Ondansetron is an anti-emetic 5-HT3 receptor antagonist being investigated for treating neonatal opioid withdrawal syndrome (NOWS). Sparse PK data were analyzed from a multicenter, double-blind clinical trial with 98 mother/neonate dyads. Pregnant women with opioid use disorder were randomized to receive either placebo or ondansetron 8 mg intravenously within 4 h of delivery. Neonates born… →
CONCLUSION: ART-001 was effective and well-tolerated in patients with SFVMs. These results support the further development of ART-001 in SFVMs and other PIK3CA-related overgrowth syndromes to confirm clinical benefits and long-term safety. →
CONCLUSIONS: Direct MRCP may be a feasible and potentially cost-effective diagnostic strategy for patients with suspected acute gallstone disease and deranged LFTs. Automated measurement of MRCP parameters shows promise in detecting obstruction. Larger trials are warranted to assess this potential. →
Mathematical reasoning remains a difficult area for artificial intelligence (AI) due to the complexity of problem-solving and the need for structured, logical thinking. While large language models (LLMs) have made significant progress, they often struggle with tasks that require multi-step reasoning. Reinforcement learning (RL) has shown promise in improving these capabilities, yet traditional methods face… →
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,… →
Recent advancements in LLMs, such as the GPT series and emerging “o1” models, highlight the benefits of scaling training and inference-time computing. While scaling during training—by increasing model size and dataset volume—has been a well-established strategy, recent findings emphasize the advantages of inference-time scaling, where additional computational resources during testing improve output quality and task… →
Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection ability, current techniques handicap themselves with three important challenges. They heavily depend on expensive and large-scale region-level annotations, which are hard to scale. Their captions are typically short and not contextually rich, which makes them… →
Developing AI systems that learn from their surroundings during execution involves creating models that adapt dynamically based on new information. In-Context Reinforcement Learning (ICRL) follows this approach by allowing AI agents to learn through trial and error while making decisions. However, this method has significant challenges when applied to complex environments with various tasks. It… →
CONCLUSIONS: This study supports the efficacy of a low-intensity psychological intervention applied in a blended format on multimorbidity in primary care. It justifies the exploration of the conceptualization of depression in type 2 diabetes as well as the analysis of the implementation of such interventions in routine clinical practice. →