Quantum dots (QDs), nanoscale semiconductor crystals, have emerged as a revolutionary class of nanomaterials with unique optical and electrochemical properties, making them highly promising for applications in disease diagnosis and treatment. Their tunable emission spectra, long-term photostability, high quantum yield, and excellent charge carrier mobility enable precise control over light emission and efficient charge utilization, which are critical for biomedical applications. This article provides a comprehensive review of recent advancements in the use of quantum dots for disease diagnosis and therapy, highlighting their potential and the challenges involved in clinical translation. Quantum dots can be classified based on their elemental composition and structural configuration. For instance, IB-IIIA-VIA group quantum dots and core-shell structured quantum dots are among the most widely studied types. These classifications are essential for understanding their diverse functionalities and applications. In disease diagnosis, quantum dots have demonstrated remarkable potential due to their high brightness, photostability, and ability to provide precise biomarker detection. They are extensively used in bioimaging technologies, enabling high-resolution imaging of cells, tissues, and even individual biomolecules. As fluorescent markers, quantum dots facilitate cell tracking, biosensing, and the detection of diseases such as cancer, bacterial and viral infections, and immune-related disorders. Their ability to provide real-time, in vivo tracking of cellular processes has opened new avenues for early and accurate disease detection. In the realm of disease treatment, quantum dots serve as versatile nanocarriers for targeted drug delivery. Their nanoscale size and surface modifiability allow them to transport therapeutic agents to specific sites, improving drug bioavailability and reducing off-target effects. Additionally, quantum dots have shown promise as photosensitizers in photodynamic therapy (PDT). When exposed to specific wavelengths of light, quantum dots interact with oxygen molecules to generate reactive oxygen species (ROS), which can selectively destroy malignant cells, vascular lesions, and microbial infections. This targeted approach minimizes damage to healthy tissues, making PDT a promising strategy for treating complex diseases. Despite these advancements, the translation of quantum dots from research to clinical application faces significant challenges. Issues such as toxicity, stability, and scalability in industrial production remain major obstacles. The potential toxicity of quantum dots, particularly to vital organs, has raised concerns about their long-term safety. Researchers are actively exploring strategies to mitigate these risks, including surface modification, coating, and encapsulation techniques, which can enhance biocompatibility and reduce toxicity. Furthermore, improving the stability of quantum dots under physiological conditions is crucial for their effective use in biomedical applications. Advances in surface engineering and the development of novel encapsulation methods have shown promise in addressing these stability concerns. Industrial production of quantum dots also presents challenges, particularly in achieving consistent quality and scalability. Recent innovations in synthesis techniques and manufacturing processes are paving the way for large-scale production, which is essential for their widespread adoption in clinical settings. This article provides an in-depth analysis of the latest research progress in quantum dot applications, including drug delivery, bioimaging, biosensing, photodynamic therapy, and pathogen detection. It also discusses the multiple barriers hindering their clinical use and explores potential solutions to overcome these challenges. The review concludes with a forward-looking perspective on the future directions of quantum dot research, emphasizing the need for further studies on toxicity mitigation, stability enhancement, and scalable production. By addressing these critical issues, quantum dots can realize their full potential as transformative tools in disease diagnosis and treatment, ultimately improving patient outcomes and advancing biomedical science.

Objective To establish a cell bank of extrahepatic cholangiocarcinoma (ECC)-derived organoids and investigate the key factors influencing the organoids generation. Methods The tumor samples from patients with portal cholangiocarcinoma (pCCA) and distal cholangiocarcinoma (dCCA) were used to isolate cells, and these cells were cultured using three-dimensional (3D) technique to establish ECC organoids. Histological characteristics of the organoids were evaluated and identified through hematoxylin-eosin (HE) and immunohistochemistry stainings. The success rates of organoids generation from different tumor types were compared. And clinical characteristics of patients between successful and failure culture groups were compared. Results The success rates of organoids establishment from pCCA and dCCA were all low, with 42.4% (14/33), 51.9% (14/27), respectively. The tumor was larger in successful group than that in failure group (P＜0.001); there was no statistical difference in tumor differentiation status, microvascular invasion, and perineural invasion between the two groups. Conclusions The successful rate of ECC-derived organoids establishment is low, and larger tumor has higher successful culture rate.

BACKGROUND:In recent years,artificial intelligence has been increasingly integrated into the diagnosis and treatment of lumbar disc herniation,enhancing the accuracy and efficiency of diagnostic procedures and diversifying therapeutic approaches.This integration has positioned artificial intelligence as a burgeoning focal point within the field.OBJECTIVE:To provide a comprehensive overview of the current applications of artificial intelligence in the diagnosis and treatment of lumbar disc herniation,to analyze the limitations of the relevant technologies.METHODS:A systematic computer-assisted literature search was performed in PubMed,CNKI,WanFang Database,and VIP Database for relevant publications regarding the application of artificial intelligence in the diagnosis and treatment of lumbar disc herniation from database inception up to August 2024.The search keywords included"lumbar disc herniation,artificial intelligence,machine learning,deep learning,big data,robot,neural network,model,algorithm."A total of 101 articles were selected based on predefined inclusion criteria and were reviewed.RESULTS AND CONCLUSION:Different artificial intelligence technologies have propelled the intelligent treatment of lumbar disc herniation,showing great potential for future development.Deep learning technology,based on advanced algorithms,constructs corresponding learning models to optimize the processing of X-ray,CT,and MRI images,achieving precise localization,identification,and analysis of degenerated intervertebral discs,and improving the accuracy of automated diagnosis.Big data technology utilizes data platforms to analyze vast medical data,simulate disease development trends,and provide a new perspective for disease assessment and prediction.Surgical robots,combined with robotic arms,3D high-definition vision systems,and 5G communication technology,support remote precise surgical operations,demonstrating significant technological advantages.In the future,the diagnosis and treatment of lumbar disc herniation by artificial intelligence will evolve towards standardization,efficiency,and precision through continuous optimization of algorithms and the professional development of data platforms.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Objective:To summarize medication safety assessment tools for clinical nurses both domestically and internationally.Methods:Guided by the Joanna Briggs Institute (JBI) scoping review methodology, a systematic search was conducted across CINAHL, Embase, PubMed, Web of Science, SinoMed, China National Knowledge Infrastructure, and WanFang Data. The search period was from the establishment of database to January 1, 2025. Medication safety assessment tools for clinical nurses were extracted, relevant content was systematically analyzed, and the retrieval results were reported in a standardized manner.Results:A total of 28 studies were included, involving 15 medication safety assessment tools for clinical nurses. Assessment methods employed multidimensional and graded self-assessment formats. Based on evaluation perspectives, these tools were categorized into six types, including operational standardization monitoring, cognitive bias calibration, environmental stress testing, capability threshold identification, reporting barrier analysis, and medication information systems. The assessment tools had high reliability and validity, multiple types, and diverse evaluation perspectives.Conclusions:Researchers should carefully select and use assessment tools based on research characteristics. It is necessary to enhance the autonomy of nursing research on medication safety, develop comprehensive and accurate clinical nurse medication safety assessment tools that are adapted to China's clinical context, and promote the improvement of nurse medication safety.

Objective To explore the action mechanism of dehydrocostus lactone(DHL)on renal interstitial fibrosis(RIF)in rats with unilateral ureteral obstruction(UUO).Methods Forty-four male SD rats were randomly divided into four groups:the sham surgery group(Sham group),the pure drug intervention group(Sham+DHL group),the experimental group(UUO+Vehicle group)and the DHL treatment group(UUO+DHL group),with 11 rats in each group.The rats underwent sham surgery,sham surgery+DHL[10 mg/(kg·d)],UUO modeling+the same volume of solvent and UUO modeling+DHL[10 mg/(kg·d)],respectively.After surgery,DHL or the same volume of solvent was administered by gavage for 14 days starting from day 2 post-surgery.Hematoxylin-eosin(HE)and Masson staining were used to observe the pathological changes in renal tissue.Immunohistochemical staining was performed to detect the expression levels of collagen I,collagen III and α-smooth muscle actin(α-SMA).Western blotting was used to detect the protein levels of the transforming growth factor(TGF)-β1/Smad2/3 pathway.Results Compared with the UUO+Vehicle group,DHL treatment alleviated renal interstitial pathological damage,reduced collagen fiber deposition,and decreased the expression of collagen I,collagen III and α-SMA.It also inhibited the expression of TGF-β1 and Smad2/3 proteins.Conclusions DHL mitigates RIF in rats by inhibiting the TGF-β1/Smad2/3 pathway,providing a new strategy for the treatment of chronic kidney disease.

Purpose To quantitatively compare the diffusion parameters of mono-and biexponential diffusion-weighted imaging models,and to obtain optimal sets of b-values in diffusion-weighted MRI for obtaining monoexponential apparent diffusion coefficient(ADC)close to perfusion-insensitive intravoxel incoherent motion(IVIM)model ADC(ADCIVIM)in identifying of pulmonary solid benign and malignant lesions.Materials and Methods IVIM was performed in 40 patients with solid nodule and masse in Xi'an Gaoxin Hospital from July 2021 to August 2022 using a 3.0T MR imager.Two experienced diagnostic radiologists subjectively evaluated the IVIM images.A single index model was used to calculate ADC values(ADC0-1 000,ADC20-1 000,ADC50-1 000,ADC80-1 000,ADC150-1 000,ADC300-1 000,ADC500-1 000,ADC300,500,1 000,ADC300,800,1 000,ADC300,500,ADC300,800 and ADC300,1 000).The reference standard ADCIVIM value were calculated using a double-exponential model.The physician's measurements between two physicians were measured.The malignant and benign groups were compared and receiver operator characteristic curve for all parameters were analyzed.Results The measurement consistency of ADC values under b value sets and ADCIVIM was very good,and the intraclass correlation coefficient was more significant than 0.75.The differences between ADCIVIM and ADC values in each b group were statistically significant(t=-6.016--2.500,all P<0.05).The area under the curve(AUC)of ADCIVIM was the largest(0.906),with an optimal threshold of 1.271×10-3 mm2/s,a sensitivity of 80.0%and a specificity of 93.0%.The diagnostic efficacy close to ADCIVIM were ADC300,800(AUC=0.891),ADC50-1 000(AUC=0.827)and ADC300,800,1 000(AUC=0.795),respectively.The optimal threshold of ADC300,800 was 1.140×10-3 mm2/s,the sensitivity and specificity were 80.0%and 93.7%,respectively.Conclusion Combining b-values 300 s/mm2 and 800 s/mm2 is recommended as routine scanning parameters for identifying the insensitive monoexponential ADC between benign and malignant solid pulmonary lesions.

Objective To develop a graded early mobility implementation pathway for critically ill adult patients in tertiary hospitals in Beijing and to preliminarily validate its feasibility and effectiveness.Methods Based on the"goal-directed"early mobility concept,a graded early mobility implementation pathway for critically ill patients was developed through evidence synthesis and the Delphi method,consisting of 3 components:patient inclusion,mobility implementation,and mobility evaluation.Using convenience sampling,patients meeting inclusion criteria in the general ICU of a tertiary hospital in Beijing from October 2024 to January 2025 were selected as participants.Among them,25 patients admitted from December 2024 to January 2025 were assigned to an experimental group and received early mobility interventions following the developed pathway.25 patients admitted from October to November 2024 served as a control group and received routine ICU mobility care.Outcomes including diaphragm excursion,muscle strength,ICU length of stay,and adverse events were compared between the 2 groups.Results The graded early mobility pathway achieved an implementation rate of 70.05％in the experimental group,significantly higher than it in the control group(P＜0.001),without increasing adverse events.Post-intervention diaphragm excursion in the experimental group was significantly greater than that in the control group(P=0.018).Conclusion The developed graded early mobility implementation pathway for ICU patients demonstrates scientific rigor and clinical practicality.It provides a reference for the widespread and effective implementation of early mobility in ICUs,standardizing its clinical application.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Objective:To investigate the incidence,clinical characteristics,and complications of Torque teno virus(TTV)in children after hematopoietic stem cell transplantation(HSCT).Methods:A total of 40 children with hematological diseases who underwent HSCT were selected,and metagenomic next-generation sequencing(mNGS)technology was used to detect the gene sequences of pathogenic microorganisms in the blood.Combined with clinical data,the characteristics of TTV infection were analyzed.Results:Among the 40 pediatric patients post-HSCT,the TTV positive rate was 42.5％(17/40).There were no statistically significant differences between the TTV-positive group and the TTV-negative group in sex,age,white blood cell count(WBC),red blood cell count(RBC),hemoglobin,platelet count,neutrophil count,lymphocyte count,and high-sensitivity C-reactive protein(all P＞0.05).The incidence of TTV infection was significantly higher in children who underwent haploidentical HSCT and in those with bone marrow stem cells(BMSC)as the transplant source(P＜0.05).However,there were no significant differences in the TTV infection rate among patients with different disease types,different HLA matching statuses,or different engraftment times of neutrophils and platelets(all P＞0.05).Among 17 children infected with TTV,13(76.5％)had co-infections with other viruses,mainly including cytomegalovirus(58.8％,10/17),human polyomavirus(41.2％,7/17),and Epstein-Barr virus(17.6％,3/17).In children with TTV infection,the most common complications were sepsis(82.4％),graft-versus-host disease(GVHD)(70.6％),pulmonary infection(41.2％),and hemorrhagic cystitis(17.6％).The incidence of GVHD in the TTV-positive group was significantly higher than that in the TTV-negative group(P＜0.05).Conclusion:TTV infection is common in children undergoing HSCT,and it is prone to be complicated with cytomegalovirus infection and GVHD,which has an important influence on the clinical outcomes.