Two case reports: EML4-ALK rearrangement large cell neuroendocrine carcinoma and literature review

Anaplastic lymphoma kinase gene (ALK) rearrangement is present in only approximately 5% of non-small cell lung cancers (NSCLCs) and is scarce in LCNEC patients. The conventional first-line treatment options are chemotherapy combined with immunotherapy or chemotherapy followed by palliative radiotherapy. In this report, we present two cases of metastatic LCNEC with EML4-ALK fusion that were treated with ALK-TKI inhibitors and demonstrated a rapid therapeutic response. Both patients were nonsmoking women who declined cytotoxic chemotherapy, underwent Next-Generation Sequencing (NGS), and confirmed EML4-ALK fusion. They were treated with alectinib as first-line therapy, and the tumors showed significant shrinkage after two months, achieving a PR (defined as a more than 30% decrease in the sum of maximal dimensions). The PFS was 22 months and 32 months, respectively, until the last follow-up. A systematic review of all previously reported cases of LCNEC with ALK mutations identified only 21 cases. These cases were characterized by being female (71.4%), nonsmoking (85.7%), diagnosed at a relatively young age (median age 51.1), and stage IV (89.5%), with an overall response rate (ORR) of 90.5%. PFS and OS were significantly longer than those treated with conventional chemotherapy/immunotherapy. Based on the clinical characteristics and the effective therapeutic outcomes with ALK inhibitors in LCNEC patients with ALK fusion, we recommend routine ALK IHC (economical, affordable, and convenient, but with higher false positives) as a screening method in advanced LCNEC patients, particularly nonsmoking females or those who are not candidates for or unwilling to undergo cytotoxic chemotherapy. Further molecular profiling is necessary to confirm these potential beneficiaries. We suggest TKI inhibitors as the first-line treatment for metastatic LCNEC with ALK fusion. Additional studies on larger cohorts are required to assess the prevalence of ALK gene fusions and their sensitivity to various ALK inhibitors

Identification of intrinsic subtype-specific prognostic microRNAs in primary glioblastoma

BACKGROUND: Glioblastoma multiforme (GBM) is the most malignant type of glioma. Integrated classification based on mRNA expression microarrays and whole–genome methylation subdivides GBM into five subtypes: Classical, Mesenchymal, Neural, Proneural-CpG island methylator phenotype (G-CIMP) and Proneural-non G-CIMP. Biomarkers that can be used to predict prognosis in each subtype have not been systematically investigated. METHODS: In the present study, we used Cox regression and risk-score analysis to construct respective prognostic microRNA (miRNA) signatures in the five intrinsic subtypes of primary glioblastoma in The Cancer Genome Atlas (TCGA) dataset. RESULTS: Patients who had high-risk scores had poor overall survival compared with patients who had low-risk scores. The prognostic miRNA signature for the Mesenchymal subtype (four risky miRNAs: miR-373, miR-296, miR-191, miR-602; one protective miRNA: miR-223) was further validated in an independent cohort containing 41 samples. CONCLUSION: We report novel diagnostic tools for deeper prognostic sub-stratification in GBM intrinsic subtypes based upon miRNA expression profiles and believe that such signature could lead to more individualized therapies to improve survival rates and provide a potential platform for future studies on gene treatment for GBM

Quantifying the impact of synoptic circulation patterns on ozone variability in northern China from April to October 2013-2017

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O-3 (MDA8 O-3) concentration was 122 +/- 11 mu g m(-3), with an increase rate of 7.88 lug mu g m(-3) yr(-1), and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 mu m(-3)), May (138 mu m(-3)) and July (132 mu g m(-3)). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb-Jenkinson method. The highly polluted weather categories included the S-W-N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 03 concentrations were 122, 126 and 128 mu g m(-3), respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O-3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %-63 % of the day-today variability in the MDA8 O-3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China.Peer reviewe

Exploring the inorganic and organic nitrate aerosol formation regimes at a suburban site on the North China Plain

Nitrate-driven aerosol pollution frequently occurs during winter over the North China Plain (NCP). Extensive studies have focused on inorganic nitrate formation, but few have focused on organic nitrates in China, preduding a thorough understanding of the nitrogen cycle and nitrate aerosol formation. Here, the inorganic (NO3,inorg) and organic nitrate (NO3,org) formation regimes under aerosol liquid water (ALW) and aerosol acidity (pH) influences were investigated during winter over the NCP based on data derived from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). The campaign-averaged concentration of the total nitrate was 53 mu g m(3), with a 13% contribution from NO3,org, which exhibited a significantly decreased contribution with increasing haze episode evolution. The diurnal cycles of NO3,inorg and NO3,org were similar, with high concentrations during the nighttime at a high ALW level, revealing the important role of aqueous-phase processes. However, the correlations between the aerosol pH and NO3,inorg (R-2 = 0.13, P <0.01) and NO3,org (R-2 = 0.63, P <0.01) during polluted periods indicated a contrasting effect of aerosol pH on inorganic and organic nitrate formation. Our results provide a useful reference for smog chamber studies and promote a better understanding of organic nitrate formation via a nthropogenic emissions. (C) 2021 Elsevier B.V. All rights reserved.Peer reviewe

Highly time-resolved chemical characterization and implications of regional transport for submicron aerosols in the North China Plain

To investigate the regional transport and formation mechanisms of submicron aerosols in the North China Plan (NCP), for the first time, we conducted simultaneous combined observations of the non-refractory submicron aerosols (NR-PM1) chemical compositions using aerosol mass spectrometer at urban Beijing (BJ) and at regional background area of the NCP (XL), from November 2018 to January 2019. During the observation period, average mass concentrations of PM1 in BJ and XL were 26.6 +/- 31.7 and 16.0 +/- 18.7 mu g m(-3) respectively. The aerosol composition in XL showed a lower contribution of organic aerosol (33% vs. 43%) and higher fractions of nitrate (35% vs. 30%), ammonium (16% vs. 13%), and chlorine (2% vs. 1%) than in BJ. Additionally, a higher contribution of secondary organic aerosol (SOA) was also observed in XL, suggesting low primary emissions and highly oxidized OA in the background area. Nitrate displayed a significantly enhanced contribution with the aggravation of aerosol pollution in both BJ and XL, which was completely neutralized by excess ammonium at both sites, that the abundant ammonia emissions in the NCP favor nitrate formation on a regional scale. In addition, a higher proportion of nitrate in XL can be attributed to the more neutral and higher oxidation capacity of the background atmosphere. Heterogeneous aqueous reaction plays an important role in sulfate and SOA formation, and is more efficient in BJ which can be attributed to the higher aerosol surface areas at urban site. Regional transport from the southwestern regions of NCP showed a significant impact on the formation of haze episodes. Beside the invasion of transported pollutants, the abundant water vapor associated with the air mass to the downwind background area further enhanced local secondary transformation and expanded the regional scope of the haze pollution in the NCP. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

Rapid formation of intense haze episodes via aerosol-boundary layer feedback in Beijing

Although much effort has been put into studying air pollution, our knowledge of the mechanisms of frequently occurring intense haze episodes in China is still limited. In this study, using 3 years of measurements of air pollutants at three different height levels on a 325m Beijing meteorology tower, we found that a positive aerosol-boundary layer feedback mechanism existed at three vertical observation heights during intense haze polluted periods within the mixing layer. This feedback was characterized by a higher loading of PM2.5 with a shallower mixing layer. Modelling results indicated that the presence of PM2.5 within the boundary layer led to reduced surface temperature, relative humidity and mixing layer height during an intensive haze episode. Measurements showed that the aerosol-boundary layer feedback was related to the decrease in solar radiation, turbulent kinetic energy and thereby suppression of the mixing layer. The feedback mechanism can explain the rapid formation of intense haze episodes to some extent, and we suggest that the detailed feedback mechanism warrants further investigation from both model simulations and field observations.Peer reviewe

PKM2 promotes glucose metabolism and cell growth in gliomas through a mechanism involving a let-7a/c-Myc/hnRNPA1 feedback loop

AbstrAct Tumor cells metabolize more glucose to lactate in aerobic or hypoxic conditions than non-tumor cells. Pyruvate kinase isoenzyme type M2 (PKM2) is crucial for tumor cell aerobic glycolysis. We established a role for let-7a/c-Myc/hnRNPA1/PKM2 signaling in glioma cell glucose metabolism. PKM2 depletion via siRNA inhibits cell proliferation and aerobic glycolysis in glioma cells. C-Myc promotes up-regulation of hnRNPA1 expression, hnRNPA1 binding to PKM pre-mRNA, and the subsequent formation of PKM2. This pathway is downregulated by the microRNA let-7a, which functionally targets c-Myc, whereas hnRNPA1 blocks the biogenesis of let-7a to counteract its ability to downregulate the c-Myc/hnRNPA1/PKM2 signaling pathway. The down-regulation of c-Myc/ hnRNPA1/PKM2 by let-7a is verified using a glioma xenograft model. These results suggest that let-7a, c-Myc and hnRNPA1 from a feedback loop, thereby regulating PKM2 expression to modulate glucose metabolism of glioma cells. These findings elucidate a new pathway mediating aerobic glycolysis in gliomas and provide an attractive potential target for therapeutic intervention

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains

Characteristics of fine particulate matter and its sources in an industrialized coastal city, Ningbo, Yangtze River Delta, China

Chemical information is essential in understanding the characteristics of airborne particles, and effectively controlling airborne particulate matter pollution, but it remains unclear in some regions due to the scarcity of measurement data. In the present study, 92 daily PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) samples as well as historical observation data of air pollutants were collected in urban Ningbo, one of important industrial cities in the coastal area of the Yangtze River Delta, China in autumn and winter (from Nov. 2014 to Feb. 2015). Various chemical species in PM2.5 were determined including water soluble ions, organic and elemental carbon and elements. Positive matrix factorization model, cluster analysis of back trajectories, potential source contribution function (PSCF) model and concentration-weighted trajectory (CWT) model were used for identifying sources, apportioning contributions from each source and tracking potential areas of sources. The results showed the PM2.5 concentration has been reducing; nonetheless, the concentrations of PM2.5 are still much higher than the World Health Organization guideline with high PM2.5 concentrations observed in autumn and winter for the past few years. During the sampling period, the average PM2.5 mass concentration was 77 μg/m3 with the major components of OC, NO3−, SO42 −, NH4+ and EC, accounting for 24.7, 18.8, 14.5, 11.8 and 6.4% in the total mass concentration, respectively. When the aerosol pollution got worse during the sampling period, the NO3−, SO42 − and NH4+ concentrations increased accordingly and NO3− appeared to increase at fastest rate. SO42 − transported from industrial areas led to slight difference in spatial distribution of SO42 − in Ningbo. More secondary organic carbon was formed and the enrichment factor values of Cu, Ag, Cd, Sn and Pb increased with the degradation of air quality. Ten types of sources were identified for PM2.5 in the autumn and winter of Ningbo, which are metallurgical industry, biomass burning and waste incineration, manufacturing related with Mo, chlor-alkli chemical industry, oil combustion, vehicular emission, secondary source, soil dust, road dust and manufacturing related with Cr, accounting for 9.4, 4.8, 9.4, 7.6, 8.1, 18.7, 27.6, 2, 7.1 and 5.2% of the total sources, respectively. There were five groups of air parcels arriving in Ningbo, of which inland air masses originating from Shandong province were associated with the highest PM2.5 concentrations. Despite the slight differences, it was obvious that the north of Jiangxi, east of Anhui, west of Jiangsu, south of Shandong were identified as major potential sources-areas of SO42 −, NO3−, NH4+, Cl−, OC and EC by both PSCF and CWT models

Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach

Objective Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations. Methods The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation ( k  = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results. Results The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance. Conclusions ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension