Avermectins are major antiparasitic agents used commercially in animal health, agriculture and human infections. To improve the fermentation efficiency of avermectins, for the first time a plasma jet ...generated by a novel atmospheric pressure glow discharge (APGD) was employed to generate mutations in Streptomyces avermitilis. The APGD plasma jet, driven by a radio frequency (RF) power supply with water-cooled and bare-metallic electrodes, was used as a new mutation method to treat the spores of S. avermitilis. The plasma jet yielded high total (over 30%) and positive (about 21%) mutation rates on S. avermitilis, and a mutated strain, designated as G1-1 with high productivity of avermectin B1a and genetic stability, was obtained. Because of the low jet temperature, the high concentrations of the chemically reactive species and the flexibility of its operation, the RF APGD plasma jet has a strong mutagenic effect on S. avermitilis. This is a proof-of-concept study for the use of an RF APGD plasma jet for inducing mutations in microbes. We have shown that the RF APGD plasma jet could be developed as a promising and convenient mutation tool for the fermentation industry and for use in biotechnology research.
Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection ...or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients.
A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability.
RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 95% confidence interval (CI) 0.923–0.993, 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability.
CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.
Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative ...determination method is not accurate enough.
We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.
The DLRN showed good discrimination of the number of LNM on all cohorts overall C-indexes (95% confidence interval): 0.821 (0.785–0.858) in the primary cohort, 0.797 (0.771–0.823) in the external validation cohorts, and 0.822 (0.756–0.887) in the international validation cohort. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).
A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.
•Evaluation of the lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC).•Deep leaning radiomic nomogram (DLRN) based on CT images can preoperatively determine the number of LNM in LAGC.•DLRN is significantly superior to the routinely used clinical N stages, tumor size, and clinical model.•DLRN is significantly associated with the overall survival of LAGC.
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