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Objective To explore the efficacy and advantages of intracranial-intracranial bypass for the treatment of complex intracranial aneurysms. Methods A total of 9 patients with complex intracranial aneurysms who underwent intracranial-intracranial bypass in Xiangya Hospital of Central South University from February 2014 to May 2020 were included, and were treated with grafts bypass or reimplantation of recipient arteries. All cases underwent CTA or DSA to detect the patent of grafts, whether aneurysms were completely clipped or resected and aneurysms recurrence occurred during the follow-up. Modified Rankin Scale (mRS) was used to evaluate the neurological prognosis at discharge and during the follow - up. Results Radial artery (RA) or great saphenous vein (GSV) was used for interposition in 4 cases, and recipient arteries were used for reimplantation in 5 cases. Of 4 cases of interposition, the cavernous sinus seoment of internal carotid artery (ICA)-GSV-ICA bypass was adopted in one case, middle cerebral artery (MCA) M2-RA-M2 bypass in one case, anterior cerebral artery (ACA) A3-GSV-A3 bypass in one case, posterior cerebral artery (PCA) P2 -RA -P2 bypass in one case. Of 5 cases of reimplantation, MCA M2 inferior trunk was reimplanted to M2 superior trunk in one case, ACA A3 was reimplanted to contralateral A3 in 2 cases, the posterior inferior cerebellar artery (PICA) was reimplanted to the anterior inferior cerebellar artery (AICA) in 2 cases. CTA in 3 d postoperatively showed all cases grafts were patent and aneurysms disappeared. After 3 months of discharge, 2 were lost to follow-up. The other 7 cases kept the patency of grafts and no new aneurysms, and were constantly followed up, with an average follow-up time of 30.71 months, and mRS score was 1 or lower. Conclusions Intracranial - intracranial bypass can be applied for complex intracranial aneurysms that are difficult for conventional surgical treatment to solve. Compared with extracranial - intracranial bypass, its grafts are shorter and its hemodynamics features are more in line with physiological conditions.

Abstract Background Hematological metastasis has been recognized as a crucial factor contributing to the high rates of metastasis and mortality observed in colorectal cancer (CRC). Notably, exosomes derived from cancer cells participate in the formation of CRC pre-metastatic niches; however, the mechanisms underlying their effects are largely unknown. While our preliminary research revealed the role of exosome-derived disintegrin and metalloproteinase 17 (ADAM17) in the early stages of CRC metastasis, the role of exosomal ADAM17 in CRC hematogenous metastasis remains unclear. Methods In the present study, we isolated and purified exosomes using ultracentrifugation and identified exosomal proteins through quantitative mass spectrometry. In vitro, co-culture assays were conducted to evaluate the impact of exosomal ADAM17 on the permeability of the blood vessel endothelium. Vascular endothelial cell resistance, the cell index, membrane protein separation, flow cytometry, and immunofluorescence were employed to investigate the mechanisms underlying exosomal ADAM17-induced vascular permeability. Additionally, a mouse model was established to elucidate the role of exosomal ADAM17 in the modulation of blood vessel permeability and pre-metastatic niche formation in vivo. Results Our clinical data indicated that ADAM17 derived from the circulating exosomes of patients with CRC could serve as a blood-based biomarker for predicting metastasis. The CRC-derived exosomal ADAM17 targeted vascular endothelial cells, thus enhancing vascular permeability by influencing vascular endothelial cadherin cell membrane localization. Moreover, exosomal ADAM17 mediated the formation of a pre-metastatic niche in nude mice by inducing vascular leakage, thereby promoting CRC metastasis. Nonetheless, ADAM17 selective inhibitors effectively reduced CRC metastasis in vivo. Conclusions Our results suggest that exosomal ADAM17 plays a pivotal role in the hematogenous metastasis of CRC. Thus, this protein may serve as a valuable blood-based biomarker and potential drug target for CRC metastasis intervention.

Abstract The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p

ObjectivesThis study aimed to assess the knowledge and awareness of nicotine, nicotine replacement therapy (NRT), and electronic cigarettes (e-cigarettes) among general practitioners with a special interest (GPwSIs) in respiratory medicine.MethodsA cross-sectional study was conducted from October 2021 to February 2022. Knowledge and awareness were compared among smokers and non-smokers, as well as different age and gender groups.ResultsThe study consisted of 102 GPwSIs from 21 cities in Sichuan Province, China. Most respondents would recommend NRT for long-term use. Only a few believed that e-cigarettes are an effective means of smoking cessation and 71.6% would not recommend e-cigarettes as a substitute for cigarettes to their patients. Additionally, the majority did not regularly provide extensive help to assist patients in quitting smoking and needed smoking cessation counseling training.ConclusionGPwSIs in respiratory medicine in China could have a relatively low level of knowledge and awareness regarding nicotine, NRT, and e-cigarettes. The study highlights the need for smoking cessation training among GPwSIs to improve their knowledge and provide better assistance to patients who want to quit smoking.

Abstract Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at the time of FNA. To bypass the staining process and expert interpretation of FNA specimens at the clinics, we developed a deep learning-based ensemble model termed FNA-Net that allows in situ screening of adequacy of unstained thyroid FNA samples smeared on a glass slide which can decrease the non-diagnostic rate in thyroid FNA. FNA-Net combines two deep learning models, a patch-based whole slide image classifier and Faster R-CNN, to detect follicular clusters with high precision. Then, FNA-Net classifies sample slides to be non-diagnostic if the total number of detected follicular clusters is less than a predetermined threshold. With bootstrapped sampling, FNA-Net achieved a 0.81 F1 score and 0.84 AUC in the precision-recall curve for detecting the non-diagnostic slides whose follicular clusters are less than six. We expect that FNA-Net can dramatically reduce the diagnostic cost associated with FNA biopsy and improve the quality of patient care.

Objective: This paper discusses the feasibility of using mineral element fingerprinting technology of to identify tobacco producing areas, screens out the effective indicators, and constructs the discrimination model of tobacco producing areas. Methods: The contents of 20 mineral elements in tobacco leaves from 11 producing areas were simultaneously determined by ICP-MS, and the data were analyzed by variance analysis, cluster analysis, principal component analysis and discriminant analysis. Results: The contents of 16 elements were significantly different among producing areas, and principal component analysis resulted in 6 principal components, with the cumulative variance contribution of 89.253%. Using linear discriminant analysis, four elementsmineral, K, Mn, Se and Ba, were screened as the effective indicators to discriminate the geographical origin of tobacco leaves. The established discriminant model could accurately distinguish tobacco leaf samples from different tobacco producing areas. Conclusion: Significant differences were showed in the contents of mineral elements in tobacco leaves from 11 producing areas. Four elements, K, Mn, Se and Ba, are important indicators for distinguishing tobacco producing areas. Mineral element fingerprinting technology can be used for distinguishing tobacco producing areas.

Abstract The skin is the largest organ in the human body. Various skin environments on its surface constitutes a complex ecosystem. One of the characteristics of the skin micro‐ecosystem is low biomass, which greatly limits a comprehensive identification of the microbial species through sequencing. In this study, deep‐shotgun sequencing (average 21.5 Gigabyte (Gb)) from 450 facial samples and publicly available skin metagenomic datasets of 2069 samples to assemble a Unified Human Skin Genome (UHSG) catalog is integrated. The UHSG encompasses 813 prokaryotic species derived from 5779 metagenome‐assembled genomes, among which 470 are novel species covering 20 phyla with 1385 novel assembled genomes. Based on the UHSG, the core functions of the skin microbiome are described and the differences in amino acid metabolism, carbohydrate metabolism, and drug resistance functions among different phyla are identified. Furthermore, analysis of secondary metabolites of the near‐complete genomes further find 1220 putative novel secondary metabolites, several of which are found in previously unknown genomes. Single nucleotide variant (SNV) reveals a possible skin protection mechanism: the negative selection process of the skin environment to conditional pathogens. UHSG offers a convenient reference database that will facilitate a more in‐depth understanding of the role of skin microorganisms in the skin.

BackgroundAccurate preoperative assessment of surgical difficulty is crucial to the success of the surgery and patient safety. This study aimed to evaluate the difficulty for endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) using multiple machine learning (ML) algorithms.MethodsFrom December 2010 to December 2022, 555 patients with gGISTs in multi-centers were retrospectively studied and assigned to a training, validation, and test cohort. A difficult case was defined as meeting one of the following criteria: an operative time ≥ 90 min, severe intraoperative bleeding, or conversion to laparoscopic resection. Five types of algorithms were employed in building models, including traditional logistic regression (LR) and automated machine learning (AutoML) analysis (gradient boost machine (GBM), deep neural net (DL), generalized linear model (GLM), and default random forest (DRF)). We assessed the performance of the models using the areas under the receiver operating characteristic curves (AUC), the calibration curve, and the decision curve analysis (DCA) based on LR, as well as feature importance, SHapley Additive exPlanation (SHAP) Plots and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.ResultsThe GBM model outperformed other models with an AUC of 0.894 in the validation and 0.791 in the test cohorts. Furthermore, the GBM model achieved the highest accuracy among these AutoML models, with 0.935 and 0.911 in the validation and test cohorts, respectively. In addition, it was found that tumor size and endoscopists’ experience were the most prominent features that significantly impacted the AutoML model’s performance in predicting the difficulty for ER of gGISTs.ConclusionThe AutoML model based on the GBM algorithm can accurately predict the difficulty for ER of gGISTs before surgery.