Browsing by Author "Pantic, Igor V. (36703123600)"

Corneal diseases and injuries, with their substantial global prevalence and adverse effects on quality of life, demographics, occupations, and lifestyles, pose a pressing healthcare challenge worldwide. Limited treatment options are unable to halt the progression of end-stage conditions, where transplantation is the ideal solution. Unfortunately, the high demand and low supply of corneal tissues, donor-recipient mismatches, and host rejections leading to graft failure limit this ideal option. As a result, there is a critical need for alternative interventions. This article aims to establish a bioengineered corneal model generated from the cadaveric decellularized extracellular matrix (dECM) that can repurpose discarded human corneal tissues to potentially increase the supply of transplantable tissues. Comparable studies have primarily focused on reendothelialization and re-epithelialization. Therefore, we hypothesize devising a method to support scaffold reinnervation emanating from prominent nerve plexi spanning the stroma to the epithelium in a patient-centered manner, using peripheral blood mononuclear cells. © 2024 The Author(s)

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, various image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (Topalovic et al.) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest Santos et al. covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature. Copyright © 2022 Pantic, Shakeel, Petroianu and Corridon.

Objectives: To train and test a Random Forest machine learning model with the ability to distinguish AI-generated from human-generated textual content in the domain of public health, and public health policy. Study design: Supervised machine learning study. Methods: A dataset comprising 1000 human-generated and 1000 AI-generated paragraphs was created. Textual features were extracted using TF-IDF vectorization which calculates term frequency (TF) and Inverse document frequency (IDF), and combines the two measures to produce a score for individual terms. The Random Forest model was trained and tested using the Scikit-Learn library and Jupyter Notebook service in the Google Colab cloud-based environment, with Google CPU hardware acceleration. Results: The model achieved a classification accuracy of 81.8 % and an area under the ROC curve of 0.9. For human-generated content, precision, recall, and F1-score were 0.85, 0.78, and 0.81, respectively. For AI-generated content, these metrics were 0.79, 0.86, and 0.82. The MCC value of 0.64 indicated moderate to strong predictive power. The model demonstrated robust sensitivity (recall for AI-generated class) of 0.86 and specificity (recall for human-generated class) of 0.78. Conclusions: The model exhibited acceptable performance, as measured by classification accuracy, area under the receiver operating characteristic curve, and other metrics. This approach can be further improved by incorporating additional supervised machine learning techniques and serves as a foundation for the future development of a sophisticated and innovative AI system. Such a system could play a crucial role in combating misinformation and enhancing public trust across various government platforms, media outlets, and social networks. © 2025 The Royal Society for Public Health

Background: Even though thrombophilias are associated with negative pregnancy outcomes (PO), there is not a consensus of when thrombophilias should be screened for, or how they affect placental vascularization during pregnancy. Therefore, the main aim of this study was to discover inherited thrombophilias (IHT) in the first trimester in women with otherwise no indications for thrombophilia screening, based on their vascularization parameters. LMWH treatment in improvement of placental vascularization and PO was also assessed. Finally, the classification of thrombophilias based on observed obstetric risks was proposed. Methods: Women were included in study based on their poor gestational sac and later utero-placental juncture vascularization signal and screening for inherited thrombophilias. LMWH were then initiated and Resistance index of Uterine artery (RIAU) was followed alongside PO (preterm birth, preeclampsia, placental abruption, intrauterine growth reduction). Study group consisted of women with combined inherited thrombophilias. Control group consisted of patients with inherited thrombophilias who have received LMWH therapy since pregnancy beginning. Findings: Out of 219 women, 93 had IHT, and 43 had combined IHT. All pregnancies both in both groups ended up with live births. Vaginal birth was more present in the control group (p <.001), and all women in study group delivered by CS. Premature birth was present in 8.4% of patients in control group, and in 32.55% of the patients in the study (p <.001). PE wasn’t noted, and only 1 case of PA in control group. In the control group, 6.5% patients had IUGR, and 32.55% in the study group (p <.05). Based on RIAU and PO, thrombophilia categories were established: S (severe), MO (moderate), MI (mild) and L (low). Higher risk thrombophilias had higher RIAU later in the pregnancy, earlier pregnancy termination and Intrauterine Growth Reduction (IUGR). Conclusions: Thrombophilias should be considered and screened when poor vascularization is noted early in the pregnancy with Doppler sonography. Intervention with LMWH prevents adverse PO in these patients. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality. Copyright © 2023 Pantic, Cumic, Valjarevic, Shakeel, Wang, Vurivi, Daoud, Chan, Petroianu, Shibru, Ali, Nesic, Salih, Butt and Corridon.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality. Copyright © 2023 Pantic, Cumic, Valjarevic, Shakeel, Wang, Vurivi, Daoud, Chan, Petroianu, Shibru, Ali, Nesic, Salih, Butt and Corridon.