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.

AIM: To investigate differences in ocular biological parameters and refractive status among children with unilateral congenital ptosis.METHODS:This was a retrospective cohort study enrolling patients with unilateral congenital ptosis in the Department of Ophthalmology, Xi'an Children's Hospital between August 2018 and March 2025, with the contralateral healthy eyes serving as the control group. All patients underwent a detailed ophthalmic examination to assess the degree of ptosis and evaluate refractive errors. Biometric measurements were performed using the IOL Master 700 biometer, including axial length(AL), central corneal thickness(CCT), anterior chamber depth(ACD), lens thickness(LT), and astigmatism(AST).RESULTS:A total of 100 eyes from 100 children(affected eye group)with unilateral congenital ptosis were enrolled, and their 100 contralateral normal eyes without ptosis were set as the control group(healthy eyes group). Among the subjects, there were 67 males(67%)and 33 females(33%), with a mean age of 6.43±2.47 y; 45 affected eyes were the right eye and 55 were the left eye. Among the 100 patients, mild, moderate, and severe ptosis accounted for 7%, 21%, and 72%, respectively. The incidence of amblyopia in patients with unilateral congenital ptosis was 32%, all of which occurred in the affected eye. Compared with the healthy eye group, K2(total)and K2(severe ptosis)in eyes with severe ptosis were significantly lower(P<0.05), indicating that the cornea in the ptotic eye was flatter. In patients with moderate ptosis, the affected eye had a significantly greater corneal thickness(CCT)compared to the healthy eye(P<0.05), indicating that the corneas of eyes with moderate ptosis were thicker. The incidence of against-the-rule and oblique astigmatism in the affected eye group(39%)was significantly higher than that in the healthy eye group(14%); there was a significant difference in the corneal astigmatism axis between the affected and healthy eye groups(P<0.01). There were no differences in refractive status or biological parameters such as AL, K1, AST, ACD, and LT between the affected and healthy eyes.CONCLUSION:Congenital ptosis can affect refractive errors, lead to oblique and against-the-rule astigmatism, increase the prevalence of amblyopia, and also result in corneal thickening and flattening. However, it has minimal effects on AL, LT, and ACD.

Lung cancer is the malignant tumor with the highest incidence and mortality rates globally. Current treatment methods for lung cancer primarily include surgery， chemotherapy， targeted therapy， and immunotherapy. However， the main limitations of these treatments are their side effects， the drug resistance， and the economic burden they impose. As a critical cancer pathway， the Janus kinase （JAK）/signal transducer and activator of transcription （STAT） signaling pathway regulates tumor occurrence and development through multiple mechanisms by influencing various downstream targets. Consequently， the JAK/STAT signaling pathway offers a promising avenue for lung cancer treatment research. Numerous studies have demonstrated that the JAK/STAT signaling pathway plays a key role in the proliferation and growth of lung cancer cells， angiogenesis， epithelial-mesenchymal transition （EMT）， metabolic alterations， remodeling of the immune microenvironment， and the development of treatment resistance. Traditional Chinese medicine （TCM） has garnered increasing attention due to its minimal side effects， low economic burden， and its potential to enhance efficacy and reduce toxicity when used in conjunction with Western medicine. In addition to traditional Chinese medicine compounds， a growing number of Chinese medicine monomers have come into the spotlight because of their more targeted effects. Numerous studies investigating the regulation of the JAK/STAT signaling pathway by TCM in the treatment of lung cancer have demonstrated that TCM can inhibit the proliferation and invasion of lung cancer cells， tumor angiogenesis， and EMT， improve the inflammatory and immunosuppressive microenvironments， and enhance treatment sensitivity by intervening in the JAK/STAT signaling pathway， thereby impeding the progression of lung cancer. In recent years， the research on the regulation of this pathway by TCM in the treatment of lung cancer has been updated rapidly. However， the summary of these studies has not been updated in time. This review summarizes and reflects on the recent research findings regarding the regulation of the JAK/STAT signaling pathway by TCM to intervene in lung cancer from three aspects， introducing the JAK/STAT pathway， elaborating the mechanism of this pathway in lung cancer， and exploring the intervention of TCM in the treatment of lung cancer through this pathway， to provide more reference for the treatment of lung cancer in the future.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Background: MicroRNA (miRNA) plays a crucial role in neuropathic pain (NP) by targeting mRNAs. This study aims to analyze the regulatory function and mechanism of miR-382-5p/dual specificity phosphatase-1 (DUSP1) axis in NP. Methods: We utilized rats with chronic constriction injury (CCI) of the sciatic nerve as the NP model. The levels of miR-382-5p and DUSP1 were reduced by intrathecal injection of lentiviral interference vectors targeting miR-382-5p and DUSP1. The mRNA levels of miR-382-5p and DUSP1 in the dorsal root ganglions (DRGs) were measured by RT-qPCR assay. The pain behavior was evaluated by mechanical nociceptive sensitivity and thermal nociceptive sensitivity. The expression levels of interleukin-6 (IL)-6, IL-1β, and tumor necrosis factor-α in the DRGs were analyzed by ELISA assay. The targeting relationship between miR-382-5p and DUSP1 was verified by DLR assay and RIP assay. Results: Compared to the Sham group, the CCI rats exhibited higher levels of miR-382-5p and lower levels of DUSP1. Overexpression of miR-382-5p significantly decreased DUSP1 levels. Reducing miR-382-5p levels can lower the mechanical nociceptive sensitivity and thermal nociceptive sensitivity of CCI rats and inhibit the over-activation of pro-inflammatory factors. Reduced miR-382-5p levels decreased NP in CCI rats. DUSP1 is the target of miR-382-5p, and down-regulation of DUSP1 reverses the inhibitory effect of reduced miR-382-5p levels on NP. Conclusions Down-regulation of miR-382-5p inhibits the over-activation of pro-inflammatory factors by targeting and regulating the expression of DUPS1, thereby alleviating NP.

Background: MicroRNA (miRNA) plays a crucial role in neuropathic pain (NP) by targeting mRNAs. This study aims to analyze the regulatory function and mechanism of miR-382-5p/dual specificity phosphatase-1 (DUSP1) axis in NP. Methods: We utilized rats with chronic constriction injury (CCI) of the sciatic nerve as the NP model. The levels of miR-382-5p and DUSP1 were reduced by intrathecal injection of lentiviral interference vectors targeting miR-382-5p and DUSP1. The mRNA levels of miR-382-5p and DUSP1 in the dorsal root ganglions (DRGs) were measured by RT-qPCR assay. The pain behavior was evaluated by mechanical nociceptive sensitivity and thermal nociceptive sensitivity. The expression levels of interleukin-6 (IL)-6, IL-1β, and tumor necrosis factor-α in the DRGs were analyzed by ELISA assay. The targeting relationship between miR-382-5p and DUSP1 was verified by DLR assay and RIP assay. Results: Compared to the Sham group, the CCI rats exhibited higher levels of miR-382-5p and lower levels of DUSP1. Overexpression of miR-382-5p significantly decreased DUSP1 levels. Reducing miR-382-5p levels can lower the mechanical nociceptive sensitivity and thermal nociceptive sensitivity of CCI rats and inhibit the over-activation of pro-inflammatory factors. Reduced miR-382-5p levels decreased NP in CCI rats. DUSP1 is the target of miR-382-5p, and down-regulation of DUSP1 reverses the inhibitory effect of reduced miR-382-5p levels on NP. Conclusions Down-regulation of miR-382-5p inhibits the over-activation of pro-inflammatory factors by targeting and regulating the expression of DUPS1, thereby alleviating NP.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Methicillin-anti staphylococcus aureus(MRSA) is one of the common pathogenic bacteria in hospital infection. Many asymptomatic MRSA carriers have been found in clinical practice, which can not only transmit the strain to others, but also cause secondary infection due to their own reasons. Decolonization measures can reduce the number of MRSA colonizers, thereby reducing the risk of endogenous infection and secondary transmission. Early identification is the first step to prevent transmission and secondary infection, which requires high accuracy and sensitivity of detection methods. Xpert MRSA/SA assay (Cepheid, Sunnyvale, CA, USA) may be a better choice, which can shorten the time of traditional methods, and has high specificity and sensitivity. Unlike other rapid detection methods, the Xpert MRSA/SA assay may be more suitable for MRSA colonisation detection.

Background: MicroRNA (miRNA) plays a crucial role in neuropathic pain (NP) by targeting mRNAs. This study aims to analyze the regulatory function and mechanism of miR-382-5p/dual specificity phosphatase-1 (DUSP1) axis in NP. Methods: We utilized rats with chronic constriction injury (CCI) of the sciatic nerve as the NP model. The levels of miR-382-5p and DUSP1 were reduced by intrathecal injection of lentiviral interference vectors targeting miR-382-5p and DUSP1. The mRNA levels of miR-382-5p and DUSP1 in the dorsal root ganglions (DRGs) were measured by RT-qPCR assay. The pain behavior was evaluated by mechanical nociceptive sensitivity and thermal nociceptive sensitivity. The expression levels of interleukin-6 (IL)-6, IL-1β, and tumor necrosis factor-α in the DRGs were analyzed by ELISA assay. The targeting relationship between miR-382-5p and DUSP1 was verified by DLR assay and RIP assay. Results: Compared to the Sham group, the CCI rats exhibited higher levels of miR-382-5p and lower levels of DUSP1. Overexpression of miR-382-5p significantly decreased DUSP1 levels. Reducing miR-382-5p levels can lower the mechanical nociceptive sensitivity and thermal nociceptive sensitivity of CCI rats and inhibit the over-activation of pro-inflammatory factors. Reduced miR-382-5p levels decreased NP in CCI rats. DUSP1 is the target of miR-382-5p, and down-regulation of DUSP1 reverses the inhibitory effect of reduced miR-382-5p levels on NP. Conclusions Down-regulation of miR-382-5p inhibits the over-activation of pro-inflammatory factors by targeting and regulating the expression of DUPS1, thereby alleviating NP.