The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective:To explore status and influencing factors of surveillance in colorectal post-polypectomy patients.Methods:Patients who underwent colorectal polypectomy in Renmin Hospital of Wuhan University between April 1, 2019 and June 30, 2019 were retrospectively studied. The surveillance information was obtained through electronic health record and telephone call. Status and influencing factors of surveillance in colorectal post-polypectomy patients were evaluated. Logistic regression model was used for multivariate analysis to determine independent risk factors influencing surveillance.Results:A total of 268 colorectal post-polypectomy patients and their surveillance information were reviewed, of whom 153 (57.09%) patients received surveillance colonoscopy, and 115 (42.91%) patients did not. Univariate analysis showed that the source of patients (outpatients VS inpatients, χ 2=5.68, P=0.017), department (others VS department of gastroenterology, χ 2=6.64, P=0.010), and the number of polyps (1/(2~4)/≥5, χ2=7.32, P=0.026) influenced the outcome of surveillance. Logistic regression model indicated that department of gastroenterology ( P=0.039, OR=2.12, 95% CI:1.04-4.34), risk level 3 ( P=0.040, OR=1.92, 95% CI:1.03-3.58) and the number of polyps ≥5 ( P=0.016, OR=2.89, 95% CI:1.22-6.83) were independent risk factors influencing surveillance. Conclusion:Patients visit the department of gastroenterology or had a risk level 3 or ≥5 polyps are more likely to opt for surveillance following the procedure.

Objective To evaluate the reliability and validity of the Chinese version of the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS)in patients with maintenance hemodialysis(MHD).Methods The general information and medical history of 84 MHD patients were collected,and the Mini-Mental State Exam(MMSE),Montreal Cognitive Assessment Scale(MoCA),and RBANS were conducted.The reliability of the scale was assessed by Cronbach α and split-half reliability.The structure and convergent validity of the scale were assessed by confirmatory factor analysis,and the RBANS scores'correlation to MoCA and MMSE scores was analyzed by Spearman correlation analysis.The predictive value of the RBANS total score on cognitive impairment(CI)was analyzed by receiver operating characteristic(ROC)curve.Results The Cronbach's alpha coefficient of the RBANS total scale was 0.896,split-half reliability was 0.911,and reliability for the five dimensions of the RBANS ranged from 0.618 to 0.791.Confirmatory factor analysis indicated that the overall fit of the five-dimensional model of the RBANS scale was acceptable(χ2/df=1.587,root mean square error of approximation=0.084,comparative fit index=0.967,incremental fit index=0.968,Tucker-Lewis index=0.947,goodness of fit index=0.891).The average variance extracted(AVE)for the five dimensions of the RBANS ranged from 0.525 to 0.863,while the composite reliability(CR)ranged from 0.733 to 0.926,indicating good convergent validity of the scale.Furthermore,Spearman correlation analysis revealed that the total RBANS score was negatively correlated to the age of MHD patients and positively correlated to years of education,as well as the total scores of MMSE and MoCA(all P<0.01).The ROC curve analysis indicated that the area under the curve(AUC)for the total RBANS score in predicting CI was 0.891(P<0.01),suggesting a high predictive value.Conclusion The Chinese version of RBANS has good reliability and validity in MHD patients,and can be used as a measure of cognitive function in MHD patients.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective:To investigate the feasibility and clinical effects of ultra early enteral nutrition (≤24 h) in critically ill children supported by extracorporeal membrane oxygenation (ECMO).Methods:A retrospective cohort study was conducted. Clinical data of 43 critically ill children who received ECMO support in the pediatric intensive care unit (PICU) of Shanghai Children′s Hospital from January 2016 to December 2023 were collected, including general information, nutritional support modalities, and enteral nutrition tolerance. Based on the timing of enteral nutrition initiation, patients were divided into the within 24 h enteral nutrition group and the after 24 h enteral nutrition group. Nutritive indicators, nutritional intake, duration of ECMO support, duration of mechanical ventilation duration, and mortality rates were compared between the 2 groups using the two independent sample t test, Mann-Whitney U test, χ2 test and Fisher′s exact test. Results:Among the 43 children, 25 were male and 18 were female, with an age of 47 (18, 97) months. There were no statistically significant differences between the within 24 h enteral nutrition group (21 cases) and the after 24 h enteral nutrition group (22 cases) in terms of age, body mass index Z score, total protein, albumin, hemoglobin levels before ECMO support, duration of ECMO support, duration of mechanical ventilation, length of PICU stay, number of enteral nutrition intolerance events, number of enteral nutrition interruption, or mortality rate (all P>0.05). The protein intake adequacy rate during ECMO support was higher in the within 24 h enteral nutrition group than in the after 24 h enteral nutrition group (0 (0, 21%) vs. 0 (0, 0), U=175.00, P<0.05). Conclusions:Ultra early enteral nutrition is safe for children supported by ECMO. Initiating enteral nutrition within 24 h can increase the proportion of days with adequate protein intake in ECMO children without increasing the occurance of enteral nutrition intolerance or interruptions.

Objective:To investigate the clinical efficacy of botulinum toxin type A（BTX-A）bladder injection in the treatment of neurogenic detrusor overactivity（NDO）with autonomic dysreflexia（AD）.Methods:The patients with spinal cord injury at or above T6，who were treated at Guangdong Provincial Work Injury Rehabilitation Hospital from January 2018 to December 2022，were included in this study prospectively. Inclusion criteria：①chronic spinal cord injury patients over 18 years old（with no progression of neurological symptoms within 3 months）；② presence of NDO and AD；③ inadequate response or intolerance to oral antimuscarinic agent（M-receptor antagonists or β 3-receptor agonists）④ perform clean intermittent catheterization to empty the bladder. Exclusion criteria：① primary disease in the acute or progressive phase；② previous surgeries that would affect lower urinary tract function，such as transurethral sphincterotomy，bladder neck resection，prostatectomy，or bladder surgery；③ allergy to BTX-A or its adjuvants，or those with allergic predisposition ④ patients who were pregnant，breastfeeding，or planning for pregnancy in the near future；⑤ patients did not accept or were unable to perform intermittent catheterization. Before treatment，all patients were required to maintain 3-5 day urine diary，along with urodynamic studies（UDS），incontinence specific quality of life instrument（I-QOL）and AD symptom severity assessment，and blood pressure monitored. Key UDS parameters recorded included maximum bladder capacity，maximum detrusor pressure during filling phase，changes in maximum systolic blood pressure（SBP）relative to baseline（ΔSBP）during UDS examination，and the frequency of 24-hour blood pressure exceeding baseline by 20 mmHg. After general anesthesia or epidural anesthesia，BTX-A（200 U）was injected into the bladder at 30 points（including the triangle）under the cystoscope using a special injection needle，6.7 U per injection，and then the catheter was kept for 3-5 days after treatment. Three months later，relevant indicators were collected and compared with pre-treatment data. Results:A total of 43 patients were included in this study，including 34 males and 9 females. The age was（39.23±13.17）years old and the disease course was（2.69±3.27）years old. There were 33 cervical and 10 thoracic cases. The American Spinal Injury Association Injury Scale score distribution was as follows：26（60%）A，4（9%）B，9（21%）C，and 4（9%）D. The presence of AD was confirmed in all patients during urodynamic examination（UDS），that was the systolic blood pressure（SBP）suddenly increased and exceeded 20 mmHg（1 mmHg = 0.133 kPa）. Before treatment，The AD symptoms severity score（consist of headache，sweating，goose bumps，anxiety and palpitation）were（14.53±2.51），Bladder-related AD frequency was 10.67 episodes/day. Baseline SBP was（103.51±9.64）mmHg，the maximum SBP was（150.40±22.75）mmHg，and the change in SBP（ΔSBP）from maximum to baseline SBP during UDS examination was（43.83±21.01）mmHg. The UDS indicated that the maximum detrusor pressure during storage phase was（54.95±24.68）cmH 2O，and the bladder capacity was（131.70±75.29）ml. Bladder diary showed the volume of catheterization each time from was（181.16±49.86）ml，and The I-QOL score was（44.07±8.60）. Three months after treatment，the AD symptoms severity score（consist of headache，sweating，goose bumps，anxiety and palpitation）were（11.37±2.39）. The frequency of bladder-related AD episodes was（7.51±2.37）episodes/day，showing statistically significant differences compared to pre-treatment（ P<0.05）.The SBP before UDS examination was（102.12±10.28）mmHg，with no statistically significant difference from baseline（ P = 0.518）. The maximum SBP in perfusion phase and the ΔSBP were（132.84±16.30）mmHg and（28.72 ± 14.02）mmHg，respectively，both demonstrating statistically significant differences（ P < 0.05）. The UDS examination revealed that the maximum detrusor pressure during the storage phase was（29.77±13.72）cmH 2O，showed a significant decrease，and the bladder capacity was（272.63±79.75）ml，which were both statistically different before and after surgery. Bladder diary showed the volume of catheterization each time was（326.74±63.71）ml；I-QOL score was（71.86±11.45），both were significant different after treatment（ P < 0.01）. Conclusion:BTX-A intravesical injection in the treatment of NDO can also alleviate the severity and frequency of bladder related AD.

Hot melt extrusion(HME)technology employs thermodynamic and kinetic principles to mix pharmaceutical polymers with crystalline drugs at high temperatures and extrude them,embedding drug molecules within the polymer matrix to form solid dispersions.Due to its solvent-free nature,capability for one-step processing,and support for continuous operation,HME has garnered significant attention in the pharmaceutical industry in recent years.This article introduced the basic principles and development history of HME technology and its marketed drugs.It reviewed the research progress of HME technology in improving drug solubility,masking taste,controlled release,targeted release,oral dispersible films,implant formulations,semi-solid formulations,and 3D printed formulations.Additionally,the article summarized the advantages and limitations of HME technology and provided an outlook on its future development.

Hot melt extrusion(HME)technology employs thermodynamic and kinetic principles to mix pharmaceutical polymers with crystalline drugs at high temperatures and extrude them,embedding drug molecules within the polymer matrix to form solid dispersions.Due to its solvent-free nature,capability for one-step processing,and support for continuous operation,HME has garnered significant attention in the pharmaceutical industry in recent years.This article introduced the basic principles and development history of HME technology and its marketed drugs.It reviewed the research progress of HME technology in improving drug solubility,masking taste,controlled release,targeted release,oral dispersible films,implant formulations,semi-solid formulations,and 3D printed formulations.Additionally,the article summarized the advantages and limitations of HME technology and provided an outlook on its future development.