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Background Spinosad, a secondary metabolite produced by Saccharopolyspora spinosa , is a polyketide macrolide insecticide with low toxicity and environmental friendliness. Owing to the high level of DNA methylation and unclear regulatory mechanisms, gene engineering to increase spinosad production is challenging. Limited improvements in yield have been observed with heterologous expression or partial overexpression of the 74-kb spinosyn gene cluster ( spn ), and research on the overexpression of the complete spinosyn gene cluster is lacking. Results The plasmid pCM265-spn was constructed using CRISPR/Cas9-mediated Transformation-Associated Recombination cloning to enable the overexpression of the complete spn gene cluster in Sa. spinosa . The engineered strain Sa. spinosa-spn achieved a 124% increase in spinosad yield (693 mg/L) compared to the wild type (309 mg/L). The overexpression of the spn gene cluster also delayed spore formation and reduced hyphal compartmentalization by influencing the transcription of related genes ( bldD , ssgA , whiA , whiB , and fstZ ). Transcriptional analysis revealed significant upregulation of genes in the spn gene cluster, thereby enhancing secondary metabolism. Additionally, optimization of the fermentation medium through response surface methodology further increased spinosad production to 920 mg/L. Conclusions This study is the first to successfully overexpress the complete spn gene cluster in Sa. spinosa , significantly enhancing spinosad production. These findings have significance for further optimization of spinosad biosynthesis.

The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set ( n  = 66) and a validation set ( n  = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient’s images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p  < 0.0001, validation set: p  = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.

[...]the absence of reverse Mendelian Randomization (MR) is a notable limitation of this study. [...]several immune cells, gut microbiota, and inflammatory factors were positively associated with KC outcomes in the study. Cuiru Li et al. found that short-chain fatty acids (SCFAs) produced by gut microbiota metabolism can regulate host immune responses and inflammatory states, thereby affecting tumorigenesis and progression (6). [...]studies have indicated that the specific composition of the gut microbiota may lead to primary resistance to immune checkpoint inhibitor (ICI) therapy (7). [...]validation is required to determine the precise role of specific bacterial species, such as Odoribacter splanchnicus (8).

PurposeCompression of the iliac vein between the iliac artery and lumbosacral vertebra can cause iliac vein compression syndrome (IVCS). The purpose of this study is to assess compression characteristics and establish a new sub-typing in asymptomatic IVCS individuals using contrast-enhanced CT.MethodsA retrospective analysis of abdomen contrast-enhanced CT images from 195 asymptomatic subjects with iliac vein compressed was investigated. Patients had no history of venous pathology, and images were collected from June 2018 to January 2019. Qualitative and quantitative characteristics of compression were examined including the location, pattern, minor diameter, area, and the percentage compression on an orthogonal section by the post-processing of multiple planar reconstruction and volume rendering.ResultsThere were 107 females and 88 males with age range 18–92 years. The most common site of iliac vein compression was localized to the left common iliac vein (LCIV) (178/195, 91.3%). Notably, four compression types (type I–IV) were established according to the compression location, with type II being the most common. The four compression types had differences in the upper limit and fluctuation range of compression. It was found that the average level of iliac vein compression was below 25%. The compression degree of the left common iliac vein in type II was relatively concentrated, and the upper limit of compression was close to 70%.ConclusionAsymptomatic iliac vein compression was categorized according to compression location. The proposal of four types might help clinicians to predict which IVCS patients would benefit from interventional therapy.

Large bowel perforation is an acute abdominal emergency requiring rapid diagnosis for proper treatment. The high mortality rate associated with large bowel perforation underlines the importance of an accurate and timely diagnosis. Computed tomography is useful for diagnosis of ingested foreign bodies, and endoscopic repair using clips can be an effective treatment of colon perforations. We herein describe a 78-year-old man with sigmoid colon perforation caused by accidental swallowing of a jujube pit. The jujube pit had become stuck in the wall of the sigmoid colon and was successfully removed by colonoscopy, avoiding an aggressive surgery. As a result of developments in endoscopic techniques, endoscopic closure has become a feasible option for the management of intestinal perforation.

Purpose In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial. Methods We retrospectively collected 256 patients with AEG from two centres. The radiomics features were extracted from the preoperative diagnostic CT images, and the feature selection method and machine learning method were applied to reduce the feature size and establish the predictive imaging features. By comparing the three machine learning methods, the best radiomics nomogram was selected, and the average AUC was obtained by 20 repeats of fivefold cross-validation for comparison. The fusion model was constructed by logistic regression combined with clinical factors. On this basis, ROC curve, calibration curve and decision curve of the fusion model are constructed. Results The predictive efficacy of fusion model for tumour invasion depth was higher than that of radiomics nomogram, with an AUC of 0.764 vs. 0.706 in the test set, P  < 0.001, internal validation set 0.752 vs. 0.697, P  < 0.001, and external validation set 0.756 vs. 0.687, P  < 0.001, respectively. The predictive efficacy of the lymph node metastasis fusion model was higher than that of the radiomics nomogram, with an AUC of 0.809 vs. 0.732 in the test set, P  < 0.001, internal validation set 0.841 vs. 0.718, P  < 0.001, and external validation set 0.801 vs. 0.680, P  < 0.001, respectively. Conclusion We have developed a fusion model combining radiomics and clinical risk factors, which is crucial for the accurate preoperative diagnosis and treatment of AEG, advancing precision medicine. It may also spark discussions on the imaging feature differences between AEG and GC (Gastric cancer).

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefore, this study aims to develop a deep learning model using portal venous phase CT images to accurately distinguish EGC without lymph node metastasis. This study included 3164 patients with gastric cancer (GC) who underwent radical surgery at two medical centers in China from 2006 to 2019. Moreover, 2.5D radiomic data and multi-instance learning (MIL) were novel approaches applied in this study. By basing the selection of features on 2.5D radiomic data and MIL, the ResNet101 model combined with the XGBoost model represented a satisfactory performance for diagnosing pT1N0 GC. Furthermore, the 2.5D MIL-based model demonstrated a markedly superior predictive performance compared to traditional radiomics models and clinical models. We first constructed a deep learning prediction model based on 2.5D radiomics and MIL for effectively diagnosing pT1N0 GC patients, which provides valuable information for the individualized treatment selection.

Spinosad, a secondary metabolite produced by Saccharopolyspora spinosa, is a polyketide macrolide insecticide with low toxicity and environmental friendliness. Owing to the high level of DNA methylation and unclear regulatory mechanisms, gene engineering to increase spinosad production is challenging. Limited improvements in yield have been observed with heterologous expression or partial overexpression of the 74-kb spinosyn gene cluster (spn), and research on the overexpression of the complete spinosyn gene cluster is lacking. The plasmid pCM265-spn was constructed using CRISPR/Cas9-mediated Transformation-Associated Recombination cloning to enable the overexpression of the complete spn gene cluster in Sa. spinosa. The engineered strain Sa. spinosa-spn achieved a 124% increase in spinosad yield (693 mg/L) compared to the wild type (309 mg/L). The overexpression of the spn gene cluster also delayed spore formation and reduced hyphal compartmentalization by influencing the transcription of related genes (bldD, ssgA, whiA, whiB, and fstZ). Transcriptional analysis revealed significant upregulation of genes in the spn gene cluster, thereby enhancing secondary metabolism. Additionally, optimization of the fermentation medium through response surface methodology further increased spinosad production to 920 mg/L. This study is the first to successfully overexpress the complete spn gene cluster in Sa. spinosa, significantly enhancing spinosad production. These findings have significance for further optimization of spinosad biosynthesis.

The global incidence of prostate cancer (PCa) is rising. Localized PCa can be managed through surgical intervention or radiotherapy, but certain patients may experience recurrence or develop metastatic disease following localized treatment. Despite aggressive therapeutic approaches, the majority of metastatic patients with PCa will eventually progress to metastatic castration-resistant PCa, with limited treatment alternatives and a dismal prognosis. The treatment options for advanced PCa are continuously evolving, yet there remains a demand for further innovative therapeutic approaches. Antibody-drug conjugates (ADCs) represent a novel class of targeted medications comprising a humanized monoclonal antibody, a linker and a cytotoxic payload. ADCs primarily bind to antigens that are upregulated on the surface of PCa cells but are minimally expressed on normal cells. At present, a variety of targets for ADCs have been identified in the treatment of PCa, encompassing prostate-specific membrane antigen, STEAP family member 1, trophoblast cell-surface antigen 2, CD46, B7-H3, tissue factor and delta-like protein 3, each with one or more specific ADC that has shown encouraging results in the PCa field. In the present review, the developmental course, composition and mechanism of action of ADCs are explored, with a specific focus on recently published studies and ongoing trials aimed at investigating the efficacy and safety of ADCs in treating PCa. Lastly, ongoing challenges in ADC development and corresponding strategies to combat them are discussed.

Background and Aims. Magnetic resonance imaging (MRI) has taken an important role in the diagnosis of inflammatory bowel diseases (IBD). In the wake of current advances in nanotechnology, the drug delivery industry has seen a surge of nanoparticles advertising high specificity in target imaging. Given the rapid development of the field, this review has assembled related articles to explore whether molecular contrast agents can improve the diagnostic capability on gastrointestinal imaging, especially for IBD. Methods. Relevant articles published between 1998 and 2018 from a literature search of PubMed and EMBASE were reviewed. Data extraction was performed on the studies’ characteristics, experimental animals, modelling methods, nanoparticles type, magnetic resonance methods, and means of quantitative analysis. Results. A total of 8 studies were identified wherein the subjects were animals, and all studies employed MR equipment. One group utilized a perfluorocarbon solution and the other 7 groups used either magnetic nanoparticles or gadolinium- (Gd-) related nanoparticles for molecular contrast. With ultrasmall superparamagnetic iron oxide (USPIO) particles and Gd-related nanoparticles, signal enhancements were found in the mucosa or with focal lesion of IBD-related model in T1-weighted images (T1WI), whereas superparamagnetic iron oxide (SPIO) particles showed a signal decrease in the intestinal wall of the model in T1WI or T2-weighted images. The signal-to-noise ratio (SNR) was employed to analyze bowel intensity in 3 studies. And the percentage of normalized enhancement was used in 1 study for assessing the severity of inflammation. Conclusion. Molecular MRI with contrast agents can improve the early diagnosis of IBD and quantitate the severity of inflammation in experimental studies.