The detection rate of solitary pulmonary nodule (SPN) is significantly increased with the widespread application of chest computed tomography (CT) scans. Therefore, there is rising demand and expectation for more accurate diagnostic tests to characterize SPN. The different diagnostic methods currently used in clinical practice have their advantages and disadvantages. This article reviews the literature pertaining to SNP diagnosis and treatment strategy and above mentioned concerns according to Fleischner society, American College of Chest Physicians (ACCP) and National Comprehensive Cancer Network (NCCN) screening guideline.

Objective To investigate the value of Habib 4X in hepatic resection. Methods The clinical outcome of 21 patients with liver disease who received liver resection at the Tianjin Third Central Hospital from November 2009 to April 2010 were retrospectively evaluated. All the operations were carried out by using Habib 4X. Results All patients received hepatectomy, including right hepatectomy in three patients, left hepatectomy in one patient, multiple segmentectomy in nine patients, single segmentectomy in seven patients and partial liver resection in one patient. All tumors were reseeted completely. The mean operation time was (50±25) minutes and the mean blood loss was(129±117)ml. No patient was transferred to ICU. Three patients were complicated with bile leakage, one with lymphatic leakage and four with pleural effusion, and they were cured by non-surgical treatment. There were no patients with postoperative hemorrhage, incision infection or hepatic failure. No mortality was observed. The mean postoperative hospital stay was(19±14)days. Conclusions Radiofrequency energy was applied along the margins of the tumor to create zones of necrosis before resection with a scalpel, offering hepatobiliary surgeons an additional method for performing liver resections with minimal blood loss, low morbidity and mortality rates. As for malignant tumors, minor or major liver resection assisted by Habib 4X is safe, and it can reduce the chance of positive incisal margin.

Intelligent drug delivery is a promising strategy for cancer therapies. In recent years, with the rapid development of synthetic biology, some properties of bacteria, such as gene operability, excellent tumor colonization ability, and host-independent structure, make them ideal intelligent drug carriers and have attracted extensive attention. By implanting condition-responsive elements or gene circuits into bacteria, they can synthesize or release drugs by sensing stimuli. Therefore, compared with traditional drug delivery, the usage of bacteria for drug loading has better targeting ability and controllability, and can cope with the complex delivery environment of the body to achieve the intelligent delivery of drugs. This review mainly introduces the development of bacterial-based drug delivery carriers, including mechanisms of bacterial targeting to tumor colonization, gene deletions or mutations, environment-responsive elements, and gene circuits. Meanwhile, we summarize the challenges and prospects faced by bacteria in clinical research, and hope to provide ideas for clinical translation.

BACKGROUND: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. METHODS: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). RESULTS: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. CONCLUSIONS A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.
Humans ; Female ; Breast Neoplasms/genetics* ; East Asian People ; Neoplasm Recurrence, Local/genetics* ; Breast ; Algorithms ; Chronic Disease ; Prognosis ; Tumor Microenvironment