Digital Commons Network^™

Diverse Case Series Of Granulomatous Peritonitis Mimicking Advanced Ovarian Cancer, Rachel L Furuya, Bobbie J Rimel, Richard Tsai, Rebecca Ann Brooks, L Stewart Massad, Premal H Thaker, John D Pfeifer

2020-Current year OA Pubs

BACKGROUND: Epithelial ovarian cancer commonly presents with vague symptoms that delay diagnosis until disease is advanced. Granulomatous peritonitis is a term used to describe granulomatous inflammation within the peritoneal cavity and mimics advanced stage ovarian cancer clinically and on imaging. The goal of this study was to examine the frequency and characteristics of cases of granulomatous peritonitis mimicking ovarian cancer at a single institution and to describe the etiology in this population.

METHODS: Eight cases were identified with pathology conformation of granulomatous disease and absence of cancer. The etiologies include pelvic tuberculosis, ruptured dermoid cyst, ruptured hemorrhagic corpus luteum, prior …

Association Between Anticoagulant-Related Bleeding And Mortality In Patients With Hematological Malignancies And Cancer-Associated Venous Thromboembolism, Tzu-Fei Wang, Suhong Luo, Brian F Gage, Martin W Schoen, Amber Afzal, Kenneth Carson, Su-Hsin Chang, Amir Mahmoud, Kristen M Sanfilippo

2020-Current year OA Pubs

INTRODUCTION: Patients with hematological malignancies are at an increased risk of severe bleeding. Anticoagulant (AC) therapy further increases this risk. Mortality after these bleeds is unclear and may differ by bleeding site. Aim To evaluate the association between bleeding and mortality in patients with hematological malignancies prescribed AC therapy for cancer-associated venous thromboembolism (VTE).

METHODS: In a nationwide cohort of US Veterans (2012-2020), we identified patients with hematological malignancies and cancer-associated VTE prescribed AC therapy. Bleeding events were identified by a previously validated algorithm using hospitalization International Classification of Disease (ICD) codes. Within 12 months of AC therapy initiation, we …

The Interplay Of Website Characteristics, Attitude, And Booking Intentions For Different Hotel Websites: A Theory Of Reasoned Action Perspective, Atefeh Charmchian Langroudi, Imran Rahman, Maryam Charmchian Langroudi, John Stephan, Parisa Mohammadi

Journal of Global Hospitality and Tourism

Using the theory of reasoned action as the underlying theoretical foundation, this study examines the direct effects of compatibility, perceived usefulness, communicability, perceived risk, and perceived playfulness on attitudes toward online booking and the indirect effects of these variables on consumers’ intention to use three different online hotel booking channels - independent hotel websites, chain hotel websites, and third-party hotel websites. Additionally, this study tests the direct effect of attitude towards online booking on consumers’ intention to use the three online booking channels. Collected data from 638 participants via Amazon M-Turk was analyzed using partial least squares based structural equation …

Strengthening The Paediatric Clinical Trial Ecosystem To Better Inform Policy And Programmes, James A. Berkley, Judd L. Walson, Glenda Gray, Fiona Russell, Zulfiqar Ahmed Bhutta, Per Ashorn, Shane A. Norris, Ebunoluwa A. Adejuyigbe, Rebecca Grais, Bernhards Ogutu

Institute for Global Health and Development

The first WHO Global Clinical Trials Forum was convened in November, 2023 to develop a shared vision of an effective global clinical trial infrastructure. The Paediatric Clinical Trials Working Group was formed to provide perspectives, identify challenges, and propose solutions to strengthen the paediatric clinical trials ecosystem. Participants represented paediatric disciplines, including infectious diseases, nutrition, neonatology, pharmacology, oncology, neurodevelopment, public health, and policy. Childhood diseases have profound lifelong effects on health, livelihoods, and societies. Investment in early childhood results in highly cost-effective changes to lifelong health, productivity, and human capital returns. Yet, there remain substantial gaps in knowledge on the …

Measuring The Extent Of Cyberbullying Comments In Facebook Groups For Mosul University Students, Kanaan J. Brakas, Mafaz Alanezi

Mesopotamian Journal of CyberSecurity

The widespread utilization of social media platforms such as Facebook, Twitter, and Instagram inside academic organizations has become fundamental for student correspondence and joint effort, yet it has simultaneously prompted an increase in cyberbullying incidents. Cyberbullying is embodied by offensive and harmful comments that sabotage the casualty's prosperity. This study plans to resolve this issue by fostering an extensive module to distinguish cases of cyberbullying inside Facebook groups of Mosul University students. Our methodology starts with the collection of data in the Arabic language, which is then exposed to careful manual handling to label comments and eliminate clamor-like copies and …

Dagchains: An Improved Blockchain Structure Based On Directed Acyclic Graph Construction And Distributed Mining, Abbas Mahdi, Furkan Rabee

Mesopotamian Journal of CyberSecurity

Blockchain has revolutionized cryptocurrency and completely changed the management of data and transactions in the digital world because of its decentralized nature, improved transparency, increased security measures, ability to facilitate commercial trading between untrusted parties, and contribution to preventing fraudulent activity. However, the primary issue with blockchain systems is their limited scalability, as they can only process a maximum of 30 transactions per second (TPS), like Ethereum and Bitcoin. In this paper, we introduce an approach using the Nakamoto protocol and the Directed Acyclic Graph (DAG) to develop an improved infrastructure known as a DAGchains that can increase the processing …

A Dynamic Dna Cryptosystem For Secure File Sharing, Kanaan J. Brakas, Mafaz Alanezi

Mesopotamian Journal of CyberSecurity

The digital age relies heavily on file sharing, which has become more important with the increasing use of digital data and the Internet. Along with this increasing importance come major and widespread security issues, especially for files containing sensitive or vital data, such as those related to commercial, military, or healthcare sectors. The most common and effective way to protect the security and privacy of shared files containing sensitive information is still encryption. With the development of high-efficiency devices and massive processing capabilities, traditional encryption methods are increasingly under pressure from modern computer security threats, particularly quantum computing. This study …

A Secure Password Based Authentication With Variable Key Lengths Based On The Image Embedded Method, Seerwan Waleed Jirjees, Farah Flayyeh Alkhalid, Ahmed Mudheher Hasan, Amjad Jaleel Humaidi, Amjad Jaleel Humaidi

Mesopotamian Journal of CyberSecurity

Passwords are widely used to secure client–server communication in authentication-based systems that are used over untrusted transmission media; thus, users' passwords are vulnerable, and systems are vulnerable to hacking. In this paper, we propose a new philosophy for secret password encryption where encryption is secure between communicating parties during a communication session while ensuring resistance to man-in-the-middle attacks and preventing dictionary attacks in violation of trustworthiness without relying on trusted third parties or other out-of-band mechanisms for authentication, which will be encrypted on the basis of the data from the image sent during authentication. The proposed encryption scheme will encode …

Development Of Real-Time Threat Detection Systems With Ai-Driven Cybersecurity In Critical Infrastructure, Noora Zidan Khalaf, Israa Ibraheem Al Barazanchi, Israa Ibraheem Al Barazanchi, A. D. Radhi, A. D. Radhi, Pritesh Shah, Ravi Sekhar

Mesopotamian Journal of CyberSecurity

Protection of infrastructure is becoming increasingly demanding, and the sophistication and severity of cyber threats are increasing daily. Traditional threat detection techniques cannot match the ever-evolving nature of cyber threats, which increases the number of false positives and attack misses. AI-driven methods address these shortfalls via the use of advanced learning algorithms to detect and respond to newly discovered threats in real time. They are largely static rule-based or signature-based attacks, and they do not perform effectively against zero-day attacks and highly organized, advanced attacks. Given the critical need to protect digital infrastructures such as energy, transport, and communications from …

Cybersecurity Risk Assessment For Identifying Threats, Vulnerabilities And Countermeasures In The Iot, Mohammed Amin Almaiah, Rami Shehab, Tayseer Alkhdour, Mansour Obeidat, Mansour Obeidat

Mesopotamian Journal of CyberSecurity

To increase the number of connected devices in IoT networks, several types of new cyber threats and attacks also arise in the IoT. Any cyber-attack can cause significant damage to IoT networks and loss of service. Therefore, identifying these threats is one of the main steps in risk assessment and should be considered to create a robust security strategy to avoid IoT network breaches. Cybersecurity assessment in IoT networks is a prime process due to the evolving nature of cyberattacks. Therefore, this research focuses on addressing the current gap by performing a comprehensive analysis to identify the critical threats, vulnerabilities …

Enhancing User Authentication Through The Implementation Of The Forestpa Algorithm For Smart Healthcare Systems, Nurul Syafiqah Zaidi, Al-Fahim Mubarak Ali, Ahmad Firdaus, Adamu Abubakar Ibrahim, Adamu Abubakar Ibrahim, Mohd Faizal Ab Razak

Mesopotamian Journal of CyberSecurity

The machine learning-based authentication model for smart healthcare systems represents a crucial step in addressing the needs of an ever-evolving healthcare industry. The need to protect sensitive patient data, ensure regulatory compliance, and reduce medical errors, especially in the context of telemedicine and remote monitoring, underscores the importance of such systems. Traditional authentication methods frequently lack sufficient security, resulting in potential breaches. Relying solely on usernames and passwords, without supplementary authentication measures, exposes systems to advanced security attacks. As it involves patients’ health and human lives, it is important to provide additional authentication, fast machine learning-based authentication models and high …

An English-Swahili Email Spam Detection Model For Improved Accuracy Using Convolutional Neural Networks, Leshan Sankaine, John G. Ndia, Dennis Kaburu

Mesopotamian Journal of CyberSecurity

E-mail has become an essential tool for digital communication, facilitating global networking and information exchange. However, spam emails, particularly those in multilingual contexts, pose a significant threat to cybersecurity. In 2023, cyber-related attacks cost Africa approximately USD 10 billion, with the Kenyan economy suffering losses of USD 383 million, 45% of which resulted from phishing and spam emails. While spam detection has been extensively studied for English, low-resource languages such as Swahili lack sufficient research and datasets. Swahili is spoken by about approximately 200 million people, mainly from East Africa. The same speakers use English as a medium of communication. …

Healthcare Security In Edge-Fog-Cloud Environment Using Blockchain: A Systematic Review, Zaid J. Al-Araji, Mahmood S. Alkhaldee, Ammar Awad Mutlag, Zaid Ali Abdulkadhim, Zaid Ali Abdulkadhim, Sharifah Sakinah Syed Ahmad, Namaa N. Hikmat, Ayad Yassen, Abdullah A. Ibrahim Al-Dulaimi, Najwa N. Hazem Al-Sheikh, Ammar Hashim Ali

Mesopotamian Journal of CyberSecurity

Context: Our domain of expertise is healthcare security, which protects the sensitive information of patients and the integrity of the healthcare services provided to them. As health record digitization grows, along with the adoption of advanced technologies, data protection becomes more complex and vital. Realizing the transformative potential of blockchain (BC) in healthcare security requires critical exploration into the prevailing centralization of sensitive patient information, shedding traditional paradigms, and embracing the digital decentralization enabled by the BC realm. Objectives: The purpose of this study was to analyse prior research and provide a comprehensive overview of the literature on BC-based healthcare …

Cnns In Image Forensics: A Systematic Literature Review Of Copy-Move, Splicing, Noise Detection, And Data Poisoning Detection Methods, Mohammed R. Subhi, Salman Yussof, Liyana Adilla Binti Burhanuddin Adilla Binti Burhanuddin, Firas Layth Khaleel, Firas Layth Khaleel

Mesopotamian Journal of CyberSecurity

Image forgery, such as copy-move and splicing, poses significant challenges to the authenticity of digital images, and this challenge is exacerbated by the rapid development of image manipulation tools. Convolutional neural networks (CNNs) have shown promise in detecting such forgeries, but limitations remain, especially in detecting small duplicate regions and low-contrast regions, as well as in dealing with optical artefacts such as noise and blur. This systematic literature review examines CNN-based approaches to detect image forgery and explores strategies to mitigate data poisoning attacks, which can compromise the integrity of machine learning models. To our knowledge, there are currently no …

Sdn-Cloud Incident Detection & Response With Segmented Federated Learning For The Iot, Anas Harchi, Hicham Toumi, Mohamed Talea

Mesopotamian Journal of CyberSecurity

The accelerated proliferation of Internet of Things (IoT) apparatuses has rendered intrusion detection and incident response progressively arduous owing to device diversity, constrained resources, and concerns regarding data confidentiality. Addressing these challenges is paramount to sustaining secure and resilient IoT ecosystems. This manuscript introduces an innovative framework that amalgamates software-defined networking (SDN) with segmented federated learning (SFL) to augment the effectiveness and reactivity of anomaly detection within the IoT. The proposed methodology delineates the federated learning (FL) process, facilitating lightweight, localized model training customized to the capabilities of individual IoT devices. The SDN is utilized to dynamically regulate network flows …

Optimized Deep Learning Model Using Binary Particle Swarm Optimization For Phishing Attack Detection: A Comparative Study, El-Sayed M. El-Kenawy, Marwa M. Eid, Hussein Lafta Hussein, Ahmed M. Osman, Ahmed M. Osman

Mesopotamian Journal of CyberSecurity

Phishing attacks manipulate users to disclose critical information, resulting in cybersecurity risks. Traditional phishing detection algorithms usually have large false positive rates and poor feature selection, degrading performance. This paper presents an optimized phishing detection framework that integrates binary particle swarm optimization (BPSO)-based feature selection (FS) with deep learning models. Six deep learning architectures were evaluated on the selected feature subset to identify the most effective model for accurate phishing classification. BPSO was used to select suitable attributes on a public Kaggle dataset with 10,000 samples, comprising phishing and legitimate website data with 48 attributes. NumDots, UrlLength, IpAddress, and NoHttps …

Ensure Privacy-Preserving Using Deep Learning, Abeer Dawood Salman, Ruqayah Rabeea Al-Dahhan

Mesopotamian Journal of CyberSecurity

Deep learning has emerged as a powerful approach for treating complex real-world challenges. However, the performance of the deep learning models is heavily reliant on access to large volumes of high-quality training data—an aspect often constrained by privacy concerns. Ensuring data availability while preserving user confidentiality remains a pressing issue. In response, cryptographic techniques like homomorphic encryption (HE), which are grounded in strict mathematical principles, present hopeful solutions for securing data on digital platforms without compromising its usability for learning models. It performs computations on encrypted data without revealing the underlying plaintext. The main attractive feature of this technique is …

An Efficient Distributed Intrusion Detection System That Combines Traditional Machine Learning Techniques With Advanced Deep Learning, Wisam Ali Hussein Salman, Chan Huah Yong

Mesopotamian Journal of CyberSecurity

The Internet of Things (IoT), as a network of connected devices, enhances modern life but also introduces significant security vulnerabilities. Addressing these challenges requires intelligent and adaptive cybersecurity systems to ensure secure communication and protection against emerging threats. Among these systems, intrusion detection systems (IDSs) play a vital role in safeguarding IoT environments by continuously monitoring network traffic, detecting abnormal activities, and identifying or preventing unauthorized access and denial-of-service (DoS) attacks. However, despite their importance, IDSs face several limitations, including high false positive and false negative rates, delayed response times to security incidents, and substantial consumption of device resources. This …

Blockchain And Federated Learning In Edge-Fog-Cloud Computing Environments For Smart Logistics, Guma Ali, Adebo Thomas, Maad M. Mijwil, Kholoud Al-Mahzoum, Kholoud Al-Mahzoum, Ayodeji Olalekan Salau, Ioannis Adamopoulos, Indu Bala, Aseed Yaseen Rashid Al-Jubori

Mesopotamian Journal of CyberSecurity

The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. …

An Artificial Intelligence-Based System For Detecting Meta Fake Profiles Via Gradient Boosting And Multilayer Perception, Ruaa A. Al-Falluji, Marwan Ali Albahar, Ahmad Mousa Altamimi

Mesopotamian Journal of CyberSecurity

The rise of metaverse platforms has renewed interest in detecting fake profiles, which pose a significant threat to digital ownership and asset transactions within these virtual environments. If digital ownership is not guaranteed, platforms risk missing the point of the metaverse. Current supervised learning techniques for fake profile detection often struggle to maintain acceptable accuracy and interpretability in practice. To address this problem, this study investigates the application of two machine learning models, multilayer perceptron and gradient boosted trees, for detecting fake profiles, with model evaluation performed via two Explainable AI (XAI) techniques, Local Interpretable Model-agnostic Explanations (LIME) and SHapley …

Anti-Cyber Childhood Exploitation: An Online Game Chat Monitoring System, Saja J. Mohammed, Awos K. Ali, Ibrahim M. Ahmed

Mesopotamian Journal of CyberSecurity

Despite its revolutionary benefits, the Internet has been utilized to abuse children through chat in online gaming. Exhibiting harmful content can negatively impact children's psychology and behaviour, particularly during their developmental years. This paper examines the psychological effects of online predation and the growing risks posed by Internet predators (with focus on children under 15 years old). This paper proposed an Anti-Cyber Childhood Exploitation (A2CE) system, a comprehensive framework designed to detect and prevent three major forms of online abuse: psychological manipulation, cyberbullying, and online grooming. Leveraging advanced Natural language processing (NLP) techniques, A2CE analyses online conversations in real time. …

Adversarial Attacks On Hybrid Attention Integrated Transfer Learning For Lung Cancer Ct Classification, Omar Ibrahim Obaid, Abdulbasit Alazawi

Mesopotamian Journal of CyberSecurity

Deep learning–based classification of lung cancer from CT images can achieve high accuracy but is vulnerable to adversarial attacks that introduce imperceptible perturbations, potentially leading to misdiagnoses. Paying attention to neural networks in transfer learning could improve both the effectiveness and resistance to change. In this paper, we propose a hybrid framework that combines a MobileNetV2 backbone with channel–spatial attention modules and white‑box adversarial testing via the fast‑focused gradient sign method (FFGSM) and projected gradient descent under an L₂ norm constraint (PGDL₂). The model was trained end‑to‑end on a stratified CT dataset of 3,451 images (normal, benign, malignant) with adversarial …

Optimizing Cybersecurity In 5g-Enabled Iot Networks Via A Resource-Efficient Random Forest Model, Zainab Ali Abbood, Aysar Hadi Oleiwi, Raghad Tariq Al-Hassani, Jenan Ayad, Jenan Ayad

Mesopotamian Journal of CyberSecurity

With the widespread deployment of 5G networks together with many Internets of Things (IoT) devices, the demand for secure space has grown substantially. The proposed research focuses on improving the existing cybersecurity solutions in 5G based IoT networks through resource-efficient implementation of the random forest (RF) model. This study evaluated an IDPS based on a completely simulated 5G-era IoT scenario. The study evaluated an IDPS using a simulated 5G-era IoT environment replicating real-world device interactions. Synthetic datasets representing normal and malicious traffic, including distributed denial-of-service (DDoS) attacks, were used for model training and testing. The performance of the RF model …

Integrating Data Visualizations To Enhance Effectiveness Of Mitigation Reports, Eric A. Kowalik

Master's Theses (2009 -)

No abstract provided.

Multi-Omic Analysis Of Biological Aging Biomarkers In Long-Term Calorie Restriction And Endurance Exercise Practitioners: A Cross-Sectional Study, Giovanni Fiorito, Valeria Tosti, Silvia Polidoro, Beatrice Bertozzi, Nicola Veronese, Edda Cava, Francesco Spelta, Laura Piccio, Dayna S Early, Daniel Raftery, Paolo Vineis, Luigi Fontana

2020-Current year OA Pubs

Calorie restriction (CR) and physical exercise (EX) are well-established interventions known to extend health span and lifespan in animal models. However, their impact on human biological aging remains unclear. With recent advances in omics technologies and biological age (BioAge) metrics, it is now possible to assess the impact of these lifestyle interventions without the need for long-term follow-up. This study compared BioAge biomarkers in 41 middle-aged and older adult long-term CR practitioners, 41 age- and sex-matched endurance athletes (EX), and 35 sedentary controls consuming Western diets (WD), through PhenoAge: a composite score derived from nine blood-biomarkers. Additionally, a subset of …

Parent-Child Agreement On Fatigue In Pediatric Otolaryngology Patients, Amy E Ensing, Amy L Zhang, Rebecca Z Lin, Emma K Landes, Henok Getahun, Judith E C Lieu

2020-Current year OA Pubs

OBJECTIVES: To investigate parent-child agreement on fatigue reporting in pediatric otolaryngology patients and whether agreement might vary by diagnosis and other patient factors.

STUDY DESIGN: Cross-sectional survey.

METHODS: Patients ages 5-18 years old being evaluated for hearing loss (HL) or obstructive sleep apnea (OSA) were recruited from a pediatric otolaryngology clinic and sleep center. Children and parents completed the Pediatric Quality of Life Inventory Multidimensional Fatigue Scale (PedsQL MFS).

RESULTS: Responses of 42 patients with HL, 49 with OSA, 10 with sleep-disordered breathing (SDB), and 34 controls were analyzed. Parent and child PedsQL MFS scores were strongly correlated (Pearson

CONCLUSION: …

A Scoping Review Of Interventions To Address Financial Toxicity In Pediatric And Adult Patients And Survivors Of Cancer, Christina Ping, D Carolina Andrade, Ashley Housten, Michelle Doering, Eliana Goldstein, Mary C Politi

2020-Current year OA Pubs

BACKGROUND: Financial toxicity (FT) is a common and significant challenge for people with cancer, impacting immediate clinical outcomes such as treatment adherence, as well as long-term outcomes such as quality of life and mortality. Multiple studies have tested interventions to address FT and develop recommendations for their implementation.

METHODS: In this scoping review, we analyzed thirty-six studies across 35,405 participants examining institution-based interventions for FT in both pediatric and adult patients and survivors of cancer in the U.S.

RESULTS: Common interventions included: financial navigation (n = 15), direct financial/medical assistance (n = 8), financial counseling or coaching (n = 5), …

Model For End Stage Liver Disease Excluding International Normalized Ratio Predicts Severe Right Ventricular Failure After Heartmate 3 Implantation In A Contemporary Cohort, David S Lambert, Ana María Picó, Justin D Vincent, Elena Deych, Erin Coglianese, Joel D Schilling, Justin M Vader, Bin Q Yang

2020-Current year OA Pubs

BACKGROUND: Right ventricular failure (RVF) after left ventricular assist devices is associated with significant morbidity and mortality. Therefore, identifying patients at risk for severe RVF is important for clinical decision-making. Current risk prediction models were not developed in contemporary populations with left ventricular assist devices and have limited clinical applicability. In this study, we sought to evaluate whether the Model for End Stage Liver Disease Excluding International Normalized Ratio (MELD-XI) can predict severe RVF after HeartMate 3 implantation.

METHODS: We retrospectively analyzed all adult patients who received HeartMate 3 left ventricular assist devices as initial implantation at 2 academic medical …

The Implications Of The American Board Of Radiology's Decision To Relinquish Its Specialty Board Designation On Prospective Authorized Medical Physicists (Amps) And Radiation Safety Officers (Rsos), Christopher J Tien, Samantha J Simiele, Joann I Prisciandaro, Jacqueline E Zoberi, Y Jessica Huang, William A Hinchcliffe, Hania A Al-Hallaq

2020-Current year OA Pubs

In order to independently supervise the medical use of byproduct material, physicists in the United States (US) must legally meet the qualifications defined by the Nuclear Regulatory Commission (NRC) in the 35th part of the tenth title of the Code of Federal Regulations (§ 10 CFR Part 35). The American Board of Radiology (ABR) relinquished its NRC-recognized specialty board (NSB) status at the end of 2023, which eliminated the NSB application pathway for those who earn ABR certification in 2024 and beyond. While these changes in NSB status are not retroactive and will not affect eligibility for diplomates who already …

Unleashing The Power Of Multiomics: Unraveling The Molecular Landscape Of Peripheral Neuropathy, Julie Choi, Zitian Tang, Wendy Dong, Jenna Ulibarri, Elvisa Mehinovic, Simone Thomas, Ahmet Höke, Sheng Chih Jin

2020-Current year OA Pubs

Peripheral neuropathies (PNs) affect over 20 million individuals in the United States, manifesting as a wide range of sensory, motor, and autonomic nerve symptoms. While various conditions such as diabetes, metabolic disorders, trauma, autoimmune disease, and chemotherapy-induced neurotoxicity have been linked to PN, approximately one-third of PN cases remain idiopathic, underscoring a critical gap in our understanding of these disorders. Over the years, considerable efforts have focused on unraveling the complex molecular pathways underlying PN to advance diagnosis and treatment. Traditional methods such as linkage analysis, fluorescence in situ hybridization, polymerase chain reaction, and Sanger sequencing identified initial genetic variants …