Rapid, high-resolution, non-destructive assessments of metabolic and morphological homogeneity uniquely identify high-grade cervical precancerous lesions.

Purpose: Two-photon microscopy (2PM) is an emerging clinical imaging modality with the potential to non-invasively assess tissue metabolism and morphology in high-resolution. This study aimed to assess the translational potential of 2PM for improved detection of high-grade cervical precancerous lesions. Experimental Design: 2P images attributed to reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and oxidized flavoproteins (FP) were acquired from the full epithelial thickness of freshly excised human cervical tissue biopsies (N = 62). Fifteen biopsies harbored high-grade squamous intraepithelial lesions (HSILs), 14 biopsies harbored low-grade SILs (LSILs), and 33 biopsies were benign. Quadratic discriminant analysis (QDA) leveraged morphological and metabolic functional metrics extracted from these images to predict the presence of HSILs. We performed gene set enrichment analysis (GSEA) using datasets available on the Gene Expression Omnibus (GEO) to validate the presence of metabolic reprogramming in HSILs. Results: Integrating metabolic and morphological 2P-derived metrics from finely sampled, full-thickness epithelia achieved a high 90.8 ± 6.1% sensitivity and 72.3 ± 11.3% specificity of HSIL detection. Notably, sensitivity (91.4 ± 12.0%) and specificity (77.5 ± 12.6%) were maintained when utilizing metrics from only two images at 12- and 72-µm from the tissue surface. Upregulation of glycolysis, fatty acid metabolism, and oxidative phosphorylation in HSIL tissues validated the metabolic reprogramming captured by 2P biomarkers. Conclusion: Label-free 2P images from as few as two epithelial depths enable rapid and robust HSIL detection through the quantitative characterization of metabolic and morphological reprogramming, underscoring the potential of this tool for clinical evaluation of cervical precancers.

Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms.

Significance: Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information. Aim: We aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images. Approach: TPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images. Results: Optimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics. Conclusions: Denoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms.

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.

Long Curriculum 3.0 in Resident Education: Comprehensive Curriculum in Pediatric and Adolescent Gynecology for Postgraduate Trainees in Obstetrics/Gynecology, Pediatrics, and Adolescent Medicine.

In 2015, the Resident Education Committee of the North American Society for Pediatric and Adolescent Gynecology published the Long Curriculum in Resident Education to provide educators with a comprehensive document to be used in postgraduate medical education. The original curriculum was designed to meet the resident learning objectives for the Council on Resident Education in Obstetrics and Gynecology, the American Board of Pediatrics, and the Royal College of Physicians and Surgeons of Canada and to provide a more intensive, broader learning experience. The curriculum was updated in 2018. This Committee Document is the third updated version (3.0) of the Long Curriculum in Resident Education.

Resident Education Curriculum in Pediatric and Adolescent Gynecology: The Short Curriculum 3.0.

Exposure to pediatric and adolescent gynecology (PAG) varies across residency programs in obstetrics and gynecology, family medicine, and pediatrics, as well as fellowship programs in adolescent medicine. Nevertheless, these programs are responsible for training residents and fellows and providing opportunities within their programs to fulfill PAG learning objectives. To that end, the North American Society for Pediatric and Adolescent Gynecology has taken a leadership role in PAG education by creating and systematically updating the Short Curriculum. This curriculum outlines specific learning objectives that are central to PAG education and lists essential resources for learners' reference. This updated curriculum replaces the previous 2018 publication with added content, resources, and updated references.

Label-free, High-Resolution Optical Metabolic Imaging of Human Cervical Precancers Reveals Potential for Intraepithelial Neoplasia Diagnosis.

While metabolic changes are considered a cancer hallmark, their assessment has not been incorporated in the detection of early or precancers, when treatment is most effective. Here, we demonstrate that metabolic changes are detected in freshly excised human cervical precancerous tissues using label-free, non-destructive imaging of the entire epithelium. The images rely on two-photon excited fluorescence from two metabolic co-enzymes, NAD(P)H and FAD, and have micron-level resolution, enabling sensitive assessments of the redox ratio and mitochondrial fragmentation, which yield metrics of metabolic function and heterogeneity. Simultaneous characterization of morphological features, such as the depth-dependent variation of the nuclear:cytoplasmic ratio, is demonstrated. Multi-parametric analysis combining several metabolic metrics with morphological ones enhances significantly the diagnostic accuracy of identifying high-grade squamous intraepithelial lesions. Our results motivate the translation of such functional metabolic imaging to in vivo studies, which may enable improved identification of cervical lesions, and other precancers, at the bedside.

Long Curriculum in Resident Education: Comprehensive Curriculum in Pediatric and Adolescent Gynecology for Postgraduate Trainees in Obstetrics/Gynecology, Pediatrics, and Adolescent Medicine.

In 2015 the Resident Education Committee published the Long Curriculum in Resident Education to provide educators with a comprehensive document to be used in post-graduate medical education. The original curriculum was designed to meet the resident learning objectives for CREOG, RCPSC and ABP and to provide a more intensive, broader learning experience. This Committee Document is an updated version of the 2015 Long Curriculum.

Noninvasive assessment of mitochondrial organization in three-dimensional tissues reveals changes associated with cancer development.

Mitochondrial organization is often altered to accommodate cellular bioenergetic and biosynthetic demands. Changes in metabolism are a hallmark of a number of diseases, including cancer; however, the interdependence between mitochondrial metabolic function and organization is not well understood. Here, we present a noninvasive, automated and quantitative method to assess mitochondrial organization in three-dimensional (3D) tissues using exclusively endogenous two-photon excited fluorescence (TPEF) and show that mitochondrial organization reflects alterations in metabolic activities. Specifically, we examine the organization of mitochondria within live, engineered epithelial tissue equivalents that mimic normal and precancerous human squamous epithelial tissues. We identify unique patterns of mitochondrial organization in the different tissue models we examine, and we attribute these to differences in the metabolic profiles of these tissues. We find that mitochondria are clustered in tissues with high levels of glycolysis and are more highly networked in tissues where oxidative phosphorylation is more dominant. The most highly networked organization is observed within cells with high levels of glutamine consumption. Furthermore, we demonstrate that mitochondrial organization provides complementary information to traditional morphological hallmarks of cancer development, including variations in nuclear size. Finally, we present evidence that this automated quantitative analysis of endogenous TPEF images can identify differences in the mitochondrial organization of freshly excised normal and pre-cancerous human cervical tissue specimens. Thus, this method could be a promising new modality to assess the role of mitochondrial organization in the metabolic activity of 3D tissues and could be further developed to serve as an early cancer clinical diagnostic biomarker.