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.

Background Neck-related work-related musculoskeletal disorders (WMSDs) are a major type of musculoskeletal disorders with a relatively high proportion. Shanghai has a large number of occupational populations; however, the occurrence of WMSDs at neck among the occupational populations across industries in this city has not been reported, and needs to be addressed. Objective To understand the occurrence of neck-related WMSDs and their influencing factors among occupational populations in 8 industries in Shanghai, and to provide a scientific basis for the prevention and control of WMSDs in this population. Methods From February 2024 to February 2025, a cross-sectional survey employed stratified cluster sampling to select 7829 employed workers from 48 enterprises across 8 industries in Shanghai. The study investigated the occurrence of neck-related WMSDs among the participants over the preceding 12 months. Logistic regression analysis was employed to examine the relationships between WMSDs and various occupational factors. Results A total of 7770 questionnaires were collected, yielding an effective response rate of 99.2%. Overall, the positive rate of neck-related WMSDs among the participants was 11.89% (924/7770). Among different industries, the environmental sanitation industry (19.48%), pharmaceutical manufacturing industry (14.57%), and cigarette manufacturing industry (13.66%) had higher prevalence of neck-related WMSDs. Univariate analysis showed that the risk of neck-related WMSDs was higher in the following groups: those with a job tenure ≥5 years in the current position (OR=1.50–1.70), females (OR=1.77), those with higher education levels (master’s degree or above, OR=2.27), and those with worse self-rated health (very poor health, OR=4.38) (P＜0.05). Multivariate logistic regression analysis identified the following independent risk factors for neck WMSDs: environmental sanitation industry (OR=7.30, 95%CI: 4.72, 11.87), higher educational attainment (master’s degree or above: OR=2.16, 95%CI: 1.36, 3.35), worse self-rated health status (very poor: OR=1.95, 95%CI: 1.31, 2.85), work performed in a single workshop (OR=1.36, 95%CI: 1.13, 1.64), understaffing (OR=1.46, 95%CI: 1.25, 1.70), slight neck forward flexion (OR=1.64, 95%CI: 1.32, 2.06), severe neck forward flexion (OR=2.55, 95%CI: 1.97, 3.30), and maintaining a fixed neck posture for a prolonged period (OR=2.28, 95%CI: 1.93, 2.70). Conversely, work rotation (OR=0.75, 95%CI: 0.65, 0.87), adequate rest (OR=0.70, 95%CI: 0.60, 0.82), and severe back flexion (OR=0.63, 95%CI: 0.46, 0.85) were protective factors against neck-related WMSDs. For the nomogram, the area under the curve (AUC)=0.75 (95%CI: 0.74, 0.77), Hosmer-Lemeshow test P=0.764, and Brier score=0.094, indicating that the model had good discrimination and calibration. Conclusion Among the occupational populations in Shanghai, the environmental sanitation industry, pharmaceutical manufacturing industry, and cigarette manufacturing industry are identified as high-risk industries for neck-related WMSDs. Industry affiliation, educational level, work organization patterns, and awkward neck working postures are confirmed as the core influencing factors for the development of neck-related WMSDs. Targeted and comprehensive intervention measures should be formulated focusing on the aforementioned high-risk industries and key influencing factors. Multi-dimensional collaborative efforts, including ergonomic optimization, improvement of labor management systems, and strengthened health education and promotion, should be implemented to effectively control the risk of neck-related WMSDs in the occupational population, and to provide robust statistical evidence for the advancement of regional occupational health prevention and control strategies.

With the rapid aging of the global population, the demand for prosthodontic rehabilitation of partial edentulism continues to increase. Among the available treatment options, removable partial dentures remain an essential clinical modality for restoring partially edentulous arches and masticatory function because of their broad indications, minimal invasiveness, and cost-effectiveness. However, unlike fixed prosthodontics, which have undergone substantial digitalization, removable partial denture therapy is characterized by dual tooth-tissue support, complex biomechanics, and highly variable anatomical morphology. As a result, the digital design and fabrication of removable partial dentures have long faced major bottlenecks, including limited standardization, steep learning curves, and heavy dependence on expert experience. In recent years, artificial intelligence (AI) has undergone a paradigm shift from early expert systems to machine learning and deep learning, creating new opportunities to address the complexity and precision demands of removable partial denture design. Based on a comprehensive literature review and our team's clinical experience, this article systematically elaborates on the specific applications and normative standards of AI throughout the entire removable partial denture fabrication workflow. In the data acquisition phase, we emphasize multimodal data fusion and digital pressure impression strategies. In the intelligent diagnosis and planning phase, deep learning is utilized to achieve the automatic identification of edentulous areas and undercuts, while machine learning and expert systems assist in abutment selection and treatment plan generation. Regarding automated design logic, the article analyzes the process from the intelligent planning of the common path of insertion to parametric design based on biomechanical optimization. At the clinical operation level, a standardized human-machine collaborative workflow of “AI generation-clinician review-local fine-tuning” is proposed. In terms of ethics and accountability, the principles of algorithmic transparency and “the clinician as the ultimate responsible subject” are emphasized. Furthermore, integrating clinical practice, this article innovatively proposes a “capability grading system for AI-assisted removable partial denture design (comprising the auxiliary analysis level, the rule-based design level, and the intelligent adaptive level)”. The aim is to offer practical recommendations for dental clinicians, dental technicians, and researchers, thereby facilitating the transition of removable partial denture prosthodontics toward greater precision, intelligence, and standardization.

Objective: To systematically evaluate the influencing factors for repeated implantation failure (RIF) after in vitro fertilization-embryo transfer (IVF-ET) in China, so as to provide the evidence for prevention of RIF. Methods: Literature on influencing factors for RIF in China were retrieved from CNKI, Wanfang Data, VIP, China Medical Literature Service System, PubMed, Web of Science, Cochrane Library and Embase from inception to September, 2024. A meta-analysis was performed using RevMan 5.3 and Stata 14.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was evaluated using Egger's test. Results: Initially 4 836 relevant articles were retrieved, and 12 of them were finally included, with a total sample size of 11 554 individuals. There were 10 case-control studies, 1 cohort study, and 1 cross-sectional study; and 10 high-quality studies and 2 medium-quality studies. The meta-analysis showed that factors including advanced age (OR=1.121, 95%CI: 1.035-1.215), prolonged infertility duration (OR=1.237, 95%CI: 1.091-1.403), abnormal hysteroscopy findings (OR=2.205, 95%CI: 1.119-4.348), positive anti-nuclear antibody (ANA) (OR=2.393, 95%CI: 1.473-3.886), and positive anti-beta2 glycoprotein Ⅰ antibody (β2-GPⅠ-Ab) (OR=2.824, 95%CI: 1.987-4.013) were associated with an increased risk of RIF; while factors including the large number of embryos transferred (OR=0.309, 95%CI: 0.098-0.973), thicker endometrium (OR=0.601, 95%CI: 0.556-0.650), and higher granulocyte colony-stimulating factor (G-CSF) levels (OR=0.657, 95%CI: 0.511-0.845) were associated with a reduced risk of RIF. Conclusion IVF-ET RIF is associated with age, infertility duration, number of embryos transferred, endometrial thickness, hysteroscopy findings, G-CSF levels, ANA and β2-GPⅠ-Ab.

Objective To analyze the layout and shielding effectiveness of medical accelerator vaults, and to provide a reference for the layout, shielding design, and optimization of protection of medical accelerator vaults. Methods Four medical accelerator radiotherapy vaults were selected. The layouts of these vaults were compared with the layout requirements in the radiation therapy protection standards. For each vault, the dose rates at four points of interest outside the shielding were calculated, including the primary shielding area, secondary shielding area, maze outer wall, and lateral shielding area. These values were then compared with the actual measurements obtained using a dose rate meter. Results All four vaults were located on the ground floor of the building and included a maze, with the auxiliary rooms all placed outside the treatment rooms. However, one vault was not located at one end of the building, and in another vault, the control room was exposed to direct irradiation of the useful beam. The calculated dose rates outside the primary shielding area ranged from 0.04 μSv/h to 0.62 μSv/h, while the measured values ranged from 0.10 μSv/h to 0.66 μSv/h, with the measured values being higher than the calculated ones. The calculated dose rates outside the secondary shielding area ranged from 0.0005 μSv/h to 0.12 μSv/h, those outside the maze outer wall ranged from 0.06 μSv/h to 0.18 μSv/h, and those outside the lateral shielding area ranged from 0.009 μSv/h to 0.15 μSv/h; the measured values were all below the detection limit. Conclusion Inadequate layouts were observed in some of the accelerator vaults. The calculated and measured dose rates at points of interest outside the vaults were both significantly lower than the shielding protection requirements in standards, indicating that the shielding designs were overly conservative. Construction units should follow the principle of optimization of radiation protection to design the layout and shielding reasonably and pay attention to the underestimation of dose rates outside the primary shielding caused by the tenth value layer.

BACKGROUND:The extracellular-regulated protein kinase 5(ERK5)signaling protein is essential for the survival of organisms,and resveratrol can promote osteoblast proliferation through various pathways.However,whether resveratrol can regulate osteoblast function through the ERK5 signaling protein needs further verification. OBJECTIVE:To explore the regulatory effect of ERK5 on the proliferation of MC3T3-E1 cells and related secreted proteins,and to further verify whether resveratrol can complete the above process by activating ERK5. METHODS:Mouse MC3T3-E1 preosteoblasts were treated with complete culture medium,XMD8-92(an ERK5 inhibitor),epidermal growth factor(an ERK5 activator),resveratrol alone,XMD8-92+EGF,and resveratrol+XMD8-92,respectively.Western blot assay was used to detect the expression of ERK5 and p-ERK5 proteins,proliferation-related proteins Cyclin D1,CDK4 and PCNA,and osteoblast-secreted proteins osteoprotegerin and receptor activator of nuclear factor-κB ligand in MC3T3-E1 cells of each group.The fluorescence intensity of ERK5,osteoprotegerin and receptor activator of nuclear factor-κB ligand in each group was detected by cell immunofluorescence staining,and cell proliferation was detected by EdU staining,respectively.The appropriate concentration and time of resveratrol intervention in MC3T3-E1 cells were determined by cell morphology observation and cell counting kit-8 assay. RESULTS AND CONCLUSION:The activation of ERK5 signaling protein could effectively promote the proliferation of MC3T3-E1 cells,up-regulate the osteoprotegerin/receptor activator of nuclear factor-κB ligand ratio.The appropriate concentration and time for resveratrol intervention in MC3T3-E1 cells was 5 μmol/L and 24 hours,respectively.Resveratrol could activate ERK5 signaling protein,thereby promoting osteoblast proliferation and up-regulating the osteoprotegerin/RANKL ratio.All these results indicate that resveratrol can promote the proliferation of MC3T3-E1 cells and up-regulate the osteoprotegerin/RANKL ratio by activating the ERK5 signaling protein.

BACKGROUND:Black phosphorus has a high degree of homology with human bone,so it has been extensively studied in the field of bone tissue engineering in recent years.Since 2014,two-dimensional black phosphorus materials have garned significant attention in the field of biomedicine due to their excellent exceptional physical,chemical,and biological properties. OBJECTIVE:To summarize the advancements made in black phosphorus-based nanomaterials for bone tissue engineering,focus on the synthesis methods,osteogenic characteristics,and applications in biomaterials pertaining to two-dimensional black phosphorus nanomaterials. METHODS:Chinese and English key words were"black phosphorus,bone tissue engineering,bone defect,bone regeneration,osteogenesis."Relevant articles in PubMed and CNKI databases from January 2014 to December 2023 were searched.After exclusion and screening,96 articles were analyzed. RESULTS AND CONCLUSION:Black phosphorus nanomaterials play an important role in bone tissue engineering due to their good biocompatibility,biodegradability,photothermal action,antibacterial ability,drug loading performance,and special osteogenic effect,and are ideal candidate materials for promoting bone regeneration.The preparation of black phosphorus nanomaterials is mainly a top-down top-layer stripping method.The main principle is to weaken the van der Waals force between the black phosphorus layers by physical or chemical means to obtain a single or less layer of phosphanse,that is,black phosphorus nanosheets or quantum dots.Black phosphate-based nanocomposites are mainly divided into hydrogels,3D printing scaffolds,composite scaffolds,electrospinning,bionic periosteum,microspheres,and bionic coatings.The research of nano-black phosphorus in bone tissue engineering is in its infancy,and still faces many challenges:the behavior of black phosphorus in vivo and the interaction mechanism with various biomolecules need to be further studied.The long-term potential toxicity of black phosphorus is unknown.The manufacturing process for black phosphorus is difficult to control.Therefore,how to develop uniform size,safe,reliable,and efficient nano black phosphorus and transform it into clinical application requires interdisciplinary research on modern biomedical technology,physicochemical technology,and precision manufacturing technology.

OBJECTIVES: To investigate the incidence of myocardial injury in children with rotavirus-induced diarrhea, analyze its risk factors, and develop a predictive model for myocardial injury. METHODS: A retrospective analysis was conducted on 203 children diagnosed with rotavirus infection at the Suzhou Wujiang District Children's Hospital from January 2021 to December 2023. The children were divided into groups based on the presence or absence of myocardial injury. Basic information and laboratory indicators at admission were collected and compared between the two groups. LASSO regression was used to screen potential risk factors, followed by multivariate logistic regression to evaluate independent factors. A nomogram model was established and validated. RESULTS: Out of 203 children with rotavirus infection, 53 cases (26.1%) showed myocardial injury. Age, severe dehydration, metabolic acidosis, red cell distribution width, and blood sodium were closely associated with myocardial injury in children with rotavirus-induced diarrhea (P<0.05). The area under the receiver operating characteristic curve for the predictive model of myocardial injury was 0.841 (95%CI: 0.777-0.905), with a sensitivity of 73.6% and specificity of 85.3%. The model curve closely fit the ideal diagonal line. Decision curve analysis showed that using the model for prediction resulted in the highest net benefit when the probability threshold was 0.18-0.98. CONCLUSIONS The model developed in this study can predict the risk of myocardial injury in children with rotavirus-induced diarrhea.
Humans ; Rotavirus Infections/complications* ; Diarrhea/etiology* ; Male ; Female ; Infant ; Retrospective Studies ; Risk Factors ; Child, Preschool ; Logistic Models ; Child

OBJECTIVES: To study the epidemiological characteristics of respiratory syncytial virus (RSV) infection in hospitalized children with community-acquired pneumonia (CAP) in Hebei Province. METHODS: Hospitalized children with CAP who tested positive for RSV and were admitted to Hebei Children's Hospital from various cities and counties across Hebei Province between January 2019 and December 2023 were included in the study. Clinical data were collected and analyzed to assess epidemiological characteristics. RESULTS: The clinical data of 43 978 children with CAP were collected, with an overall RSV detection rate of 25.98%. The detection rate was higher during the implementation of non-pharmaceutical interventions (NPIs) (30.60%) than in the non-NPIs period. Winter and spring were the primary epidemic seasons for RSV each year except in 2022. The detection rate in males (26.62%) was higher than in females (25.06%) (P<0.001). The highest detection rate (59.18%) was found in infants aged 29 days to <1 year. Single RSV infection was more common, with rhinovirus being the most frequent co-infection. CONCLUSIONS The overall RSV detection rate in Hebei Province is influenced by NPIs, being higher during their implementation. RSV predominantly circulates in winter and spring. The detection rate of RSV is higher in males and infants. RSV infection is primarily single, most often co-occurring with rhinovirus.
Humans ; Respiratory Syncytial Virus Infections/epidemiology* ; Female ; Male ; Infant ; Child, Preschool ; Seasons ; China/epidemiology* ; Infant, Newborn ; Community-Acquired Infections/epidemiology* ; Child

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms