OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

The current treatment of glioma is facing drug resistance,which limits the efficacy of traditional chemotherapy drugs.This study aims to explore the potential mechanisms of the trifluoromethylquinazoline compound(KZL204)against glioma.Through the Cell Counting Kit-8(CCK-8)assay,we found that KZL204 significantly inhibits the growth of drug-resistant cancer cells,with a 48-hour half-maximal inhibitory concentration(IC50)of 3.63±0.38 μmol/L,which is significantly better than the positive control drug temozolomide(TMZ)(IC50 value of 81.67±5.49 μmol/L).Additionally,flow cytometry analysis showed that KZL204 treatment significantly increased the apoptosis rate of drug-resistant tumor cells and arrested the cell cycle at the G2/M phase.At the same time,the Transwell assay confirmed the inhibitory effect of KZL204 on the migration and invasion of drug-resistant cancer cells.Transcriptome analysis revealed 2 435 differentially expressed genes in drug-resistant cancer cells treated with KZL204,of which 1 320 were upregulated,and 1 115 were downregulated.KEGG and GO enrichment analysis showed that these differential genes were significantly enriched in apoptosis-related signaling pathways.Further bioinformatics prediction and Venn diagram analysis identified 35 potential core targets,with the PI3K-AKT signaling pathway being the most significant among the differentially expressed genes.Quantitative real-time PCR(RT-qPCR)experiments confirmed the downregulating effects of KZL204 on genes such as CREB3L1,CSF1,CXCL5,BCL3,and the upregulating effects on genes like FOS,LT A,PTGS2,MAP2K3.Immunoblotting experiments at the protein level also confirmed the impact of KZL204 on the expression of apoptotic proteins,including the upregulation of Bax,cleaved Caspase-3 protein,and the downregulation ofAKT,Bcl-2,Caspase-3,and Caspase-8 protein expression.In summary,KZL204 significantly inhibits the growth and metastasis of drug-resistant glioblastoma and induces apoptosis and cell cycle arrest by regulating the PI3K-AKT and apoptosis-related signaling pathways,demonstrating its potential as a candidate drug against drug-resistant glioma.

Norovirus is the global leading cause of epidemic and endemic acute gastroenteritis in people of all ages.To inves-tigate the genetic diversity of GⅡ genogroup noroviruses linked to clustered infections in northeast Chongqing,we collected anal swabs or environmental smears from 11 norovirus outbreaks during 2021-2022.Norovirus RNA was detected by quantitative real-time PCR(qRT-PCR),and partial viral RdRp/capsid genes were amplified by reverse transcription PCR(RT-PCR)and sequenced.Among samples from 11 outbreaks in 4 districts and counties,55 strains of GⅡ genogroup norovirus were detected.Six genotypes were identified with an online norovirus genotyping tool(http://www.rivm.nl/mpf/norovirus/typingtool).Genotype GⅡ.17[P17]was associated with four outbreaks;the co-circulating GⅡ.17[P17]and GⅡ.1[P16]caused another out-break;GⅡ.6[P7]and GⅡ.8[P8]respectively were linked to two outbreaks;and GⅡ.3[P12]and GⅡ.2[P16]respectively ac-counted for one outbreak.Phylogenetic analysis also indicated that 55 GⅡ genogroup strains formed five clusters,with norovir-uses of identical genotypes from diverse events belonging to the same cluster,and that genetically distinct genotypes from di-verse events belonged to different clusters.Therefore,our results revealed that multiple genotypes associated with norovirus outbreaks were circulating in northeast Chongqing,and GⅡ.17[P17]was the predominant genotype linked to these out-breaks during 2021-2022.Most norovirus outbreak events were caused by single sources,and genetic relationships were demonstrated among noroviruses of identical genotypes from diverse events.

Objective:To identify the types of fear of progression in patients after percutaneous coronary intervention (PCI) based on latent profile analysis, and to explore the influencing factors of different types.Methods:Cross-sectional survey method was used to select the patients with coronary heart disease and underwent PCI in Anhui Public Health Clinical Center from April to December 2023 as the research object. The general information questionnaire, Fear of Progression Questionnaire-Short Form, Ruminative Response Scale and Brief Illness Perception Questionnaire were used to investigate them. Mplus8.3 software was used to construct the latent profile model.Results:A total of 240 patients with complete data were enrolled, including 176 males and 64 females, aged 28-84 (62.94 ± 11.20) years. The results of latent profile analysis showed that the fear of progression of patients after PCI could be divided into three latent categories: There were 59 cases (24.6%) in the low fear group, 111 cases (46.3%) in the medium fear group, and 70 cases (29.1%) in the high fear-worried family group. The results of multiple logistic regression analysis showed that compared with the low fear group, the probability of having primary school education or below was higher in the medium fear group ( OR=4.054, 95% CI 1.370-11.996) and the high fear-worry family group ( OR=5.996, 95% CI 1.562-23.014), secondary school was more likely in the moderate fear group ( OR=3.096, 95% CI 1.104-8.682, all P<0.05);Living in rural areas were more likely to be in the moderate fear group ( OR=2.587, 95% CI 1.187-5.637) and the high few-worry family group ( OR=6.958, 95% CI 2.567-18.856, all P<0.05); The probability of the first interventional therapy was higher in the moderate fear group ( OR=2.496, 95% CI 1.107-5.630) and the high fear-worry family group ( OR=4.924, 95% CI=1.809-13.402, all P<0.05). In addition, compared with the low fear group, patients with higher rumination were more likely to belong to the high few-worry family working group ( OR=1.130, 95% CI 1.055-1.210, P<0.05);Moderate fear group ( OR=1.181, 95% CI 1.046-1.334) and high fear family working group ( OR=1.349, 95% CI 1.164-1.562, all P<0.05) had a higher level of illness perception. Conclusions:There is significant heterogeneity in the fear of progression among patients after PCI. Medical staff can implement precise intervention according to the potential category characteristics of patients′ fear of progression, so as to reduce the level of fear of disease progression.

Objective To analyze the influencing factors of oral frailty in elderly patients with type 2 diabetes and construct a nomogram prediction model. Methods A total of 370 elderly patients with type 2 diabetes were selected as the research subjects, including 284 patients in the modeling group and 86 patients in the validation group. The Oral Frailty Index-8 (OF-8) Scale was used for oral frailty screening, and a score of ≥4 was considered positive for oral frailty. General information of the two groups was collected through a self-made questionnaire. Multivariate Logistic regression analysis was used to analyze the influencing factors of oral frailty in patients with type 2 diabetes, and nomogram model was constructed. The goodness-of-fit and predictive performance of the model were verified using the Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic curve (ROC). Results The incidence of oral frailty among elderly patients with type 2 diabetes was 45.4% (129/284). Age, body mass index (BMI), appendicular skeletal muscle mass index (ASMI), smoking, monthly income and subjective chewing difficulty were identified as influencing factors for oral frailty in elderly diabetic patients (P < 0.05). The areas under the ROC for internal and external validation were 0.887 (95%CI, 0.847 to 0.925) and 0.839 (95%CI, 0.755 to 0.923), respectively. The Hosmer-Lemeshow test showed that χ²=4.852, P=0.773, indicating good goodness-of-fit for the nomogram model. Conclusion Age, ASMI, BMI, smoking, monthly income and subjective chewing difficulty are influencing factors for oral frailty in elderly diabetic patients. The nomogram prediction model constructed based on these influencing factors demonstrates good performance.

ObjectiveTo investigate the satisfaction of both service providers and users in community health service centers through questionnaires， and to analyze the reasons， so as to put forward suggestions for improving satisfaction. MethodsSampling was conducted in 247 community health service centers in 16 districts of Shanghai， and data were collected from employees and patients through on-site questionnaires. ResultsA total of 10 334 eligible questionnaires were collected from community health service centers， of which 7 712 were from patients and 2 622 were from employees. In 2021， the public's satisfaction with community health service centers was 96.51 points， and the satisfaction of employees was 94.08 points. The public expressed lower satisfaction with the availability of essential drugs for outpatient services， while the staff were less satisfied with their salary and benefits， work recognition， and career development. Regarding family doctor services， 73.38% of the public had signed up for family doctor services， while 23.57% of the public did not know about the family doctor services， and 16.18% believed it was necessary to improve the publicity of basic public health services. ConclusionThe public's overall satisfaction with the service of community health services is gradually increased. The basic medical services of community health are recognized by residents. Drug allocation needs to be improved urgently， and publicity and promotion need to be further strengthened. The overall satisfaction of employees is high， but further improvement is needed in terms of salary and benefits， work recognition， and career development.

Adverse experiences in early life have long-lasting negative impacts on behavior and the brain in adulthood, one of which is sleep disturbance. As the corticotropin-releasing hormone (CRH)-corticotropin-releasing hormone receptor 1 (CRHR1) system and nucleus accumbens (NAc) play important roles in both stress responses and sleep-wake regulation, in this study we investigated whether the NAc CRH-CRHR1 system mediates early-life stress-induced abnormalities in sleep-wake behavior in adult mice. Using the limited nesting and bedding material paradigm from postnatal days 2 to 9, we found that early-life stress disrupted sleep-wake behaviors during adulthood, including increased wakefulness and decreased non-rapid eye movement (NREM) sleep time during the dark period and increased rapid eye movement (REM) sleep time during the light period. The stress-induced sleep disturbances were accompanied by dendritic atrophy in the NAc and both were largely reversed by daily systemic administration of the CRHR1 antagonist antalarmin during stress exposure. Importantly, Crh overexpression in the NAc reproduced the effects of early-life stress on sleep-wake behavior and NAc morphology, whereas NAc Crhr1 knockdown reversed these effects (including increased wakefulness and reduced NREM sleep in the dark period and NAc dendritic atrophy). Together, our findings demonstrate the negative influence of early-life stress on sleep architecture and the structural plasticity of the NAc, and highlight the critical role of the NAc CRH-CRHR1 system in modulating these negative outcomes evoked by early-life stress.
Animals ; Mice ; Corticotropin-Releasing Hormone/metabolism* ; Nucleus Accumbens/metabolism* ; Receptors, Corticotropin-Releasing Hormone/metabolism* ; Sleep ; Sleep Wake Disorders ; Stress, Psychological/complications*

OBJECTIVE: To explore the effect of a modified three-point bending fracture device for establishing a rabbit model of closed tibial fracture. METHODS: The model of closed tibial fracture was established in 40 6-month-old male New Zealand white rabbits with a body weight of 2.5 to 3.0 kg, and the model was verified at 6 weeks after operation. Five rabbits underwent pre modeling without temporary external fixation before modeling, and then were fractured with a modified three-point bending fracture device;35 rabbits underwent formal modeling. Before modeling, needles were inserted, and splints were fixed externally, and then the fracture was performed with a modified three-point bending fracture device. The fracture model and healing process were evaluated by imaging and histopathology at 2 hours, 4 weeks, and 6 weeks after operation. RESULTS: Two hours after modeling, the prefabricated module showed oblique fracture in varying degrees and the broken end shifted significantly;Except for 1 comminuted fracture, 2 curved butterfly fractures and 2 without obvious fracture line, the rest were simple transverse and oblique fractures without obvious displacement in formal modeling group. According to the judgment criteria, the success rate of the model was 85.71%. Four weeks after modeling, the fixed needle and splint of the experimental rabbits were in good position, the fracture alignment was good, the fracture line was blurred, many continuous callus growths could be seen around the fracture end, and the callus density was high. Six weeks after modeling, many thick new bone trabeculae at the fracture, marginal osteoblasts attached, and a small number of macrophages were seen under the microscope. The intramembrane osteogenesis area was in the preparation bone stage, the medullary cavity at the fracture had been partially reopened, the callus was in the absorption plastic stage, and many osteoclasts were visible. The X-ray showed that the fracture line almost disappeared, part of the medullary cavity had been opened, the external callus was reduced around, the callus was in the plastic stage, and the bone cortex was continuous. It suggests that the fracture model showed secondary healing. CONCLUSION The improved three-point bending fracture device can establish a stable rabbit model of closed tibial fracture, and the operation is simple, which meets the requirements of closed fracture model in basic research related to fracture healing.
Rabbits ; Male ; Animals ; Bony Callus ; Fracture Healing ; Tibial Fractures/surgery* ; Osteogenesis ; Radiography

Objective:To investigate whether stress hyperglycemia (SH) is an independent risk factor for the occurrence and mortality of sepsis-associated encephalopathy (SAE).Methods:From August 2016 to October 2021, sepsis patients admitted to the ICU of Guangdong Provincial People's Hospital were selected as the study subjects. According to whether they developed to SH (RBG>11.1 mmol/L) within 7 days of enrollment, the pat ients were divided into the SH group and the non-SH group for analysis. Logistic regression was used to analyze whether SH was an independent risk factor for SAE occurrence, and ROC curve was used to analyze the predictive value of SH to SAE. Kaplan-Meier curve was used to compare the 90-day survival of SAE patients with or without SH. Cox regression analysis was used to analyze the risk factors of 28-day and 90-day death in SAE patients.Results:A total of 183 sepsis patients were included, including 62 patients in the SH group and 121 in the non-SH group. Logistic regression analysis demonstrated that SH was an independent risk factor for SAE ( OR=4.452, 95% CI: 2.021-9.808, P <0.001). ROC curve demonstrated that SH could accurately predict SAE (AUC=0.831; Sensitivity=78.4%; Specificity=76.8%; and Yoden index=0.553). Kaplan-Meier curve demonstrated that the 90-day survival of SAE patients with SH significantly declined (log-rank test: P<0.01). Cox regression analysis suggested that SH was a risk factor for death at day 28 and day 90 in SAE patients (28 d, HR=2.272, 95% CI: 1.212-4.260, P=0.010; 90 d, HR=2.456, 95% CI: 1.400-4.306, P<0.01). Conclusions:SH is an independent risk factor for SAE and can predict SAE occurrence. SH significantly reduces 90-day survival and increase mortality at 28 and 90 days in SAE patients.