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Background: Prolgolimab is an IgG1 anti-PD-1 monoclonal antibody with the Fc-silencing 'LALA' mutation. The phase III DOMAJOR study assessed efficacy and safety of prolgolimab in combination with pemetrexed and platinum-based chemotherapy vs placebo in combination with pemetrexed and platinum-based chemotherapy as first-line treatment for advanced non-small cell lung cancer (NSCLC).

Methods: 292 patients with advanced non-squamous NSCLC were randomized 1:1 to receive 4 cycles of pemetrexed, platinum-based drug and either prolgolimab (3 mg/kg Q3W) or placebo followed by prolgolimab/placebo with pemetrexed until disease progression or toxicity (≤36 months).

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Background: The RAVE-Renal study was conducted to evaluate the real-world efficacy and safety of avelumab plus axitinib as a first-line therapy for patients with metastatic renal cell carcinoma (mRCC).

Methods: RAVE-Renal was a multicenter, noninterventional, ambispective study with both retrospective and prospective components. The study included adult patients with histologically confirmed mRCC, measurable disease per RECIST version 1.

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Background: With the proportion of tuberculosis cases that are extrapulmonary tuberculosis (EPTB) increasing in recent years, understanding and addressing factors contributing to the prolonged time to diagnosis (TTD) of EPTB patients is vital.

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The biomedical applications of artificial intelligence: an overview of decades of research.

J Drug Target

January 2025

Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India.

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact.

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Background: Medical marijuana (MMJ) is available in Pennsylvania, and participation in the state-regulated program requires patient registration and receiving certification by an approved physician. Currently, no integration of MMJ certification data with health records exists in Pennsylvania that would allow clinicians to rapidly identify patients using MMJ, as exists with other scheduled drugs. This absence of a formal data sharing structure necessitates tools aiding in consistent documentation practices to enable comprehensive patient care.

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