Histopathology Slides Research Articles

Classification of salivary gland biopsies in Sjögren's syndrome by a convolutional neural network using an auto-machine learning platform.

The histopathological analysis of minor salivary gland biopsies, particularly through the quantification of the Focus Score (FS), is pivotal in the diagnostic workflow for Sjögren's Syndrome (SS). AI-based image recognition using deep learning models has demonstrated potential in enhancing diagnostic accuracy and efficiency in preclinical research. The primary aim of this investigation was to utilize an auto-machine learning (autoML) platform for the automated segmentation and quantification of FS on histopathological slides, aiming to augment diagnostic precision and speed in SS. A cohort comprising 86 patients with sicca syndrome (37 diagnosed with SS based on the 2016 ACR/EULAR Classification Criteria and 49 non-SS) was selected for an in-depth histological examination. A repository of 172 slides (two per patient) was assembled, encompassing 74 slides meeting the classificatory thresholds for SS (FS ≥ 1, indicative of lymphocytic infiltration) and 98 slides showcasing normal salivary gland histology. The autoML platform utilized (Giotto, L2F, Lausanne Switzerland) employed a Convolutional Neural Network (CNN) architecture (ResNet-152) for the training and validation phases, using a dataset of 172 slides. The developed model exhibited a reliability score of 0.88, proficiently distinguishing SS cases, with a sensitivity of 89.47% (95% CI: 66.86% to 98.70%) and a specificity of 88.24% (95% CI: 63.56% to 98.54%). The model found histological slides of suboptimal quality (e.g., those compromised during fixation or staining processes) to be the most challenging for accurate classification. AutoML platforms offer a rapid and flexible approach to developing machine learning models, even with smaller datasets, as demonstrated in this study for SS. These platforms hold significant potential for enhancing diagnostic precision and efficiency in both clinical and research settings. Multicentric studies with larger patient cohorts are essential for thorough evaluation and validation of this innovative diagnostic approach.

Scalable deep learning artificial intelligence histopathology slide analysis and validation.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically detect and identify pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In the current study, a deep learning approach is developed and presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach uses a novel pyramid tiling approach to take advantage of spatial awareness around the area to be classified, while maintaining efficiency and scalability for gigapixel images. The approach is trained and validated on a wide variety of tissue types (i.e., testis, ovary, prostate, kidney) and pathologies taken from an epigenetically altered histology study at Washington State University. The newly developed procedure was optimized and validated along with comparison and validation on public histology datasets. The current developed procedure was found to be optimal and more reproducible when compared to manual procedures, and optimal to previous protocols that used fragmented tissue or slide analysis. Observations demonstrate that the deep learning histopathology analysis is significantly more efficient and accurate than standard manual histopathology analysis.

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed. This model starts by extracting the main features from the histopathology images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature elimination based on a decision tree is introduced to remove unnecessary features extracted during the feature extraction process. Adam optimizers were implemented to optimize the network’s weights during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images (WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post-treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of 98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer.

Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review

Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures. Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024. Methodology: With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique. Results: Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer. Conclusion: Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.

Determining the status of breast cancer susceptibility genes (BRCA) is crucial for guiding breast cancer treatment. Nevertheless, the need for BRCA genetic testing among breast cancer patients remains unmet due to high costs and limited resources. This study aimed to develop a Bi-directional Self-Attention Multiple Instance Learning (BiAMIL) algorithm to detect BRCA status from hematoxylin and eosin (H&E) pathological images. A total of 319 histopathological slides from 254 breast cancer patients were included, comprising two dependent cohorts. Following image pre-processing, 633,484 tumor tiles from the training dataset were employed to train the self-developed deep-learning model. The performance of the network was evaluated in the internal and external test sets. BiAMIL achieved AUC values of 0.819 (95% CI [0.673-0.965]) in the internal test set, and 0.817 (95% CI [0.712-0.923]) in the external test set. To explore the relationship between BRCA status and interpretable morphological features in pathological images, we utilized Class Activation Mapping (CAM) technique and cluster analysis to investigate the connections between BRCA gene mutation status and tissue and cell features. Significantly, we observed that tumor-infiltrating lymphocytes and the morphological characteristics of tumor cells appeared to be potential features associated with BRCA status. An interpretable deep neural network model based on the attention mechanism was developed to predict the BRCA status in breast cancer. Keywords: Breast cancer, BRCA, deep learning, self-attention, interpretability.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

β‑catenin expression in endometrioid type endometrial cancer: Expression patterns and impact on disease outcomes.

Determination of nuclear and/or cytoplasmic expression of β-catenin by immunohistochemistry in patients with endometrial cancer (EC) may constitute a potential diagnostic method for identifying patients with a catenin β1 (CTNNB1) gene mutation and those at risk of disease recurrence. The present study aimed to investigate β-catenin expression patterns in hysterectomy specimens of patients with endometrioid type EC using immunohistochemistry, and to examine the prognostic impact of β-catenin. The study was a single-institutional, retrospective cohort trial enrolling consecutive patients with a postoperative histopathological diagnosis of endometrioid EC who underwent hysterectomy between January 2015 and December 2018. Histopathology slides from 75 patients were stained with a monoclonal antibody targeting the β-catenin protein. Any percentage of nuclear staining, whether focal or diffuse, was considered 'β-catenin nuclear-positive'. The cytoplasmic staining reaction of β-catenin was assessed based on the percentage of stained cells and staining intensity. Immune-reactivity score (IRS) values were determined by multiplying the scores for the percentage of staining and staining intensity. IRS values 0 to 2 were regarded as negative expression, 3 to 4 as low expression, 6 to 8 as moderate expression, and 9 to 12 as high expression. Recurrence-free survival (RFS) was used as the prognostic endpoint. Only 2 out of 75 tissue samples (2.7%) exhibited nuclear β-catenin expression, with a low staining percentage of 5%. By contrast, cytoplasmic staining was observed in all samples (100%). According to the IRS findings, 1.3% of the samples exhibited negative cytoplasmic expression, 42.7% low expression, 38.7% moderate expression and 17.3% high expression. Cox regression analysis revealed that staining with β-catenin, either nuclear or cytoplasmic, had no impact on RFS, and stage was the sole independent prognostic factor. In conclusion, based on these results, β-catenin expression in endometrioid EC was revealed to be mostly cytoplasmic, with only 2.7% of tissue samples exhibiting nuclear expression. Overall, β-catenin expression has no impact on RFS.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Atypical presentation of Kaposi sarcoma in an HIV-negative patient: a case report and comprehensive literature review

Introduction: Kaposi’s sarcoma (KS) is a systemic disease that is marked by the presence of neoplastic lesions caused by Human Herpesvirus-8 (HHV-8) infections. KS usually impacts people with weakened immune systems, although there have been a few cases of it occurring in individuals with normal immune function. Medical records and histopathological slides of the case were retrospectively reviewed. This work has been reported based on SCARE criteria {1} . Case presentation: A 51-year-old man from Palestine came in with a single, painless, purple growth on his left forearm that had been growing quickly for six months. The patient did not have a history of using immunosuppressants, HIV infection, or engaging in unconventional sexual practices. Histopathological examination confirmed nodular-stage KS, with positive HHV-8 immunostain. The lesion was excised without complications, and the patient remains under periodic follow-up. Discussion: KS typically manifests with multiple lesions in individuals with weakened immune systems. This case showcases a unique presentation in a patient with a strong immune system and no notable risk factors. Histopathological confirmation is necessary to differentiate between benign and infectious vascular lesions when considering the diagnosis of KS. The treatment approaches can differ depending on the extent of the lesion and the condition of the patient. Conclusion: this case emphasizes the significance of considering KS as a potential cause for solitary vascular lesions, even in people who have a healthy immune system. It also emphasizes the need for a comprehensive diagnostic evaluation and personalized management.

Artificial Intelligence Applications in Medical Mycology: Current and Future

The application of artificial intelligence (AI) in the medical mycology field represents a new era in the diagnosis and management of fungal infections. AI technologies, particularly machine learning (ML) and deep learning (DL) methods, enhance diagnostic accuracy by leveraging large datasets and complex algorithms. This review examines current applications of AI in laboratory and clinical settings for fungal diagnostics. In the laboratory, AI models analyze microscopic images from potassium hydroxide (KOH) examinations, fungal culture tests, and histopathologic slides, which improves the detection rates of fungal pathogens significantly. In the clinical setting, AI assists the diagnosis of fungal infections using medical images, exhibiting high efficacy in binary classification tasks. However, challenges include small sample sizes, class imbalances, reliance on expert-labeled data, and the black box nature of AI models. Explainable AI offers potential solutions by providing human-comprehensible insights into AI decisionmaking processes. In addition, human-computer collaboration can enhance diagnostic accuracy, particularly for less experienced clinicians. The development of generative AI models, e.g., large language models and multimodal AI, promises to create extensive datasets and integrate various data sources for comprehensive diagnostics. Addressing these limitations through prospective clinical validation and continuous feedback will be essential for realizing the full potential of AI in medical mycology.

Radio-Histopathological Spectrum of Ovarian Specimens Following Cystectomy

Ovarian cysts can be benign or malignant and requires accurate diagnosis for efficient treatment. Objective: To characterize the radiological and histopathological spectrum of ovarian specimens following cystectomy. Methods: This retrospective study was conducted at Pakistan Atomic Energy Commission General Hospital, Islamabad from 1st April 2022 to 31st December 2022.Eighty patient’s samples from cystectomy patients who were suffering from ovarian cysts were included. Each patient underwent radiological examination before ovarian cystectomy through laparoscopic surgery except two cases of urgent laparotomy. Gross histopathological specimen examination was conducted. The data were analysed using SPSS version 26.0, wherein p value &lt;. 0.05 was considered as significant. Results: The mean age of the patients enrolled in this study was 35.5±5.9 years. Hemorrhagic cysts were having a reticular pattern of internal echoes with soli appearing area with concave margins and no internal flow, while endometrioma cysts were having homogenous low level internal echoes with non-solid component and tiny echogenic foci in the walls. While within the neoplastic cysts 4/8 werehaving cystic external surface and 1/8 presented with ovarian mass.The surface epithelial tumor presented of 2 cases with carcinoma detection on histopathology slides while in the germ cell tumor 1 cases each of strumaovarii, dysgerminoma and mixed germ cell tumor was observed. Conclusions: Surface epithelial tumors were the most common category of ovarian tumors and majority of the cysts were benign cystadenomas. Radiological imaging provides a precise non-invasive tool for categorizing various ovarian cysts and histopathological findings further confirms the exact category of tumors.

Dual hyperpolarized [1-13C]pyruvate and [13C]urea magnetic resonance imaging of prostate cancer

BackgroundAlthough multiparametric (mp) 1H magnetic resonance imaging (MRI) is increasingly used to detect and localize prostate cancer (PC), its correlation with tumor grade is limited. Hyperpolarized (HP) carbon-13 (13C) MR is an emerging imaging technique, which can be used to interrogate key biologic processes through in vivo detection of various HP probes. A distinct attribute of HP 13C MRI is the ability to detect multiple HP probes within a single acquisition. Here we report on the first simultaneous dual HP [1-13C]pyruvate and [13C]urea MRI with correlations to histopathologic findings in a patient with localized PC scheduled for radical prostatectomy. Material and methodsPaired HP 13C and standard mp 1H MRI were performed in a patient with biopsy-proven Gleason score 4 + 3 = 7 adenocarcinoma of the prostate scheduled for radical prostatectomy through a first-in-human pilot study of dual-agent HP MRI (NCT02526368). HP 13C MRI was performed using a clinical 3T scanner with 13C transmit-and-receive capabilities. Dynamic series of HP 13C pyruvate, lactate and urea imaging were acquired following intravenous (IV) injection of co-hyperpolarized [13C]urea (25 mM) and [1–13C]pyruvate (125 mM). The [1-13C]pyruvate-to-[1-13C]lactate conversion rate (kPL) was calculated using an inputless two-site exchange model; AUCurea was the [13C]urea signal summed over time. Following radical prostatectomy, whole-mount prostate histopathological slides were prepared and reviewed by an experienced genitourinary pathologist. ResultsFollowing informed consent, the patient underwent paired mp 1H MRI and dual-agent HP MRI. mp 1H MRI revealed a 1.2 cm lesion in the left apical posterior zone. Dual-agent HP MRI identified a focus of increased [1-13C]pyruvate-to-[1-13C]lactate conversion rate (kPL) extending from the left apical posterior peripheral zone to the right gland. A corresponding area of abnormal tissue perfusion (AUCurea) was seen in the left gland. Metabolism-perfusion mismatch (with several foci of increased kPL/AUCurea) was observed throughout the tumor. Tumor extension to the right midgland was confirmed at the time of radical prostatectomy and staining for lactate dehydrogenase-A was increased throughout the tumor relative to surrounding benign prostatic tissue. ConclusionThis first-in-human radiopathologic study demonstrates the feasibility of dual-agent HP MRI in PC patients. Simultaneous assessment of tumor metabolism and perfusion was able to detect occult disease as well as to show a significant mismatch between intra-tumoral metabolism and tissue perfusion in high-grade PC. Prospective validation of these findings is warranted.

Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset.

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some genomics datasets, we aggregate atypia patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA methylation as an integrative input source into a deep neural network for colon cancer stages classification, and samples stratification into low or high-risk survival groups. The genomics-only and integrated input features return Area Under Curve-Receiver Operating Characteristic curve (AUC-ROC) of 0.97 compared with AUC-ROC of 0.78 obtained when only image features are used for the stage's classification. A further analysis of prediction accuracy using the confusion matrix shows that the integrated features have a weakly improved accuracy of 0.08% more than the accuracy obtained with genomics features. Also, the extracted features were used to split the patients into low or high-risk survival groups. Among the 2,700 fused features, 1,836 (68%) features showed statistically significant survival probability differences in aggregating samples into either low or high between the two risk survival groups. Availability and Implementation: https://github.com/Ogundipe-L/EDCNN.

Impacts of gentamycin toxicity: nephroprotective role of guarana through different signaling pathways.

Gentamicin is a widely used aminoglycosidic antibiotic since its discovery. Like any other medication gentamicin causes unwanted side effects such as hepatotoxicity and nephrotoxicity. This study aims to examine the antioxidant effect of the guarana seed extract in protecting renal tissue. Forty male mice were divided into four groups (group one was control with free access to food and water, group two was treated orally with 300mg/kg of guarana seed extract daily, group three was injected intraperitoneally with 100mg/kg of gentamicin daily and the fourth group was co-treated with both 300mg/kg of guarana seed extract orally and injected intraperitoneally with 100mg/kg of gentamicin daily) for two weeks. Serum levels of urea, creatinine, AST, ALT, IL-1β and IL-6 have significantly elevated in the gentamicin treated group and those changes were not found in the guarana co-treated group. In gentamicin treated mice, a significant reduction was observed in two antioxidants SOD and GPX accompanied by downregulation of Ho-1 and Nrf2 while, that did not happen in the guarana seed extract co-treated group. Histopathology and immunohistochemistry slides show that the guarana seed extract prevents degenerative and necrotic events in tubular epithelial tissues caused by gentamicin toxicity. In conclusion, current data suggest that gentamicin can damage renal tissues when given at 100mg/kg/day, however, the guarana seed extract may be capable of preventing that event when cotreated with the gentamicin as a supplement.

Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis

&lt;p&gt;Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.&lt;/p&gt;

144P Analysis of a novel predictive marker of immune checkpoint response in head and neck cancer, calculated from histopathological slides through inferred transcriptomics

144P Analysis of a novel predictive marker of immune checkpoint response in head and neck cancer, calculated from histopathological slides through inferred transcriptomics

Chromoblastomycosis: A Single Centre Clinical Laboratory Experience of Seven Years (2016-2022) and Literature Review From Pakistan.

Chromoblastomycosis (CBM) is a chronic infection of skin and subcutaneous tissue. CBM cases have been reported in local literature from Pakistan with heterogenous demographic, diagnostic and therapeutic information. The objective of this study is to share the experience of CBM from a large tertiary care hospital laboratory in Pakistan. This was a retrospective observational study. Histopathology and microbiology data of suspected CBM between 2016 and 2022 was retrieved. Patients' demographics, site of involvement, histopathological findings and positive microbiology cultures were assessed. Literature search on Google Scholar, PubMed and PakMediNet was done between 1990 and 2023 with multiple terms. A total of 16 CBM cases were identified; 14 were histopathology positive and two were both histopathology and culture positive. The median age was 21 years, and 11 patients were male. The predominant site was lower extremities followed by the face. Severe acanthosis, hyperkeratosis and granuloma with sclerotic bodies were observed in all histopathology slides. Alternaria spp. and Phialophora spp. were isolated from two culture-positive cases. A total of nine cases of CBM were reported from Pakistan in PubMed non-indexed journal. CBM is not a commonly thought of disease when evaluating skin lesions in Pakistan. A high index of suspicion when assessing patients who may have a history of trauma, exposure to soil and suggestive lesions is reasonable. An integrated approach between clinicians, histopathologist and microbiologist is required to do early identification and therapeutic interventions.

Melatonin Attenuates Diabetic Retinopathy by Regulating EndMT of Retinal Vascular Endothelial Cells via Inhibiting the HDAC7/FOXO1/ZEB1 Axis

ABSTRACTDiabetic retinopathy (DR) is characterized as a microvascular disease. Nonproliferative diabetic retinopathy (NPDR) presents with alterations in retinal blood flow and vascular permeability, thickening of the basement membrane, loss of pericytes, and formation of acellular capillaries. Endothelial–mesenchymal transition (EndMT) of retinal microvessels may play a critical role in advancing NPDR. Melatonin, a hormone primarily secreted by the pineal gland, is a promising therapeutic for DR. This study explored the EndMT in retinal microvessels of NPDR and its related mechanisms. The effect of melatonin on the retina of diabetic rats was evaluated by electroretinogram (ERG) and histopathologic slide staining. Furthermore, the effect of melatonin on human retinal microvascular endothelial cells (HRMECs) was detected by EdU incorporation assay, scratch assay, transwell assay, and tube formation test. Techniques such as RNA‐sequencing, overexpression or knockdown of target genes, extraction of cytoplasmic and nuclear protein, co‐immunoprecipitation (co‐IP), and multiplex immunofluorescence facilitated the exploration of the mechanisms involved. Our findings reveal, for the first time, that melatonin attenuates diabetic retinopathy by regulating EndMT of retinal vascular endothelial cells via inhibiting the HDAC7/FOXO1/ZEB1 axis. Collectively, these results suggest that melatonin holds potential as a therapeutic strategy to reduce retinal vascular damage and protect vision in NPDR.

Histopathology underlying environmental enteric dysfunction in a cohort study of undernourished children in Bangladesh, Pakistan, and Zambia compared with United States children

BackgroundEnvironmental enteric dysfunction (EED) is an asymptomatic intestinal disorder associated with growth impairment, delayed neurocognitive development, and impaired oral vaccine responses. ObjectivesWe set out to develop and validate a histopathologic scoring system on duodenal biopsies from a cohort study of children with growth failure in Bangladesh, Pakistan, and Zambia (“EED”) with reference to biopsies from United States children with no clinically reported histologic pathology (referred to hereafter as “normal”) or celiac disease. MethodsFive gastrointestinal pathologists evaluated 745 hematoxylin and eosin slide images from 291 children with EED (mean age: 1.6 y) and 66 United States children (mean age: 6.8 y). Histomorphologic features (i.e., villus/crypt architecture, goblet cells, epithelial and lamina propria acute/chronic inflammation, Brunner’s glands, Paneth cells, epithelial detachment, enterocyte injury, and foveolar metaplasia) were used to score each histopathologic slide. Generalized estimating equations were used to determine differences between EED, normal, and celiac disease, and receiver operating characteristic curves were used to assess predictive value. ResultsBiopsies from the duodenal bulb showed higher intramucosal Brunner’s gland scores and lower intraepithelial lymphocyte scores than from the second or third parts of the duodenum (D2/3), so only D2/3 were included in the final analysis. Although 7 parameters differed significantly between EED and normal biopsies in regression models, only 5 (blunted villus architecture, increased intraepithelial lymphocytosis, goblet cell depletion, Paneth cell depletion, and reduced intramucosal Brunner’s glands) were required to create a total score percentage (TSP-5) that correctly identified EED against normal biopsies (AUC: 0.992; 95% CI: 0.983, 0.998). Geographic comparisons showed more severe goblet cell depletion in Bangladesh and more marked intraepithelial lymphocytosis in Pakistan. ConclusionsThis scoring system involving 5 histologic parameters demonstrates very high discrimination between EED and normal biopsies, indicating that this scoring system can be applied with confidence to studies of intestinal biopsies in EED.