Annals of translational medicine
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This review aimed to summarize the application of single-cell transcriptome sequencing technology in liver diseases. ⋯ With the continuous improvement of scRNA-seq technology, scRNA-seq is expected to unlock new avenues for liver biology exploration, liver disease diagnosis, and personalized treatment, which will pave the way for breakthrough innovation in personalized medicine.
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Bladder cancer (BC) is a common malignant neoplasm with a high rate of recurrence and progression, despite optimal treatment. There is a pressing need to identify new effective biomarkers for the targeted treatment of BC. ⋯ CALD1 is a potential molecular marker associated with prognosis. It promotes the malignant progression of BC and upregulates the PD-L1 expression via the JAK/STAT signaling pathway.
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In recent years, artificial intelligence (AI) or the study of how computers and machines can gain intelligence, has been increasingly applied to problems in medical imaging, and in particular to molecular imaging of the central nervous system. Many AI innovations in medical imaging include improving image quality, segmentation, and automating classification of disease. These advances have led to an increased availability of supportive AI tools to assist physicians in interpreting images and making decisions affecting patient care. ⋯ Limitations and future prospects for AI in molecular brain imaging are also discussed. Just as new equipment such as SPECT and PET revolutionized the field of medical imaging a few decades ago, AI and its related technologies are now poised to bring on further disruptive changes. An understanding of these new technologies and how they work will help physicians adapt their practices and succeed with these new tools.
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Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases. ⋯ Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.
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A simple and accurate scoring system to predict risk of blood transfusion in patients having surgical tumor resection with immediate free flap reconstruction primary surgery for oral and oropharyngeal squamous cell carcinoma (OOSCC) is lacking. Anticipating the blood transfusion requirements in patients with oral cancer is of great clinical importance. This research aimed to propose a valid model to predict transfusion requirements in patients undergoing surgery with free flap reconstruction for an OOSCC. ⋯ The use of the integer-based TRS allowed the identification of high-risk patients who may require perioperative transfusion undergoing tumor resection surgery for the treatment of OOSCC.