Treatment algorithm

MOTIVATION: Accurate inference of potential Drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features...

PURPOSE: Accurate prediction of extraprostatic extension (EPE) is crucial for decision-making in radical prostatectomy (RP), especially in nerve-sparing strategies. Martini et al. introduced a three-tier algorithm for predicting contralateral EPE in unilateral high-risk prostate cancer (PCa). The aim of the study is to externally validate this model in a multicentric European cohort of patients. METHODS: The data from 208 unilateral high-risk PCa patients diagnosed through magnetic resonance imaging (MRI)-targeted and systematic biopsies, treated with RP between January 2016 and November 2021 at eight referral centers were collected...

Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML...

Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation. In order to convert the pixels connected to the spatial domain into a multi-resolution domain, the Gabor transform is first applied to the brain test image...

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism...

BACKGROUND AND AIMS: High-power-short-duration (HPSD) ablation is an effective treatment for atrial fibrillation but poses risks of thermal injuries to the oesophagus and vagus nerve. This study investigates incidence and predictors of thermal injuries, employing machine learning. METHODS: A prospective observational study was conducted at Leipzig Heart Centre, Germany, excluding patients with multiple prior ablations. All patients received Ablation Index guided HPSD ablation and subsequent oesophagogastroduodenoscopy...

BACKGROUND: Luteolin, a common dietary flavonoid found in plants, has been shown to have anti-cancer properties. However, its exact mechanisms of action in non-small cell lung cancer (NSCLC) are still not fully understood, particularly its role in regulating broader genomic networks and specific gene targets. In this study, we aimed to elucidate the role of microRNAs (miRNAs) in NSCLC treated with luteolin, using A549 cells as a model system. MATERIALS AND METHODS: miRNA profiling was conducted on luteolin-treated A549 cells using Exiqon microarrays, with validation of selected miRNAs by qRT-PCR...

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation...

PURPOSE: To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm. MATERIALS AND METHODS: We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017...

INTRODUCTION: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement...

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape...

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting...

Medial compartment arthritis of the knee joint presents a significant clinical challenge, with diverse management options ranging from nonsurgical interventions to various surgical procedures. This comprehensive review synthesizes current evidence on the management trends in medial compartment arthritis, highlighting both nonsurgical approaches such as physical therapy, pharmacological interventions, and intra-articular injections as well as surgical interventions, including arthroscopic debridement, high tibial osteotomy, and knee arthroplasty...

OBJECTIVE: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types. METHODS: The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO)...

OBJECTIVE: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). METHODS: In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script...

INTRODUCTION: Sacral neuromodulation and posterior tibial nerve stimulation for lower urinary tract dysfunction (LUTD) and overactive bladder yield good and reliable results. However, neuromodulation research is continuously evolving because there is still need for more patient-friendly treatment options in the therapeutic management of LUTD. Pudendal neuromodulation (PNM) has been emerging as a promising alternative treatment option for the last few decades. The aim of this study is to review the current state of the art of PNM...

The AUD2 IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2 IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow...

UNLABELLED: Intracardiac echocardiography (ICE) enables cardiac imaging with a wide field of view, deep imaging depth, and high frame rate during surgery. However, strong sidelobe and grating lobe artifacts created by the ultra-compact transducer degrade its image quality, making diagnosis and monitoring of treatment difficult. Conventionally, aperture apodization algorithms are often used to suppress sidelobe and grating lobe artifacts at the expense of lateral resolution, which is undesirable in ICE...

Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications...

BACKGROUND AND OBJECTIVE: Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models. PATIENTS AND METHODS: A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital)...