This study constructed a LHCGR-CRE-luc-HEK293 transgenic cell line according to the activation of the cAMP signaling pathway after recombinant human chorionic gonadotropin binding to the receptor. The biological activity of recombinant human chorionic gonadotropin was assayed using a luciferase assay system. The relative potency of the samples was calculated using four-parameter model. And the method conditions were optimized to validate the specificity, relative accuracy, precision and linearity of the method. The results showed that there was a quantitative potency relationship of human chorinonic gonadotropin (hCG) in the method and it was in accordance with the four-parameter curve. After optimization, the conditions were determined as hCG dilution concentration of 2.5 μg·mL^-1, dilution ratio of 1∶4, cell number of 10 000-15 000 cells/well, and induction time of 6 h. The method had good specificity, relative accuracy with relative bias ranging from -8.9% to 3.4%, linear regression equation correlation coefficient of 0.996, intermediate precision geometric coefficient of variation ranging from 3.3% to 15.0%, and linearity range of 50% to 200%. This study successfully established and validated a reporter gene method to detect hCG biological activity, which can be used for hCG biological activity assay and quality control.

Objective To study the effects of the intelligent hearing-assistive system incorporated in Nuro-tron cochlear implants(CI),including the autonomic acoustic scene recognition(ASR),intelligent strategy config-uration as well as the objective and subjective hearing improvements on recipients.Methods ① To evaluate the per-formance of the ASR matule,in a sound-proof room,the preset five kinds of test audios,including speech,noise,speech in noise,pure music(without human voice)and non-pure Music(with human voice)were played.Each type of scenes included 6 to 9 5 min test files.The prediction accuracy and scene switching times were calculated.② In order to evaluate the noise-reduction performance of the ABeam technology in the speech enhancement module,13 Nurotron? CI recipients were recruited and their speech recognition rate when ABeam was"ON"and"OFF"with noise coming from 90°,180°or 270°were tested,individually.Also,their subjective hearing feedback was evaluated through visual analogue scale(VAS)evaluation.Results The ASR module achieved high prediction performance,with prediction accuracy 99％±4％,96％±9％,94％±12％,94％±15％,92％±13％for speech,noise,noisy speech,pure music and non-pure music,respectively.The scene transation times for each individual scene were 1.1 ±0.3,1.4±0.7,1.3±0.5,1.4±0.8 and 1.3±0.5,indicating that the prediction was also stable.When noise came from the sides and behind of recipients and speech signal from the front,the adaptive dual microphone noise re-duction algorithm ABeam significantly increased the speech recognition score(SRS)in 5 dB signal-to-noise(SNR)environment(P＜0.001),with an average increase of 15.92％.Especially when the noise came from 180 degree backward,the SRS increased 28.68％when ABeam was"0N",which was significantly higher than when ABeam was"OFF"(P＜0.01).Conclusion The intelligent hearing-assistive system can help CI recipients automatically configure appropriate SPSs under different environments,improving the speech intelligibility and hearing comfort.

ObjectiveTo observe the clinical effect of Jieyu Qingxin formula granules combined with remote interactive cognitive behavioral therapy for insomnia （CBT-I） on chronic insomnia of liver depression and fire-turning type. MethodThis study was a prospective randomized controlled trial. 120 patients with chronic insomnia of liver depression and fire-turning type in Lu'an traditional Chinese medicine Hospital from January 2022 to June 2023 were selected as objects. They were randomly divided into two groups，with 60 cases in each group. The control group received remote interactive CBT-I. The observation group was treated with Jieyu Qingxin formula granules on the basis of the control group. Intervention treatment lasted for four weeks，and observation lasted for six weeks. Comparison of data of each group：clinical efficacy，changes in traditional Chinese medicine （TCM） syndrome score before and after treatment，changes in insomnia severity index （ISI） score，self-rating depression scale （SDS） and self-rating anxiety scale （SAS） score changes，total sleep time，wake time，sleep latency，sleep efficiency， Actigraphy sleep parameter value changes，serum neuron specific enolase （NSE） ，adenosine，dopamine （DA）， 5-hydroxytryptamine （5-HT） level changes，and adverse reactions. ResultThe total effective rate in the observation group （92.45%，49/53） was higher than that in the control group（76.92%，40/52）， and the difference was statistically significant（χ²=4.711 1，P<0.05）. After treatment，TCM syndrome score，ISI score，SAS score， and SDS score were decreased in all groups. The total sleep time was extended，and wake time and sleep latency were shortened. The sleep efficiency was increased，but the NSE and DA levels were decreased. Adenosine and 5-HT levels were increased in all groups（P<0.05）. After treatment，compared with the control group，the observation group had lower TCM syndrome score，ISI score，SAS score， and SDS score，longer total sleep time，higher sleep efficiency，shorter wake time and sleep latency，lower NSE and DA levels， and higher adenosine and 5-HT level （P<0.05）. There was one case of nausea in the observation group and no adverse reaction in the control group during treatment. There was no significant difference between the two groups. ConclusionBy reducing NSE and DA and increasing the levels of 5-HT and adenosine，the anxiety （SAS score） and depression （SDS score） of patients can be improved， so as to improve their sleep and effectively treat chronic insomnia of liver depression and fire-turning type.

Objective:To investigate the correlation among physical activity,mild depressive symptoms and frontal alpha power asymmetry in college students.Methods:Seventy college students with mild depressive symp-toms who conformed to the standard of the Self-Rating Scale for Depression(SDS)of 53-62 and 70 normal col-lege students were recruited.The frontal alpha power was measured under quiet and closed-eye state,and the total physical activity(PA)was assessed with the International Physical Activity Questionnaire.Results:The college students with mild depressive symptoms had lower Total PA scores,right frontal alpha power and frontal alpha a-symmetry(FAA)than the normal controls(P＜0.001).In college students with mild depressive symptoms,the to-tal PA scores(r=-0.29,P＜0.05)and FAA(r=-0.41,P＜0.001)were negatively correlated with SDS scores,and the total PA scores were positively correlated with FAA(r=0.34,P＜0.01).Conclusion:The college students with mild depressive symptoms may have reduced physical activity and asymmetric right lateralization of frontal alpha power.There is a correlation among depressive symptoms,physical activity and frontal alpha power a-symmetry in college students with mild depressive symptoms.

Knee osteoarthritis has become one of the common diseases of the elderly,total knee arthroplasty(TKA)is the most effective treatment for end-stage knee osteoarthritis at present.In TKA,the effective restoration of the lower extremity alignment is one of the key factors for the success of the operation,which greatly affects the postoperative clinical effect and prosthesis survival rate of patients.Mechanical alignment is a TKA alignment method which is first proposed,recognized and widely used in TKA.In recent years,with the in-depth research on the lower limb alignment and the rapid development of com-puter technology,the alignment technology in TKA has realized the transformation from"unified"to"individualized",two-di-mensional to three-dimensional.New alignment methods,such as adjusted mechanical alignment,anatomic alignment,kine-matic alignment,inverse kinematic alignment,restricted kinematic alignment and functional alignment have been proposed to provide surgeons with more choices.However,there is no conclusion on which alignment method is the best choice.This paper summarizes the current research status,advantages and disadvantages of various alignment methods in TKA,and aims to pro-vide some reference for the selection of alignment methods in TKA.

Objective:To discuss the relationship between 18F-FDG PET/CT metabolic parameters and clinical pathological indicators and prognosis in cutaneous malignant melanoma (CMM). Methods:A total of 100 CMM patients (62 males, 38 females, age (56.5±2.5) years) who underwent 18F-FDG PET/CT scans at the Second Xiangya Hospital of Central South University from August 2013 to November 2022 were retrospectively enrolled. Clinical pathological indicators (such as primary site, TNM staging, sentinel lymph node (SLN) status) and metabolic parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG), whole-body MTV (wb-MTV), and whole-body TLG (wb-TLG)) were collected. ROC curve analyses were used to determine the PET parameters thresholds for progression-free survival (PFS) and melanoma-specific survival (MSS). Kaplan-Meier survival analysis, univariate and multivariate Cox proportional hazards regression models were used to analyze the prognosis of patients′ PFS and MSS, and a nomogram survival prediction model was constructed. Results:Results of ROC curve analyses showed that the thresholds of SUV max of primary tumor (p-SUV max), MTV of primary tumor (p-MTV), TLG of primary tumor (p-TLG), wb-MTV and wb-TLG for predicting PFS and MSS were 7.13, 2.24 cm 3, 6.98 g, 2.57 cm 3, 8.04 g and 9.09, 2.34 cm 3, 7.44 g, 2.24 cm 3, 9.17 g, respectively. Results of univariate analysis indicated that several clinical pathological indicators and metabolic parameters were prognostic risk factors for PFS and MSS. Results of multivariate analysis indicated that metastases of SLN (hazard ratio( HR)=2.54, 95% CI: 1.09-5.90; P=0.030) and wb-TLG>8.04 g( HR=2.58, 95% CI: 1.17-5.72; P=0.019) were independent prognostic risk factors for PFS, while metastases of SLN ( HR=4.53, 95% CI: 1.54-13.35; P=0.006) and wb-TLG>9.17 g ( HR=2.48, 95% CI: 1.26-4.89; P=0.009) were independent risk prognostic factors for MSS. A nomogram survival prediction model based on PET metabolic parameter (wb-TLG) and clinical pathological indicator (SLN status) can effectively predict the prognosis of CMM patients. Conclusions:Clinical pathological parameters and PET parameters are associated with the prognosis of CMM patients. SLN status is critical for prognosis.

With the rapid society development and broad recognition of "Healthy China", the demands for good life and health are increasing. Accordingly, the concept of "food and medicine homologous" have been attractive. The concept of "food and medicine homologous" has a long history in China, and is an essence of various ideas in traditional Chinese medicine, such as diet therapy, medicated diet, regimen and preventive treatment of disease, representing an important field in health science. Many studies have found that the active ingredients of "food and medicine homologous" substances are multiple types, multiple mechanisms and multiple targets, exerting their biological effects after oral administration and chemical or metabolic transformation. In this review, the chemical basis and biological principles of various "food and medicine homologous" substances were summarized as compounds, biological macromolecules and intestinal flora. By focusing on the intestinal flora, we discussed the detailed biological principles of several classic "food and medicine homologous" substances. The scientific significance of "food and medicine homologous" concept were also discussed. This review explores the concept of "food and medicine homologous" from the perspective of modern medicine, in order to provide insights for future drug development and human health.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

ObjectiveThe scale of microalgae farming industry is huge. During farming, it is easy for microalgae to be affected by miscellaneous bacteria and other contaminants. Because of that, periodic test is necessary to ensure the growth of microalgae. Present microscopy imaging and spectral analysis methods have higher requirements for experiment personnel, equipment and sites, for which it is unable to achieve real-time portable detection. For the purpose of real-time portable microalgae detection, a real-time microalgae detection system of low detection requirement and fast detection speed is needed. MethodsThis study has developed a microalgae detection system based on deep learning. A microscopy imaging device based on bright field was constructed. With imaged captured from the device, a neural network based on YOLOv3 was trained and deployed on microcomputer, thus realizing real-time portable microalgae detection. This study has also improved the feature extraction network by introducing cross-region residual connection and attention mechanism and replacing optimizer with Adam optimizer using multistage and multimethod strategy. ResultsWith cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. ConclusionThe system could achieve real-time portable microalgae detection and provide relatively accurate detection result, so it can be applied to periodic test in microalgae farming.