Identification of Escherichia coli strains using MALDI-TOF MS combined with long short-term memory neural networks.

The current study aims to develop a new technique for the precise identification of Escherichia coli strains, utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with a long short-term memory (LSTM) neural network. A total of 48 Escherichia coli strains were isolated and cultured on tryptic soy agar medium for 24 hours for the generation of MALDI-TOF MS spectra. Eight hundred MALDI-TOF MS spectra were obtained per strain, resulting in a database of 38,400 spectra. Fifty percent of the data was utilized for LSTM neural network training, with fine-tuned parameters for strain-level identification. The other half served as the test set to assess model performance. Traditional PCA dimension reduction of MALDI-TOF MS spectra indicated 47 out of 48 strains to be unclassifiable. In contrast, the LSTM neural network demonstrated remarkable efficacy. After 20 training epochs, the model achieved a loss value of 0.0524, an accuracy of 0.999, a precision of 0.985, and a recall of 0.982. When tested on the unseen data, the model attained an overall accuracy of 92.24%. The integration of MALDI-TOF MS and LSTM neural network markedly enhances the identification of Escherichia coli strains. This innovative approach offers an effective and accurate tool for MALDI-TOF MS-based strain-level identification, thus expanding the analytical capabilities of microbial diagnostics.

Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis.

BACKGROUND: Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. OBJECTIVE: In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. METHODS: We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. RESULTS: Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. CONCLUSION: The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.

Interval from transurethral resection of prostate to laparoscopic radical prostatectomy does not affect outcomes for incidental prostate cancer.

Introduction: Laparoscopic radical prostatectomy (LRP) has become a common option for the treatment of prostate cancer. The aim of our study was to examine whether LRP performed within 12 weeks of transurethral resection of the prostate (TURP) is associated with surgical difficulty or outcomes. Material and methods: A single-institutional retrospective analysis was performed on patients who underwent LRP for incidental prostate cancer after TURP between July 2009 and December 2017. The interval between TURP and LRP was determined and patients with intervals of ≤ 12 weeks were compared to those with intervals of > 12 weeks. Patient characteristics, perioperative, pathological, and postoperative functional outcomes were analyzed to determine statistically significant differences between the 2 groups. Multivariable analyses were performed to determine whether the interval between TURP and LRP was a significant independent predictor of these outcomes. Results: A total of 56 incidental prostate cancer patients detected by TURP were included in this study. No significant differences were detected in estimated blood loss, operative duration, postoperative length of stay, and rate of positive margin, Gleason score upgrading, major complications, incontinence and prostate-specific antigen (PSA) recurrence in patients with a TURP to LRP interval above and below 12 weeks. The TURP to LRP interval was not an independent predictor of outcomes during or after LRP. Conclusions: Our results showed that performing LRP within 12 weeks after TURP does not adversely influence surgical difficulty or outcomes.

A novel approach for transforming breast cancer stem cells into endothelial cells.

Tumor vascular endothelial cells play a pivotal in the tumor microenvironment, influencing the proliferation, invasion, and metastasis of tumor progression. The present study investigated a novel method for inducing the transformation of breast cancer stem cells into endothelial cells, providing a cellular model investigating anti-angiogenic mechanisms in vitro. The breast cancer cell line MCF-7 was used, and the expression of CD133 was initially detected using flow cytometry. CD133+ breast cancer cells were purified using immunomagnetic bead sorting technology, yielding an MCF-7CD133+ subpopulation. The proliferation ability of these cells was assessed using an MTT assay, while their microsphere formation ability was evaluated using a microsphere formation assay. Post-transformation in an optimized endothelial cell culture medium, expression of endothelial cell markers CD31 and CD105 were detected using flow cytometry. Endothelial cell tube formation assays and DiI-labeled acetylated low-density lipoprotein (DiI-Ac-LDL) assays were employed to analyze the endothelial cell function of the MCF-7CD133+ cells. MDM2/CEN12 gene amplification was detected through fluorescence in situ hybridization (FISH). The MCF-7 breast cancer cell line exhibited 1.7±0.3% trace cells expressing the stem cell surface marker CD133. After anti-CD133 immunomagnetic bead sorting, MCF-7CD133+ and MCF-7CD133- subpopulation cells were obtained, with CD133 expression rates of 85.6±2.8 and 0.18±0.08%, respectively. MTT assay results demonstrated that, after 7 days, the proliferation rate of MCF-7CD133+ cells was significantly higher compared with MCF-7CD133- cells. MCF-7CD133+ subpopulation cells displayed strong stem cell characteristics, growing in suspension in serum-free media and forming tumor cell spheres. In contrast, MCF-7CD133- cells failed to form microspheres. After culturing cells in endothelial cell differentiation and maintenance media, the percentage of MCF-7CD133+ cells before and after endothelial cell culture was 0.3±0.16 and 81.4±8.37% for CD31+ cells and 0.2±0.08 and 83.8±7.24% for CD105+ cells, respectively. Vascular-like structure formation and Ac-LDL phagocytosis with red fluorescence in the tube formation assays confirmed endothelial cell function in the MCF-7CD133+ cells. FISH was used to verify MDM2/CEN12 gene amplification in the induced MCF-7CD133+ cells, indicating tumor cell characteristics. The modified endothelial cell transformation medium effectively induced differentiated tumor stem cells to express vascular endothelial cell markers and exhibit endothelial functions, ideal for in vitro anti-angiogenesis research.