Personalized Treatment Plans Research Articles

Leading the way: How leading Chinese institutions drive MDT excellence in hepatocellular carcinoma.

563 Background: HCC MDTs involve specialists collaborating on patient cases to create personalized treatment plans. Implementing MDTs can be challenging in countries with limited resources and high disease burdens. In China, there were 368,000 new liver cancer cases in 2022 representative of 40% of global cases. Therefore, evaluating MDT practices at leading hospitals in China may offer valuable insights that could help improve HCC MDT practices worldwide. Methods: This study selected hospitals with distinct MDT models: one for all new patients and one for complex cases. Hospitals handling over 1,000 HCC cases annually were chosen. Desk research and virtual interviews with MDT members from three leading Chinese hospitals—The First Affiliated Hospital of USTC, The First Affiliated Hospital of SYSU, and Tongji Hospital—were conducted to analyze their MDTs and determine key best practices. Results: We identified 6 key best practice in HCC MDTs at these leading Chinese hospitals. 1) Quality Control on MDT process: Leading hospitals ensure the MDT’s efficient execution and effective outcomes. E.g., USTC established a quality control team to audit the MDT process. 2) Real-World Evidence: Leading hospitals prioritize the construction of real-world databases linked to MDT-care to inform impact on outcomes. E.g., SYSU's preliminary data shows impact of MDT care on survival rates (From 2006 to 2010, 5,627 patients who underwent radical liver cancer resection with MDT care saw their five-year survival rate reach 60%, a notable improvement over historical averages) 3) Comprehensive Teams: These hospitals typically have robust teams, involving around 10 specialties. E.g., Tongji Hospital’s MDTs involve ~10 specialties for holistic care. 4) Patient Journey Coverage: MDTs manage patients from diagnosis to rehabilitation, providing a holistic approach to patient management. 5) Guideline-based treatment decisions: They follow latest guidelines coupled with access to approved therapies to make treatment decisions 6) Training: Young physicians are invited to actively participate in MDT consultations (e.g., through case preparation and presentation, etc.). Conclusions: Evaluation of MDT practices at leading China institutes highlights key best practices that could be adopted to enhance care quality for patients with HCC. The ongoing efforts and successes of these institutions provide a model for broader adoption and standardization of MDT practices across the country.

Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review.

Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy. This review provides a comprehensive overview of the current applications of AI in onychology, focusing on its role in diagnosing onychomycosis, subungual melanoma, nail psoriasis, nail fold capillaroscopy, and nail involvement in systemic diseases. A literature review on AI in nail disorders was conducted via PubMed and Google Scholar, yielding relevant studies. AI algorithms, particularly deep convolutional neural networks (CNNs), have demonstrated high sensitivity and specificity in interpreting nail images, aiding differential diagnosis as well as enhancing the efficiency of diagnostic processes in a busy clinical setting. In studies evaluating onychomycosis, AI has shown the ability to distinguish between normal nails, fungal infections, and other differentials, including nail psoriasis, with a high accuracy. AI systems have proven effective in identifying subungual melanoma. For nail psoriasis, AI has been used to automate the scoring of disease severity, reducing the time and effort required. AI applications in nail fold capillaroscopy have aided the analysis of diagnosis and prognosis of connective tissue diseases. AI applications have also been extended to recognize nail manifestations of systemic diseases, by analyzing changes in nail morphology and coloration. AI also facilitates the management of nail disorders by offering tools for personalized treatment planning, remote care, treatment monitoring, and patient education. Despite these advancements, challenges such as data scarcity, image heterogeneity, interpretability issues, regulatory compliance, and poor workflow integration hinder the seamless adoption of AI in onychology practice. Ongoing research and collaboration between AI developers and nail experts is crucial to realize the full potential of AI in improving patient outcomes in onychology.

Prediction model establishment of prognosis factors for distant metastasis of hepatocellular carcinoma based on the SEER database.

Distant metastasis in hepatocellular carcinoma (HCC) is an important indicator of poor patient prognosis. Identifying patients who are at high risk of metastasis early on is essential for creating personalized treatment plans, yet currently, there is a scarcity of effective predictive tools. To investigate the effects of different factors on distant metastasis in HCC patients and to establish a clinical prediction model for predicting distant metastasis in HCC patients. Our study retrospectively examined 22,318 patients diagnosed with confirmed HCC from the SEER database. Prognostic factors for developing distant metastases in HCC patients were identified by univariate and multivariate logistic regression analyses. Utilizing data from a multivariate logistic regression analysis, we created a nomogram. Its predictive precision was evaluated by analyzing the calibration curve, the area under the curve (AUC) of the receiver operating characteristic curve, decision curve assessment (DCA), and Kaplan-Meier (KM) curve analysis of overall survival. Finally,the nomogram was visualized with an online calculator. We identified six independent prognostic factors: ethnicity, marital status, tumor size, survival time, surgery, and radiotherapy. The nomogram constructed from these six factors showed good calibration, discrimination, and clinical application value after calibration curve analysis, receiver operating characteristic curve analysis and DCA curve analysis. Besides, KaplanMeier survival curves also demonstrated that this nomogram had predictive accuracy. In this research, a nomogram model was created to accurately predict distant metastasis risk in patients with HCC. This study provides guidance for optimizing individual therapies and making better clinical decisions.

Exploring the role of multimodal [18F]F-PSMA-1007 PET/CT and multiparametric MRI data in predicting ISUP grading of primary prostate cancer.

The study explores the role of multimodal imaging techniques, such as [18F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research. This study conducted a retrospective analysis of 341 prostate cancer patients enrolled between 2019 and 2023, with data collected from five imaging modalities: [18F]F-PSMA-1007 PET, CT, Diffusion Weighted Imaging (DWI), T2 Weighted Imaging (T2WI), and Apparent Diffusion Coefficient (ADC). The study compared the performance of five single-modality data sets, PET/CT dual-modality fusion data, mpMRI tri-modality fusion data, and five-modality fusion data within deep learning networks, analyzing how different modalities impact the accuracy of ISUP grading prediction. To address the issue of limited data, a few-shot deep learning network was employed, enabling training and cross-validation with only a small set of labeled samples. Additionally, the results were compared with those from preoperative biopsies and clinical prediction models to further assess the reliability of the experimental findings. The experimental results demonstrate that the multimodal model (combining [18F]F-PSMA-1007 PET/CT and multiparametric MRI) significantly outperforms other models in predicting ISUP grading of prostate cancer. Meanwhile, both the PET/CT dual-modality and mpMRI tri-modality models outperform the single-modality model, with comparable performance between the two multimodal models. Furthermore, the experimental data confirm that the few-shot learning network introduced in this study provides reliable predictions, even with limited data. This study highlights the potential of applying multimodal imaging techniques (such as PET/CT and mpMRI) in predicting ISUP grading of prostate cancer. The findings suggest that this integrated approach can enhance the accuracy of prostate cancer diagnosis and contribute to more personalized treatment planning. Furthermore, incorporating few-shot learning into the model development process allows for more robust predictions despite limited data, making this approach highly valuable in clinical settings with sparse data.

Longitudinal MRI evaluation of the efficacy of non-enhanced lung cancer brain metastases

Brain metastases (BM) are the most prevalent intracranial malignancies. Approximately 30–40% of cancer patients develop BM at some stage of their illness, presenting with a high incidence and poor prognosis. Our clinical findings indicate a significant disparity in the efficacy between non-enhanced and enhanced lung cancer BM. Therefore, the aim of this study is to conduct a longitudinal MRI evaluation of the therapeutic effect of non-enhanced lung cancer BM, thereby enabling improved efficacy prediction and more personalized treatment plans. We conducted a retrospective analysis of clinical and imaging data from 72 patients with lung cancer BM. Exclusion criteria included participants with a history of primary brain tumors, other malignancies, or neurological symptoms. We performed a longitudinal MRI evaluation of 8 patients with non-enhanced BM who met the eligibility criteria. The response to systemic therapy was locally assessed using the Neuro-Oncology response assessment criteria (RANO 2.0). A total of 8 patients were included in this study, comprising 4 cases of non-enhanced BM and 4 cases featuring both enhanced and non-enhanced BM. The median follow-up time for non-enhanced BM was 19 months, with a disease control rate of 50%. In contrast, the median progression-free follow-up time for enhanced BM was 35.5 months, yielding a disease control rate of 100%. After standardized treatment in accordance with clinical guidelines, patients with enhanced BM exhibited significantly better therapeutic outcomes than those with non-enhanced BM. Cases 1–4 demonstrated non-enhanced or mildly circular-enhanced lung cancer BM, with the sizes of all 4 lesions increasing by over 60% within approximately one year, indicating disease progression. In cases 5–8, each patient presented with two lesions: enhanced (Foci 1) and non-enhanced (Foci 2) BM. Comparative analysis of Foci 1 and Foci 2 revealed significantly weaker treatment responses in non-enhanced lesions (Patients 7–8), with Patient 5 exhibiting disease progression without any response. This study demonstrates that, following standardized treatment protocols aligned with clinical guidelines, the therapeutic effect of non-enhanced lung cancer BM is inferior to that of enhanced BM. Evaluating the enhancement type of lung cancer BM is essential for efficacy prediction and holds important implications for treatment guidance.

Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer.

In this review, we explore the application of next-generation sequencing in liver cancer research, highlighting its potential in modern oncology. Liver cancer, particularly hepatocellular carcinoma, is driven by a complex interplay of genetic, epigenetic, and environmental factors. Key genetic alterations, such as mutations in TERT, TP53, and CTNNB1, alongside epigenetic modifications such as DNA methylation and histone remodeling, disrupt regulatory pathways and promote tumorigenesis. Environmental factors, including viral infections, alcohol consumption, and metabolic disorders such as nonalcoholic fatty liver disease, enhance hepatocarcinogenesis. The tumor microenvironment plays a pivotal role in liver cancer progression and therapy resistance, with immune cell infiltration, fibrosis, and angiogenesis supporting cancer cell survival. Advances in immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies have shown potential, but the unique immunosuppressive milieu in liver cancer presents challenges. Dysregulation in pathways such as Wnt/β-catenin underscores the need for targeted therapeutic strategies. Next-generation sequencing is accelerating the identification of genetic and epigenetic alterations, enabling more precise diagnosis and personalized treatment plans. A deeper understanding of these molecular mechanisms is essential for advancing early detection and developing effective therapies against liver cancer.

Novel compound heterozygous CYP17A1 mutations identified in a family with two siblings affected by 17α-hydroxylase/17,20-lyase deficiency.

17α-Hydroxylase/17,20-lyase deficiency (17OHD) is a rare form of congenital adrenal hyperplasia (CAH), caused by mutations in the CYP17A1 gene. It typically manifests clinically as variable degree of hypertension, hypokalemia, and disorders of sexual development (DSD), which can include abnormal sexual differentiation in males and sexual infantilism in females. Over 100 mutations in CYP17A1 have been identified, with most cases involving missense mutations or small deletions. This study examined the clinical features, biochemical profiles, and genetic background of two 46, XY siblings from the same family, both diagnosed with 17OHD-a scenario that is uncommonly seen in clinical practice. We performed a genetic analysis of the CYP17A1 gene in two generations of the family to confirm the diagnosis. Both patients are phenotypically female, presenting with hypertension, hypokalemia, primary amenorrhea, and absent secondary sexual characteristics. Genetic analysis revealed two novel compound heterozygous mutations in the CYP17A1 gene: R45WfsTer5 (a frameshift mutation) and L361P (a missense mutation). Neither variant is reported in the ClinVar database, and the frameshift mutation (R45WfsTer5) is newly identified. The discovery of these two novel CYP17A1 mutations expands the genetic spectrum of 17OHD and provides new insights into the genetic underpinnings of the disease. Personalized treatment plans are necessary, and the choice of glucocorticoids with stronger sodium retention effects may be required to manage hypertension and electrolyte imbalances in select patients.

Psychopathological Influences on Surgical and Clinical Outcomes in Lumbar Disk Herniation: Prediction Models and Literature Analysis

Background/Objectives: Lumbar disk herniation (LDH) significantly affects quality of life due to lower back and radiating leg pain. Surgical intervention, such as discectomy, is effective for symptom relief when conservative measures fail; however, psychological factors like anxiety, depression, and maladaptive coping strategies may negatively impact surgical outcomes. This study aims to assess the role of preoperative psychological evaluations in predicting postoperative recovery and to identify key psychological and functional predictors of surgical success. Methods: A prospective study was conducted on 888 patients undergoing microdiscectomy for LDH at Mater Olbia Hospital between December 2020 and December 2023. Preoperative evaluations included the Visual Analog Scale, Symptom Checklist 90-R, Oswestry Disability Index, and Short Form 36. Logistic regression models and ROC curve analysis were used to identify significant predictors of outcomes and evaluate model accuracy. Results: Preoperative pain levels and emotional well-being emerged as the strongest determinants of postoperative improvement in the Oswestry Disability Index. The predictive model demonstrated high specificity (90.2%) in identifying patients likely to benefit from surgery. Clinically significant improvements were achieved by 69% of patients, highlighting the importance of psychological and functional assessments. Conclusions: Preoperative psychological assessment is critical in predicting outcomes of lumbar disk herniation surgery. Addressing psychological factors preoperatively enhances recovery, supports personalized treatment planning, and improves patient education. These findings advocate for an integrated care model that considers both physical and psychological health, optimizing surgical outcomes and patient satisfaction.

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.

Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses. Bioinformatics technologies based on high-throughput data provide a crucial foundation for comprehensively understanding the biological characteristics of ischemic stroke and discovering effective predictive targets. In this study, we evaluated gene expression data from ischemic stroke patients retrieved from the Gene Expression Omnibus (GEO) database, conducting differential expression analysis and functional analysis. Through weighted gene co-expression network analysis (WGCNA), we characterized gene modules associated with ischemic stroke. To screen candidate core genes, three machine learning algorithms were applied, including Least Absolute Shrinkage and Selection Operator (LASSO), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), ultimately identifying five candidate core genes: MBOAT2, CKAP4, FAF1, CLEC4D, and VIM. Subsequent validation was performed using an external dataset. Additionally, the immune infiltration landscape of ischemic stroke was mapped using the CIBERSORT method, investigating the relationship between candidate core genes and immune cells in the pathogenesis of ischemic stroke, as well as the key pathways associated with the core genes. Finally, the key gene VIM was further identified and preliminarily validated through four machine learning algorithms, including generalized linear model (GLM), Extreme Gradient Boosting (XGBoost), RF, and SVM-RFE. This study contributes to advancing our understanding of biomarkers for ischemic stroke and provides a reference for the prediction and diagnosis of ischemic stroke.

Linking Epicardial Adipose Tissue to Atrial Remodeling: Clinical Implications of Strain Imaging

Atrial fibrillation is a prevalent cardiac arrhythmia influenced by multifactorial mechanisms, including the emerging role of epicardial adipose tissue. Left atrial epicardial adipose tissue, through its endocrine and paracrine activities, contributes to atrial remodeling by fostering inflammation, fibrosis, and electrical remodeling. Objectives: This review aims to explore the interaction between left atrial epicardial adipose tissue and atrial dysfunction, highlighting the utility of strain imaging as a diagnostic and prognostic tool in atrial fibrillation management. Additionally, it examines emerging therapeutic strategies targeting epicardial adipose tissue to improve outcomes. Methods: We analyzed recent advances in imaging techniques, with a specific focus on speckle-tracking echocardiography for non-invasive strain assessment. Strain imaging parameters, including atrial reservoir, conduit, and contractile strain, were evaluated alongside volumetric measures of epicardial adipose tissue. Emerging therapies, such as weight management and GLP-1 receptor agonists, were reviewed for their impact on left atrial epicardial adipose tissue and atrial remodeling. Results: Strain imaging demonstrates a significant association between reduced strain parameters and atrial remodeling induced by left atrial epicardial adipose tissue. Combining strain assessment with volumetric measures enhances diagnostic accuracy and stratification of patients at risk for recurrent or progressive atrial fibrillation. Emerging therapies, particularly GLP-1 receptor agonists, show promise in reducing epicardial adipose tissue volume and mitigating atrial remodeling, thereby improving catheter ablation outcomes. Conclusions: Strain imaging is a valuable tool for the early detection of atrial dysfunction and personalized treatment planning in atrial fibrillation. Integrating these imaging approaches into routine clinical practice can optimize atrial fibrillation management and improve patient outcomes.

Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity

This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective model that can assist in clinical diagnosis and treatment of vaginal relaxation, thereby enhancing women’s pelvic floor health. In total, 1184 women with VL have been randomly selected and categorized into groups using the finger measurement method. Among them, there are 383 cases of mild VL, 405 cases of moderate VL, and 396 cases of severe VL. Concurrently, 396 healthy women without VL who underwent routine health examinations have been chosen at random and assigned to the non-VL group. Based on 1580 cases, we have established LightGBM, Random Forest, XGBoost, and AdaBoost models based on training dataset using 5-fold cross-validation and GridSearch, and analyzed the performance of the models on the hold-out test dataset. The confusion matrix, precision, recall, F1 score, overall accuracy, and ROC curve of the models on the hold-out test dataset are compared. The overall accuracy of LightGBM model, RF model, XGBoost model, and AdaBoost model are 0.8987, 0.8987, 0.8987, and 0.8457, respectively. The average AUC of LightGBM model is 0.976, the one of RF model is 0.9763, the one of XGBoost model is 0.9775, and the one of AdaBoost model is 0.928. The XGBoost model has the more comprehensive and reasonable performance among the four prediction models, which can accurately distinguish between healthy, mild VL, as well as moderate VL and severe VL, which can assist doctors in diagnosing persons’ conditions more accurately, devising personalized treatment plans, avoiding unnecessary surgeries, reducing persons’ psychological stress, improving patient compliance and treatment outcomes, thus enhancing overall treatment results.

Risk assessment and management strategies for peri-implantitis in high-risk populations⁠

Peri-implantitis is a multifactorial condition characterized by inflammation and progressive bone loss surrounding dental implants, often leading to implant failure. High-risk populations, including patients with systemic conditions such as diabetes or undergoing cancer treatments, face an elevated susceptibility due to impaired immune responses and altered bone metabolism. Understanding the pathophysiological mechanisms underlying the disease is essential for effective risk assessment and management. Dysbiosis of the microbial biofilm, immune system dysregulation, and systemic factors like oxidative stress and cytokine imbalances are critical contributors to disease progression. These factors necessitate the development of targeted strategies for prevention and intervention. Risk assessment models have evolved to include advanced technologies such as artificial intelligence and decision tree regression to predict peri-implantitis in vulnerable individuals accurately. These tools incorporate systemic, local, and implant-specific parameters to enable personalized treatment planning and early intervention. Innovative management strategies have also emerged, ranging from adjunctive antimicrobial therapies, such as glycine powder air-polishing and localized drug delivery systems, to host modulation therapies and regenerative techniques. The use of advanced biomaterials, such as implants coated with bioactive peptides or antimicrobial nanoparticles, has further improved osseointegration and reduced infection rates. Regenerative approaches, including the application of growth factors like platelet-derived growth factor and bone morphogenetic proteins, have shown significant potential in repairing peri-implant defects and restoring functional stability. Emerging technologies, such as nanotechnology and bioengineered implants, offer new possibilities for real-time health monitoring and sustained therapeutic delivery at implant sites. These advancements, coupled with a deeper understanding of peri-implantitis etiology, provide a framework for optimizing outcomes in high-risk populations, addressing both the local and systemic challenges posed by the disease.

Challenges for complement functional assays in the clinical laboratory: From test validation to clinical interpretation.

Complement functional assays are essential first-tier tests for a gamut of disorders spanning from inborn errors of the immune system which lead to recurrent severe infections, to angioedema attacks, presentation of autoimmune disease, thrombotic microangiopathies and rare kidney disorders. These assays evaluate the activity of the three complement pathways and specific complement components, which helps in differential diagnosis and monitoring disease progression. The rising use of complement inhibitors for treating complement-mediated thrombotic microangiopathies has heightened the demand for personalized treatment plans and laboratory assessment of complement blockage. However, conducting these assays is challenging due to the labile nature of complement proteins, which necessitates strict handling protocols-prompt processing, cold centrifugation, and preferable storage at -80 °C. Currently, the only FDA-approved complement functional test is the classical pathway activity assay while other tests are categorized as laboratory developed tests (LDTs). Validation of LDTs requires thorough evaluation of precision, accuracy, reference intervals, clinical reportable ranges, analytical sensitivity, and specificity. Achieving harmonization across laboratories is critical but heavily relies on the methodologies and calibrators used. This article discusses the various challenges and limitations associated with complement functional assays, highlighting the need for standardization and improved practices in clinical laboratories.

New perspectives on facial asymmetries thanks to 3D vision

Modern orthodontics is undergoing a revolution with the advent of 3D imaging, offering unprecedented perspectives for the evaluation and treatment of facial asymmetries. These asymmetries, whether mandibular, maxillary, or dental, require a deeper understanding of their causes and their aesthetic and functional impact. Additionally, associated functional imbalances must be addressed for comprehensive management. Traditional diagnostic methods, such as 2D radiographs and plaster models, have significant limitations, now overcome by the use of 3D technologies (intraoral scanners, Cone Beam Computed Tomography, photogrammetry). This article aims to demonstrate how these technologies enhance the understanding and precise management of craniofacial anomalies. The clinical case presented involves a 28-year-old female. The results obtained using 3D imaging are compared with traditional methods and initial simulations, highlighting their advantages. 3D imaging opens promising perspectives in orthodontics, particularly in personalized treatments and innovative approaches such as mandibular kinetics (transition to 4D). Moreover, the increasing integration of artificial intelligence into diagnostic and treatment planning tools adds further value, streamlining decision-making and improving clinical efficiency. 3D imaging represents a turning point in the evaluation and treatment of facial asymmetries. By providing a clearer and more detailed view of anatomical structures, it allows orthodontists to design more precise and personalized treatment plans. These advancements lead to optimized clinical outcomes and improved patient compliance.

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.

P1072 The influence of anxiety and depression and the online mindfulness interventions on the quality of life among patients with Inflammatory Bowel Disease (IBD) : Web-Based Survey Study

Abstract Background Patient diagnosed with Inflammatory Bowel Disease (IBD) experience distressing psychological symptoms and a diminished quality of life (QOL). The efficacy and feasibility of online mindfulness intervention for patients with IBD is unknown.We conducted a mobile-delivered mindfulness intervention among IBD patients to study its feasibility and efficacy in lowering their anxiety or depression level and improving their QOL. Methods We utilized individuals diagnosed with IBD to investigate the correlation between the severity of depression/anxiety and their QOL. The severity of anxiety and depression symptoms was evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9), respectively. QOL was assessed using the Short Health Scale (SHS). The relationship was analyzed through regression analysis. Additionally, a randomized parallel design was employed to conduct the Online MINdfulness-based stress reduction with Daily EXercise (MINDEX) program, and its effects on QOL and psychological symptoms were studied over a period of 2 months. Results A total of 521 participants initiated the survey and 484 completed and submitted the survey. More than 80% of the patients experienced severe symptoms of anxiety or depression, as indicated by GAD-7 scores ≥ 10 or PHQ-9 scores ≥ 10. The study found that increases in GAD-7 total scores were significantly associated with increases in SHS total scores, with an R2 value of 0.43 and coefficient of 12.6 (95% CI 9.4-15.7) for UC (Ulcerative Colitis) and R2 value of 0.27 and a coefficient of 10.4 (95% CI 8.8-12.1) for CD (Crohn’s Disease). Similarly, increases in PHQ-9 scores also showed significant correlations with increases in SHS total scores, with an R2 value of 0.37 and a coefficient of 9.2 (95% CI 6.5-11.8) for UC and R2 value of 0.22 and a coefficient of 7.2 (95% CI 5.9-8.6) for CD. However, when depression severity was considered, the significant differences between SHS and PHQ-9 scores disappeared in both UC and CD. Yet, the positive correlation between GAD-7 and SHS scores remained significant in UC. Regarding the MINDEX program, the study included 30 MINDEX patients and 22 control patients who completed the program. After 2 months, the scores of GAD-7, PHQ-9, SHS between MINDEX patients and 22 control patients were not statistically significant (.052 < P < .908). Conclusion IBD significantly influenced patients’ anxiety, depression, QOL, and sleep quality. Online mindfulness interventions did not show a significant reduction in anxiety or depression. When formulating personalized treatment plans, it is crucial to take into account the patient’s overall condition to optimize treatment outcomes and enhance the patient’s QOL.

Wavefront sensing: A breakthrough for objective evaluation of dynamic accommodation in accommodative dysfunctions.

The purpose of this study was to use wavefront sensing as an objective method to detect and assess dynamic accommodation in subjects with accommodative dysfunctions and symptoms related to near-vision tasks. Sixty-three subjects were divided into control (N=18), symptomatic without any accommodative dysfunction (SWD) (N=18), infacility of accommodation (INFA) (N=6), excess of accommodation (EA) (N=9), and insufficiency of accommodation (INSA) (N=12) groups. Accommodation was stimulated in different cycles of accommodation and disaccommodation while ocular aberrations were measured. Dynamic accommodation was computed from ocular wavefront aberrations and then analysed, including response time, peak velocity, and microfluctuations. Subjects with accommodative dysfunctions showed alterations in accommodative responses compared to the control group, characterized by slower and excessive/reduced responses, as well as an increase in accommodative microfluctuations and difficulty in relaxing accommodation to different accommodative demands. The SWD group showed significant changes compared to the control group, suggesting accommodative problems not previously detected in clinical examinations and explaining the symptoms reported by these subjects. The specific patterns of the characteristics of dynamic accommodation are presented for the different accommodative dysfunctions. The objective assessment of dynamic accommodation using wavefront sensing, analysed for different accommodative demands, provides a comprehensive approach to the detection and characterisation of accommodative dysfunctions. This method enables the improvement of the precision of the diagnosis of accommodative dysfunctions and allows its detection in cases that may not be detected by current clinical examinations. In addition, this method may contribute to personalized treatment planning, potentially improving patient outcomes in clinical practice.

P0791 Development and validation of a nomogram for predicting the short-term effectiveness of Ustekinumab in patients with moderate to severe Crohn's disease

Abstract Background Ustekinumab (UST) is recommended as the first-line treatment for patients with moderate to severe Crohn's disease (CD). The effectiveness of certain patients, however, may be suboptimal and necessitate intensive treatment or modification of the treatment regimen. We sought to establish a nomogram model to predict the short-term effectiveness of UST in moderate to severe CD patients. Methods We established a derivation cohort comprising patients diagnosed with CD and treated with UST at the Sixth Affiliated Hospital of Sun Yat-sen University between May 2020 and July 2023. The patient data, including demographic and clinical characteristics as well as treatment details, were systematically collected. The achievement of clinical remission (Crohn's Disease Activity Index, CDAI < 150) after induction therapy was the endpoint observed during follow-up. Potential predictors were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Multivariate logistic regression analysis was performed to develop a nomogram model. The predictive accuracy and discriminative power of the model were evaluated using Receiver Operating Characteristics (ROC) curves and calibration curves. Decision curve analysis (DCA) was employed to assess the clinical utility of the model. Results A total of 162 moderate to severe CD patients were included in the derivation cohort. Prognostic factors, including duration, body mass index (BMI), smoking, extraintestinal manifestations (EIMs), perianal lesions (P), history of Vedolizumab therapy, and albumin levels (ALB), were identified and included in the nomogram. The model showed good discrimination and calibration on internal validation based on the bootstrap method (C-index: 0.843, 95% confidence interval [CI]: 0.768-0.903). Moreover, DCA demonstrated that the nomogram was clinically beneficial. Conclusion Our model is a useful tool for accurately predicting the short-term effectiveness of UST in moderate to severe CD patients and aids clinicians in formulating personalized treatment plans.

Three-alternative forced-choice method (3AFC) for tinnitus matching and digital frequency customized relieving sound (DFCRS) generation strategy for tinnitus management

Tinnitus is the perception of sounds without any corresponding external stimulus exists, and it is one of the most common hearing disorders. Currently, there are no objective tests for tinnitus, and thus its detection relies on the subjective reports of patients. Here, we introduce a novel three-alternative forced-choice method (3AFC) for tinnitus pitch and loudness matching. Then, we modulate the soothing music by increasing the sound intensity of two 1/3 octave bands above and below the tinnitus frequency according to a specific algorithm to generate digital frequency customized relieving sound (DFCRS) for tinnitus management. Due to the relatively large frequency interval and more frequency characteristics, the 3AFC method can significantly shorten the time required for psychoacoustic measures of tinnitus and improve the efficiency and satisfaction of subjective tinnitus tests. Based on the matched tinnitus frequency, we created a personalized treatment plan for tinnitus patients using real-time modulation of soft music. We recruited total 32 participants who listened to the modulated music (DFCRS) or the unmodulated music (served as control) for at least 2 h a day for three months, following brief instructions from audiologists. Of these, the participants who received DFCRS experienced more reduced negative impacts of tinnitus and an improved quality of life compared to those in the control group, as measured by various scales. Our patented system seamlessly integrates tinnitus assessment and treatment, offering substantial savings in medical and audiological expenses.

Hematopoietic adverse events associated with PARP inhibitors: A FAERS database study

ABSTRACT Objectives Analyze hematopoietic ADR signals of PARP inhibitors (Olaparib, Niraparib, Rucaparib, Talazoparib) using FAERS data to inform clinical practice. Methods Extracted ASCII data for these drugs from Q1 2019 to Q2 2024. Employed SMQ and PT for standardization. Screened ADR signals via ROR, PRR, and MHRA method, comparing SMQ ratios. Results Hematopoietic ADRs peaked within 30 days post-treatment, with cytopenia and leukopenia most prevalent. Niraparib showed the highest adverse event count and signal intensity. Olaparib and Talazoparib also indicated strong hematotoxicity. Conclusion PARP inhibitors vary in ADR incidence and duration, necessitating personalized treatment plans for optimized safety and rational use.