patients with colorectal cancer liver metastases (CRCLM). Using mouse models, we demonstrate that iRFA promotes tumor progression and hinders the efficacy of anti-PD-1 therapy. Immune analysis reveals that iRFA induces sustained local inflammation with predominant myeloid suppressor cells, which inhibit T cell function in tumors. Mechanistically, tumor cell-derived CCL2 is critical for the accumulation of monocytes and tumor-associated macrophages (TAMs). The crosstalk between TAMs and tumor cells enhances the CCL2 production by tumor cells. Furthermore, we find that administration of a CCR2 antagonist or the loss of CCL2 expression in tumor cells enhances the antitumor activity of PD-1 blockade, providing a salvage alternative for residual tumors after iRFA. Radiofrequency ablation is used to treat metastatic colorectal cancer. In this study, the authors show that incomplete ablation of tumours results in metastases and show in mouse models that the chemokine CCL2 recruits myeloid cells to the partially ablated tumours, which can block T cell function.

Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ.

BackgroundStudies of glymphatic dysfunction in Parkinson’s disease (PD) patients have attracted much attention in recent years. However, the relationships between glymphatic dysfunction and clinical symptoms remains unclear.ObjectivesTo determine whether the diffusion tensor image analysis along the perivascular space (DTI-ALPS) affect the severity and types of motor and non-motor symptoms in PD patients.MethodsDe novo PD patients and controls who performed both DTI and 123I-DaTscan single photon emission computed tomography (SPECT) scanning were retrieved from the international multicenter Parkinson’s Progression Marker Initiative (PPMI) cohort. Glymphatic system was evaluated by the DTI-ALPS. Motor symptoms were assessed by Movement Disorders Society Unified Parkinson’s Disease Rating Scale III (MDS-UPDRS-III). The influence of glymphatic activity on motor and non-motor symptoms was explored by multivariate linear regression models.ResultsA total of 153 PD patients (mean age 60.97 ± 9.47 years; 99 male) and 67 normal controls (mean age 60.10 ± 10.562 years; 43 male) were included. The DTI-ALPS index of PD patients was significantly lower than normal controls (Z = − 2.160, p = 0.031). MDS-UPDRS III score (r = − 0.213, p = 0.008) and subscore for rigidity (r = − 0.177, p = 0.029) were negatively correlated with DTI-ALPS index. The DTI-ALPS index was significantly associated with MDS-UPDRS-III score (β = − 0.160, p = 0.048) and subscore for rigidity (β = − 0.170, p = 0.041) after adjusting for putamen dopamine transporter availability and clinical factors.ConclusionsOur results showed distinct relationships between glymphatic dysfunction and the severity and types of PD motor symptoms, suggesting the potential of DTI-ALPS index as a biomarker for PD motor symptoms.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94–0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87–0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making. An automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by COVID-19, assess its severity, and discriminate it from other types of pneumonia.

The correlation coefficient can calculate paired correlations among different ecological indicators as a whole, but it cannot calculate the specific interval association and the correlation among multiple indicators. This paper proposed an interval association (IA) method of the remote sensing ecological index (RSEI), based on the concept lattice and frequent closed itemset. In the IA method, the ecosystem was viewed as a complex system with a hierarchical structure, and the association among multiple indicators was calculated using the information granulation of RSEI. The interval association support degree (IASD) could measure the association clustering strength of these IA concepts. Calculation of MODIS data compiled by Google Earth Engine (GEE) showed that the IA concepts of RSEI in China were primarily composed of selected middle indicator intervals in 2017. The overall eco-environmental condition in China was general when assessed through IA. The spatial distribution of the remote sensing eco-environment in China displayed strong spatial association clustering. Furthermore, the IA of RSEI focused on the first few concepts with high IASD values.

The prognosis of colorectal cancer (CRC) is largely dependent on the early detection of hepatic metastases. With the advantages of nonradioactivity and the availability of multiple scanning sequences, the efficacy of magnetic resonance imaging (MRI) in the detection of colorectal liver metastases (CRLM) is not yet clear. We performed this meta-analysis to address this issue. PubMed, Embase, and the Cochrane Library were searched for studies reporting diagnostic performance of MRI for CRLM. Descriptive and quantitative data were extracted. The study quality was evaluated for the identified studies and a random effects model was used to determine the integrated diagnosis estimation. Meta-regression and subgroup analyses were implemented to investigate the potential contributors to heterogeneity. As a result, seventeen studies were included for analysis (from the year 1996 to 2018), comprising 1121 patients with a total of 3279 liver lesions. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.90 (95% confidence intervals (CI): 0.81–0.95), 0.88 (0.80–0.92), and 62.19 (23.71–163.13), respectively. The overall weighted area under the curve was 0.94 (0.92–0.96). Using two or more imaging planes and a quantitative/semiquantitative interpretation method showed higher diagnostic performance, although only the latter demonstrated statistical significance ( P  < 0.05). Advanced scanning sequences with DWI and liver-specific contrast media tended to increase the sensitivity for CRLM detection. We therefore concluded that contemporary MRI has high sensitivity and specificity for screening CRLM, especially for those with advanced scanning sequences. Using two or more imaging planes and adopting a quantitative/semiquantitative imaging interpretation may further improve diagnosis. However, the MRI results should be interpreted with caution because of substantial heterogeneity among studies.

Epstein–Barr virus-associated diseases are important global health concerns. As a group I carcinogen, EBV accounts for 1.5% of human malignances, including both epithelial- and lymphatic-originated tumors. Moreover, EBV plays an etiological and pathogenic role in a number of non-neoplastic diseases, and is even involved in multiple autoimmune diseases (SADs). In this review, we summarize and discuss some recent exciting discoveries in EBV research area, which including DNA methylation alterations, metabolic reprogramming, the changes of mitochondria and ubiquitin-proteasome system (UPS), oxidative stress and EBV lytic reactivation, variations in non-coding RNA (ncRNA), radiochemotherapy and immunotherapy. Understanding and learning from this advancement will further confirm the far-reaching and future value of therapeutic strategies in EBV-associated diseases.

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18–30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.