目的 探讨原发性IgA肾病临床特征与病理间的关系。 方法 回顾性分析2006年1月-2008年6月经肾活检诊断为原发性IgA肾病患者109例的临床病理资料。 结果 血尿的发生及血尿的程度与肾活检病理Lee分级无关，而尿蛋白定量、症状性高血压、肾功能受损与病理学分级呈正相关，且与肾小球硬化、间质病变等病理特征呈正相关。 结论 大量蛋白尿、症状性高血压等是IgA肾病预后不良的重要因素，需要积极干预。
ObjectiveTo explore the relevant risk factors for postoperative delirium (POD) in elderly patients undergoing radical colon cancer surgery, and provide a basis for formulating postoperative prevention and treatment measures for POD. MethodsA total of 128 elderly patients diagnosed with colon cancer and underwent radical colon cancer surgery at Xindu District People’s Hospital in Chengdu from January 2018 to December 2021 were included as the study subjects. Patients were divided into two groups according to the score of Delirium Assessment Scale (4AT Scale). The basic data, main perioperative clinical data and laboratory indicators of the two groups were collected, and univariate and logistic regression analysis were carried out to determine the potential risk factors of POD in elderly patients with colon cancer after radical operation. ResultsAccording to the results of the 4AT scale score, a total score of ≥4 points was used as the threshold for determining patient POD. Among 128 patients, there were 29 patients (22.66%) with POD and 99 patients (77.34%) without POD. ① General data comparison: There was no significant difference between the two groups in gender, body mass index, years of education, hypertension, diabetes, smoking history and drinking history (P>0.05), but there was significant difference in age, preoperative mini-mental state examination (MMSE) score and American Society of Anesthesiologists (ASA) grade (P<0.05). ② Comparison of main clinical data during the perioperative period: There was no statistically significant difference between the two groups of patients in ICU treatment, nonsteroidal anti-inflammatory drug treatment, visual analogue scale, and intraoperative hypotension (P>0.05), but there was a statistically significant difference in operative time, anesthesia time, intraoperative blood loss, and dexmedetomidine treatment (P<0.05). ③ Comparison of preoperative laboratory indicators: There was no statistically significant difference between the two groups of patients in terms of hemoglobin, serum albumin, white blood cell count, prognostic nutritional index, neutrophil/lymphocyte ratio, D-dimer, and albumin to fibrinogen ratio (P>0.05). ④ The results of logistic regression analysis showed that low preoperative MMSE score [OR=0.397, 95%CI (0.234, 0.673)], long surgical time [OR=1.159, 95%CI (1.059, 1.267) ], and long anesthesia time [OR=1.138, 95%CI (1.057, 1.226) ] were independent risk factors for the occurrence of POD in elderly colon cancer patients undergoing radical surgery. ConclusionPreoperative MMSE score, operative time, and anesthesia time are closely related to the occurrence of POD in elderly colon cancer radical surgery, worth implementing key perioperative management in clinical practice to prevent and manage POD.
High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether recurrence occurs becomes an important reference for surgical planning and treatment methods for this disease. Current recurrence prediction models do not consider the potential pathological relationships between internal tissues of the entire ovary. They use convolutional neural networks to extract local region features for judgment, but the accuracy is low, and the cost is high. To address this issue, this paper proposes a new lightweight deep learning algorithm model for predicting recurrence of high-grade serous ovarian cancer. The model first uses ghost convolution (Ghost Conv) and coordinate attention (CA) to establish ghost counter residual (SCblock) modules to extract local feature information from images. Then, it captures global information and integrates multi-level information through proposed layered fusion Transformer (STblock) modules to enhance interaction between different layers. The Transformer module unfolds the feature map to compute corresponding region blocks, then folds it back to reduce computational cost. Finally, each STblock module fuses deep and shallow layer depth information and incorporates patient's clinical metadata for recurrence prediction. Experimental results show that compared to the mainstream lightweight mobile visual Transformer (MobileViT) network, the proposed slicer visual Transformer (SlicerViT) network improves accuracy, precision, sensitivity, and F1 score, with only 1/6 of the computational cost and half the parameter count. This research confirms that the proposed algorithm model is more accurate and efficient in predicting recurrence of high-grade serous ovarian cancer. In the future, it can serve as an auxiliary diagnostic technique to improve patient survival rates and facilitate the application of the model in embedded devices.