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.

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.

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.

OBJECTIVE: To explore the role of the natural compound hesperidin in glycolysis, the key ratelimiting enzyme, in colorectal cancer (CRC) cell lines. METHODS: In vitro, HCT116 and SW620 were treated with different doses of hesperidin (0-500 µmol/L), cell counting kit-8 and colone formation assays were utilized to detected inhibition effect of hesperidin on CRC cell lines. Transwell and wound healing assays were performed to detect the ability of hesperidin (0, 25, 50 and 75 µmol/L) to migrate CRC cells. To confirm the apoptotic-inducing effect of hesperidin, apoptosis and cycle assays were employed. Western blot, glucose uptake, and lactate production determination measurements were applied to determine inhibitory effects of hesperidin (0, 25 and 50 µmol/L) on glycolysis. In vivo, according to the random number table method, nude mice with successful tumor loading were randomly divided into vehicle, low-dose hesperidin (20 mg/kg) and high-dose hesperidin (60 mg/kg) groups, with 6 mice in each group. The body weights and tumor volumes of mice were recorded during 4-week treatment. The expression of key glycolysis rate-limiting enzymes was determined using Western blot, and glucose uptake and lactate production were assessed. Finally, protein interactions were probed with DirectDIA Quantitative Proteomics, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. RESULTS: Hesperidin could inhibit CRC cell line growth (P<0.05 or P<0.01). Moreover, hesperidin presented an inhibitory effect on the migrating abilities of CRC cells. Hesperidin also promoted apoptosis and cell cycle alterations (P<0.05). The immunoblotting results manifested that hesperidin decreased the levels of hexokinase 2, glucose transporter protein 1 (GLUT1), GLUT3, L-lactate dehydrogenase A, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2), PFKFB3, and pyruvate kinase isozymes M2 (P<0.01). It remarkably suppressed tumor xenograft growth in nude mice. GO and KEGG analyses showed that hesperidin treatment altered metabolic function. CONCLUSION Hesperidin inhibits glycolysis and is a potential therapeutic choice for CRC treatment.
Hesperidin/therapeutic use* ; Colorectal Neoplasms/metabolism* ; Glycolysis/drug effects* ; Animals ; Humans ; Apoptosis/drug effects* ; Mice, Nude ; Cell Movement/drug effects* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Glucose/metabolism* ; Cell Cycle/drug effects* ; Mice, Inbred BALB C ; Mice ; HCT116 Cells ; Lactic Acid

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.

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 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 value of proton density fat fraction(PDFF)and R2* based on iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification(IDEAL-IQ)sequence in the assessment of vertebral fat and trabecular structure in postmenopausal women with osteoporosis.Methods A total of 85 postmenopausal women who underwent abdominal CT scans were selected and divided into three groups using quantitative computed tomography(QCT):osteoporosis group(30 cases),osteopenia group(29 cases),and normal bone mass group(26 cases).MRI examinations were performed within 2 weeks,the sequences including lumbar sagittal IDEAL-IQ and T2* mapping.The images were uploaded to the post-processing workstation,and the mean PDFF,IDEAL-R2* and gradient echo(GRE)-R2* were calculated for the L1-L5 vertebral body.One-way ANOVA was used to analyze the differences between the parameters among the three groups,and Spearman was used to compare the correlations between PDFF and bone mineral density(BMD),R2* value,as well as BMD and R2* value.The Bland-Altman plot was drawn to evaluate the consistency of IDEAL-R2* and GRE-R2*,and the receiver operating characteristic(ROC)curve was used to further evaluate the diagnostic performance of PDFF and R2* values for osteoporosis and osteopenia.Results There were significant differences in PDFF and BMD values among normal bone mass group,osteopenia group and osteoporosis group(P＜0.05),and R2* values were reduced in the osteoporosis group compared to the normal bone mass group(P＜0.05).Correlation analysis showed that PDFF was negatively correlated with BMD(r=-0.558,P＜0.05),negatively correlated with R2*[r=-0.250(IDEAL),-0.354(GRE),both P＜0.05],and positively correlated with BMD and R2*[r=0.351(IDEAL),0.457(GRE),both P＜0.05].The Bland-Altman plots showed that the mean value of the difference between IDEAL-R2* and GRE-R2* was-4.5 Hz,and the results were highly consistent.ROC curves showed no significant difference in the diagnostic efficacy of IDEAL-R2* and GRE-R2* in the assessment of osteoporosis and osteopenia(Z=0.526,1.327,P=0.599,0.185),but the diagnostic efficacy of both was lower than PDFF[area under the curve(AUC)=0.846(osteoporosis),0.684(oseopenia)].Conclusion IDEAL-IQ sequence-derived PDFF and R2* values are effective biological indicators for one-stop assessment of vertebral fat and microscopic trabecular structure in osteoporosis,which have certain clinical value.