ObjectiveTo investigate the mechanism of the modified of Bazhentang combined with Guishentang in improving pregnancy outcomes in mouse models of embryo implantation dysfunction by regulating T helper 1/T helper 2 （Th1/Th2） immune balance. MethodsEighty ICR female mice were randomly divided into four groups （n=20 per group） on gestational day 1 （GD1）： control， model， western medicine， and traditional Chinese medicine （TCM） groups. Except for the control group， all mice received mifepristone solution （0.2 mg/mouse） via oral gavage on GD4 to induce embryo implantation dysfunction. The TCM group received a water decoction of the modified of Bazhentang combined with Guishentang （20.8 g·kg^-1）， with the western medicine group administered dydrogesterone （3.9 mg·kg^-1）， and the control/model groups given equal volumes of saline. All treatments were administered once daily from GD1 until one day before sample collection. Outcomes included implantation site counts （macroscopic observation）， pregnancy rates， body weight， endometrial histopathology （hematoxylin-eosin staining）， uterine expression of T-box expressed in T cells （T-bet）， GATA-binding protein 3 （GATA3）， interferon gamma （IFN-γ）， and interleukin-4 （IL-4） at protein （Western blot） and mRNA （real-time polymerase chain reaction， Real-time PCR） levels， serum IFN-γ and IL-4 levels （enzyme-linked immunosorbent assay， ELISA）， and Th1/Th2 immune balance evaluated by calculating T-bet/GATA3 and IFN-γ/IL-4 ratios. ResultsCompared to the control group， the model group showed no significant change in pregnancy rate but exhibited a marked reduction in average implantation sites and body weight （P<0.01）. Histopathological analysis revealed endometrial abnormalities， including decreased glandular density， stromal compaction， and absence of nucleolar vacuoles. At the molecular level， uterine tissue in the model group demonstrated significantly upregulated expression of T-bet and IFN-γ （P<0.05， P<0.01）， alongside markedly downregulated GATA3 and IL-4 expression （P<0.05， P<0.01）. Serum analysis confirmed markedly elevated IFN-γ （P<0.01） and reduced IL-4 levels （P<0.01）， resulting in significantly increased T-bet/GATA3 and IFN-γ/IL-4 ratios （P<0.01）. Compared to the model group， pregnancy rates in all treatment groups showed no significant change. Implantation sites and body weight increased substantially （P<0.01）， with restored endometrial morphology characterized by enhanced glandular density， stromal edema， and reappearance of nucleolar vacuoles. Significant downregulation of T-bet and IFN-γ （P<0.01） and upregulation of GATA3 and IL-4 （P<0.05， P<0.01） in uterine tissue were observed. Serum IFN-γ levels were significantly reduced （P<0.05， P<0.01）， while IL-4 levels were significantly elevated （P<0.05）. The Th1/Th2 ratios were significantly decreased （P<0.01）. ConclusionThe modified of Bazhentang combined with Guishentang significantly enhances the number of embryo implantation sites in mice with embryo implantation dysfunction， potentially through modulating T-bet/GATA3 expression， restoring Th1/Th2 immune balance， and improving endometrial receptivity.

The Janus kinase/signal transducers and activators of transcription (JAK-STAT) control natural killer (NK) cells development and cytotoxic functions, however, whether long non-coding RNAs (lncRNAs) are involved in this pathway remains unknown. We found that miR155HG was elevated in activated NK cells and promoted their proliferation and effector functions in both NK92 and induced-pluripotent stem cells (iPSCs)-derived NK (iPSC-NK) cells, without reliance on its derived miR-155 and micropeptide P155. Mechanistically, miR155HG bound to miR-6756 and relieved its repression of JAK3 expression, thereby promoting the JAK-STAT pathway and enhancing NK cell proliferation and function. Further investigations disclosed that upon cytokine stimulation, STAT3 directly interacts with miR155HG promoter and induces miR155HG transcription. Collectively, we identify a miR155HG-mediated positive feedback loop of the JAK-STAT signaling. Our study will also provide a power target regarding miR155HG for improving NK cell generation and effector function in the field of NK cell adoptive transfer therapy against cancer, especially iPSC-derived NK cells.

OBJECTIVES: To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression. METHODS: We collected voice data from both normal individuals and participants with subthreshold depression by asking them to read specifically chosen words and texts. From each voice sample, 384-dimensional vocal emotional feature variables were extracted, including energy feature, Meir frequency cepstrum coefficient, zero cross rate feature, sound probability feature, fundamental frequency feature, difference feature. The Recursive Feature Elimination (RFE) method was employed to select voice feature variables. Classification models were then built using the machine learning algorithms Adaptive Boosting (AdaBoost), Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Lasso Regression (LRLasso), and Support Vector Machine (SVM), and the performance of these models was evaluated. To assess generalization capability of the models, we used real-world speech data to evaluate the best speech recognition classification model. RESULTS: The AdaBoost, RF, and LDA models achieved high prediction accuracies of 100%, 100%, and 93.3% on word-reading speech test set, respectively. In the text-reading speech test set, the accuracies of the AdaBoost, RF, and LDA models were 90%, 80%, and 90%, respectively, while the accuracies of the other 3 models were all below 80%. On real-world word-reading and text-reading speech data, the classification models using AdaBoost and Random Forest still achieved high predictive accuracies (91.7% and 80.6% for AdaBoost and 86.1% and 77.8% for Random, respectively). CONCLUSIONS Analyzing vocal emotional characteristics allows effective identification of individuals with subthreshold depression. The AdaBoost and RF models show excellent performance for classifying subthreshold depression individuals, and may thus potentially offer valuable assistance in the clinical and research settings.
Humans ; Machine Learning ; Emotions ; Depression/diagnosis* ; Algorithms ; Voice ; Support Vector Machine ; Male ; Female

Objective Compared with patients with insomnia,the clinical characteristics and polysomnography(PSG)results from patients with co-morbid insomnia and obstructive sleep apnea(COMISA)were analyzed.The analysis was conducted to investigate clinical manifestations,sleep physiological characteristics,and related risk factors of COMISA patients.Methods A total of 134 patients with insomnia were enrolled and divided into two groups based on whether their apnea-hypopnea index(AHI)was≥5 events/hour:the COMISA group(69 patients)and the insomnia-only group(65 patients).The COMISA group was further divided into a mild OSA subgroup of 41 cases and a moderate-to-severe OSA subgroup of 28 cases;and then into a mild insomnia subgroup of 37 cases and a moderate-to-severe insomnia subgroup of 32 cases.The demographic characteristics,Pittsburgh sleep quality index(PSQI),self-rating depression scale(SDS),self-rating anxiety scale(SAS),Epworth sleepiness scale(ESS)and PSG results were analyzed.Results Compared with the insomnia-only group,the COMISA group had a higher proportion of men,a higher age,a higher body mass index(BMI),a higher proportion of smoking,a higher proportion of snoring,a lower average blood oxygen saturation and lowest blood oxygen saturation,a higher proportion of N2 stage,and a lower proportion of N3 stage and R stage(P<0.05).Compared with the mild OSA subgroup,the moderate-to-severe OSA subgroup had a higher BMI index,a higher proportion of snoring,a higher SAS score,a lower average blood oxygen saturation and lowest blood oxygen saturation,a higher proportion of N1 stage,and a lower proportion of N3 stage(P<0.05).Compared with the subgroup of mild insomnia,the subgroup of moderate-to-severe insomnia in the COMISA group had a higher proportion of hypnotic drug use(P<0.01),but there was no significant difference between the two groups in PSG results(P>0.05).Logistic regression analysis showed that older age(OR=1.07,P<0.01)and higher BMI(OR=1.41,P<0.01)were risk factors for the co-occurrence of OSA in insomnia patients.Conclusion Patients with COMISA have worse objective sleep quality at night than those with insomnia-only,and patients with more severe OSA have poorer objective sleep quality.In clinical practice,it is important to be aware of the risk of comorbid OSA for patients with insomnia who are older and have a higher BMI.

Objective To investigate the common diseases and the causes of diseases in Chinese Antarctic researchers,so as to provide reference for the medical support.Methods Medical records of 1 127 people with injuries and diseases who participated in four Antarctic scientific expeditions(the 31st,35th,36th,and 39th time)were retrospectively analyzed.The causes of the injuries and diseases as well as the implications for nursing were investigated.Results The top 10 diseases in the four Antarctic expeditions were acute soft tissue injury,dermatomycosis,pharyngitis,insomnia,periodontitis,gastroenteritis,motion sickness,acute upper respiratory infection,primary hypertension,and frostbite.The causes of the diseases in the four Antarctic expeditions were analyzed,and the nursing of different diseases was proposed.Conclusion It is necessary to take preventive measures based on the characteristics of injuries and diseases during Antarctic expeditions,so as to effectively prevent and treat these diseases and provide more comprehensive medical support for Antarctic scientific expeditions.

Objective To construct a global model of cardiac surface motion based on average intensity projection(AIP)image.Methods The cine magnetic resonance imaging data were divided into training set and test set for model construction and validation.The datum points were obtained on the AIP surface point cloud,and the corresponding points of each cardiac phase surface point cloud were found based on surface features.Principal component analysis was used to extract feature information,establish the mapping relationship between the datum points and the corresponding points,and construct a model for predicting each temporal phase surface point cloud from the AIP surface point cloud.Results The RMSE of the model on test set for corresponding point prediction ranged from(0.209±0.020)mm to(0.841±0.074)mm,while the Euclidean distance for each time phase surface point cloud prediction ranged from(1.399±0.029)mm to(1.658±0.100)mm.Conclusion The proposed global model exhibit high accuracy and can provide a reference for image segmentation and clinical treatments.

Objective Compared with patients with insomnia,the clinical characteristics and polysomnography(PSG)results from patients with co-morbid insomnia and obstructive sleep apnea(COMISA)were analyzed.The analysis was conducted to investigate clinical manifestations,sleep physiological characteristics,and related risk factors of COMISA patients.Methods A total of 134 patients with insomnia were enrolled and divided into two groups based on whether their apnea-hypopnea index(AHI)was≥5 events/hour:the COMISA group(69 patients)and the insomnia-only group(65 patients).The COMISA group was further divided into a mild OSA subgroup of 41 cases and a moderate-to-severe OSA subgroup of 28 cases;and then into a mild insomnia subgroup of 37 cases and a moderate-to-severe insomnia subgroup of 32 cases.The demographic characteristics,Pittsburgh sleep quality index(PSQI),self-rating depression scale(SDS),self-rating anxiety scale(SAS),Epworth sleepiness scale(ESS)and PSG results were analyzed.Results Compared with the insomnia-only group,the COMISA group had a higher proportion of men,a higher age,a higher body mass index(BMI),a higher proportion of smoking,a higher proportion of snoring,a lower average blood oxygen saturation and lowest blood oxygen saturation,a higher proportion of N2 stage,and a lower proportion of N3 stage and R stage(P<0.05).Compared with the mild OSA subgroup,the moderate-to-severe OSA subgroup had a higher BMI index,a higher proportion of snoring,a higher SAS score,a lower average blood oxygen saturation and lowest blood oxygen saturation,a higher proportion of N1 stage,and a lower proportion of N3 stage(P<0.05).Compared with the subgroup of mild insomnia,the subgroup of moderate-to-severe insomnia in the COMISA group had a higher proportion of hypnotic drug use(P<0.01),but there was no significant difference between the two groups in PSG results(P>0.05).Logistic regression analysis showed that older age(OR=1.07,P<0.01)and higher BMI(OR=1.41,P<0.01)were risk factors for the co-occurrence of OSA in insomnia patients.Conclusion Patients with COMISA have worse objective sleep quality at night than those with insomnia-only,and patients with more severe OSA have poorer objective sleep quality.In clinical practice,it is important to be aware of the risk of comorbid OSA for patients with insomnia who are older and have a higher BMI.

Objective To construct a global model of cardiac surface motion based on average intensity projection(AIP)image.Methods The cine magnetic resonance imaging data were divided into training set and test set for model construction and validation.The datum points were obtained on the AIP surface point cloud,and the corresponding points of each cardiac phase surface point cloud were found based on surface features.Principal component analysis was used to extract feature information,establish the mapping relationship between the datum points and the corresponding points,and construct a model for predicting each temporal phase surface point cloud from the AIP surface point cloud.Results The RMSE of the model on test set for corresponding point prediction ranged from(0.209±0.020)mm to(0.841±0.074)mm,while the Euclidean distance for each time phase surface point cloud prediction ranged from(1.399±0.029)mm to(1.658±0.100)mm.Conclusion The proposed global model exhibit high accuracy and can provide a reference for image segmentation and clinical treatments.

Objective:To investigate the application value of virtual reality laparoscopic simulator training in laparoscopic transabdominal preperitoneal hernioplasty (TAPP).Methods:The prospective cohort study was conducted. Twenty young physicians from The First Affiliated Hospital of Harbin Medical University with ≥3 years of clinical experience in general surgery and no foundation in laparoscopy were selected for training during July to August 2023. Physicians were divided into two groups based on random number table method. Physicians undergoing virtual reality laparoscopic simulator training were divided into the virtual reality group, and physicians undergoing regular laparoscopic simulator training were divided into the regular group. Two groups of physicians were trained using laparoscopic simulator for 10 days (2 hours for each skill, with a total of 6 hours per day for 3 skills), and the training covered basic laparoscopic surgical skills such as clamping, cutting and suturing and knotting. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups were conducted using the independent sample t test and the paired ttest was used for intra group comparison. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers, and comparison between groups were conducted using the chi-square test. Results:(1) Physicians situation before training. A total of 20 physicians were selected for eligibility. There were 13 males and 7 females, aged 31(range, 30?34)years. There are 7 males and 3 females in the virtual reality group, with age of (31.5±1.4)years and the TAPP simulation surgery time of (42±4)minutes before training. There are 6 male and 4 female students in the regular group, with age of (31.2±1.0)years and the TAPP simulation surgery time of (42±4)minutes before training. There was no significant difference in gender, age, TAPP simulation surgery time between the two groups of physicians ( P>0.05), confounding bias ensured comparability. (2) Basic skills of physicians before and after training. For physicians in the virtual reality group, the clamping score was 5.1±1.0, the cutting score was 4.9±1.0, the suturing and knotting score was 4.7±1.5 before training. The clamping score was 8.0±1.3, the cutting score was 7.9±1.5, the suturing and knotting score was 6.6±1.3 after training. There were significant differences in the above indicators before and after training ( t=?5.75, ?5.21, ?3.07, P<0.05). For physicians in the regular group, the clamping score was 5.3±1.0, the cutting score was 5.0±1.2, the suturing and knotting score was 4.3±1.5 before training. The clamping score was 7.1±1.2, the cutting score was 6.7±1.3, the suturing and knotting score was 5.7±1.1 after training. There were significant differences in the above indicators before and after training ( t=?3.73, ?3.16, ?2.42, P<0.05). (3) Completion of simulated surgical situations before and after training. The time of completing TAPP simulation surgery for virtual reality group after training was (29±3)minutes, versus (42±4)minutes before training, showing a significant difference before and after training ( t=7.69, P<0.05). The time of completing TAPP simulation surgery for regular group after training was (36±4)minutes, versus (42±4)minutes before training, showing a significant difference before and after training ( t=3.75, P<0.05). The time of completing TAPP simulation surgery of virtual reality group after training was shorter than that of regular group, showing a significant difference between the two groups ( t=?3.89, P<0.05). Conclusion:Both of virtual reality and regular laparoscopic simulator can enhance the basic laparoscopic surgical skills and the proficiency of TAPP simulation surgery of physicians, and the training effect of virtual reality laparoscopic simulators is better.

Objective:To analyze the influencing factors on the occurrence of acute kidney injury (AKI) in hospitalized patients treated with esomeprazole and to construct a risk prediction model to predict the occurrence of esomeprazole-associated AKI.Methods:The study was designed as a retrospective study. The subjects were selected from patients who were hospitalized in the First Affiliated Hospital of Shandong First Medical University from January 2018 to December 2020 and received treatment with esomeprazole. The clinical data of patients, including basic information, operations, intervention measures, medication, and laboratory test results, was collected through the hospital′s electronic medical record system. Patients were divided into AKI and non-AKI groups according to the occurrence of esomeprazole-associated AKI, and the clinical characteristics between the 2 groups were compared. The least absolute shrinkage and selection operator (LASSO regression) was used to analyze the influencing factors of esomeprazole-associated AKI. Patients were randomly divided into the training set and the test set at a 8∶2 ratio. Based on data in the training set, 5 machine learning algorithms were used to build esomeprazole-associated AKI prediction models, including logistic regression, random forest, gradient boosting machine (GBM), extreme gradient boosting, and light gradient boosting machine. Based on data in the test set, the performance of 5 models was validated through the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.Results:A total of 5 436 patients were enrolled in the study, including 3 231 males and 2 205 females, with an age of 61(51, 70) years. Esomeprazole-associated AKI occurred in 393 patients, with an incidence of 7.23%. The results of LASSO regression analysis identified 24 variables closely related to esomeprazole-associated, such as hepatic insufficiency, chronic renal insufficiency, hypoproteinemia. Based on data in the training set (4 349 patients), the esomeprazole-associated AKI risk prediction models were constructed and their predictive performance was good (all AUC>0.900). The predictive performance validation was conducted using the data in the test set (1 087 patients), and the results showed that the GBM model has the highest AUC (0.922) and relatively stable performance, with small differences in various indicators between the training and the test sets.Conclusions:The use of esomeprazole is significantly associated with AKI, and the risk is influenced by factors such as baseline renal function, comorbidities, and combined medications. The risk prediction model based on GBM algorithm is helpful for early assessment of the risk of esomeprazole-related AKI in clinical practice.