In recent years, there has been a significant surge in the prevalence of myopia at younger ages in China. Numerous studies have investigated methods for preventing and controlling myopia, including orthokeratology, low-concentration atropine eye drops, light therapy, posterior scleral reinforcement, and traditional Chinese medicine. These approaches can modulate choroidal thickness, blood flow, and target various molecular mechanisms. Orthokeratology and low-concentration atropine demonstrate a thickening effect on the choroid and regulate choroidal blood flow; the use of multi-point defocus control lenses also shows promise in thickening the choroid; the influence of light and light feeding therapy on myopia prevention and control is also reflected in the choroidal thickness and blood flow; and the traditional Chinese medicine has shown good prospect in influencing the microstructure of the choroid for myopia prevention and control. However, the long-term effects of various prevention and control measures on the choroid still need to be explored with a large sample size. This article provides an overview of various methods used to regulate the choroid and prevent myopia. The mechanisms by which these interventions act on the choroid are described to provide new insights and identity novel clinical strategies for myopia management.

Loop-mediated isothermal amplification(LAMP)is a newin vitronucleic acid amplification technique,which has been widely used in rapid detection of pathogenic bacteria,with advantages of high efficiency,simple operation and low cost.Microfluidic chip technique is a kind of miniaturized and integrated analytical technology,which integrates automation and high throughput,and can effectively avoid sample cross-contamination and has the advantages of high sample utilization rate and high cost-effectivenes.LAMP combined with microfluidic chip can detect multiple samples of the same pathogen at the same time or single samples of different pathogens at the same time,providing a new method for the rapid,field detection of pathogens.In this paper,the research progresses of LAMP microfluidic chip and its application in rapid detection of pathogenic bacteria in recent years were reviewed,and the future prospect was discussed.

Objective To explore the risk factors for medium-and long-term mortality in patients with severe community-acquired pneumonia(SCAP)based on the Medical Information Mart for Intensive Care Ⅳ(MIMIC-Ⅳ),construct a prognostic model and evaluate its predictive efficacy.Methods In this retrospective cohort study,1943 SCAP patients from the U.S.MIMIC-Ⅳdatabase(2008-2019)were randomly divided into training(n=1363)and validation(n=580)sets(7:3 ratio).Primary and secondary endpoints were 1-year and 30-/90-day all-cause mortality,respectively.Prognostic factors were selected using LASSO regression and multivariable Cox proportional hazards modeling,and a visual nomogram model was built.Model performance was assessed via C-index,receiver operator characteristic(ROC)curves,and calibration curves,and compared with the CURB-65 score.Risk stratification was validated using Kaplan-Meier analysis.Results The 30-day,90-day,and 1-year all-cause mortality rates for SCAP patients were 25.9％,34.5％,and 42.6％,respectively.Seven independent risk factors were identified:age(HR=1.037),heart rate(HR=1.007),red blood cell distribution width(RDW,HR=1.092),Acute Physiology Score Ⅲ(APS-Ⅲ,HR=1.013),cerebrovascular disease(HR=1.453),liver disease(HR=1.272),and malignancy(HR=2.007).Based on these factors,Cox regression model was constructed and nomogram was drawn,C-indices of training set and validation set were 0.710 and 0.688,respectively.For 1-year mortality prediction,the model achieved superior area under the ROC curve(AUC)values(training set:0.768;validation set:0.738)compared with CURB-65 score(training set:0.648;validation set:0.616).Kaplan-Meier survival analysis revealed significantly worse survival in high-risk group than low-risk group(P<0.0001).Conclusions Age,heart rate,RDW,APS-Ⅲ,cerebrovascular disease,liver disease,and malignant tumor were medium-and long-term mortality risk factors in SCAP patients.The prognostic model constructed based on these factors has high predictive power and provides an important clinical diagnosis and treatment reference.

Medical device imaging data augmentation is a method of expanding existing datasets by generating new data samples,which is of great significance for improving the performance of artificial intelligence(AI)medical device-related models and clinical application effects.However,traditional data augmentation methods are usually limited by the quality,realism,and diversity of generated samples.Denoising diffusion probabilistic model(DDPM)is a generative model based on the noise diffusion process,and its main idea is to generate samples with high quality by modelling the sampling process of the target distribution as a process of progressive denoising from the noise distribution.The basic principles and working mechanisms of DDPM were reviewed,the application scenarios of this method in AI medical device data augmentation were analyzed,and its advantages,challenges,and future development directions were explored to provide a reference for the field of AI medical device data augmentation.

In recent years, artificial intelligence (AI) technology represented by deep learning (DL) has developed rapidly, and smart medical care has become one of the most important application areas of AI. As the most accurate noninvasive test to assess myocardial blood flow, myocardial perfusion imaging (MPI) has important clinical values. At present, the use of DL algorithms to establish learning models for MPI images is still in the research stage, and more external verification and iterative updates are needed before it can be widely used in real time clinical practice. In this article, the application of DL algorithms in MPI is comprehensively elaborated to provide a basis and direction for further research.

Objective To develop an effective method for delivering VEGF and CD47 double-modified exosomes to treat renal damage induced by heat stroke so as to reduce and repair renal damage.Methods A plasmid fusion-expressing VEGF and CD47 targeting renal injury was constructed,transfected into rat bone marrow derived mesenchymal stem cells (BMMSCs),and then fusion-exosomes were isolated and extracted.Transmission electron microscopy,nanoparticle tracking analysis,and Western blotting were used to identify the obtained exosomes.Rats were intravenously injected with 200 μg of DiD-labeled unmodified exosomes,VEGF-modified exosomes and VEGF-CD47 double-modified exosomes,respectively,through the tail vein,and the effects of exosomes on the kidneys were detected and analyzed using a small animal in vivo imaging instrument.A total of 60 SD rats were randomly divided into 6 groups,with 10 rats in each group,that is,blank control group (group A),heat stroke-induced renal injury model receiving PBS in 12,24 and 36 h after modelling (group B),empty plasmid group (group C),Exos group (group D),ExosVEGF group (group E) and ExosVEGF-CD47.Kidney tissue and blood samples were collected in 72 h after 3 times of treatment.Pathological changes in kidney tissue were observed at the tissue level and the damage were scored.Changes in serum blood urea nitrogen (BUN)and serum creatinine (Scr)levels were detected to evaluate the therapeutic effect.Western blotting and qRT-PCR were used to analyze the expression of the pro-inflammatory factors TNF-α and NF-κB,the proliferation regulatory signaling molecules Ki67,FGF2,pAMPK and pERK,and the fibrosis regulatory molecule FGF23,in order to comprehensively analyze the effects on proliferation and inhibition of fibrosis.Results BMMSCs and ExosVEGF-CD47 were successfully isolated and characterized,and a rat model of acute kidney injury was effectively constructed.Higher fluorescence intensity was found in the kidney tissue of the Exos VEGF-CD47group than the Exos-Ctrl group and Exos VEGF group (P<0.05).In 72 h after treatment,the ExosVEGF-CD47 group had significantly lower serum BUN and Scr levels (P<0.0001),and notably lower Tubular casts score (P<0.0001),decreased levels of pro-inflammatory factors TNF-α and NF-κB (P<0.0001),up-regulated Ki67 and FGF2 expression (P<0.05),and down-regulated FGF23 expression (P<0.0001)when compared with the AKI+Exos group and AKI+ExosVEGF group.Conclusion VEGF and CD47 show promise in targeting acute kidney injury induced by heat stroke,effectively mitigate damage and facilitate repair,which may be due to exosome-mediated inhibition of renal tissue inflammation,promotion of proliferation,and inhibition of fibrosis.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.