A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI.

Management of anterior cruciate ligament revision in adults: the 2022 ESSKA consensus: part II-surgical strategy.

Purpose: The aim of this ESSKA consensus is to give recommendations based on scientific evidence and expert opinion to improve the diagnosis, preoperative planning, indication and surgical strategy in Anterior Cruciate Ligament revision.

Methods: Part 2, presented herein, followed exactly the same methodology as Part 1: the so-called ESSKA formal consensus derived from the Delphi method. Eighteen questions were ultimately asked.

Comorbidity between lung cancer and COVID-19 pneumonia: role of immunoregulatory gene transcripts in high -expressing normal lung.

Background: SARS-CoV-2 (COVID-19) elicits a T-cell antigen-mediated immune response of variable efficacy. To understand this variability, we explored transcriptomic expression of angiotensin-converting enzyme 2 (, the SARS-CoV-2 receptor) and of immunoregulatory genes in normal lung tissues from patients with non-small cell lung cancer (NSCLC).

Methods: This study used the transcriptomic and the clinical data for NSCLC patients generated during the CHEMORES study [ = 123 primary resected (early-stage) NSCLC] and the WINTHER clinical trial ( = 32 metastatic NSCLC).

Management of anterior cruciate ligament revision in adults: the 2022 ESSKA consensus part I-diagnostics and preoperative planning.

Purpose: The aim of this ESSKA consensus is to give recommendations based on evidence and expert opinion to improve diagnosis, preoperative planning, indication and surgical strategy in ACL revision.

Methods: The European expert surgeons and scientists were divided into four groups to participate in this consensus. A "literature group" (four surgeons); "steering group" (14 surgeons and scientists); "rating group" (19 surgeons) and finally "peer review group" (51 representatives of the ESSKA-affiliated national societies from 27 countries).

Risk factors and prevention strategies for shoulder injuries in overhead sports: an updated systematic review.

Purpose: The aim of this systematic review was to update the knowledge on risk factors and prevention strategies for shoulder injuries in overhead sports with special emphasis on methodological quality.

Methods: All methodological procedures were performed in line with a previous systematic review by Asker et al. (2018).

GDAP1 loss of function inhibits the mitochondrial pyruvate dehydrogenase complex by altering the actin cytoskeleton.

Charcot-Marie-Tooth (CMT) disease 4A is an autosomal-recessive polyneuropathy caused by mutations of ganglioside-induced differentiation-associated protein 1 (GDAP1), a putative glutathione transferase, which affects mitochondrial shape and alters cellular Ca homeostasis. Here, we identify the underlying mechanism. We found that patient-derived motoneurons and GDAP1 knockdown SH-SY5Y cells display two phenotypes: more tubular mitochondria and a metabolism characterized by glutamine dependence and fewer cytosolic lipid droplets.

Machine learning and conventional statistics: making sense of the differences.

The application of machine learning (ML) to the field of orthopaedic surgery is rapidly increasing, but many surgeons remain unfamiliar with the nuances of this novel technique. With this editorial, we address a fundamental topic-the differences between ML techniques and traditional statistics. By doing so, we aim to further familiarize the reader with the new opportunities available thanks to the ML approach.

iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain.

Parkinson's Disease Phenotypes in Patient Neuronal Cultures and Brain Organoids Improved by 2-Hydroxypropyl-β-Cyclodextrin Treatment.

Background: The etiology of Parkinson's disease (PD) is only partially understood despite the fact that environmental causes, risk factors, and specific gene mutations are contributors to the disease. Biallelic mutations in the phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK1) gene involved in mitochondrial homeostasis, vesicle trafficking, and autophagy are sufficient to cause PD.

Objectives: We sought to evaluate the difference between controls' and PINK1 patients' derived neurons in their transition from neuroepithelial stem cells to neurons, allowing us to identify potential pathways to target with repurposed compounds.

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons.

The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques.

Machine learning methods in sport injury prediction and prevention: a systematic review.

Purpose: Injuries are common in sports and can have significant physical, psychological and financial consequences. Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention. The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention.

Integrated, automated maintenance, expansion and differentiation of 2D and 3D patient-derived cellular models for high throughput drug screening.

Patient-derived cellular models become an increasingly powerful tool to model human diseases for precision medicine approaches. The identification of robust cellular disease phenotypes in these models paved the way towards high throughput screenings (HTS) including the implementation of laboratory advanced automation. However, maintenance and expansion of cells for HTS remains largely manual work.

Proteomic response of inflammatory stimulated intestinal epithelial cells to in vitro digested plums and cabbages rich in carotenoids and polyphenols.

Due to their anti-oxidant and anti-inflammatory potential, polyphenol and carotenoid-rich plant foods have been suggested as promising phytochemicals in the prevention of or as adjuvants regarding inflammatory bowel diseases (IBD). In the present study, we investigated whether plum (Italian Plum, Prunus cocomilla), or cabbage (Kale, Brassica oleracea var. sabellica), selected for their high phytochemical content, are able to reduce inflammation in cellular models of the intestinal epithelium, employing proteomic methods.