The Proctor's reporting guideline for implementation strategies represents a landmark framework in the field of implementation science, aiming to address the issue of inconsistent reporting in implementation research by standardizing the naming, definition, and operationalization of implementation strategies, thereby enhancing the credibility and utility of research findings. This paper provides an in-depth interpretation of the core connotations of this reporting guideline and illustrates its application in developing interview outlines and specifying implementation strategies, using a brief smoking cessation intervention project as a case study. Through this reporting guideline, abstract recommendations for implementation are systematically transformed into clear, multidimensional operational guides, significantly improving the transparency of strategy connotations and the replicability of actual execution. Meanwhile, the case study highlights the flexibility of the guideline, which allows researchers to adapt the content and format of strategies based on local resources and cultural contexts, thus enhancing practical adaptability while maintaining scientific rigor. However, the application of Proctor's reporting guideline still faces challenges, primarily manifested in the potential confusion surrounding the constructs of temporality and dose in practice, as well as the challenges that the inherent flexibility of the guideline may pose to the assessment of fidelity and effectiveness. Despite these limitations, the reporting guideline remains a vital tool for implementation research; future efforts should focus on optimizing its application—through refining operational guidelines, standardizing flexible adaptations, and involving stakeholders—to better guide implementation studies and continuously promote high-quality development in the field.

OBJECTIVES: To investigate the changes in blood levels of calcium and bone metabolism biochemical markers in patients with primary osteoporosis receiving treatment with denosumab. METHODS: Seventy-three patients with primary osteoporosis treated in our Department between December, 2021 and December 2023 were enrolled. All the patients were treated with calcium supplements, vitamin D and calcitriol in addition to regular denosumab treatment every 6 months. Blood calcium, parathyroid hormone (PTH), osteocalcin (OC), type I procollagen amino-terminal propeptide (PINP), and type I collagen carboxy-terminal telopeptide β special sequence (β‑CTX) data before and at 3, 6, 9, and 12 months after the first treatment were collected from each patient. RESULTS: Three months after the first denosumab treatment, the bone turnover markers (BTMs) OC, PINP, and β-CTX were significantly decreased compared to their baseline levels by 39.5% (P<0.001), 56.2% (P<0.001), and 81.8% (P<0.001), respectively. At 6, 9, and 12 months of treatment, OC, PINP, and β-CTX remained significantly lower than their baseline levels (P<0.001). Blood calcium level was decreased (P<0.05) and PTH level increased (P<0.05) significantly in these patients at months of denosumab treatment, but their levels were comparable to the baseline levels at 6, 9, and 12 months of the treatment (P>0.05). CONCLUSIONS Denosumab can suppress BTMs and has a good therapeutic effect in patients with primary osteoporosis, but reduction of blood calcium and elevation of PTH levels can occur during the first 3 months in spite of calcium supplementation. Blood calcium and PTH levels can recover the baseline levels as the treatment extended, suggesting the importance of monitoring blood calcium and PTH levels during denosumab treatment.
Humans ; Denosumab/therapeutic use* ; Calcium/blood* ; Parathyroid Hormone/blood* ; Biomarkers/blood* ; Osteoporosis/blood* ; Osteocalcin/blood* ; Procollagen/blood* ; Female ; Collagen Type I/blood* ; Peptide Fragments/blood* ; Bone Density Conservation Agents/therapeutic use* ; Bone and Bones/metabolism* ; Male ; Middle Aged ; Vitamin D ; Peptides/blood* ; Aged

Objective To develop a machine learning(ML)-based prediction model for assessing the therapeutic effects of resveratrol(RES)on the pathological damage of acute pancreatitis(AP),and to optimize RES administration strategies for AP through validation using an animal model.Methods AAn ML-based prediction model was constructed using published data.Interpretability analysis was applied to identify high-efficacy zones within the parameter space of administration dose and frequency,which was followed by rigorous screening to select the optimal dosing strategy that balanced therapeutic efficacy and experimental feasibility.A total of 32 C57BL/6 mice were randomly assigned to 4 groups(n=8 per group),including a control group(Ctrl),an AP model group induced by caerulein(CER)and referred to as CER-AP,a treatment group receiving RES via intraperitoneal injection(RES i.p.),and a treatment group receiving RES via intragastric gavage(RES i.g.).The Ctrl group received intraperitoneal injection of normal saline.The CER-AP and the treatment groups were induced with 10 intraperitoneal injections of CER at 50 μg/kg.RES was administered to the RES i.p.and RES i.g.groups according to the optimal dose and timing predicted by the ML model.Blood and tissue samples were collected 12 hours after the experiment started.Results The gradient boosting decision tree model,optimized via Hyperopt,yielded the best performance,predicting that the optimal dose and administration frequency were 19.992 mg/kg and 3.828 times,respectively.Accordingly,a regimen of 20 mg/kg RES,administered four times,was used in the animal experiments.Compared with the Ctrl group,the CER-AP group exhibited higher pancreatic pathology scores and elevated levels of serum amylase,lipase,pancreatic myeloperoxidase,and trypsin,with all differences reaching statistical significance(all P<0.05).The administration of 20 mg/kg RES via both intraperitoneal injection and intragastric gavage mitigated pancreatic inflammatory cell infiltration and necrosis,improved the overall pathology score,and reduced serum amylase,lipase,and pancreatic myeloperoxidase levels to varying degrees(all P<0.05).Conclusion A regimen of 20 mg/kg RES administered four times effectively alleviates the severity of CER-induced AP.The therapeutic benefits appear to arise from a multi-target regulatory network that simultaneously suppresses inflammatory cascades,mitigates oxidative stress,and reduces apoptosis,thereby reducing pancreatic tissue damage and systemic inflammatory responses.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

Objective:To develop the Competency Assessment Questionnaire for Clinical Teachers of Traditional Chinese Medicine Specialist Nurses and to test its reliability and validity.Methods:The first draft of the questionnaire was formed through literature review and expert consultation based on the competency theory. Convenience sampling was used to survey 218 clinical teachers of traditional Chinese medicine specialist nurses in 5 ClassⅢ Grade A hospitals in Zhejiang and Jiangsu provinces from June to July 2024 to test the reliability and validity of the questionnaire.Results:The Competency Assessment Questionnaire for Clinical Teachers of Traditional Chinese Medicine Specialist Nurses contained 5 dimensions, including professionalism and personal attributes, professional knowledge and skills, clinical practice ability, clinical teaching ability, and research management ability, with a total of 35 items. The scale-level content validity index was 0.875. The Cronbach's α coefficient was 0.974, the folded half reliability was 0.845, and the retest reliability was 0.912.Conclusions:The Competency Assessment Questionnaire for Clinical Teachers of Traditional Chinese Medicine Specialist Nurses has good reliability and validity, and can be used as a valid tool for evaluating the core competencies of clinical teachers of traditional Chinese medicine specialist nurses.

BACKGROUND:Mitophagy is closely associated with the development of idiopathic pulmonary fibrosis,but its mechanism remains unclear.OBJECTIVE:To investigate the biological mechanism of mitophagy in idiopathic pulmonary fibrosis and provide ideas for the risk prediction of idiopathic pulmonary fibrosis and subtype differentiation.METHODS:The mitophagy-related genes in idiopathic pulmonary fibrosis were obtained through GEO and Reactome Pathway databases.The mitophagy-related characteristic genes in idiopathic pulmonary fibrosis were screened based on intergroup differences and random forest model.GO functional enrichment analysis and KEGG,Reactome with WIKI pathway enrichment analyses were performed by g:Profiler database.Mitophagy subtypes in idiopathic pulmonary fibrosis were distinguished by consensus clustering method and immune infiltration analysis was performed.The mitophagy-related key gene was screened.Finally,the predictive value of mitophagy-related key gene for the risk of idiopathic pulmonary fibrosis was quantified by alignment diagram and the correlation between mitophagy-related key gene and clinical characteristics of idiopathic pulmonary fibrosis was explored.RESULTS AND CONCLUSION:(1)A total of 13 genes related to mitophagy in idiopathic pulmonary fibrosis were identified and 5 characteristic genes were screened,containing PINK1,RPS27A,SRC,HIF1A,and CDH6.(2)GO analysis was mainly involved in ubiquitin protein ligase binding,and cellular response to hypoxia.Pathway enrichment analysis was mainly involved in PINK1-PRKN mediated mitophagy,NOTCH signaling pathway,signaling by EGFR and angiogenesis.(3)HIF1A had significant expression differences between subtypes,which might serve as a key gene for the differentiation of mitophagy subtypes of idiopathic pulmonary fibrosis.(4)Immune infiltration analysis suggested that myeloid-derived suppressor cell,neutrophil and type 1 T helper cell might have infiltration differences between subtypes,while HIF1A was positively correlated with multiple immune cells.(5)Alignment diagram suggested that the risk of idiopathic pulmonary fibrosis might be predicted by the expression level of HIF1A.(6)Clinical characteristics analysis indicated patients with high expression of HIF1A might have poorer lung function and more severe fibrosis.It is concluded that PINK1,RPS27A,SRC,HIF1A,and CDH6 may influence the development of idiopathic pulmonary fibrosis through mitophagy,in which HIF1A may serve as a key gene for risk prediction with clinical subtype differentiation and HIF1A is strongly associated with the lung function of patients.

Objective:To screen high risk children for lysosomal storage diseases (LSD) in southern China and analyze the spectrum characteristics of LSD in this region.Methods:A cross-sectional study was conducted. A total of 7 435 children at high risk of LSD were screened at Guangzhou Women and Children′s Medical Center, Guangzhou Medical University from January 2009 to December 2024. The activities of 22 lysosomal enzymes from peripheral blood leukocytes or plasma were measured by fluorescence or colorimetric assays with synthetic substrates to screen for 24 LSD subtypes.Results:Among the 7 435 high risk children, 759 children were diagnosed with LSD (10.2%). The diagnosed cases included 506 males and 253 females, with an age at diagnosis of 3.0 (2.5, 5.5) years. The common disease types were mucopolysaccharidosis (MPS) (390 cases (51.4%)), sphingolipidoses (269 cases (35.4%)), glycogen storage disease (62 cases (8.2%)), and mucolipidosis types Ⅱ and Ⅲ (29 cases (3.8%)). Among the positive cases, 21 disease subtypes were identified. The 5 frequent subtypes, in descending order, were MPS type Ⅱ (197 cases (26.0%)), Gaucher disease (111 cases (14.6%)), MPS type ⅣA (87 cases (11.5%)), glycogen storage disease type Ⅱ (62 cases (8.2%)), and metachromatic leukodystrophy (MLD) (49 cases (6.5%)). The rarest subtypes were mannosidosis, multiple sulfatase deficiency and Wolman disease, each with 1 case (0.1%).Conclusions:Enzyme activity screening is essential for diagnosing high risk children with LSD. In Southern China, the most common LSD subtypes are MPS Ⅱ, Gaucher disease, MPS ⅣA, glycogen storage disease type Ⅱ, and MLD, while mannosidosis, multiple sulfatase deficiency and Wolman disease are the rarest.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

BACKGROUND:Mitophagy is closely associated with the development of idiopathic pulmonary fibrosis,but its mechanism remains unclear.OBJECTIVE:To investigate the biological mechanism of mitophagy in idiopathic pulmonary fibrosis and provide ideas for the risk prediction of idiopathic pulmonary fibrosis and subtype differentiation.METHODS:The mitophagy-related genes in idiopathic pulmonary fibrosis were obtained through GEO and Reactome Pathway databases.The mitophagy-related characteristic genes in idiopathic pulmonary fibrosis were screened based on intergroup differences and random forest model.GO functional enrichment analysis and KEGG,Reactome with WIKI pathway enrichment analyses were performed by g:Profiler database.Mitophagy subtypes in idiopathic pulmonary fibrosis were distinguished by consensus clustering method and immune infiltration analysis was performed.The mitophagy-related key gene was screened.Finally,the predictive value of mitophagy-related key gene for the risk of idiopathic pulmonary fibrosis was quantified by alignment diagram and the correlation between mitophagy-related key gene and clinical characteristics of idiopathic pulmonary fibrosis was explored.RESULTS AND CONCLUSION:(1)A total of 13 genes related to mitophagy in idiopathic pulmonary fibrosis were identified and 5 characteristic genes were screened,containing PINK1,RPS27A,SRC,HIF1A,and CDH6.(2)GO analysis was mainly involved in ubiquitin protein ligase binding,and cellular response to hypoxia.Pathway enrichment analysis was mainly involved in PINK1-PRKN mediated mitophagy,NOTCH signaling pathway,signaling by EGFR and angiogenesis.(3)HIF1A had significant expression differences between subtypes,which might serve as a key gene for the differentiation of mitophagy subtypes of idiopathic pulmonary fibrosis.(4)Immune infiltration analysis suggested that myeloid-derived suppressor cell,neutrophil and type 1 T helper cell might have infiltration differences between subtypes,while HIF1A was positively correlated with multiple immune cells.(5)Alignment diagram suggested that the risk of idiopathic pulmonary fibrosis might be predicted by the expression level of HIF1A.(6)Clinical characteristics analysis indicated patients with high expression of HIF1A might have poorer lung function and more severe fibrosis.It is concluded that PINK1,RPS27A,SRC,HIF1A,and CDH6 may influence the development of idiopathic pulmonary fibrosis through mitophagy,in which HIF1A may serve as a key gene for risk prediction with clinical subtype differentiation and HIF1A is strongly associated with the lung function of patients.