Bile-derived exosome noncoding RNAs as potential diagnostic and prognostic biomarkers for cholangiocarcinoma

Background

Cholangiocarcinoma (CCA) is one of the most aggressive malignancies, lacking novel diagnostic and prognostic biomarkers. Exosome noncoding RNAs (ncRNA) were previously proposed as a potential source of biomarkers in several cancers. This study aimed to interpret the value of specific bile-derived ncRNA as predictors for early diagnosis and prognosis of CCA.

Methods

We recruited 100 patients who received endoscopic retrograde cholangiopancreatography at our hospital for bile duct obstruction due to CCA (n = 50) and biliary stone (n = 50). They were further divided into training set and validation set (3:2). A panel of CCA-specific ncRNAs including 5 miRNAs (PMID: 30165035) and 2 lncRNAs (PMID: 29050258) were detected in both serum and bile exosomes. The diagnostic accuracy was assessed using the area under the receiver operating characteristic curve. Logistic analysis was used to classify the potential predictors of CCA and further establish the diagnostic model. And the prognostic value of the ncRNAs was also assessed.

Results

Exosomes were successfully collected from bile and serum. Exosomal miR-141-3p, miR-200a-3p, miR-200c-3p in serum and bile, as well as miR-200b-3p and ENST00000588480.1 in bile showed AUCs of >0.70 in the diagnosis of CCA. Bile exosomal miR-200c-3p displayed the best diagnostic value with the AUC of 0.87. The combination of serum CA19-9 into the model could increase the AUC to 0.906. Bile exosomal miR-200a-3p and miR-200c-3p were found to be independent predictors of CCA. Among exosomal ncRNAs in human bile and blood, 3 (serum and bile exosomal miR-200c-3p, bile exosomal miR-200a-3p) showed significant value in predicting cancer recurrence and 1 (serum exosomal miR-200c-3p) had great predictive ability of cancer death. High levels of serum exosomal miR-200c-3p showed unfavorable tumor-free survival and overall survival.

Conclusion

The bile exosomal miR-200 family, particularly miR-200c-3p, was verified to be a potential biomarker for the early detection of CCA. The diagnostic ability of exosomal ncRNAs in human bile is better than that in blood. Moreover, high levels of bile exosomal miR-200a-3p, miR-200c-3p, and serum exosomal miR-200c-3p represented adverse clinical outcomes.

Reply to the 'Comment on "Realization of the Zn3+ oxidation state"' by Y. Shang, N. Shu, Z. Zhang, P. Yang and J. Xu, Nanoscale, 2022, , DOI: 10.1039/D1NR07031B

In a recent paper (https://doi.org/10.1039/D1NR02816B), we suggested that Zn can assume a +3-oxidation state when interacting with super-electrophilic clusters, BeB₁₁(CN)₁₂ and BeB₂₃(CN)₂₂. In a comment to our paper (https://doi.org/10.1039/D1NR07031B), Shang et al. have questioned this suggestion. Using density functional theory with the TPSSh functional and def2-SVP basis sets in the Gaussian16 software and semiempirical localized orbital bonding analysis (LOBA), the authors have made three major claims: (1) the oxidation state of Zn in Zn[BeB₁₁(CN)₁₂] and Zn[BeB₂₃(CN)₂₂] is +2; (2) electron affinities are not reliable to probe the oxidation states; and (3) our results are "misleading" because these are based on the VASP code. According to these authors, VASP is not suitable for small clusters because it uses projected augmented wave (PAW) pseudopotentials. In the following, we show that these claims are invalid, caused by both misunderstanding and the authors' use of a lower-level theory.

Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs

Background. Twelve lead ECGs are a core diagnostic tool for cardiovascular diseases. Here, we describe and analyse an ensemble deep neural network architecture to classify 24 cardiac abnormalities from 12 lead ECGs.Method. We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions. The deep features were augmented with age and gender features in the final fully connected layers. Output thresholds for each class were set using a constrained grid search. To determine why the model made incorrect predictions, two expert clinicians independently interpreted a random set of 100 misclassified ECGs concerning left axis deviation.Results. Using the bespoke weighted accuracy metric, we achieved a 5-fold cross-validation score of 0.684, and sensitivity and specificity of 0.758 and 0.969, respectively. We scored 0.520 on the full test data, and ranked 2nd out of 41 in the official challenge rankings. On a random set of misclassified ECGs, agreement between two clinicians and training labels was poor (clinician 1:κ= -0.057, clinician 2:κ= -0.159). In contrast, agreement between the clinicians was very high (κ= 0.92).Discussion. The proposed prediction model performed well on the validation and hidden test data in comparison to models trained on the same data. We also discovered considerable inconsistency in training labels, which is likely to hinder development of more accurate models.

[Stereotactic body radiation therapy for patients with lung and liver oligometastases from colorectal cancer: a phase Ⅱ trial]

Objective: To explore the safety and effectiveness of stereotactic body radiation therapy (SBRT) for oligometastases from colorectal cancer (CRC). Methods: This is a prospective, single-arm phase Ⅱ trial. Patients who had histologically proven CRC, 1 to 5 detectable liver or lung metastatic lesions with maximum diameter of any metastases ≤5 cm were eligible. SBRT was delivered to all lesions. The primary endpoint was 3-year local control (LC). The secondary endpoints were treatment-related acute toxicities of grade 3 and above, 1-year and 3-year overall survival (OS) and progression free survival (PFS). Survival analysis was performed using the Kaplan-Meier method and Log rank test. Results: Petients from 2016 to 2019 who were treated in Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. Forty-eight patients with 60 lesions were enrolled, including 37 liver lesions and 23 lung lesions. Forty-six patients had 1 or 2 lesions, with median diameter of 1.3 cm, the median biologically effective dose (BED(10)) was 100.0 Gy. The median follow-up was 19.5 months for all lesions. Twenty-five lesions developed local failure, the median local progression free survival was 15 months. The 1-year LC, OS and PFS was 70.2% (95% CI, 63.7%~76.7%), 89.0% (95% CI, 84.3%~93.7%) and 40.4% (95%CI, 33.0%~47.8%). The univariate analysis revealed that planning target volume (PTV) and total dose were independent prognostic factors of LC (P<0.05). For liver and lung lesions, the 1-year LC, OS and PFS was 58.7% and 89.4% (P=0.015), 89.3% and 86.5% (P=0.732), 30.5% and 65.6% (P=0.024), respectively. No patients developed acute toxicity of grade 3 and above. Conclusion: SBRT is safe and effective treatment method for oligometastases from CRC under precise respiratory motion management and robust quality assurance.

[Bioinformatics screening and analysis of key differentially expressed genes characteristics in nonalcoholic fatty liver disease]

Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.

[Carbon peak, carbon neutrality: strategic opportunities for China's health system]

Excessive emission of greenhouse gases is the most important reason for global warming. Severe climate change significantly threatens human health. World Health Organization estimated that 12.6 million deaths were attributable to modifiable environmental factors and climate change would lead to 0.25 million deaths annually during 2030-2050. All countries in the world try to reduce greenhouse gas emission to mitigate adverse impacts of climate change by following the Paris Agreement, through setting goals and measures of carbon peak, and carbon neutrality. China plays a very important role in the campaign of global Carbon Peak and Carbon Neutrality. Climate change is also a global health emergency, and Carbon Peak and Carbon Neutrality will be a strategic opportunity for China's health system. The goals of Carbon Peak and Carbon Neutrality are harmonized with those of China's health system, both aiming to provide high-quality health security for all Chinese people. China's health system should try to not only reduce its own carbon footprints, but also make significant contributions to the global Carbon Peak and Carbon Neutrality campaign.

[Comparison of preoperative chemotherapy with concurrent chemoradiotherapy combined with TME for 305 patients with locally advanced rectal cancer]

Objective: To retrospectively analyze the long-term efficacy and prognostic factors of preoperative chemotherapy (PCT) or chemoradiotherapy (PCRT) combined with total mesorectal excision in locally advanced rectal cancer. Methods: Clinical pathology data of 305 patients with localized advanced rectal cancer admitted to the Cancer Hospital, Chinese Academy of Medical Sciences from 2006 to 2018 were collected, of whom 246 patients received PCRT (PCRT group), 59 patients received PCT (PCT group). Kaplan-Meier and Log rank test were used for the survival analysis, Cox regression model was used for multivariate analysis, and the prognosis of two groups of patients were compared by the propensity score matching (PSM). Results: In the whole group of 305 patients, 20 cases of tumors located in the upper part of the rectum and at the junction of rectum and colon, 96 cases in the middle of the rectum and 189 cases in the lower part of the rectum. PCRT group included 38 cases of cT2-3 phase, 11 cases of cT4a stage, 10 cases of cT4b stage, while the cases in PCT group were 184, 0 and 62 cases, respectively, the difference is statistically significant (P<0.05). The R0 excision rates of PCRT group and PCT group were 100% (246/246) and 96.6% (57/59), respectively, and the total pathological remission rates were 13.4% and 3.3%, respectively (P<0.05). After PSM, the 3-year survival rates of PCRT group and the PCT group were 86.6% and 89.9% (P>0.05), respectively, and the progression-free survival rates were 74.6% and 77.2% (P>0.05), local recurring free survival rates were 100% and 92.3% (P>0.05), distant metastasis free survival rate were 75.6% and 77.3% (P>0.05). Pre-treatment N-positive, N-degeneration and MRF-positive were all associated with total survival (P<0.05). Conclusion: In the PCRT group, with a higher proportion of patients with stage T4b and lower rectal cancer, the long-term efficacy of PCRT was similar to that of PCT, and higher R0 excision rate and pathological complete response rate could be obtained.

Maxillary molar root and canal morphology of Neolithic and modern Chinese

OBJECTIVE

This study aimed to characterize Neolithic human maxillary molars from archeological remains at the Jiaojia site, Shandong, China, and compare their ultrastructural features with sex and age-matched modern locals.

DESIGN

Maxillary first (n = 86) and second (n = 80) molars in 5000-year-old individuals (n = 50) from the Jiaojia site were scanned by cone-beam computed tomography (CBCT). Sex and age-matched control groups were assigned from oral surgical patients at Shandong University. Images were analyzed for crown size, root length, root morphology, canal inter-orifice distances, mesiobuccal canal morphology, and second mesiobuccal (MB2) canal prevalence and location. Neolithic and modern values were compared statistically using Chi-squared and Mann-Whitney test at p < .05.

RESULTS

Crown and root size were smaller, and canal inter-orifice distances were shorter in Neolithic maxillary molars than their modern counterparts. For mesiobuccal roots, Weine's Type I single canals were the most prevalent in Neolithic and modern first and second molars. MB2 canal prevalence were not significantly different (p > .05) in Neolithic (53.3%) or modern (60.5%) first molars, and Neolithic (11.3%) or modern (21.3%) second molars. But, MB2 prevalence was significantly higher for modern than ancient male first (p = .032) and second (p = .005) molars. Additionally, MB2 were located more mesially and closer to MB1 in Neolithic than modern molars.

CONCLUSIONS

Maxillary molar root and canal morphology of ancient 5000-year-old remains at the Jiaojia site resemble that of local patients. A trend towards larger tooth size, and more dispersed MB2 canals over this short evolutionary period warrants additional investigation.

Deep learning for improving non-destructive grain mapping in 3D

Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.