Objective:By combining image memory with educational games, electrocardiogram (ECG) images were integrated with card games to simplify complex ECG knowledge and visualize abstract concepts. This approach stimulated students' motivation to learn within the game, thereby enhancing their cognitive abilities and aiding them in mastering ECG-related knowledge.Methods:Through literature search and brainstorming, ECG learning flash cards and game process were designed. Using the convenience sampling method, 55 medical students from a university were selected to conduct the self-designed questionnaire before and after using ECG flash cards to evaluate the efficacy of flash cards in enhancing ECG learning outcomes. A descriptive qualitative study was conducted on the satisfaction with the flash cards.Results:Before the intervention, there was no significant difference in the score of students from different majors. For students who had studied ECG within 1 month and 6 months, the difference in correct recognition rate between them was relatively small, which were 57.50% and 54.51%, respectively. For students who had not studied ECG, the correct recognition rate after training was only 18.06%. After the intervention, 42 valid questionnaires were collected. The overall correct rate of ECG recognition increased by 17.86%. The correct recognition rate of ventricular fibrillation increased significantly from 30.91% to 64.29%; the correct recognition rate of supraventricular tachycardia was improved only by 5.50%. The correct ECG recognition rate was significantly higher after the intervention than before the intervention ( P<0.01). The results of qualitative interviews indicated that the ECG game cards were interesting, practical, and generalizable. However, there was room for improvement in the distinctiveness of key graphics, the richness of information on cards, and the diversity of rule design. Conclusions:ECG recognition is hard to master for beginners. The use of ECG flash cards simplifies the process of ECG recognition and stimulates students' initiative in learning, thus improving the overall correct ECG recognition rate.

Objective:To explore the mediating effect of self-rated health status between living space and social frailty of the elderly in community, and to provide reference for intervention strategies.Methods:From December 2023 to March 2024, 550 community elderly in Yandun Community, Yicheng Community and Binhu Community in Baohe District of Hefei City were selected as the research objects by convenient sampling method. The general information questionnaire, self-rated health questionnaire, social frailty index and living space assessment scale were used to investigate a cross-sectional survey. Spearman correlation analysis was used to explore the correlation between self-rated health, living space and social frailty of community elderly. Bootstrap method was used to test the mediating effect.Results:A total of 542 valid questionnaires were collected. Among the 542 elderly residents in the community, aged 65-94 (71.69 ± 6.54) years, there were 267 males and 275 females. Among the 542 community elderly, the self-rated health score, living space score and social frailty index were (2.52 ± 0.95), (73.39 ± 16.77) and 0.0 (0.0, 3.0), respectively. Spearman analysis showed that the total score of living space was negatively correlated with the scores of self-rated health and social frailty ( r=-0.477, -0.538, both P<0.01), and the score of self-rated health was positively correlated with the score of social frailty ( r=0.711, P<0.01).The mediating effect results showed that the living space could play a partial mediating effect on social frailty through the mediating variable of self-rated health (-0.028), accounting for 49.12% of the total effect. Conclusions:The self-rated health status of the elderly in the community has an indirect mediating effect between living space and social frailty. Medical workers should take the improvement of the living space mobility of the elderly as the starting point, and improve their self-rated health level through diversified intervention means, so as to reduce the occurrence of social frailty and promote active aging.

Objective:To investigate the current status of subjective well-being among stroke patients, and to explore the pathways and effects of influencing factors using structural equation model, so as to provide reference for improving subjective well-being among stroke patients.Methods:From July to November 2024, the stroke patients admitted to the First Affiliated Hospital of Bengbu Medical University, the Second Affiliated Hospital of Bengbu Medical University, Hefei First People′s Hospital were selected by convenience sampling method. A cross-sectional survey was conducted using a general demographic questionnaire, General Well-Being Scale, Cognitive Reserve Index questionnaire, Social Support Rating Scale, Stroke Symptom Cluster Scale, and FRAIL Scale, and AMOS 26.0 was used to analyse the pathways and effects of influencing factors of subjective well-being.Results:A total of 435 questionnaires were collected, 410 were valid.Among 410 cases, 266 case were males, 144 were females, with an age of (65.96 ± 12.15) years. The subjective well-being scores of stroke patients were (72.58 ± 11.66) points. Cognitive reserve and social support were positively correlated with subjective well-being ( r = 0.517, 0.554, both P<0.01), while symptom burden and frailty were negatively correlated with subjective well-being ( r = -0.687, -0.670, both P<0.01). Path analysis showed that symptom burden, frailty, cognitive reserve, and social support had a direct impact on subjective well-being (path coefficients were -0.500, -0.266, 0.148, and 0.144, respectively, all P<0.05), while cognitive reserve, social support, and symptom burden had an indirect impact on subjective well-being (path coefficients were 0.287, 0.249, and 0.108, respectively, all P<0.05). Conclusions:The subjective well-being of stroke patients is influenced by multiple factors, with symptom burden being an important factor affecting subjective well-being. Intervention strategies such as improving cognitive reserve, strengthening social support systems, and preventing frailty can improve the subjective well-being of patients.

Objective:By combining image memory with educational games, electrocardiogram (ECG) images were integrated with card games to simplify complex ECG knowledge and visualize abstract concepts. This approach stimulated students' motivation to learn within the game, thereby enhancing their cognitive abilities and aiding them in mastering ECG-related knowledge.Methods:Through literature search and brainstorming, ECG learning flash cards and game process were designed. Using the convenience sampling method, 55 medical students from a university were selected to conduct the self-designed questionnaire before and after using ECG flash cards to evaluate the efficacy of flash cards in enhancing ECG learning outcomes. A descriptive qualitative study was conducted on the satisfaction with the flash cards.Results:Before the intervention, there was no significant difference in the score of students from different majors. For students who had studied ECG within 1 month and 6 months, the difference in correct recognition rate between them was relatively small, which were 57.50% and 54.51%, respectively. For students who had not studied ECG, the correct recognition rate after training was only 18.06%. After the intervention, 42 valid questionnaires were collected. The overall correct rate of ECG recognition increased by 17.86%. The correct recognition rate of ventricular fibrillation increased significantly from 30.91% to 64.29%; the correct recognition rate of supraventricular tachycardia was improved only by 5.50%. The correct ECG recognition rate was significantly higher after the intervention than before the intervention ( P<0.01). The results of qualitative interviews indicated that the ECG game cards were interesting, practical, and generalizable. However, there was room for improvement in the distinctiveness of key graphics, the richness of information on cards, and the diversity of rule design. Conclusions:ECG recognition is hard to master for beginners. The use of ECG flash cards simplifies the process of ECG recognition and stimulates students' initiative in learning, thus improving the overall correct ECG recognition rate.

Objective To explore the potential profiles of symptom burden among stroke patients and to analyze the differences in the characteristics of different classes of stroke patients,providing references for clinical nursing practice.Methods A convenience sampling method was used to select 485 stroke patients treated at 4 tertiary-level general hospitals in Anhui Province from July to December 2024 as the study population.The general information questionnaire,Stroke Symptom Cluster Scale,Personal Mastery Scale,and Cognitive Reserve Index questionnaire.Latent profile analysis was employed to explore the categories of symptom burden among stroke patients,and multiple logistic regression was used to assess the influence factors of each category.Results A total of 456 valid questionnaires were collected,with a valid response rate of 94.02％.Symptom burden among stroke patients can be divided into 4 latent profiles:low symptom burden group(69.08％),multiple symptom burden group(8.12％),moderate burden-physical activity impairment group(11.18％),and moderate burden-emotional and cognitive language impairment group(11.62％).The patient's age,number of stroke episodes,number of chronic diseases,systemic inflammation response index,personal mastery,and cognitive reserve were the factors influencing the latent profiles of symptom burden in stroke patients(P＜0.05).Conclusion The symptom burden of stroke patients shows significant heterogeneity.Medical staff can develop targeted nursing interventions based on the category characteristics and influencing factors of the symptom burden in stroke patients.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective:To explore the latent categories and influencing factors of health activation among community-dwelling older adults with chronic diseases.Methods:Older patients with chronic diseases attending three community health centers in Hefei City from August to December 2023 were selected for the study using the convenience sampling method. The survey utilized the General Information Questionnaire, Consumer Health Activation Index, Chinese version of the Life-Space Assessment, Social Support Rating Scale, and Simplified Coping Style Questionnaire. Mplus 8.3 software was applied for latent profile analysis to examine patients' health activation, and unordered multicategory Logistic regression was used to explore the factors influencing different categories of health activation.Results:A total of 382 questionnaires were distributed, and 368 valid questionnaires were recovered, with a valid recovery rate of 96.34%. Three latent categories of health activation existed in community-dwelling older adults with chronic diseases, which were named as low health activation-high efficiency-coping inability group (16.85%, 62/368) , medium health activation group (27.99%, 103/368) , and high activation perception-action group (55.16%, 203/368) . Multicategory logistic regression analysis indicated that place of residence, age, self-rated health status, life space, social support, and active coping strategies were significant factors influencing the health activation of different latent categories of community-dwelling older adults with chronic diseases (all P<0.05) . Conclusions:The health activation of community-dwelling older adults with chronic diseases can be classified into three latent categories. Primary healthcare providers should identify the characteristics of patients in different categories and implement targeted nursing interventions to enhance their health activation levels.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Depression is a common psychiatric disorder characterized by persistent low mood or mental disorders. Current treatments primarily focus on regulating neurotransmitter levels， but their effectiveness is limited. The mechanisms underlying its onset are complex， and there is no unified consensus. Abnormal signaling pathway transmission plays a crucial role in the development of depression， involving multiple pathways， including Toll-like receptor 4/nucleotide-binding oligomerization domain-like receptor protein 3 （TLR4/NLRP3）， nuclear factor-κB （NF-κB）， Janus kinase/signal transducer and activator of transcription （JAK/STAT）， mitogen-activated protein kinase/extracellular signal-regulated kinase （MAPK/ERK）， brain-derived neurotrophic factor/tyrosine kinase receptor B （BDNF/TrkB）， cyclic AMP/protein kinase A/cAMP response element-binding protein （cAMP/PKA/CREB）， and others. Traditional Chinese medicine（TCM） is based on a holistic approach and the principle of treatment based on the differentiation of syndromes， regulating the balance of multiple systems and organ functions from a macroscopic perspective. This approach has shown unique advantages in the treatment of depression. TCM attributes the onset of depression to dysfunction of the organ systems， involving liver Qi stagnation， heart spirit deficiency， kidney essence depletion， and spleen dysfunction. TCM compound treatments focus on soothing the liver， strengthening the spleen， calming the heart， and replenishing essence， with formulas such as Xiaoyaosan， Zishui Qinggan Yin， and Chahu Jia Guizhi Longgu Muli Tang. The active components of Chinese herbs mainly aim to tonify and regulate Qi， such as salidroside， ginsenoside Rb₁， astragaloside， and muscone. External TCM treatments， primarily acupuncture， aim to open the orifices and invigorate the spirit. Acupoints such as Baihui， Shenting， and Yintang are commonly used. Additionally， massage and moxibustion therapy can intervene in depression by regulating signaling pathways. This article reviews the core role of signaling pathways in the development of depression and the mechanism of TCM regulation of signaling pathways to intervene in depression， aiming to discover new therapeutic approaches that can improve the symptoms of depressed patients.

ObjectiveTo observe the prevention and control effect of processed Polygonatum cyrtonema on depression induced by chronic unpredictable mild stress （CUMS） in female rats. MethodsForty rats were assigned into control， model， and low-， medium-， and high-dose processed P. cyrtonema groups according to the random number table method， with 8 rats in each group. The rat model of depression was established with the CUMS method. The body mass， open field test， forced swimming test， Morris water maze test， levels of neurotransmitters ［dopamine （DA）， 5-hydroxytryptamine （5-TH）， and acetylcholine （ACh）］， serum levels of sex hormones ［gonadotropin-releasing hormone（GnRH）， testosterone （T）， and estradiol （E₂）］ and inflammatory factors ［tumor necrosis factor-α （TNF-α）， interleukin （IL）-6， and IL-10］， and mRNA and protein levels of factors in the brain-derived neurotrophic factor （BDNF）/tyrosine kinase receptor B （TRKB）/cAMP-response element binding protein （CREB） pathway were employed to evaluate the effect of processed P. cyrtonema on the CUMS-induced depression in female rats. ResultsThe body mass， open field test results， and forced swimming test results showed that the rat model of depression was successfully established. The comparison of behaviors， neurotransmitters， sex hormones， inflammatory factors， and neural pathways among groups showed that processed P. cyrtonema had different effects of preventing the development of depression in female rats. SPSS 25 was used for statistical analysis of error and significance. T test was conducted between groups. Each treatment group showed significant therapeutic effect compared with the model group （P<0.05）. Processed P. cyrtonema elevated the level of 5-TH （P<0.01） and lowered the levels of DA and ACh （P<0.01） in the brain tissue of female rats. In addition， it reduced the serum levels of GnRH， T， E₂， TNF-α， and IL-6 （P<0.05） and up-regulated the mRNA levels of BDNF and TRKB in the rat brain. ConclusionProcessed P. cyrtonema has a non-hyperactive preventive effect on CUMS-induced depression in rats， which provides a theoretical basis for the development of processed P. cyrtonema as a functional food product.