Background: Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism.Methods: We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children’s Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search.Results: We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported.Conclusion: We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.

Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. This study utilizes intelligent prediction models to explore the key features of titanium implant materials in macrophage polarization. Impact Statement: This pilot study provided some insights into the great potential of machine learning in exploring bone immunomodulatory biomaterials. Introduction: Titanium materials are commonly utilized as bone replacement materials to treat missing teeth and bone defects. The immune response caused by implant materials after implantation in the body has a double-edged sword effect on osseointegration. Macrophage polarization has been extensively explored to understand early material-mediated immunomodulation. However, understanding of implant material surface properties and immunoregulations remains limited due to current experimental settings, which are based on trial-by-trial approaches. Artificial intelligence, with its capacity to analyze large datasets, can help explore complex material–cell interactions. Methods: In this study, the effect of titanium surface properties on macrophage polarization was analyzed using intelligent prediction models, including random forest, extreme gradient boosting, and multilayer perceptron. Additionally, data extracted from the newly published literature were further input into the trained models to validate their performance. Results: The analysis identified “cell seeding density”, “contact angle”, and “roughness” as the most important features regulating interleukin 10 and tumor necrosis factor α secretion. Additionally, the predicted interleukin 10 levels closely matched the experimental results from newly published literature, while the tumor necrosis factor α predictions exhibited consistent trends. Conclusion: The polarization response of macrophages seeded on titanium materials is influenced by multiple factors, and artificial intelligence can assist in extracting the key features of implant materials for immunoregulation.

Objective: Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. Methods: We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged $$\ge$$≥ 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. Results: We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. Conclusion: The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.

Acute myeloid leukemia (AML) is a highly heterogeneous malignant hematological neoplasm. Although standard diagnostic procedures have been established, traditional methods still face limitations with regard to efficiency, accuracy, and standardization. In recent years, artificial intelligence (AI) has demonstrated notable advantages in medical image analysis, flow cytometry interpretation, and genetic data modeling, offering new approaches for adjunctive diagnosis of AML. This review systematically summarizes recent research advances in adjunctive diagnosis of AML, categorizing current AI-based approaches based on data modality into three groups: blood smear image analysis, flow cytometry data interpretation, and genetic data modeling. We focus on the application strategies, diagnostic performance, and limitations of these approaches. Studies have shown that AI not only enhances diagnostic efficiency and reduces subjective bias, but also holds promise in identifying novel biomarkers. Nevertheless, current models still suffer from limited generalizability and insufficient clinical interpretability. Future efforts should prioritize data standardization, improve model transparency, and facilitate the seamless integration of AI systems into clinical workflows to support precision diagnosis and treatment of AML.

The exponential growth of biomedical literature has rendered traditional screening methods inefficient and unsustainable, making knowledge discovery akin to finding a needle in a haystack. While recent advances in artificial intelligence (AI) offer new opportunities for rapid literature retrieval, many clinicians and researchers lack familiarity with these tools. In this study, we optimized LitSuggest, a user-friendly, code-free AI-based literature screening system, and established a standardized operational workflow. Using the field of organoid-based bone tissue engineering as a case study, the optimized system achieved an accuracy of 98.83%, precision of 76.19%, recall of 83.33%, and an F1-score of 79.60%, while reducing manual screening workload by over 90%. Furthermore, we innovatively integrated correlation scoring into literature analysis, revealing that China and the United States are leading contributors to bone organoid regeneration research, and that complex and genetic disease organoid models hold significant research potential. This AI-driven approach enables researchers to focus on high-value literature, improving efficiency while guiding future research in bone organoid regeneration and broader biomedical fields.