Prediction Tool

PURPOSE: It is unclear whether the Geriatric-8 (G8) has the accuracy to preselect patients for complete geriatric assessment, and has the ability to predict adverse outcomes in patients with colorectal cancer (CRC). We therefore aimed to determine whether the G8, or other variables present in the medical record, are applicable in predicting 30-day adverse outcomes in older patients undergoing surgery for CRC. METHODS: We performed a retrospective cohort study involving patients ≥70 years who had surgery for CRC between 2018 and 2020 in a general hospital in the Netherlands...

BACKGROUND: Aspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke. METHODS: We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke...

OBJECTIVE: To identify neurobehavioral symptom profiles among persons with chronic traumatic brain injury (TBI) using the Behavioral Assessment Screening Tool (BAST) and to consider participant characteristics that differ between profile groups. SETTING: Community. PARTICIPANTS: Participants (n = 615) were English-speaking adults (≥18) and had a self-reported history of at least one TBI of any severity. DESIGN: Secondary analysis of cross-sectional data...

Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values. The ANN models were developed to predict six variables: spot size in the x -axis, y -axis, major axis, minor axis, and relative positional errors in the x -axis and y -axis...

OBJECTIVE: To assess the cost-effectiveness of alternative approaches to diagnose and treat obstructive sleep apnea (OSA) in patients with traumatic brain injury (TBI) during inpatient rehabilitation. SETTING: Data collected during the Comparison of Sleep Apnea Assessment Strategies to Maximize TBI Rehabilitation Participation and Outcome (C-SAS) clinical trial (NCT03033901) on an inpatient rehabilitation TBI cohort were used in this study. STUDY DESIGN: Decision tree analysis was used to determine the cost-effectiveness of approaches to diagnosing and treating sleep apnea...

BACKGROUND: The objective of this study is to examine the application of AI algorithms in detecting OPMD and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes. MATERIALS AND METHODS: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD...

Aim: Proof-of-concept study, highlighting the clinical diagnostic ability of FT-IR compared with MALDI-TOF MS, combined with WGS. Materials & methods: 104 pathogenic isolates of Neisseria meningitidis , Streptococcus pneumoniae , Streptococcus pyogenes and Staphylococcus aureus were analyzed. Results: Overall prediction accuracy was 99.6% in FT-IR and 95.8% in MALDI-TOF-MS. Analysis of N. meningitidis serogroups was superior in FT-IR compared with MALDI-TOF-MS. Phylogenetic relationship of S. pyogenes was similar by FT-IR and WGS, but not S...

PURPOSE: The use of 3D-printing in every field of medicine is expanding, notably as an educational tool. The aim of this study was to assess how visuospatial abilities (VSA) of students may impact learning helped with 3D-printed models. METHODS: Participants were undergraduate medical school students during their clinical rotation in oral and maxillofacial surgery in two French Universities. Students were included prospectively and consecutively from September 2021 to June 2023...

Medicinal plants are integral to traditional medicine systems world-wide, being pivotal for human health. Harvesting plant material from natural environments, however, has led to species scarcity, prompting action to develop cultivation solutions that also aid conservation efforts. Biotechnological tools, specifically plant tissue culture and genetic transformation, offer solutions for sustainable, large-scale production and enhanced yield of valuable biomolecules. While these techniques are instrumental to the development of the medicinal plant industry, the challenge of inherent regeneration recalcitrance in some species to in vitro cultivation hampers these efforts...

Bioaccumulation predictions can be substantially improved by combining in vitro metabolic rate measurements derived from rainbow trout hepatocytes and/or hepatic S9 fractions with quantitative structure-activity relationship (QSAR) modeling approaches. Compared with in vivo testing guidelines Organisation for Economic Co-operation and Development (OECD) 305 and Office of Chemical Safety and Pollution Prevention (OCSPP; an office of the US Environmental Protection Agency) 850.1730, the recently adopted OECD test guidelines 319A and 319B are in vitro approaches that have the potential to provide a time- and cost-efficient, humane solution, reducing animal use while addressing uncertainties in bioaccumulation across species...

OBJECTIVE: To investigate the association of a novel post-match muscle pain map, named Muscle Pain Code (MPC), with the risk of subsequent time-loss muscle injury in a cohort of professional football (soccer) players. METHODS: The MPC classifies pain in four codes: code 0, ' no pain ;' code 1, ' generalized muscle pain ;' code 2, ' diffused site muscle pain ;' and code 3, ' specific site muscle pain .' Over four consecutive seasons, MPC was collected on the second post-match day and players were followed for occurrence of time-loss muscle injury over the next five days...

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures...

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs...

BACKGROUND: We aimed to establish a novel model using a radiomics analysis of magnetic resonance (MR) images for predicting osteoporosis. PURPOSE: To investigate the effectiveness of a radiomics approach utilizing magnetic resonance imaging (MRI) of the lumbar spine in identifying osteoporosis. MATERIAL AND METHODS: In this retrospective study, a total of 291 patients who underwent MRI were analyzed. Radiomics features were extracted from the MRI scans of all 1455 lumbar vertebrae, and build the radiomics model based on T2-weighted (T2W), T1-weighted (T1W), and T2W + T1W imaging...

Artificial intelligence (AI) de novo molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular generation and optimization strategy based on a low activity lead compound. This process integrates fragment growth-based reaction templates, while target docking and drug-likeness prediction were simultaneously performed. This comprehensive approach considers molecular similarity, internal diversity, synthesizability, and effectiveness, thereby enhancing the quality and efficiency of molecular generation...

OBJECTIVES: This study aimed to examine Ki-67's correlation with clinicopathological characteristics of head and neck squamous cell carcinoma (HNSCC), evaluate its prognostic significance, and develop a Ki-67 integrated prognostic model. METHODS: The retrospective study included 764 HNSCC patients hospitalized from 2012 to 2022. Data were sourced from medical records and immunohistochemical analysis of surgical specimens. RESULTS: Ki-67 expression was significantly associated with sex, pathological grade, clinical stage, and metastasis, but not with age or recurrence...

BACKGROUND: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. OBJECTIVES: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index...

Two-component systems are key signal-transduction systems that enable bacteria to respond to a wide variety of environmental stimuli. The human pathogen, Streptococcus pneumoniae (pneumococcus) encodes 13 two-component systems and a single orphan response regulator, most of which are significant for pneumococcal pathogenicity. Mapping the regulatory networks governed by these systems is key to understand pneumococcal host adaptation. Here we employ a novel bioinformatic approach to predict the regulons of each two-component system based on publicly available whole-genome sequencing data...

BACKGROUND: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. METHODS: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT...

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. Bioinformatic tools could improve efficiency of miRNA research, while current bioinformatic tools are in lack of sufficient accuracy. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in the bioinformatical tools...