Liquid biopsy using ascitic fluid and pleural effusion supernatants for genomic profiling in gastrointestinal and lung cancers.

BACKGROUND: Precision medicine highlights the importance of incorporating molecular genetic testing into standard clinical care. Next-generation sequencing can detect cancer-specific gene mutations, and molecular-targeted drugs can be designed to be effective for one or more specific gene mutations. For patients with special site metastases, it is particularly important to use appropriate samples for genetic profiling. This study aimed to determine whether genomic profiling using ASC and PE is effective in detecting genetic mutations. METHODS: Tissues, plasma, ascites (ASC) supernatants, and pleural effusion (PE) samples from gastrointestinal cancer patients with peritoneal metastasis and lung cancer patients with pleural metastasis were collected for comprehensive genomic profiling. The samples were subjected to next-generation sequencing using a panel of 59 or 1021 cancer-relevant genes panel. RESULTS: A total of 156 tissues, 188 plasma samples, 45 ASC supernatants, and 1 PE samples from 304 gastrointestinal cancer patients and 446 PE supernatants, 122 tissues, 389 plasma samples, and 45 PE sediments from 407 lung cancer patients were analyzed. The MSAF was significantly higher in ASC and PE supernatant than that in plasma ctDNA (50.00% vs. 3.00%, p < 0.0001 and 28.5% vs. 1.30%, p < 0.0001, respectively). The ASC supernatant had a higher actionable mutation rate and more actionable alterations than the plasma ctDNA in 26 paired samples. The PE supernatant had a higher total actionable mutation rate than plasma (80.3% vs. 48.4%, p < 0.05). The PE supernatant had a higher frequency of uncommon variations than the plasma regardless of distant organ metastasis. CONCLUSION: ASC and PE supernatants could be better alternative samples when tumor tissues are not available, especially in patients with only peritoneal or pleural metastases.

Dynamics of transcription-translation coordination tune bacterial indole signaling.

Indole signaling is an important cross-species communication pathway in the mammalian gut. In bacteria, upon induction by tryptophan, the molecular sensor (tnaC) controls indole biosynthesis by precisely coordinating dynamics of the corresponding macromolecular machineries during its transcription and translation. Our understanding of this regulatory program is still limited owing to its rapid dynamic nature. To address this shortcoming, we adopted a massively parallel profiling method to quantify the responses of 1,450 synthetic tnaC variants in the presence of three concentrations of tryptophan in living bacterial cells. The resultant dataset enabled us to comprehensively probe the key intermediate states of macromolecular machineries during the transcription and translation of tnaC. We also used modeling to provide a systems-level understanding of how these critical states collectively shape the output of this regulatory program quantitatively. A similar methodology will likely apply to other poorly understood dynamics-dependent cis-regulatory elements.

SIRT2 plays complex roles in neuroinflammation neuroimmunology-associated disorders.

Neuroinflammation and neuroimmunology-associated disorders, including ischemic stroke and neurodegenerative disease, commonly cause severe neurologic function deficits, including bradypragia, hemiplegia, aphasia, and cognitive impairment, and the pathological mechanism is not completely clear. SIRT2, an NAD+-dependent deacetylase predominantly localized in the cytoplasm, was proven to play an important and paradoxical role in regulating ischemic stroke and neurodegenerative disease. This review summarizes the comprehensive mechanism of the crucial pathological functions of SIRT2 in apoptosis, necroptosis, autophagy, neuroinflammation, and immune response. Elaborating on the mechanism by which SIRT2 participates in neuroinflammation and neuroimmunology-associated disorders is beneficial to discover novel effective drugs for diseases, varying from vascular disorders to neurodegenerative diseases.

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid-Structure Interaction.

Based on expert system theory and fluid-structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid-structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

Intravoxel incoherent motion diffusion-weighted imaging for early assessment of combined anti-angiogenic/chemotherapy for colorectal cancer liver metastases.

Background: To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the early assessment of colorectal cancer liver metastases (CRLM). Methods: A total of 34 patients with pathologically confirmed unresectable CRLM were enrolled. All participants uniformly received capecitabine and oxaliplatin (CAPOX) plus bevacizumab chemotherapy as standard first-line treatment for advanced colorectal cancer (CRC). Participants underwent 1.5-T conventional magnetic resonance imaging (MRI) and IVIM-DWI sequence scans with 9 b values (0 to 1,000 s/mm2) before treatment and at 3 weeks of treatment, and conventional MRI scans were performed at 6 and 12 weeks after the initial treatment. The IVIM-DWI parameters in the tumor target area were extracted using image post-processing software, including perfusion fraction (f), true diffusion coefficient (D), and false diffusion coefficient (D*). The response assessment was based on the Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 by measuring the longest tumor diameter on dynamic contrast-enhanced (DCE) T1-weighted images. Results: According to the RECIST v. 1.1 criteria, the 34 participants were divided into a response group (n=16) and a non-response group (n=18). In the response group, the f value was significantly lowered (P=0.001) and the D value was significantly increased after treatment (P=0.002). In the non-response group, the D value was increased slightly after treatment (P=0.039), and there was no significant difference in the f value and the D* value. In addition, the f value at baseline was significantly greater in the response group than in the non-response group (0.221±0.033 vs. 0.175±0.040; P=0.001). The receiver operating characteristic (ROC) curve analysis showed that the percentage change of the f value obtained the largest area under the curve (AUC =0.797), and the AUC obtained by the Fisher's linear discriminant analysis (FLDA) method (Δf & ΔD combination) was 0.819. Conclusions: The IVIM-DWI parameters (f values and D values) provided effective assessment of the therapeutic effect of CAPOX combined with bevacizumab in patients with CRLM at an early stage, and the f value of the pre-treatment tumor area was shown to be useful for predicting the treatment response of patients.

Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study.

OBJECTIVE: The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer. METHODS: Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007-2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov-Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram. RESULTS: Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram. CONCLUSION: We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.