Objective Mesenteric panniculitis is an idiopathic , uncommon disease involving the adipose tissue of mesentery .The etiology , diagnosis and treatment are still unnoticed .We thus reported a case series to improve the understanding of this rare disorder .Methods We retrospectively analyzed the clinical data of 12 patients with mesenteric panniculitis including manifestation , diagnosis, treatment and prognosis.Results We found a male predominance (M∶F 3∶1) with the median age of 58 years old at diagnosis.The most common symptom was abdominal pain (9/12), followed by abdominal distension (3/12) and weight loss (3/12).Physical examination was unremarkable in the majority of patients (8/12).C reactive protein (9/12) and erythrocyte sedimentation rate (10/12) were normal in majority of patients.CT findings were of much diagnostic value .All patients had small intestinal mesentery involvement and multi-nodular appearance with increased fat density .Pseudo-capsule sign ( 8/12 ) and fat halo sign (6/12) were common.Pathological diagnosis was obtained in 4 cases showing fat tissue inflammation with local necrosis and fibrosis .Six cases all received prednisone , 2 with combined cyclophosphamide , 1 with azathioprine, 1 with tripterygium wilfordii .Short-term clinical response was achieved in all cases , but two patients relapsed .Conclusions Mesenteric panniculitis occurs predominantly in middle-aged and elderly . Abdominal pain is the leading symptom .Inflammatory markers are often normal while computed tomography is the most important diagnostic tool .Surgery combined with cortical steroid and immunosuppressant agents is effective.

OBJECTIVE To explore a new model of intelligent medication education for pharmaceutical outpatient clinics by constructing dynamic HTML web pages through the DeepSeek-V3-0324 large-scale model. METHODS Clinical pharmacists integrated key clinical information such as patients’ basic information， medication history and medication precautions in real time， and generated a standardized medication education list through the DeepSeek-V3-0324 large-scale model and manual review. RESULTS The DeepSeek-V3-0324 large-scale model was applied in the pharmaceutical outpatient clinics to generate a personalized medication education list， which could effectively solve the disunity of pharmacy guidance caused by the lack of standardization of medication education and the difference of individualized experience of pharmacists in the traditional pharmaceutical outpatient clinics in the face of complex cases， and medication errors caused by forgetting or misremembering information among certain special patient populations after receiving medication education. CONCLUSIONS The transformation and application of artificial intelligence technology in pharmaceutical outpatient clinics is an innovation of pharmaceutical outpatient service means， which can provide patients with immediate and personalized medication education and improve the quality of pharmaceutical care. However， it is also necessary to face the lag of database update and the lack of risk management， as well as the lack of diversification of medication education lists.