Citations

Ovarian cancer (OC) has the highest mortality rate among gynecologic malignant tumors and is clinically difficult to cure. Traditional Chinese medicine (TCM) has shown unique advantages in the treatment of OC. However, there is currently a lack of normative guidance documents for the treatment of OC with integrated traditional Chinese and western medicine. The guidelines are the first comprehensive approach to diagnosing and treating OC by integrating TCM with western medicine. Adhering to evidence-based principles, the guidelines draw on the most recent advancements in OC treatment from both domestic and international researches. The guidelines formulate a variety of therapeutic methods, including oral Chinese herbal medicine, acupuncture, and external treatments, providing a treatment strategy for the combined use of TCM and western medicine in OC. Furthermore, the guidelines establish a diagnostic and treatment pathway for the holistic management of OC with TCM throughout the disease’s progression. The guidelines were officially published in the China Association of Chinese Medicine in January 2023, which provided a scientific basis for the formulation of clinical diagnosis and treatment strategies and promoted integration with international academic standards. This paper interpreted the features of the guidelines and the 7 PICO issues (Population, Intervention, Comparison, Outcome) in the whole TCM management of OC, aiming to help TCM and western medicine practitioners better understand and apply the guidelines, and provide better medical services for patients with OC.

Breast cancer is the most common malignancy among women worldwide, posing a serious threat to women’s physical and mental health. To address this significant global public health challenge, the Guidelines for the Integrated Diagnosis and Treatment of Breast Cancer with Traditional Chinese and Western Medicine working group, adhering to the principles of evidence-based medicine, has proposed a comprehensive and holistic diagnostic and therapeutic pathway. This pathway integrates the core concepts of traditional Chinese medicine (TCM), such as holistic views and syndrome differentiation and treatment, with the latest advances in breast cancer treatment from both domestic and international perspectives. It objectively evaluates the role of integrated Chinese and western medicine in the all-encompassing and full-cycle management of breast cancer across different phases: the acute phase (perioperative, chemotherapy, and radiotherapy periods), early consolidation phase, late palliative care phase, and follow-up period addressing accompanying symptoms (eg, cancer-related fatigue, bone and joint symptoms). Highlighting the unique features of TCM, the guidelines provide a model for integrated breast cancer treatment for clinicians at various levels of health care institutions. This article offers a detailed interpretation of the guideline’s key highlights and 11 PICO questions concerning TCM management throughout the breast cancer continuum, aiming to enhance the understanding, application, and promotion of the guidelines.

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Methods: Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. Conclusions: These findings highlight GAI’s potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use.

Abstract Objective The rapid evolution of the COVID-19 virus has led to the development of different vaccine shots, each designed to combat specific variants and enhance overall efficacy. While vaccines have been crucial in controlling the spread of the virus, they can also cause adverse events (AEs). Understanding these relationships is vital for vaccine safety monitoring and surveillance. Methods In our study, we collected data from the Vaccine Adverse Event Reporting System (VAERS) and social media platforms (Twitter and Reddit) to extract relationships between COVID-19 vaccine shots and adverse events. The dataset comprised 771 relation pairs, enabling a comprehensive analysis of adverse event patterns. We employed state-of-the-art GPT models, including GPT-3.5 and GPT-4, alongside traditional models such as Recurrent Neural Networks (RNNs) and BioBERT, to extract these relationships. Additionally, we used two sets of post-processing rules to further refine the extracted relations. Evaluation metrics including precision, recall, and F1-score were used to assess the performance of our models in extracting these relationships accurately. Results The most commonly reported AEs following the primary series of COVID-19 vaccines include arm soreness, fatigue, and headache, while the spectrum of AEs following boosters is more diverse. In relation extraction, fine-tuned GPT-3.5 with Sentence-based Relation Identification achieved the highest precision of 0.94 and a perfect recall of 1, resulting in an impressive F1 score of 0.97. Conclusion This study advances biomedical informatics by showing how large language models and deep learning models can extract relationships between vaccine shots and adverse events from VAERS and social media. These findings improve vaccine safety monitoring and clinical practice by enhancing our understanding of post-vaccination symptoms. The study sets a precedent for future research in natural language processing and biomedical informatics, with potential applications in pharmacovigilance and clinical decision-making.

Abstract Introduction COVID-19, a global health crisis, prompted rapid vaccine development. Bivalent vaccines by Pfizer-BioNTech and Moderna target both original and Omicron strains. Despite vaccination efforts, the persistence of adverse events underscores the importance of ongoing safety monitoring. Method We collected COVID-19 bivalent vaccine adverse event reports from the Vaccine Adverse Event Reporting System (VAERS) (September 15, 2022, to September 1, 2023). The Medical Dictionary for Regulatory Activities (MedDRA) categorized reported symptoms into corresponding concepts. Statistical analyses included zero-truncated Poisson regression for estimating symptom rates, logistic regression for identifying risk factors, and Spearman correlation for assessing non-parametric relationships between adverse events in different System Organ Classes (SOCs). Results Our analysis of COVID-19 bivalent vaccine adverse events identified significant correlations between SOCs. Notably, a positive correlation (ρ = 0.3) linked Infections (SOC 1) with Respiratory disorders (SOC 13), while a negative correlation (ρ=-0.3) connected Investigations (SOC 23) with Injury (SOC 24). Zero-truncated Poisson modeling estimated an average of 6.179 symptoms per individual, varying by age, gender, and vaccine. Gender and age significantly influence reported adverse events associated with COVID-19 bivalent vaccines. Females generally experienced higher odds for most SOCs compared to males, except for cardiac and product-related issues. Older adults showed a higher likelihood of symptoms across various SOCs, while children and adolescents had distinct susceptibility patterns. Additionally, Moderna and Pfizer vaccines were associated with different SOCs, with Moderna linked mainly to immune and skin disorders and Pfizer associated primarily with cardiac and metabolic issues. Conclusion In conclusion, our study aligned self-reported items with widely-accepted medical terminology and rigorously analyzed adverse events following COVID-19 bivalent vaccine using advanced statistical methods. The significant findings provide crucial insights, guiding evidence-based strategies and interventions for vaccine safety.

Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.

PURPOSE To investigate the clinical behavior of breast cancer in young BRCA carriers according to the specific BRCA gene ( BRCA1 v BRCA2 ) and the association of the timing of genetic testing (before v at diagnosis) with prognosis. METHODS This was an international, multicenter, hospital-based, retrospective cohort study that included 4,752 patients harboring germline pathogenic/likely pathogenic variants (PVs) in BRCA1 or BRCA2 , who were diagnosed with stage I-III invasive breast cancer at 40 years or younger between January 2000 and December 2020 in 78 centers worldwide (ClinicalTrials.gov identifier: NCT03673306 ). RESULTS Compared with BRCA2 carriers (n = 1,683), BRCA1 carriers (n = 3,069) had more frequently hormone receptor–negative (74.4% v 15.5%) and high-grade (77.5% v 49.1%) tumors. Similar outcomes were observed in BRCA1 and BRCA2 carriers but with a different pattern and risk of disease-free survival events over time. Compared with patients tested for BRCA at diagnosis (ie, between 2 months before and up to 6 months after diagnosis; n = 1,671), those tested before diagnosis (ie, any time up to 2 months before diagnosis; n = 411) had smaller tumors (T1: 61.3% v 32.4%), less nodal involvement (N0: 65.9% v 50.8%), less frequently received chemotherapy (84.4% v 92.9%), and axillary dissection (37.5% v 47.4%). Patients tested before diagnosis had better overall survival (OS; unadjusted hazard ratio [HR], 0.61 [95% CI, 0.40 to 0.92]); however, this result lost statistical significance after adjustment for potential confounders including tumor stage (adjusted HR, 0.74 [95% CI, 0.47 to 1.15]). CONCLUSION This global study provides evidence on the different clinical behavior of breast cancer in young BRCA1 and BRCA2 carriers. Identifying a BRCA PV in healthy individuals was associated with earlier-stage breast cancer diagnosis and lower treatment burden, as well as better unadjusted OS.

Background: Electronic Health Records (EHRs) encompass valuable data essential for disease prediction. The application of artificial intelligence (AI), particularly deep learning, significantly enhances disease prediction by analyzing extensive EHR datasets to identify hidden patterns, facilitating early detection. Recently, numerous foundation models pretrained on extensive data have demonstrated efficacy in disease prediction using EHRs. However, there remains some unanswered questions on how to best utilize such models, especially with very small fine-tuning cohorts. Methods: We utilized Med-BERT, an EHR-specific foundation model, and reformulated the disease binary prediction task into a token prediction task and a next visit mask token prediction task to align with Med-BERT’s pretraining task format in order to improve the accuracy of pancreatic cancer (PaCa) prediction in both few-shot and fully supervised settings. Results: The reformulation of the task into a token prediction task, referred to as Med-BERT-Sum, demonstrated slightly superior performance in both few-shot scenarios and larger data samples. Furthermore, reformulating the prediction task as a Next Visit Mask Token Prediction task (Med-BERT-Mask) significantly outperformed the conventional Binary Classification (BC) prediction task (Med-BERT-BC) by 3% to 7% in few-shot scenarios with data sizes ranging from 10 to 500 samples. These findings highlight that aligning the downstream task with Med-BERT’s pretraining objectives substantially enhances the model’s predictive capabilities, thereby improving its effectiveness in predicting both rare and common diseases. Conclusions: Reformatting disease prediction tasks to align with the pretraining of foundation models enhances prediction accuracy, leading to earlier detection and timely intervention. This approach improves treatment effectiveness, survival rates, and overall patient outcomes for PaCa and potentially other cancers.

Abstract Advances in genomic medicine have accelerated the identification of mutations in disease- associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models have been generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models can increase their predictive power. We carried out an exhaustive mutational analysis of the disease gene PMM2 and subjected structural models of each variant to MDS. AI models trained on this dataset outperformed existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, was also able to predict the pathogenicity of several PMM2 mutations currently considered of unknown significance. We believe this new model will help alleviate the burden of unknown variants in genomic medicine.