400x Magnification Research Articles

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.

Impact of Acidic and Alkaline Environments on the Surface Morphology of Biodentine and White Mineral Trioxide Aggregate - An In-vitro Study.

The physical and chemical properties of calcium silicate cement might be affected due to exposure to acidic or alkaline conditions during clinical use. The present study aimed to evaluate the effect of acidic and alkaline environments on the surface morphology of biodentine (BD) and white mineral trioxide aggregate (wMTA). Disc-shaped specimens of BD (n = 30) and wMTA (n = 30) were prepared in a metal mould and wrapped in pieces of gauze. They were divided into three sub-groups according to the storage media: group A, soaked in sterile distilled water at a pH of 7.0; group B, exposed to butyric acid buffered at pH 4.0; and group C, exposed to calcium hydroxide solution buffered at pH 12.0. The specimens were incubated for 7 days at 37°C, followed by examination under scanning electron microscopy at 1000x and 5000x magnification to characterise the microstructural morphology. Definite changes were seen in the microstructure of BD and wMTA on exposure to acidic and alkaline pH. The microstructure of wMTA tends to exhibit reduced cohesion when exposed to an acidic environment, especially when compared to an alkaline pH. Acidic pH exerts a milder influence on the morphological structure of BD when contrasted with its effects on wMTA. Biodentine may emerge as a more prudent choice than wMTA for utilisation in inflamed periapical regions.

An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%.

Application of a Modified SegFormer Architecture for Gleason Pattern Segmentation

A key element of morphological assessment in prostate cancer (PCa) diagnostics is the Gleason score, which is based on the architecture of tumor growth in biopsy or prostatectomy samples. During this process, biopsies from the prostate's pathological areas are evaluated to identify the most common and severe architectural patterns (referred to as Gleason patterns), each of which is assigned a score in the Gleason grading system. The resulting total score serves as a critical predictor for disease progression and metastasis. This research focuses on enhancing the efficiency of morphological examination in PCa by utilizing automatic segmentation of Gleason patterns in whole slide images (WSI) in *.svs format, with 40x magnification and an average spatial resolution of 0.258 microns per pixel. Segmentation was performed using a neural network with a SegFormer architecture, supplemented by an EdgeNeXt (small variant) subnet. The proposed solution successfully identified Gleason patterns of grades 3, 4, and 5. The best model performance for detecting combined Gleason patterns 3, 4, 5, and 4, 5 was achieved with input images sized 512x512 (tile size) and a batch size of 8, resulting in an F-score of 0.835 and 0.769, respectively.

LungHist700: A dataset of histological images for deep learning in pulmonary pathology

Accurate detection and classification of lung malignancies are crucial for early diagnosis, treatment planning, and patient prognosis. Conventional histopathological analysis is time-consuming, limiting its clinical applicability. To address this, we present a dataset of 691 high-resolution (1200 × 1600 pixels) histopathological lung images, covering adenocarcinomas, squamous cell carcinomas, and normal tissues from 45 patients. These images are subdivided into three differentiation levels for both pathological types: well, moderately, and poorly differentiated, resulting in seven classes for classification. The dataset includes images at 20x and 40x magnification, reflecting real clinical diversity. We evaluated image classification using deep neural network and multiple instance learning approaches. Each method was used to classify images at 20x and 40x magnification into three superclasses. We achieved accuracies between 81% and 92%, depending on the method and resolution, demonstrating the dataset’s utility.

7850 Developmental ZNHIT3 expression in mice embryos in Mayer-Rokitansky-Küster-Hauser (MRKH) Syndrome related organs

Abstract Disclosure: D. Kira: None. B. Eroglu, MS, PhD: None. Z. Hawkins: None. L.P. Chorich: None. S. Brakta: None. L.C. Layman: None. Introduction: MRKH is a severe form of the Mullerian duct (MD) anomaly where the upper 2/3 of the vagina and the uterus fail to develop. Individuals with MRKH still have normally functioning ovaries and normal chromosomes. MRKH may be isolated Mullerian aplasia (type 1) or associated with renal, skeletal, cardiac, and/or auditory dysfunction (type 2). The etiology of MRKH syndrome remains unknown for most probands, although a genetic contribution is likely. Most evidence suggests autosomal dominant inheritance with incomplete penetrance. WNT4 and HNF1B pathogenic variants have been identified in some MRKH individuals. Recently, our lab identified heterozygous (HTZ) ZNHIT3 loss of function variants in persons with MRKH, Both variants predict functional changes in the ZNHIT3 C-terminus and impair protein expression in vitro. Previously, other investigators found biallelic variants in the N-terminus of ZNHIT3 in individuals with PEHO syndrome, a severe autosomal recessive neurodevelopmental disorder characterized by extreme cerebellar atrophy. We are currently characterizing a novel Znhit3 knockout (KO) mouse model, in which homozygous KO mice exhibit embryonic lethality. We have demonstrated ZNHIT3 RNA expression in the human uterus, heart, kidney, and skeleton, but developmental expression of ZNHIT3 in wild type (WT) animals has not been studied. In the WT mouse embryo, the kidney forms between E8.0 and E11, while the MDs form through invagination of epithelium ∼E11.5 and extend along the Wolffian duct until reaching the cloaca at ∼E13.5. Hypothesis: We hypothesize that ZNHIT3 is involved in MD development and expressed in the urogenital system and MRKH-related organs during embryonic development of WT mice. Method: Timed mating of WT mice produced embryos for analysis. Embryos were dissected out at E11.5, 12.5 and 13.5, fixed in 4% PFA, embedded in paraffin, and sectioned sagittally at 5mm thickness. H&E staining was done to identify sections showing kidney, genital tubercle, skeleton and cerebellum. Immunohistochemistry was also done using a ZNHIT3 antirabbit antibody and DAPI as nuclear counter stain. Imaging was performed using the Keyence fluorescence microscope (BZ-X800) at 10x and 40x magnification. Results: At all 3 time points, ZNHIT3 protein expression was observed in the developing urogenital organs including kidneys and bipotential gonads, as well as in vertebral bones and cerebellum. Heart protein expression studies are ongoing. Conclusion: ZNHIT3 protein expression is seen as early as E11.5 and is present in MRKH-related organs, suggesting it may play a role in the development of urogenital and MRKH-related organs. These findings provide the basis to compare expression in the Znhit3 KO mouse, a mouse model for MRKH. Presentation: 6/2/2024

Dermoscopic Distinction of Tinea Incognito on the Face and Topical Steroid Damaged Face: A Cross-Sectional Study in Skin of Color

Abstract Background: Dermatophytosis is widespread in India due to recalcitrant and resistant infection. Tinea incognito (TI) is modified dermatophytosis due to the inadvertent use of topical steroids (TS). Similarly, topical steroid-damaged face (TSDF) is caused by prolonged use of TS. Distinction of TI and TSDF is difficult when the face is affected. Dermoscopy can assist in the differentiation of both by revealing characteristic features. We evaluated the dermoscopic features in TI affecting the face and TSDF. Aims and Objectives: To evaluate the clinical and dermoscopic features of TI affecting the face and TSDF. To observe the involvement of eyelids in TI as opposed to TSDF. Materials and Methods: The study was conducted in a tertiary hospital after obtaining ethical clearance and informed consent. Patients with signs of TI or TSDF were enrolled and demographic data were collected. Patients who had applied TS/combination creams were included. A handheld dermoscope with 10x magnification was used. A potassium hydroxide mount was used to confirm the diagnosis of TI. Results: Out of 80 patients, 27 males and 53 females were present. The mean duration of application was 8.25 ± 7 months. Clinical features were pruritus, erythema, scaling, and burning sensation. Eyelid involvement was noted in TI while it was spared in TSDF. On dermoscopy, morphological features were predominant in TI whereas vascular structures were found in TSDF in addition to white rosettes. Conclusion: Dermoscopy distinguishes TI from TSDF comprehensively by demonstrating definitive features. In addition, the involvement of the eyelid is an excellent clinical sign of TI.

Nicotine in Vapor Exposure Decreases Hippocampal Neurons in Rats

Background: Recently, many people have considered electronic cigarettes to be a safer alternative to conventional tobacco cigarettes, even though they still contain harmful substances such as nicotine, which is associated with a decrease in hippocampal neurons. Objective: This study aimed to examine the effect of exposure to e-cigarettes on hippocampal neurons in rats. Methods: An experimental study was performed using 26 tissue samples obtained from Wistar rats. The rats were randomly assigned to four groups and treated over an eight-week period as follows: the control group (K) (n=7) had no vapor exposure; the V0 group (n=6) received vapor without nicotine (0 mg/ml); the V6 group (n=7) was exposed to vapor and 6 mg/ml nicotine; and the V12 group (n=6) was exposed to vapor and 12 mg/ml nicotine. Hippocampal neurons were examined using hematoxylin-eosin staining at 400x magnification across 7 fields of view. Data were analyzed using the Kruskal-Wallis test with the Mann-Whitney post-hoc. Results: The number of hippocampal neurons was significantly reduced in both the V12 and V0 groups compared to that in the control group (p<0.05). The lowest number of hippocampal neurons was observed in the V0 group (0 mg/ml nicotine), followed by the V12 group (12 mg/ml), and then the V6 group (6 mg/ml).Conclusion: The present study showed that the variation of nicotine in vapor exposure decreases hippocampal neurons in rats.

Correlation between Kupffer Cell Infiltration and Liver Parenchymal Cell Damages in Immunosuppressed Drugs-Induced Rats

The liver is the largest organ in the body, composed of both parenchymal and non-parenchymal cells. Chemical substances and various drugs can induce liver injury and involve Kupffer cells which are non-parenchymal cells that release biologically active substances, promoting pathological processes. This study aimed to evaluate the correlation between the number of Kupffer cells and liver parenchymal cell damages in immunosuppressed, drug-induced rats. The study was conducted from July to December 2019 at the Oral Biology Laboratory of the Faculty of Dentistry and the Biochemical Laboratory of the Faculty of Medicine at Universitas Hang Tuah Surabaya. Twelve healthy male Wistar rats were divided into two groups: Healthy (H) and Immunosuppressed Drug-Induced (ID) groups. Immunosuppression was induced using dexamethasone (0.5 mg/day/rat), administered orally for 14 days, combined with tetracycline (1%/day/rat). Liver samples from all rats were examined for Kupffer cell count and parenchymal cell damages were assessed using a light microscope with 400x magnification. Results revealed a significant difference in the number of Kupffer cells and liver parenchymal cell damages between the H and ID groups (p<0.05). Pearson correlation analysis indicated a significant correlation between Kupffer cell number and parenchymal cell damages (p=0.000). Continuous administration of immunosuppressive drugs may activate Kupffer cells, leading to damage of liver parenchymal cells. In conclusion, the infiltration of Kupffer cells is associated with liver parenchymal cell damages, mediated by various factors in the immunosuppressed drug-induced rat model.

SSResNeXt: A Novel Deep Learning Architecture for Multi-class Breast Cancer Pathological Image Classification

Multi-class classification of breast cancer pathological images remains challenging due to complex image features and limited datasets. This study proposes SSResNeXt, a novel deep learning architecture incorporating a new Small-SE-ResNeXt Block with asymmetric convolutions and channel attention mechanisms. Evaluated on the BreaKHis dataset, SSResNeXt achieves state-of-the-art performance with accuracies of 95.2%, 94.0%, 92.7%, and 93.5% for 40X, 100X, 200X, and 400X magnification scales, respectively. Comparative experiments demonstrate SSResNeXt's superior performance over existing models, including ResNet and Swin Transformer variants. The proposed architecture offers improved feature extraction capabilities for breast cancer pathological images without significantly increasing model complexity, providing a promising tool for computer-aided diagnosis systems.

Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.

The study's objective was to develop an automated method for a histopathology recognition model for odontogenic keratocysts (OKC) and non-keratocyst (Non-KC) in jaw cyst sections stained with hematoxylin (H) and eosin (E) on a tiny bit of incisional biopsy prior to surgery. This hastens the speed and precision of diagnosis to patients. Also, navigates the clinicians with the therapeutic doctrine. To build such a system and to increase the accuracy of the existing models, the edge attention CNN model with Keras functional API was implemented which efficiently analyzes the texture information of the images. Approximately 2861 microscopic images at a 40X magnification were taken from 54 OKC, 23 Dentigerous cysts (DC), and 20 Radicular cysts. The model was trained using both RGB and canny edge-detected images. The model gave a good accuracy of 96.8%, which is suitable for real-time. Histopathological images are better analyzed through textural features. The proposed edge attention CNN highlights the edges, making texture analysis more precise. The suggested method will work for OKC and Non-KC diagnosis automation systems. The use of a whole slide imaging scanner has the potential to increase accuracy and remove human bias.

Foliar epidermal micro-morphology of selected brinjal varieties: A study on leaf surface characteristics

Studies on the leaf epidermal morphological characteristics were conducted on the three varieties of brinjal (Solanum melongena L.) belonging to the family Solanaceae. The varieties used in the study were Bhavani Gold, Purple Round and Haritha. Fresh mature brinjal leaves were randomly selected from the three experimental genotypes, and the foliar epidermal features were studied by using light microscopy and SEM imaging. Measurements were taken with an ocular and stage micrometer using 10X and 40X magnifications. The photographs were taken from mounted slides. The stomatal count and each measurement represent the average of ten readings. The upper and lower epidermal surfaces of leaf were studied for stomatal features including, stomatal distribution, stomatal type, stomatal index, stomatal pore length, stomatal pore width, guard cell length, guard cell width, subsidiary cell length and width, epidermal cell type, epidermal cell length and width, trichome type, trichome length and width. Amphistomatous, anisocytic, and with more stomata on the lower epidermal surface than the upper epidermal surface were present in all leaf varieties. On the leaves, the abaxial stomatal index ranged from 68.19 mm-2 (Bhavani Gold) to 56.67 mm-2 (Purple Round) to 58.8 mm-2 (Haritha). While on the adaxial surface, it varied from 59.67 mm-2 (Bhavani Gold), 53.46 mm-2 (Purple Round) and 56.67 mm-2 (Haritha). The three varieties of this study exhibited similarities in the pattern of epidermal cell on the adaxial and abaxial surfaces and were irregular in shape. Among the three varieties epidermal cell become irregular in outline and trichomes were present on both leaf surfaces. It becomes stellate, non-glandular, glandular and 8-12 in number. The highest trichome length was obtained in Bhavani Gold (63 μm) followed by Haritha (57.5 μm) and Purple Round (43 μm) on the upper epidermis. On the lower epidermal region maximum length was obtained in Bhavani Gold (65.5 μm) followed by Haritha (53 μm) and Purple Round (44 μm).

Degradation of Microplastics by Zinc Oxide Nanoparticles Synthesized Using Piper longum Leaf Extract.

Background: The environmental hazards posed by microplastics have drawn considerable concern due to their buildup in ecosystems. Microplastics accumulate in human saliva, skin, and hair. Developing effective technology for managing and degrading microplastics remains a substantial challenge. In a concerted attempt to save the ecology, this study explores the photocatalytic breakdown of common microplastics like polystyrene (PS) microspheres and polyethylene (PE) using green-synthesized zinc oxide nanoparticles (ZnO NPs) under UV light exposure. Aim: To synthesize and characterize zinc oxide nanoparticles prepared using the extract of the leaves of Piper longumand qualitatively assess the photocatalytic degradation potential of the nanoparticles under light microscopy. Material and Methods:A fresh extract of P. longum leaves was used as a reducing agent to synthesize zinc oxide nanoparticles. Fourier transform infrared (FTIR) analysis, X-ray diffraction (XRD) analysis, UV-Vis spectra analysis, and scanning electron microscopy (SEM) analysis were performed to characterize the nanoparticles. Microplastics were isolated from the saliva of 50 healthy patients and were purified and filtered. In a six-well microtiter plate, 0.5 μg of varying concentrations of nanoparticles were added. After fixing with 15% formaldehyde, microplasticswere subjected to UV irradiation for 2 hours with different concentrations of ZnO nanoparticles (25, 50, 75,and 100 µg). Custom photoreactors activated the photocatalysts to degrade the microplastic pollutants. The six-well microtiter plate was viewed under 40x magnification in a light microscope to observe microplastic morphology after 24 hours of degradation. Results: The FTIR spectrum showed distinct peaks at 890.51 cm⁻¹, indicating the involvement of C-N in-plane vibrations of amino acids. XRD analysis revealed three distinct diffraction peaks at 31.68°, 34.39°, and 36.33°, corresponding to the hexagonal wurtzite structure of ZnO nanoparticles. The synthesized ZnO nanoparticles ranged from 50 to 90 nm in size, viewed at 100x magnification on SEM. The highest degradation of microplastics was observed ataZnO NPconcentration of 100 µL, with the ZnO NPs 50-90 nm in size. Conclusion: Zinc oxide nanoparticles synthesized using Piper longum leaf extract effectively degrade microplastics, with the highest degradation observed at a 100 µL concentration of ZnO nanoparticles and optimal degradation occurring at a concentration of 75 µL.

Deep learning classification method for boar sperm morphology analysis.

Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics. To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide. Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy. Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification. We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

Comparative Evaluation of Different Obturation Techniques for Root Canal Filling of Permanent Teeth: An In-Vitro Study.

Achieving success in root canal (RC) therapy relies on three key components: comprehensive cleaning of the canal, efficient disinfection, and proper filling of the canal space. The present study aimed to compare the efficacy of four obturation techniques: single cone (SC), GuttaCore (GC), cold lateral condensation (LC), and C Point systemfor RC filling of permanent teeth. 76 extracted human mandibular first premolars were divided into four groups, with 19 teeth in each group (group A, obturation with SC technique; group B, obturation with GC; group C, obturation with LC; and group D, obturation with C Point system). The samples were marked at 4 mm and 8 mm from the root apex using a marker and calliper, sectioned horizontally, and analyzed under a stereomicroscope at 20x magnification. The mean percentage (%) of gutta-percha (GP)-filled area was compared using a two-way analysis of variance (ANOVA), followed by the Bonferroni post-hoc test. The mean percentage of GP-filled area at a distance of 4 mm from the apex was highest in group B (0.86±0.04; 95% CI: 0.845-0.881), followed by group D (0.70±0.07; 95% CI: 0.664-0.736), group A (0.61±0.05; 95% CI: 0.595-0.642), and least in group C (0.58±0.09; 95% CI: 0.543-0.627), and the difference was statistically significant (p≤0.05). The post-hoc pairwise comparison of groups at 4 mm revealed that there were statistically significant differences between group D and groups B, C, and A (p ≤ 0.001). The mean percentage of the GP area at a distance of 8 mm from the apex was highest in group B (0.81±0.10; 95% CI: 0.761-0.861), followed by group D (0.75±0.07; 95% CI: 0.725-0.792), group A (0.69±0.07; 95% CI: 0.658-0.729), and lowest in group C (0.65±0.10; 95% CI: 0.607-0.709), and this difference was statistically significant (p≤0.05). Post-hoc pairwise comparison of groups at 8 mm revealed that there was a statistically significant difference between groups D and C (p=0.006), whereas no statistically significant differences were noted between groups D and B (p=0.473). Furthermore, no statistically significant differences were noted between groups C and A at 4 mm and 8 mm (p>0.05). Obturation with the GC system provided the best results in terms of the percentage of GP-filled area at 4 mm. However, at 8 mm from the apical region, both the GC and C Point systems provided similar results.

Abstract P169: SGLT2 Colocalizes on the Nucleus Tractus Solitarii Neurons in Normotensive and Hypertensive Rats in a Location-Dependent Manner

Introduction/Background: Hypertension affects ~45% adults in the US and is associated with a high sodium diet. The nucleus tractus solitarii (nTS) in the brainstem influences blood pressure through the interplay between glutamate and GABA. Sodium-glucose cotransporter 2 (SGLT2) is a protein recently found within the nTS, and its expression in the nTS may impact blood pressure. In this study, we investigated the distribution of SGLT2 in the nTS of normotensive and hypertensive rats, focusing on its presence in glutamatergic (Glu) and GABAergic neurons. Within the nTS, increases in glutamate lowers blood pressure, and increases of GABA raises blood pressure. The interaction between SGLT2 and glutamate/GABAergic neurons within the nTS is not well-understood. Hypothesis: We hypothesize that SGLT2 colocalizes on both Glu and GABAergic nTS neurons but increases due to a high salt diet. Methods: Male Dahl-Salt Sensitive Rats were fed a normal 0.4% salt diet (NSD) for 2 weeks and randomly placed to a NSD or a high 4% salt diet (HSD) for 7 weeks. After 9 weeks total, the brainstem section containing the nTS of each rat was isolated at 9AM. Goals/Aims: We used immunohistochemistry to identify either Glu (CAMKII) and GABAergic (GAD67) and localize SGLT2. Using a confocal microscope, a Z-stack image was compiled at 40x magnification and colocalization analysis was completed to determine if SGLT2 is found greater on Glu or GABAergic neurons. Results: We examined 3 areas within the nTS: caudal, rostral, and mid (area postrema, a region where cardiorespiratory afferent information is received). We first found that SGLT2 colocalizes to different extents on both Glu and GABAergic nTS neurons. We used Pearson’s R correlation to determine the strength and linear relationship between cell type and SGLT2. In GABAergic neurons, we found a positive correlation (r=0.319) in NSD, which decreased significantly in HSD (p=0.013, t-test) in the nTS at area postrema. Conversely, within the caudal nTS, SGLT2 colocalizes to a greater extent on Glu neurons in HSD (r=0.434) and lower in NSD (r=0.308), (p=0.04, t-test). Conclusions: In our model, we find that SGLT2 colocalization with Glu and GABAergic neurons is present within the nTS in a location-dependent manner, which may affect its function in the development of hypertension. Further experiments exploring the SGLT2 within normotensive and hypertensive rats may better reveal the function of the protein within the nTS.

Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study

The diagnosis of early-stage mycosis fungoides (MF) is challenging due to shared clinical and histopathological features with benign inflammatory dermatoses (BIDs). Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classification, but this field is largely unexplored for cutaneous lymphomas. This study evaluates DL in distinguishing early-stage MF from BIDs using a unique dataset of 924 hematoxylin and eosin-stained whole-slide images from skin biopsies, including 233 early-stage MF and 353 BID patients. All MF patients were diagnosed after clinicopathological correlation. The classification accuracy of weakly-supervised DL models was benchmarked against three expert pathologists. The highest performance on a temporal test set was at 200x magnification (0.25 μm per pixel resolution), with a mean area-under-the-curve of 0.827 ± 0.044 and a mean balanced accuracy of 76.2 ± 3.9%. This nearly matched the 77.7% mean balanced accuracy of the three expert-pathologists. Most (63.5%) attention heatmaps corresponded well with the pathologists’ region-of-interest. Considering the difficulty of the MF versus BID classification task, the results of this study show promise for future applications of weakly-supervised DL in diagnosing early-stage MF. Achieving clinical-grade performance will require larger multi-institutional datasets and improved methodologies, such as multimodal DL with incorporation of clinical data.

Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images.

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision.

In vivo study: black cumin extract (Nigella sativa) on antral follicle diameter and granulosa cell layer thickness as an effort to prevent infertility

Background: Infertility is a health problem that affects approximately 10-15% of couples trying to achieve pregnancy worldwide. Various studies have shown that Nigella sativa (N. Sativa) has many health benefits, including reproductive health. However, studies examining the effectiveness of N. Sativa in preventing folliculogenesis disorders have never been reported.Purpose: This study aims to determine the benefits of administering N. Sativa in preventing folliculogenesis disorders as measured by the diameter of the antral follicles and the thickness of the granulosa cell layer.Methods: This research design uses an experiment with a randomized posttest-only controlled group design approach. There were 27 experimental animals (mice) divided into three groups, namely the negative control group (9 animals) and the positive control group (9 animals), as well as the treatment group (9 animals). The positive control group was exposed to two cigarettes of cigarette smoke per day. The treatment group was exposed to cigarette smoke and N. Sativa 300 mg/KgBW/day for 28 days. After that, the mice were sacrificed to take their ovaries. Then, the ovarian organ samples were stained with Hematoxylin-eosin and examined for the diameter of the antral follicles and the thickness of the granulosa cell layer in the antral follicles with photo slides at 400x magnification in 5 fields of view, statistical analysis using the Independent T-test.Results: There were significant differences in antral follicle diameter and granulosa cell thickness in mice exposed to cigarette smoke and N. Sativa. Administration of N. Sativa extract can increase granulosa cell thickness through estrogenic function. Administration of N. Sativa extract showed estrogenic activity by increasing serum estradiol levels. N. Sativa heterotrophic activity cannot be separated from the flavonoid and phenolic compounds contained in N. Sativa, which have been proven to have high estrogenic activity.Conclusion: N. Sativa extract is efficacious in improving the diameter of antral follicles and the thickness of granulosa cells caused by exposure to cigarettes.

Investigating the effect of SiO2 thin films with sculptured topographies on the alignment properties of liquid crystals

In the present work, sculptured thin films (STF) based on silicon dioxide (SiO2) induced on the indium tin oxide (ITO) coated substrates via glancing angle deposition (GLAD) technique using the physical vapor deposition (PVD) process. Our main motive to perform this work is to address the issues we have faced with the traditional alignment method and give a try to overcome those problems. Keeping these challenges in our mind, we are giving here an alternative to traditional method and explored this different alignment technique with GLAD process. We have fabricated a liquid crystal device (LCD) in a way of electrically-controlled birefringence (ECB) and placed the engineered substrates in anti-parallel alignment. Morphological, optical and electro-optic behaviour of resulting device has been studied in the ∼5 μm thick device. Effect of applied voltage on the morphology and electro-optic behaviour of liquid crystal has been established. Interestingly, LC material demonstrates electrically switchable birefringence colours viz. brown, green, pink, purple and mauve colour, under applied electric field in room temperature at 10X magnification under polarizing optical microscope. A very nice extinction and contrast has been obtained with fabricated ECB device, which elucidate the device quality with impeccable alignment without any disclination defects. Besides, the electro-optic characteristics it also shows a significant impact on the liquid crystal properties. The obtained results demonstrate nice optical retardation, a lower Frederick's threshold voltage i.e. 1.5 V and 19.03 dB higher contrast respectively. An impressive switching response time τon of 5.4 ms and τoff = 600 µs at 20 V has attained with respective device as well. Such change in parameters may correspond to the higher electrical torque experienced by the LC molecules on the application of applied field. Anchoring energy and pretilt angle of respective system has been calculated and correlated with observed behaviour. Thus, engineered device with this superior alignment method show remarkable electro-optic results which can open a new way for the development of fast LCD for advanced optical device applications.