Objective:To construct a classification management model on the basis of Kraljic matrix for electrocardiogram(ECG)monitoring equipment in emergency intensive care unit(ICU),so as to explore its application value in the management for ECG monitoring equipment in emergency ICU.Methods:The classification management model on the basis of index for ECG monitoring equipment in emergency ICU was constructed.According to two classification dimensions included the market supply risk and the self-value,an indicator system of classification management,which aimed at strategic materials with high value and high risk,leverage materials with high value and low risk,bottleneck materials with low value and high risk,and conventional materials with low value and low risk,was constructed.A total of fifty-one ECG monitoring equipment in the emergency ICU of The People's Hospital of Longhua of Shenzhen from January to December 2023 were selected,and they were managed respectively by conventional management mode(25 sets)and classification management mode(26 sets)according to different management modes.The standardization level of operation management for equipment,the occurrence of safety risk and the level of management for equipment of the two management modes were compared,and the satisfaction of 30 relative personnel,who used and managed these equipment,for classification management of equipment also were compared.Results:The average values of the percentage of standardization level of normality of equipment operation,disinfection and sterilization,maintenance and fault repair of using classification management mode were respectively(91.58±4.33)%,(92.1±3.28)%,(91.49±3.54)%and(92.58±3.32)%,all of which were higher than those of conventional management mode,and the differences were statistically significant(t=12.537,15.706,14.196,18.946,P<0.05),repsectively.The average incidences of the risk of pressure injury,electrical injury and body fluid extravasation of adopting classification management mode were respectively(2.54±0.87)%,(3.02±0.82)%and(1.29±0.65)%,all of which were lower than those of adopting conventional management mode,and the differences were statistically significant(t=22.825,17.453,24.424,P<0.05),respectively.The satisfaction scores of 30 relative management personnel,who used equipment on the process rationality,system standardization and quality effectiveness,of adopting classification management mode were respectively(94.26±3.54),(92.57±4.36)and(91.87±3.69),all of which were higher than those of conventional management mode,and the differences were statistically significant(t=14.052,13.991,13.551,P<0.05),respectively.The reasonable placement rate,recording rate of standardization,and intact rate of equipment in the 26 equipment by adopting classification management mode were respectively 92.31%,92.31%and 88.46%,all of which were significantly higher than those by adopting conventional management mode,and the differences were statistical significant(x2=12.052,10.398,11.338,P<0.05).Conclusion:The classification management model of ECG monitoring equipment in emergency ICU can increase the management efficiency for the equipment in operating room of hospital,and improve the operation quality of equipment,and enhance the safety of equipment in clinical use,and the standardization of operation management for equipment.

Objective To explore the effect of Lokomat robotic-assisted gait training on lower limb motor function in children with hemiplegia.Methods From October,2023 to January,2025,a total of 52 children with hemiplegia admitted to Beijing Bo'ai Hospital were randomly divided into control group(n=26)and observation group(n=26).Both groups received conven-tional rehabilitation therapy,while the observation group additionally received Lokomat robotic-assisted gait training,for four weeks.Before and after intervention,the self-selected walking speed(SWS)and maximum walking speed(MWS)of 10-meter Walk Test,6-minute walking distance(6MWD),Physiological Cost Index(PCI),as well as gait line length asymmetry ratio,single support line asymmetry ratio,stance phase asymmetry ratio and step length ratio were compared.Results After intervention,SWS,MWS and 6MWD improved in both groups(|Z|>2.910,P<0.01),and were better in the the observation group than in the control group(|Z|>2.069,P<0.05);PCI significantly decreased in both groups(|Z|>4.458,P<0.001),and was lower in the observation group than in the control group(Z=-2.435,P<0.05);the gait line length asymmetry ratio,single support line asymmetry ratio and stance phase asymmetry ratio improved in both groups(Z=3.398,|t|>2.211,P<0.05),and were better in the observation group than in the control group(Z=2.802,|t|>2.107,P<0.05).Conclusion Lokomat robotic-assisted gait training can effectively improve walking speed and endurance in children with hemiplegia,reduce energy expenditure,enhance walking efficiency,and promote gait symmetry,thereby fa-cilitating symmetrical gait patterns.

Circadian rhythm is an endogenous biological clock mechanism that enables organisms to adapt to the earth’s alternation of day and night. It plays a fundamental role in regulating physiological functions and behavioral patterns, such as sleep, feeding, hormone levels and body temperature. By aligning these processes with environmental changes, circadian rhythm plays a pivotal role in maintaining homeostasis and promoting optimal health. However, modern lifestyles, characterized by irregular work schedules and pervasive exposure to artificial light, have disrupted these rhythms for many individuals. Such disruptions have been linked to a variety of health problems, including sleep disorders, metabolic syndromes, cardiovascular diseases, and immune dysfunction, underscoring the critical role of circadian rhythm in human health. Among the numerous systems influenced by circadian rhythm, the skin—a multifunctional organ and the largest by surface area—is particularly noteworthy. As the body’s first line of defense against environmental insults such as UV radiation, pollutants, and pathogens, the skin is highly affected by changes in circadian rhythm. Circadian rhythm regulates multiple skin-related processes, including cyclic changes in cell proliferation, differentiation, and apoptosis, as well as DNA repair mechanisms and antioxidant defenses. For instance, studies have shown that keratinocyte proliferation peaks during the night, coinciding with reduced environmental stress, while DNA repair mechanisms are most active during the day to counteract UV-induced damage. This temporal coordination highlights the critical role of circadian rhythms in preserving skin integrity and function. Beyond maintaining homeostasis, circadian rhythm is also pivotal in the skin’s repair and regeneration processes following injury. Skin regeneration is a complex, multi-stage process involving hemostasis, inflammation, proliferation, and remodeling, all of which are influenced by circadian regulation. Key cellular activities, such as fibroblast migration, keratinocyte activation, and extracellular matrix remodeling, are modulated by the circadian clock, ensuring that repair processes occur with optimal efficiency. Additionally, circadian rhythm regulates the secretion of cytokines and growth factors, which are critical for coordinating cellular communication and orchestrating tissue regeneration. Disruptions to these rhythms can impair the repair process, leading to delayed wound healing, increased scarring, or chronic inflammatory conditions. The aim of this review is to synthesize recent information on the interactions between circadian rhythms and skin physiology, with a particular focus on skin tissue repair and regeneration. Molecular mechanisms of circadian regulation in skin cells, including the role of core clock genes such as Clock, Bmal1, Per and Cry. These genes control the expression of downstream effectors involved in cell cycle regulation, DNA repair, oxidative stress response and inflammatory pathways. By understanding how these mechanisms operate in healthy and diseased states, we can discover new insights into the temporal dynamics of skin regeneration. In addition, by exploring the therapeutic potential of circadian biology in enhancing skin repair and regeneration, strategies such as topical medications that can be applied in a time-limited manner, phototherapy that is synchronized with circadian rhythms, and pharmacological modulation of clock genes are expected to optimize clinical outcomes. Interventions based on the skin’s natural rhythms can provide a personalized and efficient approach to promote skin regeneration and recovery. This review not only introduces the important role of circadian rhythms in skin biology, but also provides a new idea for future innovative therapies and regenerative medicine based on circadian rhythms.

To analyze the epidemiological characteristics of COVID-19 in Fujian Province from the 49th week of 2022 to the 5th week of 2023,after further optimization of China's COVID-19 prevention and control measures on December 7,2022(the 49th week of 2022),this study used multi-dimensional surveillance data to dynamically assess population infection levels and their changing trends.The aim of the study was to provide a scientific basis for early warning of epidemic risk,medical resource allocation,and evalu-ation of socio-economic impact.A multi-source data surveillance system was constructed,encompassing surveillance of fever clinics at medical institutions(weekly collection of visits,positive nucleic acid and antigen test results,inpatients,and severe cases in sec-ondary or above hospitals),population nucleic acid test monitoring(weekly person-times and positivity rates of single-tube tests from the provincial system),sentinel hospital monitoring(weekly proportion of influenza-like illness visits at 18 sentinel hospitals and re-lated viral testing data),and monitoring of novel coronavirus variants(weekly systematic collection of genomic sequences of local and imported cases).Line charts were plotted weekly,and time series analysis,molecular epidemiological methods,and an improved SEIAR model were used to simulate epidemic spread.During the study period,the COVID-19 epidemic in Fujian Province exhibited three distinct stages.In the infection peak stage(52nd week of 2022),the provincial fever clinic visits reached 606 893 person-times,and a 49.2%positivity rate in population single-tube nucleic acid tests and 63.8%positivity rate in sentinel hospital monitoring were observed.In the medical load peak stage(2nd week of 2023),274 460 inpatients and 28 487 severe cases were recorded.In the epidemic decline stage(4th to 5th weeks of 2023),fever clinic visits decreased by 96.3%with respect to the peak,the single-tube nucleic acid test positivity rate decreased to 6.3%,and the sentinel hospital COVID-19 nucleic acid test positivity rate was 6.4%.All 508 sequenced local cases were Omicron variants,predominantly BA.5.2 and its sub-lineages(67.4%).Among 56 imported se-quenced cases,BA.5.2 and its sub-lineages accounted for 50.0%,and 16.1%comprised nine variants of interest including XBB and BQ.The model predicted the infection peak in the 52nd week of 2022,whereas the hospitalization peak lagged by approximately 10.6 days.Multi-source data monitoring revealed a three-stage development of the COVID-19 epidemic in Fujian.The BA.5.2 strain was dominant during the epidemic.The combination of multi-source monitoring data and modeling provides important references for epi-demic prevention and control,and highlights the need to improve the monitoring system in follow-up.

Objective To evaluate answers to typical clinical questions related to neuro-ophthalmology generated by Artificial Intelligence(AI)Large Language Models(LLM)and to explore the performance of neuro-ophthalmology-related questions on LLM in a multidimensional manner using objective and expert assessment.Methods Multicenter,random-ized,cross-sectional pilot study.Thirty typical questions related to neuro-ophthalmology were selected based on four per-spectives:definition,etiology,clinical manifestations and signs,and treatment and prognosis,and were analyzed quantita-tively using Deepseek,Wenxin Yiyin 4.0,Doubao,and Kimi 1.5,which are four open-source LLMs in China,and quantita-tively analyzed with objective assessment;and quantitatively rated by three ophthalmologists using expert assessment for 120 answer texts.Three ophthalmology experts quantitatively scored the 120 answer texts.Three ophthalmologists quantita-tively scored the 120 answer texts.Level 3,5,and 4 Likert scales were developed according to the completeness,accura-cy,professionalism,relevance,and criticality of the question texts,respectively.The best-performing LLM was selected,and its performance was observed across the four types of questions.Additionally,three other experts assessed whether the best-performing one could be evaluated as a substitute for real-world doctor-patient communication.Results In the objective Chinese text reading difficulty analysis,the differences in total word count among the four LLMs were statistically significant(all P＜0.001).Of the four LLMs,Kimi 1.5 performed the best,with frequencies of 61％,29％,and 41％for the highest scores in completeness(3),accuracy and professionalism(5),and relevance and usefulness(4),respective-ly.Kimi 1.5 performed more consistently on the questions on the four areas of neuro-ophthalmologic disorders:definition,etiology,clinical manifestations and signs,treatment,and prognosis,with no between-group differences(P＞0.05).Con-clusion Chinese language LLMs have great potential in the clinical application of neuro-ophthalmology.Kimi 1.5 outper-forms other LLMs in terms of completeness,accuracy,professionalism,relevance,and usefulness,but it still cannot re-place real-world doctor-patient communication.There is a need to explore new diagnostic and therapeutic model of AI+physician in the future.

To analyze the epidemiological characteristics of COVID-19 in Fujian Province from the 49th week of 2022 to the 5th week of 2023,after further optimization of China's COVID-19 prevention and control measures on December 7,2022(the 49th week of 2022),this study used multi-dimensional surveillance data to dynamically assess population infection levels and their changing trends.The aim of the study was to provide a scientific basis for early warning of epidemic risk,medical resource allocation,and evalu-ation of socio-economic impact.A multi-source data surveillance system was constructed,encompassing surveillance of fever clinics at medical institutions(weekly collection of visits,positive nucleic acid and antigen test results,inpatients,and severe cases in sec-ondary or above hospitals),population nucleic acid test monitoring(weekly person-times and positivity rates of single-tube tests from the provincial system),sentinel hospital monitoring(weekly proportion of influenza-like illness visits at 18 sentinel hospitals and re-lated viral testing data),and monitoring of novel coronavirus variants(weekly systematic collection of genomic sequences of local and imported cases).Line charts were plotted weekly,and time series analysis,molecular epidemiological methods,and an improved SEIAR model were used to simulate epidemic spread.During the study period,the COVID-19 epidemic in Fujian Province exhibited three distinct stages.In the infection peak stage(52nd week of 2022),the provincial fever clinic visits reached 606 893 person-times,and a 49.2%positivity rate in population single-tube nucleic acid tests and 63.8%positivity rate in sentinel hospital monitoring were observed.In the medical load peak stage(2nd week of 2023),274 460 inpatients and 28 487 severe cases were recorded.In the epidemic decline stage(4th to 5th weeks of 2023),fever clinic visits decreased by 96.3%with respect to the peak,the single-tube nucleic acid test positivity rate decreased to 6.3%,and the sentinel hospital COVID-19 nucleic acid test positivity rate was 6.4%.All 508 sequenced local cases were Omicron variants,predominantly BA.5.2 and its sub-lineages(67.4%).Among 56 imported se-quenced cases,BA.5.2 and its sub-lineages accounted for 50.0%,and 16.1%comprised nine variants of interest including XBB and BQ.The model predicted the infection peak in the 52nd week of 2022,whereas the hospitalization peak lagged by approximately 10.6 days.Multi-source data monitoring revealed a three-stage development of the COVID-19 epidemic in Fujian.The BA.5.2 strain was dominant during the epidemic.The combination of multi-source monitoring data and modeling provides important references for epi-demic prevention and control,and highlights the need to improve the monitoring system in follow-up.

Objective To explore the effect of Lokomat robotic-assisted gait training on lower limb motor function in children with hemiplegia.Methods From October,2023 to January,2025,a total of 52 children with hemiplegia admitted to Beijing Bo'ai Hospital were randomly divided into control group(n=26)and observation group(n=26).Both groups received conven-tional rehabilitation therapy,while the observation group additionally received Lokomat robotic-assisted gait training,for four weeks.Before and after intervention,the self-selected walking speed(SWS)and maximum walking speed(MWS)of 10-meter Walk Test,6-minute walking distance(6MWD),Physiological Cost Index(PCI),as well as gait line length asymmetry ratio,single support line asymmetry ratio,stance phase asymmetry ratio and step length ratio were compared.Results After intervention,SWS,MWS and 6MWD improved in both groups(|Z|>2.910,P<0.01),and were better in the the observation group than in the control group(|Z|>2.069,P<0.05);PCI significantly decreased in both groups(|Z|>4.458,P<0.001),and was lower in the observation group than in the control group(Z=-2.435,P<0.05);the gait line length asymmetry ratio,single support line asymmetry ratio and stance phase asymmetry ratio improved in both groups(Z=3.398,|t|>2.211,P<0.05),and were better in the observation group than in the control group(Z=2.802,|t|>2.107,P<0.05).Conclusion Lokomat robotic-assisted gait training can effectively improve walking speed and endurance in children with hemiplegia,reduce energy expenditure,enhance walking efficiency,and promote gait symmetry,thereby fa-cilitating symmetrical gait patterns.

Objective To evaluate answers to typical clinical questions related to neuro-ophthalmology generated by Artificial Intelligence(AI)Large Language Models(LLM)and to explore the performance of neuro-ophthalmology-related questions on LLM in a multidimensional manner using objective and expert assessment.Methods Multicenter,random-ized,cross-sectional pilot study.Thirty typical questions related to neuro-ophthalmology were selected based on four per-spectives:definition,etiology,clinical manifestations and signs,and treatment and prognosis,and were analyzed quantita-tively using Deepseek,Wenxin Yiyin 4.0,Doubao,and Kimi 1.5,which are four open-source LLMs in China,and quantita-tively analyzed with objective assessment;and quantitatively rated by three ophthalmologists using expert assessment for 120 answer texts.Three ophthalmology experts quantitatively scored the 120 answer texts.Three ophthalmologists quantita-tively scored the 120 answer texts.Level 3,5,and 4 Likert scales were developed according to the completeness,accura-cy,professionalism,relevance,and criticality of the question texts,respectively.The best-performing LLM was selected,and its performance was observed across the four types of questions.Additionally,three other experts assessed whether the best-performing one could be evaluated as a substitute for real-world doctor-patient communication.Results In the objective Chinese text reading difficulty analysis,the differences in total word count among the four LLMs were statistically significant(all P＜0.001).Of the four LLMs,Kimi 1.5 performed the best,with frequencies of 61％,29％,and 41％for the highest scores in completeness(3),accuracy and professionalism(5),and relevance and usefulness(4),respective-ly.Kimi 1.5 performed more consistently on the questions on the four areas of neuro-ophthalmologic disorders:definition,etiology,clinical manifestations and signs,treatment,and prognosis,with no between-group differences(P＞0.05).Con-clusion Chinese language LLMs have great potential in the clinical application of neuro-ophthalmology.Kimi 1.5 outper-forms other LLMs in terms of completeness,accuracy,professionalism,relevance,and usefulness,but it still cannot re-place real-world doctor-patient communication.There is a need to explore new diagnostic and therapeutic model of AI+physician in the future.

Objective:To construct a classification management model on the basis of Kraljic matrix for electrocardiogram(ECG)monitoring equipment in emergency intensive care unit(ICU),so as to explore its application value in the management for ECG monitoring equipment in emergency ICU.Methods:The classification management model on the basis of index for ECG monitoring equipment in emergency ICU was constructed.According to two classification dimensions included the market supply risk and the self-value,an indicator system of classification management,which aimed at strategic materials with high value and high risk,leverage materials with high value and low risk,bottleneck materials with low value and high risk,and conventional materials with low value and low risk,was constructed.A total of fifty-one ECG monitoring equipment in the emergency ICU of The People's Hospital of Longhua of Shenzhen from January to December 2023 were selected,and they were managed respectively by conventional management mode(25 sets)and classification management mode(26 sets)according to different management modes.The standardization level of operation management for equipment,the occurrence of safety risk and the level of management for equipment of the two management modes were compared,and the satisfaction of 30 relative personnel,who used and managed these equipment,for classification management of equipment also were compared.Results:The average values of the percentage of standardization level of normality of equipment operation,disinfection and sterilization,maintenance and fault repair of using classification management mode were respectively(91.58±4.33)%,(92.1±3.28)%,(91.49±3.54)%and(92.58±3.32)%,all of which were higher than those of conventional management mode,and the differences were statistically significant(t=12.537,15.706,14.196,18.946,P<0.05),repsectively.The average incidences of the risk of pressure injury,electrical injury and body fluid extravasation of adopting classification management mode were respectively(2.54±0.87)%,(3.02±0.82)%and(1.29±0.65)%,all of which were lower than those of adopting conventional management mode,and the differences were statistically significant(t=22.825,17.453,24.424,P<0.05),respectively.The satisfaction scores of 30 relative management personnel,who used equipment on the process rationality,system standardization and quality effectiveness,of adopting classification management mode were respectively(94.26±3.54),(92.57±4.36)and(91.87±3.69),all of which were higher than those of conventional management mode,and the differences were statistically significant(t=14.052,13.991,13.551,P<0.05),respectively.The reasonable placement rate,recording rate of standardization,and intact rate of equipment in the 26 equipment by adopting classification management mode were respectively 92.31%,92.31%and 88.46%,all of which were significantly higher than those by adopting conventional management mode,and the differences were statistical significant(x2=12.052,10.398,11.338,P<0.05).Conclusion:The classification management model of ECG monitoring equipment in emergency ICU can increase the management efficiency for the equipment in operating room of hospital,and improve the operation quality of equipment,and enhance the safety of equipment in clinical use,and the standardization of operation management for equipment.