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Filter by:Artificial intelligence (AI) is becoming prevalent in modern medicine and psychiatry. AI is based on a wide variety of computer algorithms classified under machine learning (ML). The purpose of the present study is to evaluate the potential for mental health diagnosis using AI. In the first part of the study, the AI will conduct an interview with standardized patients [SP] (actors) presenting a psychiatric illness. The AI will present a differential diagnosis and treatment plan. Immediately afterward, the actors will be interviewed by a board-certified psychiatrist, who will also give a differential diagnosis and a treatment plan. The results of the AI and psychiatrist will be compared. In the second part of the study, AI will examine patients coming for consultation by a psychiatrist in the inpatient units, outpatient units, or in the emergency room (ER) at Sheba Medical center. The AI results will be compered to the psychiatrist diagnosis.
Multicenter Prospective Cohort Study of Twin Maternal-Child Dyads in China (ChiTwiMC) is supported by National Key Research and Development Program of China - Reproductive Health and Women's and Children's Health Protection Project. This project is funded by the Ministry of Science and Technology of China under grant number 2023YFC2705900. The ChiTwiMC cohort is led by Professor Wei Yuan from the Department of Gynecology and Obstetrics at Peking University Third Hospital.
This protocol outlines a planned mixed methods feasibility trial which will be conducted to examine the feasibility and acceptability of a physiotherapy-led exercise-based telerehabilitation programme for groups of people with mixed chronic health conditions.
to evaluate the ability of the Optical genome Mapping (OGM) approach to detect simple and complex constitutional chromosomal aberrations of clinical relevance, which had previously been identified with standard diagnostic approaches (karyotyping, FISH, CNV-microarray) in the context of neurodevelopmental disorders (NDDs) with/wo congenital anomalies (CA)
The goal of this prospective, case-control study is to discover the specific "omics" biomarkers of early stage of lung cancer using the non-invasive samples (breath, urine and serum) in a total of 200 subjects (100 healthy controls and 100 lung cancer patient). The main questions it aims to answer are: - Which are the "omics" biomarkers that characterize the early stage of lung cancer? - How to Translate Laboratory Data into Clinical Data? For each participant we will collected the breath, urine and blood samples. In lung cancer patients group the samples will be sample before lung cancer resection. The samples of Breath, urine and serum will be analysed using different type of analysis: eNose and the Gas Chromatography combined with Ion Mass Spectrometry (GC/IMS). Moreover, Serum will be analyzed by mass-spectrometry-based proteomics. The purpose of these analyses will be to find biomarkers capable of distinguishing the early-stage of lung cancer from the healthy group. Followup will be performed to evaluate the possible change of the volatolomic and proteomic profile.
An Open, Dose-escalation, Phase 1b Clinical Trial to Evaluate the Safety and Efficacy of EN001 in Patients with Charcot-Marie-Tooth Disease type 1A (CMT1A)
The aim of this randomised clinical trial is to evaluate the short and longterm effects of a transdiagnostic mentalization-based intervention (Lighthouse MBT Parenting Program) compared to care as usal (CAU) for parents with a mental disorder in adult mental health service.
- This study aim to develope a diagnostic method of pancreatic cancer by using a reagent for analyzing purine metabolite (Hypoxanthine, Xanthine) in urine. - It is safe and cost effective compare to radiologic or blood test. It can be used for initial screening test for healty population.
Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.
As a diagnostic test, this study aims to explore the effectiveness of systems based on wearable devices in identifying mood disorders in children and adolescents.