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.

Alzheimer's disease(AD)is a progressive,irreversible neurodegenerative disorder characterized by gradual brain cell degeneration,leading to progressive decline in cognitive function and ultimately death.Early identification and intervention are critical to AD diagnosis.In recent years,deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis.To achieve accurate early diagnosis and subsequent classification of AD,researchers have integrated deep learning with magnetic resonance imaging to develop more precise models.By analyzing and synthesizing relevant domestic and international literature,this review introduces commonly used public datasets and evaluation criteria for AD,analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods,and highlights the roles of techniques such as convolutional neural networks,transfer learning,attention mechanisms,and multimodal approaches in AD classification.It also discusses the advantages,limitations,and developmental trends of deep learning in AD classification,aiming to provide new insights for the application of deep learning in AD research.

Objective To investigate the expression levels and diagnostic value of long non coding RNA(lncRNA)DNA damage inducible transcript 4 like protein 4 antisense RNA1(lncRNA DDIT4-AS1)and long chain non coding RNA(lncRNA)radial spoke head protein 9 homolog 4(RSPH9-4)in serum of patients with bacterial meningitis.Methods A total of 94 patients with meningitis in the Fifth Hospital of Deyang City from May 2022 to May 2024 were regarded as the bacterial meningitis group.According to the severity of damage to the central nervous system caused by bacterial meningitis,they were further separated into a general group(n=63)and a severe group(n=31).94 patients with suspected meningitis upon admission but excluded meningitis after examination were selected as the non bacterial meningitis group.A total of 94 patients diagnosed with bacterial meningitis in the Fifth Hospital of Deyang City from May 2022 to May 2024 were selected as the bacterial meningitis group 94 healthy volunteers who underwent physicalexamination were as the control group.Real-time fluorescence quantitative PCR(qRT-PCR)method was applied to detect the expression levels of lncRNA DDIT4-AS1 and lncRNA RSPH9-4 in serum samples of all personnel.Spearman analysis was applied to analyze the correlation between serum levels of lncRNA DDIT4-AS1 and lncRNA RSPH9-4 with the severity of bacterial meningitis.Multivariate Logistic analysis was applied to analyze the influencing factors of bacterial meningitis.Receiver operating characteristic(ROC)curve was plotted to analyze the diagnostic value of serum lncRNA DDIT4-AS1 and lncRNA RSPH9-4 levels for bacterial meningitis.Results Compared with the control group,the expression levels of serum lncRNA DDIT4-AS1(1.35±0.27,1.67±0.35 vs 1.01±0.18)and lncRNA RSPH9-4(1.30±0.25,1.56±0.32 vs 1.00±0.19)were increased in non bacterial meningitis group and bacterial meningitis group(t=10.159,16.259;9.263,14.589),compared with the non bacterial meningitis group,the expression levels of serum lncRNA DDIT4-AS1 and lncRNA RSPH9-4 in the bacterial meningitis group were increased(t=7.019,6.208),and the differences were statistically significant(all P<0.05),respectively.Compared with the general group,the expression levels of serum lncRNA DDIT4-AS1(1.88±0.38 vs 1.56±0.31)and lncRNA RSPH9-4(1.79±0.35 vs 1.45±0.28)were increased in the severe group,and the differences were statistically significant(t=4.361,5.088,all P＜0.05).Spearman correlation analysis results showed that serum levels of lncRNA DDIT4-AS1,lncRNA RSPH9-4 were positively correlated with the severity of bacterial meningitis(r=0.524,0.548,all P<0.05).The expression levels of PCT,LDH and CRP in patients with bacterial meningitis were higher than those in patients without bacterial meningitis,and the differences were statistically significant(t=7.459,4.234,8.565,all P<0.05).Multivariate Logistic analysis showed that PCT,LDH,CRP,lncRNA DDIT4-AS1 and lncRNA RSPH9-4 were influencing factors for bacterial meningitis(Wald χ2=4.768～12.035,all P＜0.05).The receiver operating characteristic(ROC)curve results showed that the combined diagnosis of serum lncRNA DDIT4-AS1 and lncRNA RSPH9-4 levels for bacterial meningitis had a significantly larger area under the curve(AUC)than the diagnosis of lncRNA DDIT4-AS1 and lncRNA RSPH9-4 alone,and the differences were statistically significant(Z=2.426,2.545,all P<0.05).Conclusion The expression levels of serum lncRNA DDIT4-AS1 and lncRNA RSPH9-4 are elevated in patients with bacterial meningitis,and the combination of the two has high diagnostic value for the disease.

Objective To screen and analyze key genes in rabbit corneal alkali burns based on transcriptomics se-quencing technology and bioinformatics techniques.Methods Thirty healthy male New Zealand rabbits were randomly di-vided into 2 groups(n=15 per group):The control group received no intervention,while the alkali burn group underwent corneal alkali burn modeling.Histological evaluation of corneal tissues was performed via hematoxylin-eosin(HE)stai-ning.Transcriptome sequencing was conducted for library construction and sequencing.Differentially expressed genes(DEGs)were identified using DESeq2,followed by Gene Ontology(GO)functional enrichment analysis and Kyoto Ency-clopedia of Genes and Genomes(KEGG)pathway analysis.A protein-protein interaction(PPI)network was constructed to screen hub genes,and RT-PCR was employed to validate mRNA expression levels of key genes.Results HE staining revealed orderly arranged corneal stromal layers and scattered stromal cells in the control group,whereas the alkali burn group exhibited stromal edema,thickened collagen fibers with loose/disorganized alignment,and increased fibroblast and inflammatory cell infiltration.Compared to the control group,1 827 significant DEGs were identified in the alkali burn group,including 1 495 upregulated and 332 downregulated genes.GO analysis showed biological processes such as immune response,plasma membrane,and identical protein binding.KEGG analysis indicated that DEGs were enriched in pathways related to cancer,lipid and atherosclerosis,and neuroactive ligand-receptor interaction.The PPI network screened 11 key genes:neutrophil cytosolic factor 1(NCF1),neutrophil cytosolic factor 2(NCF2),matrix metallopeptidase 2(MMP2),ma-trix metallopeptidase 9(MMP9),interleukin-1α(IL-1α),interleukin-1β(IL-1β),interleukin-6(IL-6),interleukin-8(CXCL8),cluster of differentiation 4(CD4),C-C motif ligand 2(CCL2)and tumor necrosis factor(TNF).RT-PCR valida-tion revealed that the mRNA expression levels of key genes in the corneal tissues of the alkali burn group were significantly higher than those in the control group(all P＜0.05),consistent with the transcriptomic sequencing results.Conclusion Based on the rabbit corneal alkali burn model,this study identified 11 key genes(NCF1,NCF2,MMP2,MMP9,IL-1α,IL-1β,IL-6,CXCL8,CD4,CCL2 and TNF)through transcriptomics and bioinformatics analysis.

Objective To observe the effects of acupuncture at"Weizhong"on angiogenesis of chronic blunt injury of lumbar muscle in rats;To explore the possible mechanism of acupuncture in the treatment of chronic lumbar skeletal muscle injury based on PI3K/AKT/HIF-1α signaling pathway.Methods A total of 24 SD male rats were established chronic blunt injury of lumbar muscle model,and were randomly divided into model group,Weizhong group and non-acupoint group,with 8 rats in each group.Another 8 rats of the same age were selected as the blank group.In the Weizhong group,acupuncture intervention was performed on both sides of"Weizhong",while in the non-acupoint group,acupuncture intervention was performed on both sides of"Weizhong"inwardly by 0.5 cm,once a day for 20 minutes each time,for 2 consecutive weeks.The blank group and model group adopted the same grasping fixed posture without any intervention.The body mass and limb grip strength of each group were measured,the lumbar muscle tissue morphology was observed by HE staining,the content of NO in lumbar muscle tissue was detected by ELISA,the expression of VEGF in lumbar muscle tissue was observed by immunofluorescence,the expression of PI3K/AKT/HIF-1α pathway protein in lumbar muscle tissue was detected by Western blot.Results Compared with the blank group,body mass and limb grip strength of model group decreased(P<0.01),cell structure of waist skeletal muscle was loosely arranged,red blood cells and nuclear exudation were observed,NO content and positive expression of VEGF in lumbar muscle tissue decreased(P<0.01),and the protein expressions of p-PI3K/PI3K,p-AKT/AKT and HIF-1α in lumbar muscle tissue were down-regulated(P<0.01).Compared with the model group,the body mass and limb grip strength of rats in Weizhong group increased(P<0.05,P<0.01),the arrangement of skeletal muscle cells in the waist was more compact and orderly,and the content of NO and positive expression of VEGF in lumbar muscle tissue were increased(P<0.01),the protein expressions of p-PI3K/PI3K,p-AKT/AKT and HIF-1α in lumbar muscle tissue were up-regulated(P<0.01).The effects of Weizhong group was better than those in non-acupoint group(P<0.05,P<0.01).Conclusion Acupuncture at"Weizhong"can effectively promote the repair of chronic blunt injury of lumbar muscle in rats.The mechanism may be related to activating the PI3K/AKT/HIF-1α pathway to regulate angiogenesis and improve vascular injury.

Fatigue driving is a major cause of traffic accidents,which poses a great threat to public safety and property.In order to reduce the losses caused by fatigue driving,many researchers have devoted themselves to the study about fatigue driving,such as driver behavior monitoring,brainwave monitoring,eye tracking and facial expression analysis.Each of these methods has its own advantages and disadvantages.Behavioral monitoring reflects the fatigue state by analyzing the driver's driving habits and facial expression,which is easy to operate but prone to be affected by the external environment.Brainwave monitoring is more accurate and can detect the fatigue state in real time,but the equipment is complicated and costly,which limits its large-scale application.The detection based on eye-tracking and facial expression analysis also has a certain potential for application,but errors may occur under different light conditions.Herein the review introduces the effects of fatigue on driving ability and compares the research results of various fatigue driving detection methods by searching,collating,analyzing and summarizing the relevant literatures at home and abroad.Various detection methods are analyzed and summarized,and it is pointed out that the fatigue driving detection method based on multi-feature information fusion will become a research hotspot.

Fatigue driving is a major cause of traffic accidents,which poses a great threat to public safety and property.In order to reduce the losses caused by fatigue driving,many researchers have devoted themselves to the study about fatigue driving,such as driver behavior monitoring,brainwave monitoring,eye tracking and facial expression analysis.Each of these methods has its own advantages and disadvantages.Behavioral monitoring reflects the fatigue state by analyzing the driver's driving habits and facial expression,which is easy to operate but prone to be affected by the external environment.Brainwave monitoring is more accurate and can detect the fatigue state in real time,but the equipment is complicated and costly,which limits its large-scale application.The detection based on eye-tracking and facial expression analysis also has a certain potential for application,but errors may occur under different light conditions.Herein the review introduces the effects of fatigue on driving ability and compares the research results of various fatigue driving detection methods by searching,collating,analyzing and summarizing the relevant literatures at home and abroad.Various detection methods are analyzed and summarized,and it is pointed out that the fatigue driving detection method based on multi-feature information fusion will become a research hotspot.

Theory and technology of Medical Immunology are widely used in scientific research.Our teaching and research group uses experimental teaching of Medical Immunology as a platform to carry out practice of scientific research project-based experi-mental system among"5+3"integration students.By completing a mini-project research including experimental design-experimental operation-research article writing,students cultivated scientific research thinking and exercised scientific research practice ability,and generally reported that the course is very difficult,but after completing it,it is very rewarding.

High myopia is a refractive abnormality characterized by excessive elongation of the eye axis.The sclera,as an important part of the eye axis regulation,plays a key role in the occurrence and development of high myopia.In this pa-per,based on the domestic and international articles,the pathological changes and related regulatory molecules of the scle-ra in high myopia were systematically summarized from the features of scleral lesions in high myopia and the gene regula-tion and signaling pathways affecting the ultrastructure and mechanical properties of the sclera,thus providing a theoretical basis for the study of the pathological mechanism of high myopia.

Alzheimer's disease(AD)is a progressive,irreversible neurodegenerative disorder characterized by gradual brain cell degeneration,leading to progressive decline in cognitive function and ultimately death.Early identification and intervention are critical to AD diagnosis.In recent years,deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis.To achieve accurate early diagnosis and subsequent classification of AD,researchers have integrated deep learning with magnetic resonance imaging to develop more precise models.By analyzing and synthesizing relevant domestic and international literature,this review introduces commonly used public datasets and evaluation criteria for AD,analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods,and highlights the roles of techniques such as convolutional neural networks,transfer learning,attention mechanisms,and multimodal approaches in AD classification.It also discusses the advantages,limitations,and developmental trends of deep learning in AD classification,aiming to provide new insights for the application of deep learning in AD research.