The exoskeleton robot for lower limb medical rehabilitation in foreign countries and China was introduced in terms of the research status,structure and working principle,and analysis was carried out over its key technologies.It's pointed out the exoskeleton robot for lower limb medical rehabilitation would be enhanced in energy endurance,safety and comfort,individualized and intelligent control,modularity and lightweight design.[Chinese Medical Equipment Journal,2025,46(1):88-100]

Objective:To develop fluorescent nanoprobes with aggregation-induced emission characteristics and to systematically evaluate their optical properties, biosafety, anti-tumor activity, and imaging capability, thereby assessing their potential for early precision diagnosis and treatment of oral cancer in mice.Methods:Control probes (PEG@TPD) were prepared by encapsulating ( E)-4-(2-(4′-(1-phenyl-2,2-bis(4-methoxyphenyl)vinyl)biphenyl-4-yl)vinyl)-4-(dicyanomethylene)-4 H-chromene (TPD) using 1,2-distearoyl- SN-glycerol-3-phosphoethanolamine- N-polyethylene glycol 2000-maleimide as the carrier. Fluorescent nanoprobes (GE11-PEG@TPD) were subsequently fabricated by surface modification with the targeting GE11 peptide. The morphology and particle size of the nanoprobes were characterized by transmission electron microscopy and dynamic light scattering. The optical properties of the nanoprobes were analyzed using ultraviolet-visible spectrophotometry and fluorescence spectrophotometry. Mouse squamous carcinoma SCC-7 cells were randomly divided into six groups by the random number table method. The PBS, PEG@TPD, and GE11-PEG@TPD groups were not treated with light, while the PBS+L, PEG@TPD+L, and GE11-PEG@TPD+L groups were exposed to white light (25 W/cm 2, 10 min) at a nanoprobe concentration of 20 μg/ml (based on TPD concentration). Cell survival rate was assessed by the cell counting kit-8 assay. Cellular uptake, intracellular reactive oxygen species levels, and cytotoxicity were evaluated using laser scanning confocal microscopy. The apoptosis rate was evaluated by cell apoptosis assay. Twelve 6-week-old female C3H/HeN mice were randomly divided into two groups: PEG@TPD-1 group and GE11-PEG@TPD-1 group, with 6 mice in each group. Subcutaneous oral cancer models were established by injecting SCC-7 cell suspensions into the dorsal region of mice in two groups. Each mouse was intravenously administered 200 μl of PEG@TPD or GE11-PEG@TPD solution (1 mg/ml, based on TPD concentration). Tumor boundaries and scope were visualized using a small animal in vivo imaging system. At the optimal imaging time point, three mice from each group were euthanized, and major organs and tumor tissues were collected to measure probe accumulation. Statistical comparisons between two groups were performed using independent samples t-tests, while one-way or two-way analysis of variance was applied for multiple group comparisons. Results:Both PEG@TPD and GE11-PEG@TPD exhibited a relatively regular sphere, with average particle sizes of (92.76±8.80 and 117.50±6.40) nm, respectively. PEG@TPD showed two obvious absorption peaks at 352 and 444 nm. GE11 peptide showed a polypeptide characteristic absorption peak at 280 nm, GE11-PEG@TPD showed three characteristic absorption peaks at 280, 352 and 444 nm. Under dark conditions, cell survival rate remained above 80% even at a concentration of 160 μg/ml. After light irradiation, cell survival rate in the PEG@TPD+L group at 20 and 40 μg/ml [(68.2±5.2)% and (48.6±7.1)%] were higher than those in the GE11-PEG@TPD+L group [(55.0±2.8)% and (30.0±9.2)%], with statistically significant differences ( P<0.05, 0.01). At incubation time points of 2, 4, and 6 h, the relative fluorescence intensity of the GE11-PEG@TPD group (119.4±10.2, 192.9±14.2, and 234.1±4.8) were higher than those of the PEG@TPD group (98.6±7.5, 163.8±3.1, 204.6±11.2), with statistically significant differences (all P<0.05). The relative fluorescence intensity of the PEG@TPD+L and GE11-PEG@TPD+L group (68.5±4.7 and 86.8±10.0) were higher than those in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups (6.1±8.0, 7.6±1.8, 4.7±4.2 and 21.1±7.6), with statistically significant differences (all P<0.01). And the difference between the GE11-PEG@TPD+L and PEG@TPD+L groups was also statistically significant ( P<0.05). Viable cell proportions in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups all exceeded 95.0%, while those in the PEG@TPD+L and GE11-PEG@TPD+L groups decreased to (11.1±3.7)% and (4.3±1.1)%, respectively, with a statistically significant difference between them ( P<0.05). The apoptotic cell proportions in the PEG@TPD+L and GE11-PEG@TPD+L groups [(40.5±4.3)% and (55.3±7.4)%] were higher than those in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups [(27.3±2.0)%, (28.2±1.9)%, (28.6±1.2)%, and (29.7±3.0)%], with statistically significant differences ( P<0.05, 0.01). Moreover, the difference between the GE11-PEG@TPD+L and the PEG@TPD+L groups was also statistically significant ( P<0.01). The mean fluorescence intensities of the GE11-PEG@TPD-1 group at 1, 3, 5, 8, and 24 h, as well as in ex vivo tumor tissues[(5.2±0.8, 5.9±0.7, 6.6±1.0, 7.9±0.6, 7.8±0.7 and 20.6±3.5)×10 6 p/s/cm 2/sr] were all higher than those in the PEG@TPD-1 group [(3.2±0.7, 4.2±0.7, 4.6±0.9, 5.1±0.9, 4.7±0.9 and 14.2±1.8)×10 6 p/s/cm 2/sr], with statistically significant differences ( P<0.05, 0.01). Conclusions:The fluorescent nanoprobes exhibit uniform particle size, high photostability, and good biocompatibility. They demonstrate significant tumor-killing effects at the cellular level and possess tumor-targeting capability in vivo, showing promising application potential for the early precision diagnosis and treatment of oral cancer.

BACKGROUND: Durvalumab after chemoradiotherapy (CRT) failed to bring survival benefits to patients with epidermal growth factor receptor ( EGFR ) mutations in PACIFIC study (evaluating durvalumab in patients with stage III, unresectable NSCLC who did not have disease progression after concurrent chemoradiotherapy). We aimed to explore whether locally advanced inoperable patients with EGFR mutations benefit from tyrosine kinase inhibitors (TKIs) and the optimal treatment regimen. METHODS: We searched the PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases from inception to December 31, 2022 and performed a meta-analysis based on a Bayesian framework, with progression-free survival (PFS) and overall survival (OS) as the primary endpoints. RESULTS: A total of 1156 patients were identified in 16 studies that included 6 treatment measures, including CRT, CRT followed by durvalumab (CRT-Durva), TKI monotherapy, radiotherapy combined with TKI (RT-TKI), CRT combined with TKI (CRT-TKI), and TKI combined with durvalumab (TKI-Durva). The PFS of patients treated with TKI-containing regimens was significantly longer than that of patients treated with TKI-free regimens (hazard ratio [HR] = 0.37, 95% confidence interval [CI], 0.20-0.66). The PFS of TKI monotherapy was significantly longer than that of CRT (HR = 0.66, 95% CI, 0.50-0.87) but shorter than RT-TKI (HR = 1.78, 95% CI, 1.17-2.67). Furthermore, the PFS of RT-TKI or CRT-TKI were both significantly longer than that of CRT or CRT-Durva. RT-TKI ranked first in the Bayesian ranking, with the longest OS (60.8 months, 95% CI = 37.2-84.3 months) and the longest PFS (21.5 months, 95% CI, 15.4-27.5 months) in integrated analysis. CONCLUSIONS: For unresectable stage III EGFR mutant NSCLC, RT and TKI are both essential. Based on the current evidence, RT-TKI brings a superior survival advantage, while CRT-TKI needs further estimation. Large randomized clinical trials are urgently needed to explore the appropriate application sequences of TKI, radiotherapy, and chemotherapy. REGISTRATION PROSPERO; https://www.crd.york.ac.uk/PROSPERO/ ; No. CRD42022298490.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; ErbB Receptors/genetics* ; Lung Neoplasms/drug therapy* ; Mutation/genetics* ; Protein Kinase Inhibitors/therapeutic use* ; Chemoradiotherapy ; Antibodies, Monoclonal/therapeutic use*

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

The exoskeleton robot for lower limb medical rehabilitation in foreign countries and China was introduced in terms of the research status,structure and working principle,and analysis was carried out over its key technologies.It's pointed out the exoskeleton robot for lower limb medical rehabilitation would be enhanced in energy endurance,safety and comfort,individualized and intelligent control,modularity and lightweight design.[Chinese Medical Equipment Journal,2025,46(1):88-100]

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To evaluate the quality of critical patient handover practice between emergency room and intensive care unit (ICU) nurses, and to provide a basis for structured handover process.Methods:From March to July 2023, a total of 223 pairs of nurses in emergency room and ICU (including EICU) of 5 Class 3 Grade A general hospitals in Suzhou were selected as the research objects by using cross-sectional survey method and convenience sampling method. Self-designed general information questionnaire and Patient Handover Practice Quality Scale were used to investigate the included 223 pairs of nurses in emergency room and ICU on the current situation of handover time and quality.Results:A total of 211 pairs of nurses were included, including 286 females (67.8%) and 136 males (32.2%). The average age of emergency department nurses was (27.31 ± 2.17) years old, and ICU nurses was (26.96 ± 3.04) years old. The total scores of the patient handover practice Quality Scale for nurses in the emergency room and ICU were (45.25 ± 6.26) and (43.55 ± 7.19) points respectively, and the scores of the information transmission dimension were (20.47 ± 5.43) and (17.66 ± 3.45) points. The scores of common understanding dimension were (7.59 ± 2.31) and (8.58 ± 2.46) points. The scores of work atmosphere dimension were (7.93 ± 2.11) and (8.39 ± 2.29) points. The scores of handover situation dimension were (5.33 ± 1.30) and (5.70 ± 1.53) points, and the differences were statistically significant ( t values were - 6.35-4.22, all P<0.05). There were statistically significant differences in the scores of handover practice quality between emergency room nurses and ICU nurses according to specialization, education background, working years and job category ( t values were - 4.91-2.56, all P<0.05). Conclusions:Emergency room nurses and ICU nurses have different requirements and expectations for handover procedures, so it is necessary to build a structured handover practice framework and carry out personalized handover practice training, in order to achieve the consistency of handover content and improve the quality of critical patients handover practice.

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

Objective:To explore the therapeutic efficacy and factors influencing the sequential combination of nucleos(t)ide analogues (NAs) with pegylated interferon alpha (Peg-IFN-α) in the treatment of patients with chronic hepatitis B (CHB).Methods:144 CHB cases with NAs treatment for more than 1 year, HBV DNA < 20 IU/ml, hepatitis B surface antigen (HBsAg) quantification < 3 000 IU/ml, treated with a sequential combination of Peg-IFN-α treatment for 48 to 96 weeks, and followed up were selected from the Fifth Medical Center of the PLA General Hospital between May 2018 and May 2020. Intention-to-treat analysis was used to measure the HBsAg clearance rate at 96 weeks. The Kaplan-Meier method was used to compute the cumulative HBsAg clearance rate at 96 weeks. Univariate and multivariate logistic regression were used to analyze the factors influencing HBsAg clearance at 48 weeks of sequential combination therapy. Univariate and multifactorial COX proportional hazard models were used to analyze the factors influencing HBsAg clearance following 96 weeks of prolonged PEG-IFN-α treatment. The receiver operating characteristic curve was used to assess the predictive value of factors influencing HBsAg clearance. A Mann-Whitney U test was used to compare the measurement data between groups. The count data was compared using the χ2 test between groups. Results:41 (28.47%) cases achieved HBsAg clearance at 48 weeks of sequential combination therapy. The HBsAg clearance rate at 96 weeks was 40.28% (58/144) by intention-to-treat analysis. The Kaplan-Meier method computed that the cumulative HBsAg clearance rate at 96 weeks was 68.90%. Multivariate logistic regression analysis showed that HBsAg quantification at baseline ( OR = 0.090, 95% CI: 0.034-0.240, P < 0.001) and a 24-week drop in HBsAg level ( OR = 7.788, 95% CI: 3.408-17.798, P < 0.001) were independent predictors of HBsAg clearance in CHB patients treated sequentially in combination with NAs and Peg-IFN-α for 48 weeks. Receiver operating characteristic curve analysis showed that the baseline HBsAg quantification [area under the receiver operating characteristic curve (AUC), 0.911, 95% CI: 0.852-0.952)] and 24-week drop in HBsAg level (AUC = 0.881, 95% CI: 0.814-0.930) had equally good predictive value for 48-week HBsAg clearance, but there was no statistically significant difference between the two ( Z = 0.638, P = 0.523). The value of the combination of baseline HBsAg quantification and 24-week drop in HBsAg level (AUC = 0.981, 95% CI: 0.941-0.997) was superior to that of single baseline HBsAg quantification ( Z = 3.017, P = 0.003) and 24-week drop in HBsAg level ( Z = 3.214, P = 0.001) in predicting HBsAg clearance rate at 48 weeks. Multivariate COX proportional hazards model analysis showed that HBsAg quantification at 48 weeks ( HR = 0.364, 95% CI: 0.176-0.752, P = 0.006) was an independent predictor of HBsAg clearance with a prolonged course to 96 weeks of Peg-IFN-α treatment. Conclusion:The HBsAg clearance rate can be accurately predicted with baseline HBsAg quantification combined with a 24-week drop in HBsAg level in patients with CHB who are treated with a sequential combination of NAs and Peg-IFN-α therapy for 48 weeks. Prolonging the course of Peg-IFN-α treatment can enhance the HBsAg clearance rate's capability. An independent predictor of HBsAg clearance is HBsAg quantification at 48 weeks of sequential combination therapy with a prolonged course of 96 weeks of Peg-IFN-α treatment.

Objective:To systematically review the association between delivery mode and exclusive breastfeeding rate during hospitalization and within the first six months of life.Methods:Observational studies on the association between delivery mode and feeding pattern were searched from PubMed, Web of Science, Cochrane Library, EBSCO, China Biomedical Literature Database, CNKI, Wanfang Database, and VIP Database from inception to October 2022. Two independent reviewers screened the literature, extracted data, and assessed the quality of included studies using Critical Appraisal Tools published by Joanna Briggs Institute or Newcastle-Ottawa Quality Scale (NOS). This meta-analysis was performed using R 4.1.0 software. Fixed-effect or random-effect models were used to pool data. Egger test and funnel plot were used to assess publication bias.Results:A total of 34 studies involving 597 203 subjects were included, including 22 cross-sectional studies and 12 cohort studies. All of the 22 cross-sectional studies were B-level quality, and eleven out of the 12 included cohort studies scored 7 points or above on the NOS scale with high quality. The results of meta-analysis showed that the likelihood of exclusive breastfeeding during hospitalization of women who had cesarean section was lower than those who delivered vaginally ( OR=0.33, 95% CI: 0.22-0.50, P<0.001); and so was the likelihood of exclusive breastfeeding at six months postpartum ( OR=0.61, 95% CI: 0.47-0.79, P<0.001). Conclusion:Current evidence suggests that cesarean section is a disadvantage to exclusive breastfeeding during hospitalization and within six months after delivery.