Characteristics and utility of high-resolution optical coherence microscopy images of endocervical canal lesions.

OBJECTIVES: To investigate optical coherence microscopy (OCM) imaging features and the application value of these high-resolution images for identifying endocervical canal lesions (ECLs), which is a clinical dilemma in cervical cancer screening programs. METHODS: In total, 520 OCM images were obtained by scanning the cervical canal lesions with an ultra-high-resolution OCM system (204 specimens from 73 patients). The OCM morphologic characteristics of ECLs were observed and summarized, and then 3 researchers performed a diagnostic test of OCM images of cervical canal lesions. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, 95% confidence interval of each parameter, and interinvestigator agreement (κ) were calculated. RESULTS: Normal endocervix, cysts, squamous metaplasia, high-grade squamous intraepithelial lesions involving glands, and invasive carcinoma had distinct OCM characteristics, which correlated well with corresponding H&E histologic sections. The accuracy, sensitivity, and specificity of the 3 researchers were 90.6%, 89.3% (95% CI, 86.5%-91.7%) and 91.6% (95% CI, 89.2%-93.5%), respectively. The positive predictive value was 90.1% (95% CI, 87.3%-92.4%), and the negative predictive value was 90.9% (95% CI, 88.5%-92.9%), with almost perfect agreement (κ = 0.874). CONCLUSIONS: The application of the OCM system in cervical canal lesions is feasible and could help improve detection of occult ECLs in cervical cancer screening programs. This study lays the foundation for further research on OCM in cervical canal lesions in vivo, which also has a potential impact on projecting pathologic evaluation beyond what is currently possible, perhaps globally.

Assessment of Efficacy and Accuracy of Cervical Cytology Screening With Artificial Intelligence Assistive System.

The role of artificial intelligence (AI) in pathology offers many exciting new possibilities for improving patient care. This study contributes to this development by identifying the viability of the AICyte assistive system for cervical screening, and investigating the utility of the system in assisting with workflow and diagnostic capability. In this study, a novel scanner was developed using a Ruiqian WSI-2400, trademarked AICyte assistive system, to create an AI-generated gallery of the most diagnostically relevant images, objects of interest (OOI), and provide categorical assessment, according to Bethesda category, for cervical ThinPrep Pap slides. For validation purposes, 2 pathologists reviewed OOIs from 32,451 cases of ThinPrep Paps independently, and their interpretations were correlated with the original ThinPrep interpretations (OTPI). The analysis was focused on the comparison of reporting rates, correlation between cytological results and histologic follow-up findings, and the assessment of independent AICyte screening utility. Pathologists using the AICyte system had a mean reading time of 55.14 seconds for the first 3000 cases trending down to 12.90 seconds in the last 6000 cases. Overall average reading time was 22.23 seconds per case compared with a manual reading time approximation of 180 seconds. Usage of AICyte compared with OTPI had similar sensitivity (97.89% vs 97.89%) and a statistically significant increase in specificity (16.19% vs 6.77%) for the detection of cervical intraepithelial neoplsia 2 and above lesions. When AICyte was run alone at a 50% negative cutoff value, it was able to read slides with a sensitivity of 99.30% and a specificity of 9.87%. When AICyte was run independently at this cutoff value, no sole case of high-grade squamous intraepithelial lesions/squamous cell carcinoma squamous lesion was missed. AICyte can provide a potential tool to help pathologists in both diagnostic capability and efficiency, which remained reliable compared with the baseline standard. Also unique for AICyte is the development of a negative cutoff value for which AICyte can categorize cases as "not needed for review" to triage cases and lower pathologist workload. This is the largest case number study that pathologists reviewed OOI with an AI-assistive system. The study demonstrates that AI-assistive system can be broadly applied for cervical cancer screening.

Comprehensive overview of genotype distribution and prevalence of human papillomavirus in cervical lesions.

Across cervical squamous and glandular lesions, a spectrum of human papillomavirus (HPV) genotypes has been identified. This review aims to provide a comprehensive summary detailing the distribution and profile of HPV genotypes detected in cervical lesions, leveraging insights from histological and cytological findings. High-risk HPV (HR-HPV) genotypes exhibit varying degrees of oncogenic potential, with HPV16 and HPV18 identified as the most prevalent and oncogenic types. The distribution of HR-HPV genotypes varies among different degrees of the cervical lesions and varies between squamous and glandular neoplasia. HPV16 is predominantly associated with severe lesions (precancers and carcinomas), while HPV18 demonstrates a significantly higher prevalence in endocervical as compared with squamous neoplasia. The distribution of HR-HPV in severe squamous lesions is complex, involving many HR-HPV genotypes in addition to HPV16, while the distribution of HR-HPV genotypes in endocervical glandular lesions is mainly limited in HPV18 and HPV16. Large datasets from China have identified the three most common HR-HPV genotypes in this population as stratified by diagnostic category: HPV52, HPV16, HPV58 in histologically negative cases and cervical intraepithelial neoplasia 1 (CIN1); HPV16, HPV52, HPV58 in CIN2/3; HPV16, HPV58, HPV52 or HPV18 in squamous cell carcinoma (SCC); HPV16, HPV18 and HPV52 in endocervical adenocarcinoma in situ (AIS), invasive adenocarcinoma, as well as mixed squamous and glandular lesions. HPV33 is the fourth most common HPV type in CIN2/3 and SCC, while HPV45 occurs more commonly in AIS and adenocarcinoma, compared with squamous lesions. The prevalence and distribution of multiple HR-HPV coinfections vary across different cervical diseases. The clinical significance and pathogenesis of these multiple HR-HPV infections remain uncertain, although recent two large studies demonstrate that multiple HR-HPV infections are not associated with cumulatively higher risk of high-grade cervical squamous lesion development, suggesting competitive and/or cooperative interactions among HPV genotypes. Extensive HPV genotyping aids in risk assessment and optimising clinical approaches for women with mild abnormalities in Pap cytology. Women with atypical squamous cells of undetermined significance (ASC-US) and low-grade squamous intraepithelial lesion (LSIL) Pap test results and with the infection of some HR-HPV genotypes carry a very low risk of high-grade cervical lesions. HPV genotyping can allow for risk stratification and triage optimisation for these HR-HPV-positive women. Women with atypical glandular cell (AGC) Pap test results showed a specific HPV genotyping pattern and extended HPV genotyping may be helpful for the clinical management of AGCs. Continual advancements in clinical guidelines integrating extended genotyping would increase diagnostic accuracy and refine strategies in clinical management.

Associations of Single Versus Multiple Human Papillomavirus Infections With the Prevalence of Cervical Intraepithelial Neoplasia 2/3 and Squamous Cell Carcinoma Lesions: Human Papillomavirus Type-Specific Attribution.

The risk of developing cervical squamous lesions in women with multiple high-risk human papillomavirus (hrHPV) infections is uncertain. The aim of this retrospective study was to investigate the type-specific attribution and phylogenetic effects of single and multiple hrHPV subtypes in cervical squamous lesions. All cases with cervical histopathologic diagnosis and human papillomavirus (HPV) genotyping results in the 6 months preceding biopsy from October 2018 to December 2022 were studied and analyzed. Over the study period, 70,361 cases with histopathologic follow-up and prior HPV genotyping were identified. The hrHPV-positive rate was 55.6% (39,104/70,361), including single hrHPV detected in 27,182 (38.6%), 2 types of hrHPV detected in 8158 (11.6%), and 3 types of hrHPV detected in 2486 (3.5%). Among 16,457 cases with a histologically diagnosed squamous lesion (cervical intraepithelial neoplasia 1: 11411; cervical intraepithelial neoplasia 2/3: 4192; squamous cell carcinoma: 854 cases), the prevalence of single hrHPV infection increased, but the rate of multiple concomitant hrHPV infections showed negative association as the degree of squamous lesions increased. Among women with a single HPV16 infection, cervical intraepithelial neoplasia 2/3 and squamous cell carcinoma (CIN2+) diagnostic rate was 30.6%, and it increased to 47.6% when coinfected with HPV33 (P < .001) but significantly decreased when coinfected with all other hrHPV types (P < .05). By comparing CIN2+ diagnostic rates in 40 most common 2 types of hrHPV infections with related single hrHPV infection, CIN2+ rates were decreased in 12 combinations (30.0%), equivalent in 26 combinations (65.0%), and increased in 2 combinations (5.0%). The cases with 3 types of HPV infections reduced the risk for CIN2+ compared with related single HPV infections. HPV16+52+53, HPV16+52+68, HPV16+52+51, HPV16+39+52, and HPV16+58+53 significantly decreased the risk of CIN2+ compared with HPV16 single infection (P < .05). This study demonstrates that multiple hrHPV infections are not associated with cumulatively higher risk for CIN2+ development, suggesting that oncogenic progression of multiple hrHPV-associated cervical squamous lesions is neither synergistic nor a cumulative effect at the phylogenetic level, possibly a way of competitive interference.

A real-time computer-aided diagnosis method for hydatidiform mole recognition using deep neural network.

BACKGROUND AND OBJECTIVE: Hydatidiform mole (HM) is one of the most common gestational trophoblastic diseases with malignant potential. Histopathological examination is the primary method for diagnosing HM. However, due to the obscure and confusing pathology features of HM, significant observer variability exists among pathologists, leading to over- and misdiagnosis in clinical practice. Efficient feature extraction can significantly improve the accuracy and speed of the diagnostic process. Deep neural network (DNN) has been proven to have excellent feature extraction and segmentation capabilities, which is widely used in clinical practice for many other diseases. We constructed a deep learning-based CAD method to recognize HM hydrops lesions under the microscopic view in real-time. METHODS: To solve the challenge of lesion segmentation due to difficulties in extracting effective features from HM slide images, we proposed a hydrops lesion recognition module that employs DeepLabv3+ with our novel compound loss function and a stepwise training strategy to achieve great performance in recognizing hydrops lesions at both pixel and lesion level. Meanwhile, a Fourier transform-based image mosaic module and an edge extension module for image sequences were developed to make the recognition model more applicable to the case of moving slides in clinical practice. Such an approach also addresses the situation where the model has poor results for image edge recognition. RESULTS: We evaluated our method using widely adopted DNNs on an HM dataset and chose DeepLabv3+ with our compound loss function as the segmentation model. The comparison experiments show that the edge extension module is able to improve the model performance by at most 3.4% regarding pixel-level IoU and 9.0% regarding lesion-level IoU. As for the final result, our method is able to achieve a pixel-level IoU of 77.0%, a precision of 86.0%, and a lesion-level recall of 86.2% while having a response time of 82 ms per frame. Experiments show that our method is able to display the full microscopic view with accurately labeled HM hydrops lesions following the movement of slides in real-time. CONCLUSIONS: To the best of our knowledge, this is the first method to utilize deep neural networks in HM lesion recognition. This method provides a robust and accurate solution with powerful feature extraction and segmentation capabilities for auxiliary diagnosis of HM.

Nomograms for predicting overall survival and cancer-specific survival in patients with invasive micropapillary carcinoma: Based on the SEER database.

BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer that lacks a prognostic prediction model. Its treatment and prognostic factors remain controversial. Our study aimed to develop nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in IMPC patients. METHODS: A total of 2149 patients confirmed to have IMPC between 2003 and 2018 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were used to identify significant independent prognostic factors. The nomograms were used to predict 3- and 5-year OS and CSS. The training and validation cohorts were used to verify the nomograms internally and externally. The predictive capability of the nomograms was evaluated by the consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve. RESULTS: In the study, 2149 IMPC patients were randomized to a training group (n = 1611) and a validation group (n = 538). Age, T stage, N stage, ER, radiotherapy, and surgery were identified as independent prognostic factors for OS and CSS. These variables were selected to construct nomograms for IMPC. The C-index (0.768 for OS and 0.811 for CSS) and the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomograms. Additionally, DCA showed that the nomograms had higher clinical value than traditional TNM tumor staging. CONCLUSIONS: The models can accurately predict the prognosis of IMPC patients and can aid in providing individualized treatment for patients.

Study on the application of optical coherence microscopy in Hirschsprung's disease.

To explore the clinical application value of optical coherence microscopy (OCM) in Hirschsprung's disease. 109 HSCR patients were recuited in a Chinese hospital from January 2018 to July 2021. All the recruited patients underwent barium enema angiography preoperatively and the resected diseased intestinal tubes were evaluated intraoperatively. The OCM and the histopathological examination were performed successively on the surgical specimens, and the OCM images were compared with the relevant tissue sections to characterize different lesions. 10 non-HSCR fetal colorectal tissues at the same period were retained for OCM, the characteristics of which with and without HSCR under OCM imaging were analyzed. In the OCM images of in vitro tissue, it can be clearly observed that the scattering degree of HSCR narrow segment mucosal is high, glands and crypt structures are reduced or even atrophy, and the scattering degree of submucosal and intermuscular is low; In the dilated segment, the low scattering and high scattering are complex, and the muscle layer is obviously hypertrophy and structural disorder. Compared with the pathological findings, the OCM sensitivity, Kappa value, and AUC area reached 92.66%, 0.63, and 0.91, respectively. OCM can quickly and clearly display the structure of all layers of colorectal tissue, which is highly consistent with the corresponding histopathological examination results and has high sensitivity. which will provide a more reliable basis for OCM diagnosis of early HSCR, targeted biopsy and location of operative treatment, and has a certain potential for clinical application.

Survival nomogram for endometrial cancer with lung metastasis: A SEER database analysis.

Purpose: The lung is the most common distant metastatic organ in patients with endometrial cancer (EC) but is rarely reported. This study examines the association between clinical characteristics and overall survival (OS) in EC with lung metastasis. Methods: Patients with EC who had accompanying lung metastasis were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Univariate and multivariate Cox regression were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assess OS outcomes related to EC with lung metastasis. A Cox proportional hazards nomogram model for OS was constructed and validated. The calibration plot, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the discriminative ability and clinical benefit of the novel nomogram. Kaplan-Meier curves and scatter diagram analysis were used to investigate the risk stratifications of the nomogram. Results: Overall, 1542 EC patients with lung metastasis between 2010 and 2017 were included and randomly divided into training and validation cohorts. A nomogram model was constructed using the clinical characteristics of tumor grade, histological type, surgery, adjuvant chemotherapy, adjuvant radiation, brain metastasis and liver metastasis. The concordance indexes (C-indexes) were 0.750 (95% CI, 0.732-0.767) and 0.743 (95% CI, 0.719-0.767) for the training cohort and validation cohort, respectively. Calibration plots and DCA showed good clinical applicability of the nomogram. The areas under the curves (AUCs) were 0.803 and 0.766 for 1-year and 3-year OS, respectively, indicating that the nomogram model had a stable discriminative ability. An online calculator of our nomogram is available on the internet at https://endometrialcancer.shinyapps.io/DynNomapp/. Additionally, patients in the high-risk group had a significantly worse OS than those in the low-risk group. Conclusion: An easy-to-use, highly accurate nomogram was developed for predicting the prognosis of EC patients with lung metastasis.

Multi-center clinical study using optical coherence tomography for evaluation of cervical lesions in-vivo.

In this prospective study of an in-vivo cervical examination using optical coherence tomography (OCT), we evaluated the diagnostic value of non-invasive and real-time OCT in cervical precancerous lesions and cancer diagnosis, and determined the characteristics of OCT images. 733 patients from 5 Chinese hospitals were inspected with OCT and colposcopy-directed biopsy. The OCT images were compared with the histological sections to find out the characteristics of various categories of lesions. The OCT images were also interpreted by 3 investigators to make a 2-class classification, and the results were compared against the pathological results. Various structures of the cervical tissue were clearly observed in OCT images, which matched well with the corresponding histological sections. The OCT diagnosis results delivered a sensitivity of 87.0% (95% confidence interval, CI 82.2-90.7%), a specificity of 84.1% (95% CI 80.3-87.2%), and an overall accuracy of 85.1%. Both good consistency of OCT images and histological images and satisfactory diagnosis results were provided by OCT. Due to its features of non-invasion, real-time, and accuracy, OCT is valuable for the in-vivo evaluation of cervical lesions and has the potential to be one of the routine cervical diagnosis methods.

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms.

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3,300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76.91 ± 1.17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i.e., normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84.50% accuracy in the four-class classification task, as well as an AUC of 0.9829 with a 77.97% (95% confidence interval, CI, 65.27%∼87.71%) sensitivity and 100% (95% CI, 97.42%∼100.00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.

Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue.

OBJECTIVE: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. METHODS: 497 high-resolution three-dimensional (3-D) OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information [e.g., age and human papillomavirus (HPV) results], and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. RESULTS: An 88.3 ± 4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task [low-risk (normal, ectropion, and LSIL) versus high-risk (HSIL and cancer)], the CADx method achieved an area-under-the-curve value of 0.959 with an 86.7 ± 11.4% sensitivity and 93.5 ± 3.8% specificity. CONCLUSION: The proposed deep-learning-based CADx method outperformed four human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. SIGNIFICANCE: Label-free OCM imaging, combined with deep-learning-based CADx methods, holds a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.

Effects of let-7c on the proliferation of ovarian carcinoma cells by targeted regulation of CDC25a gene expression.

MicroRNAs serve a role in the development of ovarian cancer (OC). The present study investigated whether let-7c is able to regulate the proliferation of OC cells by targeting cell division cycle 25A (CDC25a). The reverse transcription-quantitative polymerase chain reaction was performed to detect the expression of let-7c in OC specimens. Let-7c agomir was transfected into OC cells, and the proliferation and apoptosis of OC cells were detected. A dual-luciferase assay and western blotting were performed to analyze whether CDC25a was the target gene of let-7c as well as its interaction site. The results revealed that, in OC tissue, let-7c was downregulated when compared with normal ovarian tissue. A Cell Counting Kit-8 (CCK8) assay, colony formation assay and flow cytometry demonstrated that increased expression of let-7c was able to inhibit the proliferation and increase the apoptosis of OC cells. Western blotting revealed that upregulated let-7c is able to decrease the expression of CDC25a, and a dual-luciferase assay and a recovery assay demonstrated that let-7c was able to regulate the expression of the 3' untranslated region of CDC25a. Therefore, the roles of let-7c in inhibiting the proliferation and promoting the apoptosis of OC cells may be realized through the regulation of the expression of CDC25a. The results of the present study revealed that let-7c may be a novel target in the diagnosis and treatment of OC.

Ultrahigh-resolution optical coherence microscopy accurately classifies precancerous and cancerous human cervix free of labeling.

Cervical cancer remains the fourth most common cause of cancer worldwide and the third leading cause of cancer deaths for women in developing countries. Traditional screening tools, such as human papillomavirus and Pap tests, cannot provide results in real-time and cannot localize suspicious regions. Colposcopy-directed biopsies are invasive in nature and only a few sites of the cervix may be chosen for investigation. A non-invasive, label-free and real-time imaging method with a resolution approaching that of histopathology is desirable for early detection of the disease. Methods: Ultrahigh-resolution optical coherence microscopy (OCM) is an emerging imaging technique used to obtain 3-dimensional (3-D) "optical biopsies" of biological samples with cellular resolution. In this study, 497 3-D OCM datasets from 159 specimens were collected from 92 patients. Results: Distinctive patterns for normal cervix, squamocolumnar junction, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL) and invasive cervical lesions were clearly observed from OCM images, which matched well with corresponding histological slides. OCM images demonstrated a sensitivity of 80% (95% confidence interval, CI, 72%-86%) and a specificity of 89% (95% CI, 84%-93%) for detecting high-risk lesions (HSIL and invasive lesions) when blindly tested by three investigators. A substantial inter-observer agreement was observed (κ=0.627), which showed high diagnostic consistency among three investigators. Conclusion: These results laid the foundation for future non-invasive optical evaluation of cervical tissue in vivo, which could lead to a less invasive and more effective screening and "see-and-treat" strategy for the management of cervical cancer.

Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.

This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10 × 10 mm2 (10, 000 × 10, 000 pixels) for each sample. Corresponding H&E histology was obtained for each sample and used to provide ground truth diagnosis. 4310 small OCM image blocks (500 × 500 pixels) each paired with corresponding H&E histology was extracted from large-field OCM images and labeled with one of the five different classes: adipose tissue (n = 347), fibrous stroma (n = 2,065), breast lobules (n = 199), carcinomas (pooled from all sub-types, n = 1,127), and background (regions outside of the specimens, n = 572). Our experiments show that by integrating a selected set of LBP and the two new variant (ALBP and BLBP) features at multiple scales, the classification accuracy increased from 81.7% (using LBP features alone) to 93.8% using a neural network classifier. The integrated feature was also used to classify large-field OCM images for tumor detection. A receiver operating characteristic (ROC) curve was obtained with an area under the curve value of 0.959. A sensitivity level of 100% and specificity level of 85.2% was achieved to differentiate benign from malignant samples. Several other experiments also demonstrate the complementary nature of LBP and the two variants (ALBP and BLBP features) and the significance of integrating these texture features for classification. Using features from multiple scales and performing feature selection are also effective mechanisms to improve accuracy while maintaining computational efficiency.

Optical Coherence Tomography for Brain Imaging and Developmental Biology.

Optical coherence tomography (OCT) is a promising research tool for brain imaging and developmental biology. Serving as a three-dimensional optical biopsy technique, OCT provides volumetric reconstruction of brain tissues and embryonic structures with micrometer resolution and video rate imaging speed. Functional OCT enables label-free monitoring of hemodynamic and metabolic changes in the brain in vitro and in vivo in animal models. Due to its non-invasiveness nature, OCT enables longitudinal imaging of developing specimens in vivo without potential damage from surgical operation, tissue fixation and processing, and staining with exogenous contrast agents. In this paper, various OCT applications in brain imaging and developmental biology are reviewed, with a particular focus on imaging heart development. In addition, we report findings on the effects of a circadian gene (Clock) and high-fat-diet on heart development in Drosophila melanogaster. These findings contribute to our understanding of the fundamental mechanisms connecting circadian genes and obesity to heart development and cardiac diseases.

Association of FTO Mutations with Risk and Survival of Breast Cancer in a Chinese Population.

Recently, several studies have reported associations between fat mass and obesity-associated (FTO) gene mutations and cancer susceptibility. But little is known about their association with risk and survival of breast cancer in Chinese population. The aim of this study is to examine whether cancer-related FTO polymorphisms are associated with risk and survival of breast cancer and BMI levels in controls in a Chinese population. We genotyped six FTO polymorphisms in a case-control study, including 537 breast cancer cases and 537 controls. FTO rs1477196 AA genotype had significant decreased breast cancer risk [odds ratio (OR) = 0.54, 95% confidence interval (CI): 0.34-0.86] compared to GG genotype, and this association was only found in women with BMI < 24 kg/m(2) (OR = 0.41, 95% CI: 0.22-0.76); and rs16953002 AA genotype conferred significant increased breast cancer risk (OR = 1.80, 95% CI: 1.23-2.63) compared to GG genotype. Haplotype analysis showed that FTO TAC haplotype (rs9939609-rs1477196-rs1121980) had significant reduced breast cancer risk (OR = 0.76, 95% CI: 0.62-0.93) compared with TGC haplotype. But we failed to find any association between FTO polymorphisms and breast cancer survival. These findings suggest that variants in FTO gene may influence breast cancer susceptibility.

AP-2α suppresses invasion in BeWo cells by repression of matrix metalloproteinase-2 and -9 and up-regulation of E-cadherin.

Preeclampsia complicates 5-10% of pregnancies and is a leading cause of maternal/fetal morbidity and mortality. Although the cause is unknown, the reduced migration/invasion of extravillous trophoblasts is generally regarded as a key feature of preeclampsia genesis. The present study examined the expression of activator protein-2α (AP-2α), tissue inhibitor of metalloproteinase 2 (TIMP-2), matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), and E-cadherin in severe preeclamptic placentas and normal placentas using real-time PCR and immunohistochemistry. The expression levels of AP-2α, TIMP-2, and E-cadherin were elevated, while MMP-2 and MMP-9 levels were decreased in severe preeclamptic placentas when compared with normal placentas. To explore the underlying molecular mechanisms, BeWo cells were transfected with an AP-2α-expression construct as well as a siRNA against AP-2α. The over-expression of AP-2α decreased the invasive abilities of BeWo cells. AP-2α induction was followed by the induction of TIMP-2 and E-cadherin and a significant reduction of MMP-2 and MMP-9. Whereas in AP-2α-silencing BeWo cells, we observed the decreased expression of TIMP-2 and E-cadherin and the increased expression of MMP-2 and MMP-9. We presume that AP-2α may suppress trophoblast invasion by repression of MMP-2 and MMP-9 and up-regulation of E-cadherin, thus leading to shallow placentation in severe preeclampsia.

[Correlation of p38 mitogen-activated protein kinase signal transduction pathway to uPA expression in breast cancer].

BACKGROUND & OBJECTIVE: p38 mitogen-activated protein kinase (p38MAPK) signal transduction pathway regulates the expression of various metastasis-related genes. Urokinase-type plasminogen activator (uPA) plays an important role in cancer invasion and metastasis. This study was to explore the correlation of p38MAPK signal transduction pathway to uPA expression in breast cancer. METHODS: SP immunohistochemistry was used to test the expression of p-p38, p-Akt, p-Erk, and uPA in 60 specimens of breast cancer. Western blot was used to detect the protein levels of p-p38 and uPA in breast cancer MDA-MB-231 and MCF-7 cells and the protein level of uPA in MDA-MB-231 cells after blocking p38MAPK signal transduction pathway by SB203580, a specific inhibitor of p38MAPK. The correlations of p-p38, p-Akt, p-Erk, and uPA expression to the clinicopathologic characteristics of breast cancer, and the correlations of p-p38, p-Akt, and p-Erk expression to uPA expression were analyzed. The mechanism of p38MAPK signal transduction pathway regulating the protein expression of uPA in breast cancer cells was investigated. The correlation of p-p38 and uPA expression to prognosis of breast cancer was analyzed. RESULTS: The positive rates of p-p38, p-Akt, p-Erk, and uPA proteins in breast cancer tissues were 56.7%, 95.0%, 93.3%, and 60.0%, respectively. The expression of p-p38 was positively correlated to the expression of uPA (r=0.316, P<0.05), while the expression of p-Akt and p-Erk was not related to uPA expression. The expression of p-p38 and uPA was correlated to lymph node metastasis and TNM stage (P<0.05), but not to patients' age and tumor size (P>0.05). The expression of p-Akt and p-Erk were correlated to lymph node metastasis (P<0.05), but not to TNM stage, patients' age, and tumor size (P>0.05). The protein levels of p-p38 and uPA in MDA-MB-231 cells were higher than that in MCF-7 cells. SB203580 concentration-dependently inhibited p38MAPK pathway and induced uPA protein expression. The expression of p-p38 and uPA was negatively correlated to prognosis of breast cancer (log-rank=4.98, 5.40, P=0.026, 0.020). CONCLUSIONS: p38MAPK signal transduction pathway might improve breast cancer progression by up-regulating uPA expression, and might be an important route in invasion and metastasis of breast cancer. p-p38 and uPA might help to evaluate prognosis of breast cancer.