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Echinoderm skeletons are composed of calcium carbonate ossicles that join in a variety of ways to form flexible or, more rarely, fixed joints. Ossicle "fusion" in echinoderms has been widely reported in the literature to form various types of fixed joint, but fusion in the sense of chemical union (ankylosis) of the calcitic ossicles has rarely been demonstrated. The arm skeleton of ophiuroids is primarily composed of a series of vertebral ossicles; each vertebra is a compound ossicle that consists of paired ambulacral ossicles united by a fixed joint, often reported to be fused. Development of vertebral ossicles in the amphiurid brittlestar Ophiophragmus filograneus from the arm tip to the oral frame was examined using scanning electron microscopy to follow ontogeny of the vertebra. As an ambulacral ossicle grew, its stereomal trabeculae interdigitated and interlocked by hooking around those of its paired ambulacral, forming the characteristic sinuous suture line central to ophiuroid vertebrae. This three‐dimensional interlocking of stereom formed the joint between paired ambulacrals. With further growth of the vertebra, limited fusion of trabeculae of the paired ambulacrals added to the structure of the joint, primarily at the articular surfaces between successive vertebrae. The joint found here with interlocked trabeculae between ambulacral ossicles of O. filograneus appears to be the same type described in the literature in some echinoids and other ophiuroids. This unique type of fixed joint is described here and named the "campylogomphosis" (Greek: campylos, bent; gomphos, bolt). This newly recognized joint might have implications in echinoderm phylogeny, comparative biology, medicine, and materials science. [ABSTRACT FROM AUTHOR]

New guidelines on the management of chronic cough have been published in Australia and the United Kingdom. These guidelines reflect the evolving understanding of cough and emphasize the need for a more comprehensive approach to diagnosis and treatment. The traditional model of attributing chronic cough to conditions like asthma and gastroesophageal reflux disease is inadequate, and a new paradigm suggests that chronic cough is a neuroinflammatory or neuropathic disorder. Treatment options include repurposed drugs and novel medications that target peripheral airway receptors. Non-pharmacological approaches, such as behavioral techniques, can also be effective. The guidelines recommend tailoring investigations and treatments based on individual patient characteristics and avoiding unnecessary tests and empirical treatments. The hope is that these new guidelines will lead to improved outcomes for patients with chronic cough. [Extracted from the article]

Background Methods Results Conclusion Prompt diagnosis of choledocholithiasis is crucial for reducing disease severity, preventing complications and minimizing length of stay. Magnetic resonance cholangiopancreatography (MRCP) is commonly used to evaluate patients with suspected choledocholithiasis but is expensive and may delay definitive intervention. To optimize patient care and resource utilization, we have developed five machine learning models that predict a patients' risk of choledocholithiasis based on clinical presentation and pre‐MRCP investigation results.Inpatients admitted to the Royal Hobart Hospital from 2018 to 2023 with a suspicion of choledocholithiasis were included. Exclusion criteria included prior hepatobiliary surgery, known hepatobiliary disease, or incomplete records. Variables related to clinical presentation, laboratory testing, and sonographic or CT imaging were collected. Four machine learning techniques were employed: logistic regression, XGBoost, random forest, and K‐nearest neighbours. The three best performing models were combined to create an ensemble model. Model performance was compared against the American Society for Gastrointestinal Endoscopy (ASGE) choledocholithiasis risk stratification guidelines.Of the 222 patients included, 113 (50.9%) had choledocholithiasis. The most successful models were the random forest (accuracy: 0.79, AUROC: 0.83) and ensemble (accuracy and AUROC: 0.81). Every model outperformed the ASGE guidelines. Key variables influencing the models' predictions included common bile duct diameter, lipase, imaging evidence of cholelithiasis, and liver function tests.Machine learning models can accurately assess a patient's risk of choledocholithiasis and could assist in identifying patients who could forgo an MRCP and proceed directly to intervention. Ongoing validation on prospective data is necessary to refine their accuracy and clinical utility. [ABSTRACT FROM AUTHOR]