Microscopy Research and Technique

Synthesis and Characterization of Thymol Carbon Nanodot Functionalized Silver Nanoparticles (ThCND‐AgNPs) and Evaluation of Their Antiproliferative, Anti‐Invasive, and Apoptotic Effects on OVCAR‐3 Ovarian Cancer Cells

ABSTRACTOvarian cancer belongs to the category of gynecological malignancies and unfortunately holds the distinction of being the most aggressive among them. It is ranked as the fifth highest cause of cancer‐related deaths in women worldwide. The utilization of metal nanoparticles (NPs) linked with natural herbal molecules in biomedical applications has been on the rise. Thymol carbon nanodot functionalized silver nanoparticles (ThCND‐AgNPs) were synthesized in an original manner and subjected to thorough characterization, including analysis of their size, morphology, and elemental composition. The aim of this study is to investigate the effects of the ThCND‐AgNPs on cell proliferation, invasion, and apoptotic gene expressions in OVCAR‐3 ovarian cancer cells. The effect of ThCND‐AgNPs on cell viability in OVCAR cells was determined in a dose‐ and time‐dependent manner using the XTT method. The effect on the expression changes of apoptotic‐related genes was assessed through the Real‐time PCR method, while the anti‐invasive activity was measured using the matrigel invasion chamber assay. The ThCND‐AgNP molecule exhibited a dose‐ and time‐dependent reduction in cell proliferation in OVCAR‐3 cells. The IC50 values were determined to be 388.53 μg/mL at 24 h and 145.683 μg/mL at 48 h. Furthermore, the molecule was found to reduce cell invasion by 51.12% compared with the control group in OVCAR‐3 cells. In terms of apoptotic‐related genes, Bcl‐2 expression was downregulated, while BAX, CASPASE‐3, ‐8, and ‐9 expressions were unregulated. In conclusion, the obtained data reveal the potential antiproliferative, apoptotic, and anti‐invasive effects of our original ThCND‐AgNP molecule in ovarian cancer. While these results need further confirmation through more detailed experiments, they will provide insights for future studies.

PET‐CT versus MRI in the diagnosis of lymph node metastasis of cervical cancer: A meta‐analysis

AbstractTo compare the clinical application value of positron emission tomography–computed tomography (PET‐CT) and magnetic resonance imaging (MRI) in the diagnosis of cervical cancer lymph node metastasis. We searched PubMed and other databases for the studies comparing the use of PET‐CT and MRI for the diagnosis of cervical cancer lymph node metastasis up to January 20, 2021. We strictly followed the inclusion and exclusion criteria to screen the literature and extract the data. Quality Assessment of Diagnostic Accuracy Studies (QUADAS)‐2 tool was used for quality evaluation of included studies, and Revman 5.3 and Stata 15.0 software were used for evaluating heterogeneity, synthesize sensitivity (SEN), specificity (SPE), positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and the area under the curve (AUC) and comparing the pretest and posttest probabilities. Finally, 11 studies were included for meta‐analysis. The synthesized results indicated that the SEN value of PET‐CT was 0.65 (0.60 ~ 0.69) and SPE was 0.93 (0.91 ~ 0.94), and the SEN value of MRI was 0.58 (0.54 ~ 0.63) and SPE was 0.91 (0.90 ~ 0.92). AUC of PET‐CT was 0.824, which was significantly higher than that of MRI (AUC = 0.702; p < .05). The subgroup analysis showed that the AUC value of the study based on study design and use of blinding methods was not statistically significant (all p > .05). There was no obvious publication bias in the synthesized analysis of the diagnostic value of PET‐CT and MRI (all p > .05).Highlights To compare positron emission tomography–computed tomography (PET‐CT) and magnetic resonance imaging (MRI) in diagnosis of cervical cancer lymph node metastasis. Synthesize sensitivity value of PET‐CT was comparable with that of MRI. Area under the curve of PET‐CT was significantly higher than that of MRI. There was no obvious publication bias in synthesized analysis.

PathoCoder: Rethinking the Flaws of Patch‐Based Learning for Multi‐Class Classification in Computational Pathology

ABSTRACTPathology‐based decision support systems in clinical settings have faced impediments from data preparation beforehand, large‐scale manual annotations, and poor domain generalization. We report a unified hybrid framework with only raw, slide‐level label images. The method, which we termed PathoCoder, comprises core feature extractors, a feature combiner/reduction, and a supervised classifier. It is trained (through 5‐fold cross‐validation) on 2452 SurePath cervical liquid‐based whole‐slide captures, provided from Mendeley repository. Tests resulted in 98.37%, 98.37%, 98.41%, and 98.37% in accuracy, precision, recall, and F1, respectively. Extensive experiments validate the proposed scheme and versatility enough to accommodate epithelial ovarian tumor histotypes. Our method paves the way for more accelerated advancements in pathology AI by reducing patch/pixel‐based annotation and good tissue quality dependency. Its applicability spans diverse classification tasks with varying tissue content and holds potential for real‐world implementation.

Transfer Learning With Adam Gold Rush Optimization for Endometrial Disease Classification Using Histopathological Image

ABSTRACTUterine cancer also referred to as endometrial cancer, which significantly impacts the female reproductive organs. The early diagnosis increases the survival rates and also prevents the progression of endometrial cancer thereby, the novel Transfer Learning based Convolution Neural Network with Adam Gold Rush Optimization (TL‐CNN_AdGRO) is proposed to classify endometrial cancer using histopathological images. The histopathological image is fed to the preprocessing phase, which uses an Adaptive Weighted Mean Filter (AWMF). Next, the segmentation of endometrial cancer is utilized by the Directional Connectivity Network (DConn‐Net). Following segmentation, feature mining is carried out, which includes Local Boundary Summation Pattern (LBSP) and Local Gaber Binary Pattern Histogram Sequence Features (LGBPHS). Finally, the endometrial cancer classification is achieved using TL‐CNN by employing hyperparameters from the Xception model. Here TL‐CNN is trained by AdGRO algorithm, which is the combination of Adam Optimizer and Gold Rush Optimization. Compared to existing models, the proposed model achieves superior performance with an accuracy of 91.876%, a True Positive Rate (TPR) of 93.987%, and a True Negative Rate (TNR) of 89.876% for K‐sample 8. The results confirm the effectiveness of TL‐CNN_AdGRO, also it demonstrates strong performance, ensures robustness, improves the early detection of endometrial cancer, and making it a promising approach for histopathological image analysis.

Optimized Transfer Learning With Hybrid Feature Extraction for Uterine Tissue Classification Using Histopathological Images

ABSTRACTEndometrial cancer, termed uterine cancer, seriously affects female reproductive organs, and the analysis of histopathological images formed a golden standard for diagnosing this cancer. Sometimes, early detection of this disease is difficult because of the limited capability of modeling complicated relationships among histopathological images and their interpretations. Moreover, many previous methods do not effectively handle the cell appearance variations. Hence, this study develops a novel classification technique called transfer learning convolution neural network with artificial bald eagle optimization (TL‐CNN with ABEO) for the classification of uterine tissue. Here, preprocessing is done by the median filter, followed by image enhancement by the multiple identities representation network (MIRNet). Moreover, pelican crow search optimization (PCSO) is used for adapting weights in MIRNet, where PCSO is generated by combining the crow search algorithm (CSA) and pelican optimization algorithm (POA). Then, segmentation quality assessment (SQA) helps in tissue segmentation, and deep convolutional neural network (DCNN) helps in parameter selection that is trained by fractional PCSO (FPCSO). Furthermore, feature extraction is done and, finally, cell classification is done by TL with CNN, which is trained by the proposed ABEO algorithm. Here, ABEO is newly developed by the integration of the bald eagle search (BES) algorithm and artificial hummingbird algorithm (AHA). Furthermore, ABEO + TL‐CNN achieved a high accuracy of 89.59%, a sensitivity of 90.25%, and a specificity of 89.89% by utilizing the cancer image archive dataset.

Computerized morphometry analysis of epithelial fimbriae nuclear symmetry in BRCA carriers may identify patients at risk for developing ovarian cancer

AbstractSerous ovarian tumors may originate in epithelial cells of the fallopian tubes. Computerized morphometry was able to find significant alterations in the fallopian tube epithelium of healthy BRCA carriers. The purpose of this study was to identify a subgroup of BRCA carriers that may be at risk of developing serous ovarian cancer by evaluation of the epithelial nuclear symmetry in the fallopian tubes. Four groups of patients were analyzed; healthy patients, ovarian cancer patients, BRCA carriers, and BRCA noncarriers. All fallopian tubes appeared normal by H&E examination. The ImageProPlus software was used to assess the nuclear symmetry of 65 fimbriae epithelium cells and an artificial neural network algorithm aided in detecting a subpopulation among fimbriae of healthy BRCA carriers at risk for ovarian cancer. Significant differences were found between healthy patients and ovarian cancer patients, and between BRCA carriers and noncarriers. The algorithm was able to accurately predict BRCA carriers with associated ovarian cancer based on fallopian tube nuclear symmetry characteristics. These results reinforce the hypothesis that fimbriae epithelial cells of BRCA carriers may undergo early‐stage changes that could predict the risk of progression toward malignancy.