Xiaoying Wang

Background: The toxicities associated with immune checkpoint inhibitors (ICIs) can affect nearly all organ systems. Although previous studies have examined specific types of immune-related adverse events (irAEs) associated with ICIs, comprehensive evaluations of the clinical profiles of irAEs using data from the FDA Adverse Event Reporting System database over an extended period have been lacking. Methods: Duplicate records and reports submitted by nonprofessionals were removed for data cleaning. Employing three disproportional statistical methods (Reporting Odds Ratio, Bayesian Confidence Propagation Neural Network, and Multi-Item Gamma Poisson Shrinker) with MedDRA version 27.1 classification, we identified and ranked adverse event signals across System Organ Class (SOC), Standardized MedDRA Query (SMQ), and Preferred Term (PT). Results: A total of 320,556 adverse event reports associated with eight ICIs, involving 123,210 patients, were included in the analysis. At the SOC level, endocrine disorders and hepatobiliary disorders exhibited the strongest signals, while cardiac and respiratory toxicities showed the highest mortality risks across vital organ systems. At the SMQ level, the most prominent signals included noninfectious encephalitis, hypothyroidism, noninfectious myocarditis/pericarditis, eosinophilic pneumonia, and interstitial lung disease. At the PT level, the leading signals were immune-mediated lung disease, immune-mediated enterocolitis, immune-mediated hepatitis, (immune-mediated) myocarditis, hypophysitis, and adrenal insufficiency. Conclusions: This pharmacovigilance study provides a systematic analysis of the clinical spectrum and reporting characteristics of ICI-related irAEs, comparing relative risks across vital organ systems and laying the foundation for interdisciplinary collaboration among oncologists and relevant specialists.

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications.

Medication-related osteonecrosis of the jaw (MRONJ) is a rare and severe adverse drug reaction (ADR) commonly seen in people taking drugs prescribed for metastatic cancer and osteoporosis. Prior studies only analyzed this ADR utilizing the public database Food and Drug Administration Adverse Event Reporting System (FAERS) by different stages (2010-2014, 2015-2021); a more comprehensive study is needed to analyze the MRONJ cases and the associated drugs from a longer time span. We conducted a retrospective pharmacovigilance analysis for all reported MRONJ cases between 2010 and 2023 in FAERS, using preferred terms and the primary suspect drug as searching conditions. Thus, this study aimed to analyze the MRONJ cases and the associated drugs more comprehensively. The reporting odds ratios (RORs) and 95% CIs were calculated for each queried drug. To distinguish the signal levels, we calculated the expected information component (EIC) and its 95% CI, using the Bayesian confidence propagation neural network (BCPNN) methods. We identified 22 846 MRONJ cases. A total of 15 drug classes including 30 suspect drugs showed different positive signal levels; among these drugs, 8, 5, and 17 had strong, medium, and weak intensity signals (+++, ++, and +), respectively. Drug classes involved bisphosphonate, RANKL inhibitor, radiotherapy drug, monoclonal antibody for cancer, corticosteroid, tyrosine kinase inhibitor, mammalian target of rapamycin inhibitor, aromatase inhibitor, cyclin-dependent kinase 4/6 inhibitor, immunomodulator, microtubule inhibitor, selective estrogen receptor modulator, sclerostin monoclonal antibody, estrogen receptor antagonist, and cytotoxic drug. Bisphosphonate, RANKL inhibitor, and radiotherapy drug exhibited higher risk than other classes with higher ROR or EIC 95% CI lower limit. Females had higher MRONJ incidence than males, and the mean age was 67.33 ± 11.71 yr. Compared with previous research, this study identified more drug classes and more novel medications with positive MRONJ signals that warrant further attention.