Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study.

Background And Purpose:  Hand fractures are commonly presented in emergency departments, yet diagnostic errors persist, leading to potential complications. The use of artificial intelligence (AI) in fracture detection has shown promise, but research focusing on hand metacarpal and phalangeal fractures remains limited. We aimed to train and evaluate a convolutional neural network (CNN) model to diagnose metacarpal and phalangeal fractures using plain radiographs according to the AO/OTA classification system and custom classifiers.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years.

Plasma H3Cit-DNA Discriminates Between Cancer and Inflammation in a Cohort of Patients with Unspecific Cancer Symptoms.

Cancer detection is challenging, especially in patients with unspecific cancer symptoms. Biomarkers could identify patients at high risk of cancer. Prior studies indicate that neutrophil extracellular traps (NETs) are associated with cancer, but also with autoimmune and infectious diseases.

Development and validation of an artificial intelligence model for the classification of hip fractures using the AO-OTA framework.

Background And Purpose: Artificial intelligence (AI) has the potential to aid in the accurate diagnosis of hip fractures and reduce the workload of clinicians. We primarily aimed to develop and validate a convolutional neural network (CNN) for the automated classification of hip fractures based on the 2018 AO-OTA classification system. The secondary aim was to incorporate the model's assessment of additional radiographic findings that often accompany such injuries.

Artificial intelligence can be used in the identification and classification of shoulder osteoarthritis and avascular necrosis on plain radiographs: a training study of 7,139 radiograph sets.

Background And Purpose: Knowledge concerning the use AI models for the classification of glenohumeral osteoarthritis (GHOA) and avascular necrosis (AVN) of the humeral head is lacking. We aimed to analyze how a deep learning (DL) model trained to identify and grade GHOA on plain radiographs performs. Our secondary aim was to train a DL model to identify and grade AVN on plain radiographs.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies.

Many growth factors and cytokines signal by binding to the extracellular domains of their receptors and driving association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affect signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo-designed fibroblast growth factor receptor (FGFR)-binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca release and mitogen-activated protein kinase (MAPK) pathway activation.

Effect of a national infection control programme in Sweden on prosthetic joint infection incidence following primary total hip arthroplasty: a cohort study.

Objectives: Prosthetic joint infection (PJI) is a serious complication following total hip arthroplasty (THA) entailing increased mortality, decreased quality of life and high healthcare costs.The primary aim was to investigate whether the national project: Prosthesis Related Infections Shall be Stopped (PRISS) reduced PJI incidence after primary THA; the secondary aim was to evaluate other possible benefits of PRISS, such as shorter time to diagnosis.

Design: Cohort study.

Comparison of Comorbidity Models Within a Population-Based Cohort of Older Adults With Non-Hodgkin Lymphoma.

Purpose: Compare the association of individual comorbidities, comorbidity indices, and survival in older adults with non-Hodgkin lymphoma (NHL), including in specific NHL subtypes.

Methods: Data source was SEER-Medicare, a population-based registry of adults age 65 years and older with cancer. We included all incident cases of NHL diagnosed during 2008-2017 who met study inclusion criteria.

ChatGPT-4 generates orthopedic discharge documents faster than humans maintaining comparable quality: a pilot study of 6 cases.

Background And Purpose: Large language models like ChatGPT-4 have emerged. They hold the potential to reduce the administrative burden by generating everyday clinical documents, thus allowing the physician to spend more time with the patient. We aimed to assess both the quality and efficiency of discharge documents generated by ChatGPT-4 in comparison with those produced by physicians.

Optimization of mHealth behavioral interventions for patients with chronic lymphocytic leukemia: the HEALTH4CLL study.

Purpose: This pilot study of a diet and physical activity intervention (HEALTH4CLL) was conducted to reduce fatigue and improve physical function (PF) in patients with chronic lymphocytic leukemia (CLL).

Methods: The HEALTH4CLL study used a randomized factorial design based on the multiphase optimization strategy (MOST). Patients received diet, exercise, and body weight management instructional materials plus a Fitbit and were randomized to undergo one of 16 combinations of 4 evidence-based mHealth intervention strategies over 16 weeks.

Timing of Surgery for Proximal Humeral Fracture Treated with Shoulder Hemiarthroplasty, Best Results with Surgery Within 2 Weeks.

Background: Preoperative delay may affect the outcome of proximal humerus fractures treated with shoulder hemiarthroplasty. There is currently no consensus for the recommended preoperative time interval. The aim was to examine how the time to surgery with shoulder hemiarthroplasty after a proximal humerus fracture affected the patient-reported outcome.

A single-cell atlas of CD19 chimeric antigen receptor T cells.

Li et al. present a resource of single-cell RNA sequencing (scRNA-seq) data from the infusion products of relapsed or refractory large B cell lymphoma (rrLBCL) patients treated with standard-of-care axicabtagene ciloleucel and identify features that are significantly different between products from responders and non-responders at 3-month followup by PET/CT, an important landmark for long-term outcomes.

Influence of treatment intensity and medical comorbidities in older adults with peripheral T cell lymphoma.

We conducted a population-based study of patients >65 years, diagnosed 2008-2017, with peripheral T-cell lymphoma (PTCL) using SEER-Medicare. Associations between PTCL subtype, treatment regimen, comorbidity, and mortality were assessed using the Kaplan-Meier method and multivariable Cox regression. Amongst the 2,546 patients, the median age was 77 years (interquartile range, 71-83).

Functional outcomes are restored a decade after a distal radius fracture: a prospective long-term follow-up study.

We performed an 11-13-year prospective follow-up of patients after a distal radial fracture (DRF) to investigate the association between fracture malunion, radiocarpal osteoarthritis and clinical outcome. In total, 292 patients responded to patient-reported outcome measures; of them, 242 underwent clinical examination. Clinical outcomes improved with time.

Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle.

In this study, we present a deep learning model for fracture classification on shoulder radiographs using a convolutional neural network (CNN). The primary aim was to evaluate the classification performance of the CNN for proximal humeral fractures (PHF) based on the AO/OTA classification system. Secondary objectives included evaluating the model's performance for diaphyseal humerus, clavicle, and scapula fractures.

Timing of adverse events in patients undergoing acute and elective hip arthroplasty surgery: a multicentre cohort study using the Global Trigger Tool.

Objective: To explore timing in relation to all types of adverse events (AEs), severity and preventability for patients undergoing acute and elective hip arthroplasty.

Design: A multicentre cohort study using retrospective record review with Global Trigger Tool methodology in combination with data from several registers.

Setting: 24 hospitals in 4 major regions of Sweden.

Association Between Radiographic and Clinical Outcomes Following Distal Radial Fractures: A Prospective Cohort Study with 1-Year Follow-up in 366 Patients.

Background: Several studies of distal radial fractures have investigated final displacement and its association with clinical outcomes. There is still no consensus on the importance of radiographic outcomes, and published studies have not used the same criteria for acceptable alignment. Previous reports have involved the use of linear or dichotomized analyses.