BackgroundPatients with depressive episode and schizophrenia have a high risk of suicide. The sleep electroencephalogram power spectral density characteristics of patients with depressive episode accompanied by suicidal behavior and those with schizophrenia may be different， but there is currently a lack of direct comparative studies on these two groups of patients. ObjectiveTo compare the sleep electroencephalogram power spectral density between depressive episode and schizophrenic patients with suicidal behavior， in order to provide references for exploring predictive indicators of suicidal behavior. MethodsFrom June 2018 to December 2020， 20 patients with depressive episode and 20 patients with schizophrenia who had committed suicide within the past month and were treated at the outpatient department of Shenzhen Kangning Hospital were selected. All of them met the diagnostic criteria for depressive episode or schizophrenia as defined in the International Classification of Diseases， tenth edition （ICD-10）. Using a random sampling method， 20 volunteers with matching gender and age to the patient groups were selected from the Cuiping community in Shenzhen as the control group. The subjective sleep of the patients was evaluated using the Insomnia Severity Index （ISI）， the Dysfunctional Belief and Attitude about Sleep （DBAS）， the Disturbing Dreams and Nightmare Severity Index （DDNSI）， and the Epworth Somnolence Scale （ESS）. The objective sleep of the patients was assessed using polysomnography. The sleep electroencephalogram was filtered and the power spectral density of the brain wave was analyzed and processed for all the subjects. The subjective and objective sleep conditions of the two patient groups were compared， and the sleep electroencephalogram power spectral density of the patient groups and the control group were also compared. ResultsA comparison of subjective and objective sleep conditions between patients with depressive episode accompanied by suicidal behavior and patients with schizophrenia accompanied by suicidal behavior showed no statistically significant differences （P>0.05）. Comparisons of sleep electroencephalogram power spectral density in the W stage （average power of α wave， total power of δ wave， average power of δ wave， average power of θ wave）， N1 stage （average power of β wave， total power of α wave， total power of δ wave）， N2 stage （total power of α wave， average power of α wave， total power of δ wave， average power of δ wave）， N3 stage （average power of α wave， average power of δ wave）， and R stage （total power of α wave， average power of α wave， total power of δ wave， average power of δ wave） between patients with depressive episode accompanied by suicidal behavior， patients with schizophrenia accompanied by suicidal behavior， and the control group showed statistically significant differences （P<0.05 or 0.01）. The total power of δ wave in the W stage and the average power of β wave and δ wave in the N1 stage were higher in two patient groups were higher than those of the control group. The total power of α wave and the average power of α wave in the N2 stage were lower than those of the control group， while the average power of δ wave was higher than that of the control group. The average power of α wave in the N3 stage of both patient groups were lower than that of the control group， while the average power of δ wave was higher than that of the control group. The total power and average power of α wave in the R stage were lower than those of the control group， while the total power and average power of δ wave were higher than those of the control group. All the differences were statistically significant. Patients with depressive episode accompanied by suicidal behavior had higher average powers of α wave， δ wave， and θ wave in the W stage compared with the control group， while the total power of α wave in the N1 stage was lower in the former group. All these differences were statistically significant （P<0.05）. ConclusionThe depressive episode patients accompanied by suicidal behavior have highly overlapping sleep electroencephalogram abnormal patterns with those of schizophrenia patients， mainly manifested as a general decrease in α wave power （N2， N3， R stage） and a general increase in δ wave power （W， N1， N2， N3， R stage） as well as β wave power in N1 stage. At the same time， patients with depressive episode accompanied by suicidal behavior also show specific changes， including an increase in the average power of α and θ waves during the wakefulness period （W stage）， and a decrease in the total power of α wave in N1 stage. ［Funded by Guangdong Province High-level Clinical Key Specialty （with supporting funds from Shenzhen City） （number， SZGSP013）； Shenzhen Key Medical Discipline （number， SZXK041）； Shenzhen Clinical Medicine Research Center Project （number， 20210617155253001）］

Objective To explore an artificial intelligence (AI)-based method for automated hand hygiene monitoring and to compare the effectiveness of three algorithms (UniFormerV2, TDN, C3D) in recognizing hand hygiene steps in surgical settings, thereby aiding hospital infection control. Methods From April to October 2024, we non-invasively collected 641 video recordings of healthcare staff performing hand hygiene at four-bay scrub sinks in two tertiary hospitals using overhead HD cameras. The dataset was annotated by five trained experts for model training and validation. Results Following training on 385 samples, internal validation (n=119) showed the C3D model achieved 81% accuracy, 87% recall, and an 83% F1-score. The TDN model achieved 93%, 91%, and 92% for the same metrics. The UniFormerV2 model outperformed both, with an accuracy, recall, and F1-score of 93%—an improvement of over 10 percentage points compared to traditional CNNs (TDN, C3D). It also achieved an 84% accuracy in external validation, demonstrating strong generalization. Conclusion The UniFormerV2 model is more accurate than CNN-based models for hand hygiene step recognition and shows robust performance in external validation. It presents a viable tool for healthcare facilities to enhance hand hygiene management, ultimately improving medical quality and patient safety.

Objective To report the diagnosis of a canine distemper virus outbreak among a colony of cynomolgus monkeys at an experimental monkey farm in 2019. MethodsA total of 46 samples were collected from 21 diseased cynomolgus monkeys (exhibiting symptoms such as facial rash, skin scurf, runny nose, and diarrhea) and from one deceased monkey at an experimental monkey breeding farm in South China in late 2019, including serum, skin rash swabs, and anticoagulated whole blood, liver, lung, and skin tissues were submitted for testing. All submitted samples were tested for canine distemper virus gene fragments using real-time quantitative PCR, while immunohistochemical staining was performed to detect canine distemper virus nucleoprotein in lung tissues. The skin tissue of the deceased monkey was ground and sieved. The filtrate was inoculated into a monolayer MDCK cell line for virus isolation. Then, whole-genome sequencing was performed to identify the isolated virus. The Clustal Omega tool was used to align and analyze the homology of different Asian canine distemper virus isolates. A phylogenetic tree was constructed, followed by genetic evolutionary analysis. ResultsClinical retrospective analysis revealed that the diseased cynomolgus monkeys exhibited symptoms similar to those observed in cynomolgus monkeys infected with measles virus. Necropsy findings showed red lesions in the lungs and significant hemorrhage in the colonic mucosa. Real-time quantitative PCR detected canine distemper virus nucleic acid in the serum, skin rash swabs of the infected monkeys, and various tissue samples of the deceased monkey, all of which tested positive. Calculation based on the standard curve formula indicated the viral load was highest in the skin tissue. Immunohistochemical staining of the deceased monkey's lung tissue demonstrated aggregation of CDV nucleoprotein in alveolar epithelial cells, bronchi, and bronchioles. A CDV strain was isolated from the skin tissue of the deceased monkey. Phylogenetic analysis indicated that this strain shares the closest relationship (98.86%) with the Asian-1 type canine distemper virus strain CDV/dog/HCM/33/140816, previously identified in dogs in Vietnam. ConclusionBased on comprehensive analysis of clinical symptoms, nucleic acid detection, viral protein immunohistochemistry, and whole-genome sequencing results, the diagnosis confirms that the cynomolgus monkeys in this facility are infected with canine distemper virus. It is recommended to include canine distemper virus as a routine surveillance target in captive monkey populations. Additionally, this study provides a foundation for further research on the molecular biological characteristics of canine distemper virus.

Objective To report the diagnosis of a canine distemper virus outbreak among a colony of cynomolgus monkeys at an experimental monkey farm in 2019. MethodsA total of 46 samples were collected from 21 diseased cynomolgus monkeys (exhibiting symptoms such as facial rash, skin scurf, runny nose, and diarrhea) and from one deceased monkey at an experimental monkey breeding farm in South China in late 2019, including serum, skin rash swabs, and anticoagulated whole blood, liver, lung, and skin tissues were submitted for testing. All submitted samples were tested for canine distemper virus gene fragments using real-time quantitative PCR, while immunohistochemical staining was performed to detect canine distemper virus nucleoprotein in lung tissues. The skin tissue of the deceased monkey was ground and sieved. The filtrate was inoculated into a monolayer MDCK cell line for virus isolation. Then, whole-genome sequencing was performed to identify the isolated virus. The Clustal Omega tool was used to align and analyze the homology of different Asian canine distemper virus isolates. A phylogenetic tree was constructed, followed by genetic evolutionary analysis. ResultsClinical retrospective analysis revealed that the diseased cynomolgus monkeys exhibited symptoms similar to those observed in cynomolgus monkeys infected with measles virus. Necropsy findings showed red lesions in the lungs and significant hemorrhage in the colonic mucosa. Real-time quantitative PCR detected canine distemper virus nucleic acid in the serum, skin rash swabs of the infected monkeys, and various tissue samples of the deceased monkey, all of which tested positive. Calculation based on the standard curve formula indicated the viral load was highest in the skin tissue. Immunohistochemical staining of the deceased monkey's lung tissue demonstrated aggregation of CDV nucleoprotein in alveolar epithelial cells, bronchi, and bronchioles. A CDV strain was isolated from the skin tissue of the deceased monkey. Phylogenetic analysis indicated that this strain shares the closest relationship (98.86%) with the Asian-1 type canine distemper virus strain CDV/dog/HCM/33/140816, previously identified in dogs in Vietnam. ConclusionBased on comprehensive analysis of clinical symptoms, nucleic acid detection, viral protein immunohistochemistry, and whole-genome sequencing results, the diagnosis confirms that the cynomolgus monkeys in this facility are infected with canine distemper virus. It is recommended to include canine distemper virus as a routine surveillance target in captive monkey populations. Additionally, this study provides a foundation for further research on the molecular biological characteristics of canine distemper virus.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

Objective: To investigate the predictive value of EZH2 expression for malignant transformation in oral leukoplakia (OLK) and to provide a reference for clinical practice. Methods: This study was approved by the institutional ethics committee, and informed consent was obtained from all participants. A total of 114 patients diagnosed with OLK by pathological examination and treated at our hospital between November 2020 and July 2022 were initially enrolled. After excluding those with incomplete data or follow-up, 105 participants were included in the final analysis, comprising 14 in the high EZH2 expression group and 91 in the low EZH2 expression group. Histopathological examination of oral mucosa and immunohistochemical detection of EZH2 protein expression were performed. The follow-up period was 30 months; participants were followed until malignant transformation occurred or until the end of follow-up, at which point they were withdrawn from the study. The exposure factor was the level of EZH2 protein expression, and the outcome was the malignant transformation rate of OLK. Differences in EZH2 expression levels and transformation outcomes were analyzed. Results: There were no statistically significant differences between the high and low EZH2 expression groups in terms of age, sex, history of systemic disease, lifestyle habits, psychological status, diet, and sleep conditions (P > 0.05). Lesions in the high EZH2 expression group were mainly located on the ventral tongue, while in the low EZH2 expression group, they were more commonly found on the dorsal tongue and buccal mucosa. The malignant transformation rate was 28.6% (4/14) in the high expression group and 8.8% (8/91) in the low expression group; these differences were not statistically significant (P=0.053). In univariate Cox regression analysis, the risk of malignant transformation in the high EZH2 expression group was 3.647 times that of the low EZH2 expression group (HR = 3.647, 95% CI: 1.097-12.120, P<0.05). Kaplan-Meier survival analysis showed that over the 30-month follow-up period, the cancer-free survival rate in the high EZH2 expression group was 19.8% lower than in the low expression group, and the difference was statistically significant (P<0.05). In multivariate Cox regression analysis, only moderate and severe epithelial dysplasia were identified as independent risk factors for malignant transformation. The risk of malignant transformation in the moderate and severe dysplasia groups was 10.695 and 13.623 times higher, respectively, than in the mild dysplasia group (HR = 10.695, 95% CI: 2.270-50.396, P<0.05; HR=13.623, 95% CI: 1.918-96.774, P<0.05). EZH2 high expression was not an independent risk factor in the multivariate model (HR= 2.528, 95% CI: 0.752-8.500, P = 0.134). Conclusion High EZH2 protein expression is a risk factor for the malignant transformation of OLK but does not have independent predictive value.

OBJECTIVE To analyze the influencing factors of voriconazole trough concentration and adverse drug reactions （ADR） in renal transplant recipients. METHODS Data from inpatients who received voriconazole and therapeutic drug monitoring in our hospital between January 2022 and August 2023 were retrospectively analyzed. Patients were divided into renal transplant group and non-renal transplant group based on transplantation status. A 1∶1 propensity score matching （PSM） method was used to balance differences in baselines between the two groups. Voriconazole trough concentrations， target attainment rate， clinical efficacy， and ADR were compared between the two groups. Multiple linear regression （backward） was used to analyze the factors influencing voriconazole trough concentrations in the renal transplant group. Univariate analysis and binary Logistic regression were used to identify independent risk factors for ADR in the renal transplant group. RESULTS After PSM， 48 patients were included in each group. There were no statistically significant differences in the mean voriconazole trough concentration， target attainment rate or efficacy rate between the two groups （P＞0.05）. The total incidence of ADR was significantly higher in the renal transplant group than in the non-renal transplant group （P＜0.05）. Multiple linear regression analysis showed that age， average daily dose， pulmonary infection， total bilirubin during medication， day-1 loading dose， use of the original drug， concomitant immunosuppressant use， and the occurrence of ADR were factors influencing voriconazole trough concentration in renal transplant patients （P＜0.05）. Binary Logistic regression analysis showed that abnormal direct bilirubin during medication [OR＝7.747， 95%CI （1.334， 45.005）， P＝0.023] was an independent risk factor for ADR in renal transplant patients receiving voriconazole. CONCLUSIONS Age， average daily dose， pulmonary infection， use of the original drug， day-1 loading dose， total bilirubin during medication， concomitant immunosuppressant use， and the occurrence of ADR are the factors influencing voriconazole trough concentration in renal transplant patients. Furthermore， patients with abnormal direct bilirubin during medication are more susceptible to ADR.

AIM: To analyze the reasons and clinical outcomes of intraocular lens(IOL)exchange.METHODS:This retrospective case series study included 47 patients(53 eyes)who underwent IOL exchange surgery at the Department of Ophthalmology, the First Affiliated Hospital of Zhengzhou University, between April 2020 and May 2024, aged from 5 to 87(44.8±3.6)years old. Patients' demographics, surgical indications, surgical techniques, preoperative and postoperative uncorrected visual acuity(UCVA)and best-corrected visual acuity(BCVA), as well as postoperative complications were recorded.RESULTS:The reasons for IOL exchange included refractive error(18 cases, 23 eyes, 43%), IOL dislocation(12 cases, 13 eyes, 25%), IOL opacification(12 cases, 12 eyes, 23%), neuroadaptation failure(3 cases, 3 eyes, 6%), and patient dissatisfaction with visual quality(2 cases, 2 eyes, 4%). The surgical techniques for IOL exchange included in-the-bag IOL fixation(16 eyes, 30%), ciliary sulcus fixation(27 eyes, 51%), and scleral suture fixation(10 eyes, 19%). There was statistical significant difference between preoperative UCVA(LogMAR)and UCVA(LogMAR)at 1 d postoperatively(1.03±0.64 vs 0.50±0.46, P<0.05), and there was statistical significant difference between preoperative BCVA(LogMAR)and BCVA(LogMAR)at 1 mo postoperatively(0.41±0.37 vs 0.17±0.21, P<0.05). Postoperative complications included posterior capsule opacification in 2 eyes and IOL dislocation in 1 eye.CONCLUSION:Refractive error, IOL dislocation, and IOL opacification were the three most common reasons for IOL exchange. Although less frequent, factors such as neuroadaptation issues associated with multifocal IOLs and patient-reported visual quality dissatisfaction reflect growing expectations for improved visual outcomes. IOL exchange surgery, though technically challenging, demonstrates favorable clinical efficacy and a low complication rate, representing an effective intervention for managing postoperative IOL-related issues following cataract surgery.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult