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This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

This study evaluated various NLP models for detecting bias in medical curricula, finding that fine-tuned BERT models perform well, whereas LLMs, despite being state-of-the-art in many tasks, are unsuitable for this application.

This study presents a system for evaluating semantic similarity between sentences by using token-based matching and improved similarity measures beyond WordNet.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This article reviews challenges in interpreting observational COVID-19 data due to biases from non-random sampling, discussing strategies to address these biases but reporting no new results.

In this study, researchers developed a novel computational framework using deep reinforcement learning to identify strategies for cellular reprogramming in gene regulatory networks, showing its effectiveness in a model of immune response against infection.

In this study, researchers used genetic data from over 52,000 men to identify over 250 genetic loci associated with severe hair loss and developed a predictive algorithm for determining hair loss risk.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

This thesis presents research conducted in partial fulfillment of a Master's degree in Software Systems Engineering but does not report new empirical findings.

The Brigham Eyebrow Tool for Alopecia is a simple and reliable way to measure eyebrow hair loss.

PRDX5 enzyme may contribute to alopecia areata by affecting oxidative stress and autoimmunity.

This study developed a universal ballistic trajectory model for unguided projectiles that considers both aerodynamics and altitude effects, showing comparable results to existing trajectory models and radar measurements.

This study explored a mechanism for making Turing pattern formations more predictable and robust by isolating pattern modes to prevent their sensitivity to initial conditions, which can lead to different outcomes from minor initial changes.

This study found that an ML model incorporating factors like Breslow thickness and age improved cutaneous malignant melanoma prognosis predictions compared to TNM staging, with a C-index of 80% versus 66.6% for TNM alone, suggesting ML's potential for personalized prognostication.

This study found that the WNMFDDA model effectively predicts drug-disease associations, achieving high accuracy in cross-validation and confirming most candidate associations through existing databases.

In this study, researchers reconstituted the face of Bayard, a historical French knight, using cranial characteristics, DNA analyses for skin, eye, and hair traits, and digital analysis of his portrait.

This study explored using large language models to automatically identify monkeypox cases from electronic medical records, finding that models based on DeepSeek features performed better than traditional methods, with logistic regression showing high accuracy in detecting key symptoms like fever and rash.

This study found that while a modified BASP classification for pattern hair loss could classify previously unrecognized types, it was less accurate and harder to use than the existing BASP classification.

This article discusses how DNA-based phenotyping is used by police to create visual profiles of suspects from crime scene samples, but reports no new research findings.

This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.

This study demonstrates that modeling clone size independently of time offers analytical advantages for understanding cell distribution and mutation frequency in epithelial progenitors, extending applications beyond neutral models.

This study found that 2 percent topical minoxidil significantly increased nonvellus hair count and showed moderate regrowth compared to placebo in women with mild to moderate androgenetic alopecia, although participants did not notice the difference.

This study found that online materials about alopecia areata and its treatments are generally difficult to understand, with information on JAK inhibitors being particularly challenging, highlighting a need for clearer educational resources for patients.

This study identified common and unexpected adverse events associated with ritlecitinib in real-world use, providing insights into its safety profile for treating severe alopecia areata.

This study examined different logistic damping effects in a chemotaxis system related to Alopecia Areata and found that certain conditions guarantee a unique, globally bounded classical solution, while others ensure a weak solution, offering new insights into preventing blow-ups in this context.

This study developed a framework to handle missing participant data in systematic reviews for continuous outcomes, finding varied impacts on confidence in effect estimates across reviewed cases.

In this study, researchers identified over 250 new genetic loci linked to severe male pattern baldness, and developed a prediction algorithm that could accurately differentiate between those with severe and no hair loss.