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Plain Language Summary What is this summary about? This PLSP provides a short summary of an original scientific article that presented results from the PRIMA study after 3.5 years of follow-up time. The original article was published in the European Journal of Cancer in 2023. The PRIMA study included adult patients with newly diagnosed advanced high-risk ovarian cancer whose tumors shrunk or became undetectable after treatment with chemotherapy with or without surgery. The PRIMA study evaluated how well the drug niraparib, also known as Zejula, worked at delaying or preventing ovarian cancer from coming back (recurring) or getting worse (progressing) compared with placebo (a substance with no effects that a doctor gives to a patient instead of a drug). The first results from the PRIMA study were published in 2019, when patients had participated in the PRIMA study for about 1.2 years. The article this PLSP is based on reports longer-term data from the PRIMA study, when patients had participated in the PRIMA study for about 3.5 years. Patients were monitored (or followed) for a longer time to understand how well niraparib continued to work and to evaluate whether the safety of niraparib changed with additional time being monitored. What were the results? Patients who took niraparib had more time before their cancer came back or got worse than patients who took placebo. In terms of safety, no new types of side effects with niraparib treatment were observed with additional time being monitored as part of the PRIMA study. What do the results mean? These results support that niraparib remains an important treatment option to help delay the cancer from coming back or getting worse in patients with newly diagnosed advanced ovarian cancer that responded to initial treatment. Clinical Trial Registration:NCT02655016 (PRIMA study) (ClinicalTrials.gov) [ABSTRACT FROM AUTHOR]

The diagnosis of oral lichen planus (OLP) poses many challenges due to its nonspecific clinical symptoms and histopathological features. Therefore, the diagnostic process should include a thorough clinical history, immunological tests, and histopathology. Our study aimed to enhance the diagnostic accuracy of OLP by integrating direct immunofluorescence (DIF) results with clinical data to develop a multivariate predictive model based on the Artificial Neural Network. Eighty patients were assessed using DIF for various markers (immunoglobulins of classes G, A, and M; complement 3; fibrinogen type 1 and 2) and clinical characteristics such as age, gender, and lesion location. Statistical analysis was performed using machine learning techniques in Statistica 13. The following variables were assessed: gender, age on the day of lesion onset, results of direct immunofluorescence, location of white patches, locations of erosions, treatment history, medications and dietary supplement intake, dental status, smoking status, flossing, and using mouthwash. Four statistically significant variables were selected for machine learning after the initial assessment. The final predictive model, based on neural networks, achieved 85% in the testing sample and 71% accuracy in the validation sample. Significant predictors included stress at onset, white patches under the tongue, and erosions on the mandibular gingiva. In conclusion, while the model shows promise, larger datasets and more comprehensive variables are needed to improve diagnostic accuracy for OLP, highlighting the need for further research and collaborative data collection efforts. [ABSTRACT FROM AUTHOR]