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Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Results: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. Conclusion: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments. [ABSTRACT FROM AUTHOR]

Acinetobacter johnsonii is an uncommon cause of endophthalmitis. This case report describes a 40-year-old male admitted with pain, redness, and vision loss in his right eye after an open globe injury by a steel fragment. Clinical assessment confirmed post-traumatic endophthalmitis with an intraocular foreign body. The patient underwent a vitreous biopsy, lensectomy, vitrectomy, and intravitreal antibiotics, followed by laser photocoagulation and foreign body extraction via the pars plana. Acinetobacter johnsonii was isolated from the vitreous culture. A combination of vancomycin, levofloxacin and ceftazidime was administered, leading to reduced infection and inflammation. Postoperatively at one month, the patients' best-corrected visual acuity had improved to 20/63. The anterior segment exhibited no inflammation, the vitreous cavity was clear, and the retina with hemorrhage and laser treatment remained stable. The one-year follow-up confirmed the continued stability of the ocular condition. Acinetobacter johnsonii , a rare cause of endophthalmitis often linked to trauma or surgery, should be recognized as a possible pathogen in post-traumatic endophthalmitis cases, meriting clinical consideration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hu, Huang, Li, Fang, Li and Feng.)