Biology subjects

Khosravi, P.

Publications and source records attributed to Khosravi, P..

7 recordsLinked to original sources

bioRxiv2026

Kinetics of Hypoglycemia in Diabetes Patients Informs Development of New Modes of Glucagon Therapy

Insulin therapy revolutionized the care of patients with diabetes starting [~]100 years ago, yet insulin-induced hypoglycemia remains a serious life-threatening complication of insulin therapy. Glucagon is a highly effective treatment; however current dosage forms remain under-utilized due to poor patient compliance. The development of improved and situation-specific glucagon therapies remains challenging due to the poor drug stability and incomplete knowledge of the kinetics of different hypoglycemic events. Thus, we analyzed continuous glucose monitor (CGM) data from 1135 patients with type 1 diabetes (T1D) representing 246.18 patient years. We show that a surprisingly large proportion of hypoglycemic episodes (20-30%) are follow-on events resulting from under-treatment of prior events, and that the average duration of independent hypoglycemic events can last up to 79 to 108 minutes. We further show that the kinetics of hypoglycemic onset and persistence varies significantly by patient history, severity, time of occurrence. Guided by these findings, we recognize the opportunity to develop high-density, readily-soluble, and thermostable (ReST) solid glucagon formulations, and painless application-specific microneedle-patches that are in line with the timing needs of T1D patients who are awake and asleep. Thus, we demonstrate (1) on-demand patches for rapid prevention or treatment of mild hypoglycemia during the day, and (2) enzyme-driven hypoglycemia-responsive patches supporting autonomous glucagon release during the night. We show excellent in vitro glucagon stability, loading, and release kinetics of both systems and demonstrate their ability to treat hypoglycemia in diabetic animals. The engineering of these delivery systems demonstrates the potential of human CGM data and solid glucagon formulations to enable new modes of glucagon therapy, thereby expanding the clinical role of glucagon beyond the emergency setting.

bioengineering↗

bioRxiv2025

Monolithic Shape-Shifting Absorbable Implants for Long-Term Contraception

Reversible contraceptives empower women to prevent unintended pregnancies and enable family planning. However, the need for frequent dosing with pills or injections often leads to suboptimal medication adherence and reduced effectiveness-an issue common to many chronic conditions. Long-acting drug delivery implants offer a compelling alternative by enabling autonomous, multi-year drug release, thereby improving real-world adherence and treatment outcomes. However, user acceptability and access are limited by need for invasive insertion and surgical end-of-life removal, particularly in low-resource settings, as well as by limited drug loading and suboptimal drug utilization efficiency, which constrain both the duration of therapy and the range of drugs that can be effectively delivered. To address these limitations, we developed the Monolithic Shape-shifting Absorbable Implants for Chronic Care (MoSAIC) platform-a minimally invasive, fully bioresorbable system that integrates compacted drug formulations with a space-efficient device architecture. This approach reduces implant size, eliminates the need for surgical removal, and prolongs therapeutic duration compared to existing implants. We develop compacted formulations of the contraceptive drug levonorgestrel (LNG), and other poorly water-solubility drugs, demonstrating exceptional drug loading (100% w/w) and multi-year sustained drug release via surface-mediated dissolution in rats. When incorporated into MoSAIC devices, these formulations enable high-efficiency drug loading and zero-order drug release kinetics with geometrically tunable rates and durations. As a result, MoSAIC systems can be designed to be smaller, less invasive, and/or longer lasting than current contraceptive implants such as Jadelle(R) and Nexplanon(R). The MoSAIC platform expands access to reversible contraception and supports long-term medication adherence, with the potential to improve health outcomes and quality of life. More broadly, it provides a flexible approach for delivering other potent, low-solubility therapeutics and lays the foundation for a "dose it and forget it" paradigm in chronic disease management, where adherence is designed into the therapy itself.

bioengineering↗

bioRxiv2021

Weakly-Supervised Tumor Purity Prediction FromFrozen H&E Stained Slides

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. In addition, tumor purity has been shown to be correlated with survival outcomes for several diseases. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a slide, using multiple and different types of cancer. This approach allows for a flexible analysis of tumors from whole slide imaging (WSI) of histology hematoxylin and eosin (H&E) slides. Our model predicts tumor type with high accuracy (greater than 80% on an independent test cohort), and tumor purity at a higher accuracy compared to a comparable fully-supervised approach (0.1335 MAE on an independent test cohort). In addition to tumor purity prediction, our approach can identify high resolution tumor regions within a slide, to enrich tumor cell selection for downstream analyses. This model could also be used in a clinical setting, to stratify tumors into high and low tumor purity, using different thresholds, in a cancer-dependent manner, depending on what purity levels correlate with worse disease outcomes. In addition, this approach could be used in clinical practice to select the best tissue block for sequencing. Overall, this approach can be used in several different ways to analyze WSIs of tumor H&E sections.

bioinformatics↗

bioRxiv2018

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.\n\nTo test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images. Using clinical data for 2,182 embryos, we then created a decision tree that integrates clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.\n\nIn conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

bioinformatics↗

bioRxiv2018

Molecular genetic analysis of Steroid Resistant Nephrotic Syndrome: Detection of a novel mutation

Background: Nephrotic syndrome is one of the most common kidney diseases in childhood. About 20% of children are steroid-resistant NS (SRNS) which progress to end-stage renal disease (ESRD). More than 53 genes are associated with SRNS which represent the genetic heterogeneity of SRNS. This study was aimed to screen disease causing mutations within NPHS1 and NPHS2 and evaluate new potential variants in other genes.\n\nMethod: In first phase of study, 25 patients with SRNS were analyzed for NPHS1 (exon 2, 26) and all exons of NPHS2 genes by Sanger sequencing. In the second phase, whole exome sequencing was performed on 10 patients with no mutations in NPHS1 and NPHS2.\n\nResult: WES analysis revealed a novel mutation in FAT1 (c.10570C>A; Q3524K). We identified 4 pathogenic mutations, located in exon 4 and 5 of NPHS2 gene in 20% of patients (V180M, P118L, R168C and Leu156Phe). Also our study has contributed to the descriptions of previously known pathogenic mutations across WT1 (R205C) and SMARCAL1 (R764Q) and a novel polymorphism in CRB2.\n\nConclusion: Our study concludes that mutations of exon 4 and 5 NPHS2 gene are common in Iranian and some other ethnic groups. We suggest conducting WES after NPHS2 screening and further comprehensive studies to identify the most common genes in the development of SRNS, which might help in Clinical impact on management in patients with SRNS.\n\nDetection of a novel mutation in SRNS

genetics↗

bioRxiv2018

Breast Cancer Histopathological Image Classification: A Deep Learning Approach

Breast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. Automated classification of cancers using histopathological images is a chciteallenging task of accurate detection of tumor sub-types. This process could be facilitated by machine learning approaches, which may be more reliable and economical compared to conventional methods.\n\nTo prove this principle, we applied fine-tuned pre-trained deep neural networks. To test the approach we first classify different cancer types using 6, 402 tissue micro-arrays (TMAs) training samples. Our framework accurately detected on average 99.8% of the four cancer types including breast, bladder, lung and lymphoma using the ResNet V1 50 pre-trained model. Then, for classification of breast cancer sub-types this approach was applied to 7,909 images from the BreakHis database. In the next step, ResNet V1 152 classified benign and malignant breast cancers with an accuracy of 98.7%. In addition, ResNet V1 50 and ResNet V1 152 categorized either benign- (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) or malignant- (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) sub-types with 94.8% and 96.4% accuracy, respectively. The confusion matrices revealed high sensitivity values of 1, 0.995 and 0.993 for cancer types, as well as malignant- and benign sub-types respectively. The areas under the curve (AUC) scores were 0.996,0.973 and 0.996 for cancer types, malignant and benign sub-types, respectively. Overall, our results show negligible false negative (on average 3.7 samples) and false positive (on average 2 samples) results among different models. Availability: Source codes, guidelines and data sets are temporarily available on google drive upon request before moving to a permanent GitHub repository.

bioinformatics↗

bioRxiv2017

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.\n\nIn this study, we utilize various computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers.\n\nOur classification pipeline utilizes a basic architecture of CNN, Googles Inceptions within three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. These strategies include training the last layer of Googles Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Googles Inception architectures, Inception-V1 and Inception-V3.\n\nWe demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Googles Inceptions even in presence of extensive tumor heterogeneity. Our pipeline on average achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer types, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-lab/CNN_Smoothie.

bioinformatics↗