London Bridge Hospital

About: London Bridge Hospital is a healthcare organization based out in London, United Kingdom. It is known for research contribution in the topics: Antiphospholipid syndrome & Systemic lupus erythematosus. The organization has 107 authors who have published 122 publications receiving 4523 citations.

Does fracture-extension into the pars interarticularis alter outcomes in odontoid failure? a technical note on pars interarticularis osteotomy and atlantoaxial distraction arthrodesis

Abstract: Odontoid process fractures can extend rostral into the C2 arch. We investigated the clinical impact of a concurrent fracture of the pars interarticularis on odontoid failure. To overcome the surgical challenges related to the morphology of these fractures, we describe a novel surgical strategy using atlantoaxial joint distraction arthrodesis. We conducted a single centre cohort study of 13 consecutive patients with odontoid fractures extending into the pars treated between June 2016 and June 2018. Criteria for a stable fibrous non-union were: Atlanto-Dens Interval (ADI) 14 mm and lack of symptomatic motion at the fracture site. Atlantoaxial instability was defined as greater than 50% subluxation across the C1-C2 joint. Return to pre-injury performance status was considered a satisfactory clinical outcome. The mean age of the patient population was 77.2 years (SD 11.9). The mean follow-up time was 15 months (SD 5.2). 69% had an associated atlantoaxial instability (P-value 0.0005). Cervical orthosis treatment was associated with a high non-union rate (70%) (P-value 0.04) although it did not affect the overall clinical outcome. 2 cases presented with cord compression were treated surgically with pars interarticularis osteotomy and atlantoaxial distraction arthrodesis. Odontoid fracture with extension into the pars interarticularis often present with atlantoaxial instability and may result in stable fibrous non-union if treated non-operatively. The C1–C2 segment can be stabilised with atlantoaxial distraction arthrodesis achieved through an osteotomy of the pars interarticularis.

Biological therapies in lupus

Abstract: The use of biologics in lupus has lagged far behind the experience of these agents in rheumatoid arthritis (RA). Lessons learnt in RA (and increasingly in seronegative spondyloarthritis) include the observation that in general the agents now in use are safe, even in the medium to longer term and that they can be used in place of other remedies, if necessary. Furthermore, they can be used in tandem with other biologics, reaching the stage where ‘‘treat to target’’ is now regarded as achievable. Experience in lupus has been less positive. As Nandkumar and colleagues remind us in this special issue of Lupus, ‘‘while several promising treatments have emerged, only one drug, belimumab, has passed regulatory muster’’. Possible reasons for this time lag are many – lupus being less common than RA, the clinical complexity of systemic lupus erythematosus (SLE), the need for additional immunosuppressives in many cases, and of course, the many disappointments in clinical trials along the way. However, there is cause for optimism. Anti-B cell agents, rituximab and now belimumab, despite early trial setbacks, are both successful additions to our lupus treatment repertoire. They promise to profoundly alter lupus clinical practice. One example is the ‘‘no-steroid’’ regime for lupus nephritis being trialed by Liz Lightstone and her team using an initial two pulses of rituximab (and a pulse of methylprednisolone) followed by maintenance mycophenolate mofetil (MMF) and no oral steroids. With deepening understanding of the mechanisms leading to lupus activity, a rapidly increasing number of agents are currently in various stages of trial. Already lessons are being learnt – the heightened risk of infections when biologics are combined with conventional immunosuppressive drugs such as cyclophosphamide and MMF. Plus the (obvious) risk of failure in a drug trial when the background dosage of steroids is high. In this special issue of Lupus, Professor Dan Wallace, whose clinical experience in this area is second to none, has brought together a number of the leaders in the field of new directed therapies in SLE. For me, this special issue brings us an important snapshot of the current status of ‘biologics’ in lupus.

Tackling Lung Cancer: Advanced Image Analysis and Deep Learning for Early Detection

Abstract: <p>This study utilizes advanced techniques such as image processing, deep learning, and metaheuristics to detect lung cancer in its early stages. The proposed model achieves higher detection rates and outperforms expert physicians, demonstrating superior accuracy, sensitivity, and specificity. It also excels in lung segmentation tasks compared to a traditional CNN model.</p>

&lt;b&gt;Performance Evaluation of Machine Learning Models for Prostate Cancer Detection&lt;/b&gt;

Abstract: This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.