OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.

Alzheimer disease(AD)is a neurological disease characterized by cognitive decline.AD continuum refers to the dynamic development progression from early pathological changes to obvious clinical symptoms,covering the continuous spectrum of subjective cognition decline(SCD)stage,mild cognitive impairment(MCI)stage and dementia stage.As one of the key brain regions involved in early stage,hippocampus(HP)in AD continuum is closely related to the progression of disease.MRI has been widely used in the study of HP in the AD continuum.The progresses in structural and functional MRI research of HP changes in AD continuum were reviewed in this article.

Objective To explore how digital and intelligent technologies can optimize the storage allocation and picking paths for Traditional Chinese medicine decoction pieces.Methods Based on prescription information and medicine data from the hospital information system(HIS),this study employs MATLAB programming to use an integer linear programming(ILP)algorithm for optimizing the storage allocation of Traditional Chinese medicine decoction pieces.Additionally,a greedy algorithm is applied to optimize the picking paths to reduce the picking distance for pharmacists.Finally,an independent samples paired t-test is used to analyze the experimental data to verify the significance of the optimizations.Results A total of 7 734 prescriptions were collected from the HIS.The results of storage optimization showed that the total distance before optimization was(87.58±0.29)m,which was reduced to(85.35±0.28)m after optimization(P﹤0.000 1).The picking path optimization results showed that the picking path was(85.35±0.28)m before optimization and(40.06±0.11)m after optimization(P﹤0.000 1).The results of the independent samples paired t-test indicate that the path was significantly reduced after both storage and picking path optimizations,with statistical significance.Conclusion By using digital and intelligent methods,informatics pharmacists can effectively shorten the picking paths for Traditional Chinese medicine decoction pieces,improve dispensing efficiency,and reduce patient waiting times.

The aim of this study is to establish a rapid,efficient,and sensitive method for detecting the infectious laryngotracheitis virus(ILTV).The DNA of ILTV was extracted and used as a tem-plate to develop a recombinant enzyme-mediated isothermal amplification(RAA)fluorescence de-tection method for ILTV through optimization of conditions,sensitivity analysis,and repeatability assessment.Additionally,the nucleic acids of avian influenza virus(AIV),IBV,and Newcastle dis-ease virus(NDV)were detected to verify the specificity of this method.Finally,this method was applied to analyze 59 clinical samples collected from multiple large-scale chicken farms in Hebei Province,and the results were compared with those obtained from real-time fluorescence quantifi-cation(qPCR)and PCR methods according to national standards.The results showed that the RAA detection method established in this study had a reaction system of 25.0 μL buffer,2.1 μL primer,0.6 μL probe,5.0 μL magnesium acetate,and 5.0 μL template.The reaction temperature was 39 ℃ and the amplification time was within 20 minutes.The sensitivity of this method was 101 copies/μL,and the specificity detection was 100％.Testing of 59 clinical samples showed that 17 were detected positive by both RAA fluorescence and qPCR,and 12 were detected by PCR,and the detection rate of RAA(fluorescence)was consistent with real-time fluorescence quantification and qPCR,which was higher than that of the PCR assay.The research results indicate that the RAA fluorescence method has a short detection time,good specificity and sensitivity,and can be used for rapid detection of ILTV.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To establish an evaluation model for the radon reduction effect of activated carbon adsorption in localized underground spaces, to guide the rational application of the activated carbon adsorption method for radon reduction in localized underground spaces.Methods:For both intermittent and continuous adsorption-based radon reduction method in localized underground spaces, a theoretical model was constructed for evaluating the of radon reduction effec. By means of this modle, the influence factors on the radon reduction effect were analyzed such as radon concentration, space volume, and air exchange rate with the external environment. Experimental validation of the theoretical model was conducted under typical conditions.Results:The radon exhalation rate from concrete surface in localized underground spaces was inversely linear relationship with the indoor radon concentration. However, the slope of this relationship was very small: when the radon concentration decreased from 2 018 Bq/m 3 to 0, the exhalation rate only increased slightly from 4.20 to 4.46 Bq·m -2·h -1, indicating a minimal change. At the same air exchange rate and in the same space volume, the initial radon concentration had little impact on the adsorption-based radon reduction effect, thus suggesting that the radon generation rate from the enclosure surface could be negligible in the evaluation. In contrast, the adsorption flow rate of a radon reduction device and the air exchange rate between the localized spaces and surrounding environment had significant fluence on radon reduction effect. In the same sealed space and at the same adsorption flow rate, the difference in effect between intermittent and continuous adsorption method was of insignificance. However, in localized spaces with connectivity to surrounding environment, the continuous adsorption significantly outperforms intermittent adsorption, achieving faster radon reduction and better suitability for larger spaces. Simulated experiments validated that the theoretical model for evaluating adsorption-based radon reduction effect was reliable. Conclusions:The research findings provide theoretical guidance on selecting appropriate adsorption-based radon reduction devices for localized underground spaces of different volumes and varing connectivity conditions, in order to reduce the radon concentration within localized spaces to expected levels.

Objective To identify the measurement time,assessment tools,changing trends,factors in longitudinal studies on post stroke fatigue.Methods Web of Science,PubMed,CINAHL,Embase,CNKI,CBM,Wanfang,and VIP database were retrieved from inception until December 15,2024.Results A total of 41 papers were included.The measurement time points used with high frequency were 3,6,and 12 months after stroke.10 assessment tools were retrieved and the fatigue severity scale was most selected.The majority of the studies supported that the incidence of post stroke fatigue showed a decreasing trend within 6 months after stroke,an increasing trend from 6 to 12 months,and a decreasing or steady decreasing trend from 12 months.Post stroke fatigue is influenced by demographic,disease,physiologic,psychological,and coping factors.Conclusion The longitudinal studies on post stroke fatigue focused less than 1 year after stroke,but the recovery period is understudied and specific assessment tools need to be further explored.The trend of post stroke fatigue varies at different stages and exists heterogeneity.Future studies should be optimized to explore influence factors.

Alzheimer's disease(AD),a neurodegenerative disorder,manifests pathological changes in the brain even during the asymptomatic stage.As the pathological burden intensifies,patients experience functional decline in multiple cognitive domains,including memory,language,spatial perception,executive function,and calculation,and may also exhibit emotional abnormalities.Once AD progresses,treatment becomes extremely chal-lenging.Therefore,early diagnosis and accurate prediction of disease conversion are core tasks in the prevention and treatment of AD,and they are also urgent scientific research challenges to be overcome.Deep learning(DL)models demonstrate considerable advantages in the diagnosis,prediction,classification,and feature extraction of AD,offering new hope for solving this challenging problem.This research commences with a concise introduction to the outcomes of AD and the fundamental knowledge of deep learning.Subsequently,it offers an overview of the imaging studies on the utilization of deep learning for predicting disease transformation from two perspectives.Firstly,it systematically summarizes the existing DL models that have demonstrated innovation in the classification and prediction performance of AD.Secondly,it provides a comprehensive outline of the DL fusion models applied to the diagnosis,classification,and prediction of AD.Finally,this paper expounds upon the impending challenges in the research of this domain.This article demonstrates that deep learning models is cutting-edge trends in the ex-ploration of AD research.

Objective:To establish an evaluation model for the radon reduction effect of activated carbon adsorption in localized underground spaces, to guide the rational application of the activated carbon adsorption method for radon reduction in localized underground spaces.Methods:For both intermittent and continuous adsorption-based radon reduction method in localized underground spaces, a theoretical model was constructed for evaluating the of radon reduction effec. By means of this modle, the influence factors on the radon reduction effect were analyzed such as radon concentration, space volume, and air exchange rate with the external environment. Experimental validation of the theoretical model was conducted under typical conditions.Results:The radon exhalation rate from concrete surface in localized underground spaces was inversely linear relationship with the indoor radon concentration. However, the slope of this relationship was very small: when the radon concentration decreased from 2 018 Bq/m 3 to 0, the exhalation rate only increased slightly from 4.20 to 4.46 Bq·m -2·h -1, indicating a minimal change. At the same air exchange rate and in the same space volume, the initial radon concentration had little impact on the adsorption-based radon reduction effect, thus suggesting that the radon generation rate from the enclosure surface could be negligible in the evaluation. In contrast, the adsorption flow rate of a radon reduction device and the air exchange rate between the localized spaces and surrounding environment had significant fluence on radon reduction effect. In the same sealed space and at the same adsorption flow rate, the difference in effect between intermittent and continuous adsorption method was of insignificance. However, in localized spaces with connectivity to surrounding environment, the continuous adsorption significantly outperforms intermittent adsorption, achieving faster radon reduction and better suitability for larger spaces. Simulated experiments validated that the theoretical model for evaluating adsorption-based radon reduction effect was reliable. Conclusions:The research findings provide theoretical guidance on selecting appropriate adsorption-based radon reduction devices for localized underground spaces of different volumes and varing connectivity conditions, in order to reduce the radon concentration within localized spaces to expected levels.

Alzheimer's disease(AD),a neurodegenerative disorder,manifests pathological changes in the brain even during the asymptomatic stage.As the pathological burden intensifies,patients experience functional decline in multiple cognitive domains,including memory,language,spatial perception,executive function,and calculation,and may also exhibit emotional abnormalities.Once AD progresses,treatment becomes extremely chal-lenging.Therefore,early diagnosis and accurate prediction of disease conversion are core tasks in the prevention and treatment of AD,and they are also urgent scientific research challenges to be overcome.Deep learning(DL)models demonstrate considerable advantages in the diagnosis,prediction,classification,and feature extraction of AD,offering new hope for solving this challenging problem.This research commences with a concise introduction to the outcomes of AD and the fundamental knowledge of deep learning.Subsequently,it offers an overview of the imaging studies on the utilization of deep learning for predicting disease transformation from two perspectives.Firstly,it systematically summarizes the existing DL models that have demonstrated innovation in the classification and prediction performance of AD.Secondly,it provides a comprehensive outline of the DL fusion models applied to the diagnosis,classification,and prediction of AD.Finally,this paper expounds upon the impending challenges in the research of this domain.This article demonstrates that deep learning models is cutting-edge trends in the ex-ploration of AD research.