OBJECTIVE: The primary aim of this study was to assess the effectiveness of King's Goal Attainment Theory in the management of newly diagnosed patients with type 2 diabetes. This research sought to develop a collaborative model for blood glucose management, integrating the expertise and roles of physicians, nurses, and patients. The model is designed to enhance the synergy in health care provision, ensuring a comprehensive approach to diabetes management.

METHODS: In this study conducted at Changzhou Traditional Chinese Medicine Hospital between January 2022 and February 2023, eligible patients were randomized into a control group or an online feedback group. The control group received standard care, while the online feedback group participated in a King's Theory of Goal Attainment-based online teach-back program, enhanced by "Internet + Nursing" strategies. This included an interactive platform for goal planning, video content sharing, comprehension assessment, misconception correction, and patient-driven recaps of disease information. Health monitoring was facilitated through the "Internet + Nursing" platform. The study focused on comparing changes in glucose metabolism and emotional disorder symptoms between the groups to evaluate the intervention's effectiveness.

Methods: Retrospective analysis of data of seven patients with oral and maxillofacial tumors who underwent surgery between January 2019 and January 2021 using a combination of mixed reality and surgical navigation. Virtual surgical planning and navigation plan were based on preoperative CT datasets. Through IGT-Link port, mixed reality workstation was synchronized with surgical navigation, and surgical planning data were transferred to the mixed reality workstation. Osteotomy lines were marked with the aid of both surgical navigation and mixed reality images visualized through HoloLens. Frozen section examination was used to ensure negative surgical margins. Postoperative CT datasets were obtained 1 week after the surgery, and chromatographic analysis of virtual osteotomies and actual osteotomies was carried out. Patients received standard oncological postoperative follow-up.

Results: Of the seven patients, four had maxillary tumors and three had mandibular tumors. There were total of 13 osteotomy planes. Mean deviation between the planned osteotomy plane and the actual osteotomy plane was 1.68 ± 0.92 mm; the maximum deviation was 3.46 mm. Chromatographic analysis showed error of ≤3 mm for 80.16% of the points. Mean deviations of maxillary and mandibular osteotomy lines were approximate (1.60 ± 0.93 mm vs. 1.86 ± 0.93 mm). While five patients had benign tumors, two had malignant tumors. Mean deviations of osteotomy lines was comparable between patients with benign and malignant tumors (1.48 ± 0.74 mm vs. 2.18 ± 0.77 mm). Intraoperative frozen pathology confirmed negative resection margins in all cases. No tumor recurrence or complications occurred during mean follow-up of 15.7 months (range, 6-26 months).

CASE SUMMARY: We reported a case of sclerosing odontogenic carcinoma of the maxilla in a 62-year-old woman, who presented with an indolent right palatal swelling, which progressively increased in size over 7 years. Right subtotal maxillectomy with surgical margins of approximately 1.5 cm was performed. The patient remained disease free for 4 years following the ablation surgery. Diagnostic workups, treatment, and therapeutic outcomes were discussed.

APPROACH: In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of deep learning radiomics in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks (CNN) to extract visual features as radiomics for multi-category classification based on Breast Imaging Reporting and Data System (BI-RADS). Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity.

MAIN RESULTS: To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of deep learning radiomics; and, (ii) improve the readability of generated medical reports.

METHODS: Sprague-Dawley rats were divided into four groups: Sham, AMI, AMI treated with PBS (AMI-PBS), and AMI treated with pirfenidone (AMI-PFD) (n=12 each). AMI was induced via coronary artery ligation. The AMI-PFD and AMI-PBS groups received pirfenidone and PBS for 14 days, respectively. Cardiac function, fibrosis, serum cytokines, collagen and elastin content, and their ratios were assessed. Cardiac fibroblasts (CFs) from neonatal rats were categorized into control, hypoxia-induced (LO), LO+PBS, and LO+PFD groups. ELISA measured inflammatory factors, and RT-PCR analyzed collagen and elastin gene expression.

RESULTS: The AMI-PFD group showed improved cardiac function and reduced serum interleukin-1β (IL-1β), IL-6, and transforming growth factor-β (TGF-β). Type I and III collagen decreased by 22.6 % (P=0.0441) and 34.4 % (P=0.0427), respectively, while elastin content increased by 79.4 % (P=0.0126). E/COLI and E/COLIII ratios rose by 81.1 % (P=0.0026) and 88.1 % (P=0.0006). CFs in the LO+PFD group exhibited decreased IL-1β, IL-6, TGF-β, type I and III collagen, with increased elastin mRNA, enhancing the elastin/collagen ratio.