Objective To develop an early automated differential diagnosis between Parkinson's disease(PD)and multiple system at-rophy-Parkinsonism(MSA-P)using a non-invasive combination of voice signal analysis and artificial intelli-gence.Methods From July,2023 to February,2025,a total of 48 MSA-P patients and 76 PD patients with a course of less than five years were recruited from Beijing Tiantan Hospital,Capital Medical University.Voice features,such as glot-tal,phonatory,articulatory,prosodic,phonological and representation learning-based features were extracted from eleven voice tasks.A data-driven approach was used to identify the most discriminative features,which were utilized to construct diagnostic models using a variety of machine learning models.The diagnostic model with the strongest discriminative efficiency was selected.Results The logistic regression model showed the best performance.For early-stage patients with a course less than two years,the diagnostic accuracy,precision and recall rate between PD and MSA-P were 92.5%,95.9%and 92.2%,respectively.For all the patients with a course less than five years,the logistic regression model achieved an accu-racy of 89.1%,a precision of 91.6%,and a recall rate of 92.4%.Even when features extracted from a single speech paradigm were used for analysis,the diagnostic accuracy could still reach 77.7%.Conclusion Voice signals analysis is potential in the early differential diagnosis of PD and MSA-P.

Objective:To systematically review the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures.Methods:PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Database and VIP Database were systematically searched to collect literature on the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures from inception to June 30, 2024. The languages were limited to Chinese and English. Two researchers screened the literature according to the inclusion and exclusion criteria. Data extraction was performed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS), encompassing basic study characteristics, model development features, and model performance metrics. The predictors, validation methods, presentation formats, and predictive performance of the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures were evaluated. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess risk of bias and applicability of the included studies.Results:A total of 11 studies, comprising of 16 prediction models, were included, with a total sample size of 283-1 508 patients and a pulmonary infection incidence rate of 5.4%-16.25%. The independent predictive factors repeatedly included in the models were age, American Society of Anesthesiologists (ASA) scale, preoperative comorbidities, chronic obstructive pulmonary disease (COPD), preoperative albumin level, white blood cell count (WBC), and C-reactive protein (CRP) level. The models were internally validated in 7 studies and externally validated in 3. The models were visualized in the form of a nomogram in 7 studies and a web-based risk calculator in 1. Model prediction performance was analyzed: (1) In terms of the discrimination, 9 studies reported the area under the receiver operating characteristic curve (AUC), with the overall AUC range of 0.664-0.905. (2) In terms of the calibration, 5 studies had Hosmer-Lemeshow test, with the P-values all above 0.05; 2 studies reported the calibration plots, with the slopes close to 1 and the Brier scores of 0.016 and 0.112; 4 studies reported the sensitivity of the models of 73.91%-92.40% and specificity of 57.10%-92.41%. According to PROBAST, all 11 studies exhibited certain risk of bias while maintaining favorable applicability. Conclusions:Age, ASA scale, preoperative comorbidities, COPD, preoperative albumin level, WBC, and CRP level are found to be independent predictive factors repeatedly reported in the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures. The existing models demonstrate a robust overall prediction performance despite certain risks of bias.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

Objective:This study aims to construct an evaluation index system suitable for the core competency of Clinical Research Coordinators (CRCs) in Investigator-Initiated Trials (IITs) in China.Methods:This study developed a system framework through the Onion Model, literature research, and expert interviews, utilized the Delphi method to build the index system. and analyzed the weight of each indicator through the Analytic Hierarchy Process (AHP).Results:Four first-level indicators were basic knowledge (0.143), job skills (0.300 8), professional quality (0.483 9), and personality traits (0.072 3). Besides, 18 second-level indicators and 49 third-level indicators were developed through the Delphi method. According to the third round expert′s consultation, the average scores of all indexes were >3.50, the authoritative coefficient was 0.86, the coefficient of variation of each index was <0.30, and Kendall coefficients of concordance were 0.183~0.366 ( P<0.001). The consistency ratios of single-sort were<0.1, and the overall sort of all indexes was 0.043 7, which showed good logical reliability. Conclusions:This evaluation index system for Clinical Research Coordinators is of great scientific sense. It provides IIT-conducting investigators in institutions with a proficient assessment tool to help them find qualified and reliable CRCs.

Objective:To systematically review the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures.Methods:PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Database and VIP Database were systematically searched to collect literature on the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures from inception to June 30, 2024. The languages were limited to Chinese and English. Two researchers screened the literature according to the inclusion and exclusion criteria. Data extraction was performed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS), encompassing basic study characteristics, model development features, and model performance metrics. The predictors, validation methods, presentation formats, and predictive performance of the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures were evaluated. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess risk of bias and applicability of the included studies.Results:A total of 11 studies, comprising of 16 prediction models, were included, with a total sample size of 283-1 508 patients and a pulmonary infection incidence rate of 5.4%-16.25%. The independent predictive factors repeatedly included in the models were age, American Society of Anesthesiologists (ASA) scale, preoperative comorbidities, chronic obstructive pulmonary disease (COPD), preoperative albumin level, white blood cell count (WBC), and C-reactive protein (CRP) level. The models were internally validated in 7 studies and externally validated in 3. The models were visualized in the form of a nomogram in 7 studies and a web-based risk calculator in 1. Model prediction performance was analyzed: (1) In terms of the discrimination, 9 studies reported the area under the receiver operating characteristic curve (AUC), with the overall AUC range of 0.664-0.905. (2) In terms of the calibration, 5 studies had Hosmer-Lemeshow test, with the P-values all above 0.05; 2 studies reported the calibration plots, with the slopes close to 1 and the Brier scores of 0.016 and 0.112; 4 studies reported the sensitivity of the models of 73.91%-92.40% and specificity of 57.10%-92.41%. According to PROBAST, all 11 studies exhibited certain risk of bias while maintaining favorable applicability. Conclusions:Age, ASA scale, preoperative comorbidities, COPD, preoperative albumin level, WBC, and CRP level are found to be independent predictive factors repeatedly reported in the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures. The existing models demonstrate a robust overall prediction performance despite certain risks of bias.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

Objective:This study aims to construct an evaluation index system suitable for the core competency of Clinical Research Coordinators (CRCs) in Investigator-Initiated Trials (IITs) in China.Methods:This study developed a system framework through the Onion Model, literature research, and expert interviews, utilized the Delphi method to build the index system. and analyzed the weight of each indicator through the Analytic Hierarchy Process (AHP).Results:Four first-level indicators were basic knowledge (0.143), job skills (0.300 8), professional quality (0.483 9), and personality traits (0.072 3). Besides, 18 second-level indicators and 49 third-level indicators were developed through the Delphi method. According to the third round expert′s consultation, the average scores of all indexes were >3.50, the authoritative coefficient was 0.86, the coefficient of variation of each index was <0.30, and Kendall coefficients of concordance were 0.183~0.366 ( P<0.001). The consistency ratios of single-sort were<0.1, and the overall sort of all indexes was 0.043 7, which showed good logical reliability. Conclusions:This evaluation index system for Clinical Research Coordinators is of great scientific sense. It provides IIT-conducting investigators in institutions with a proficient assessment tool to help them find qualified and reliable CRCs.

Objective To develop an early automated differential diagnosis between Parkinson's disease(PD)and multiple system at-rophy-Parkinsonism(MSA-P)using a non-invasive combination of voice signal analysis and artificial intelli-gence.Methods From July,2023 to February,2025,a total of 48 MSA-P patients and 76 PD patients with a course of less than five years were recruited from Beijing Tiantan Hospital,Capital Medical University.Voice features,such as glot-tal,phonatory,articulatory,prosodic,phonological and representation learning-based features were extracted from eleven voice tasks.A data-driven approach was used to identify the most discriminative features,which were utilized to construct diagnostic models using a variety of machine learning models.The diagnostic model with the strongest discriminative efficiency was selected.Results The logistic regression model showed the best performance.For early-stage patients with a course less than two years,the diagnostic accuracy,precision and recall rate between PD and MSA-P were 92.5%,95.9%and 92.2%,respectively.For all the patients with a course less than five years,the logistic regression model achieved an accu-racy of 89.1%,a precision of 91.6%,and a recall rate of 92.4%.Even when features extracted from a single speech paradigm were used for analysis,the diagnostic accuracy could still reach 77.7%.Conclusion Voice signals analysis is potential in the early differential diagnosis of PD and MSA-P.

Objective To understand the status quo of nutrition working ability in primary medical and health institutions of Chongqing City.Methods Three primary medical institutions were randomly extracted from each of 39 districts and counties of Chongqing City,and Primary Nutrition Service Capacity Survey Form and Nutrition Work Capacity Survey System were adopted to conduct the questionnaire survey.Then the sur-vey results were analyzed.Results A total of 117 primary medical institutions were surveyed,in which 86 in-stitutions(73.50％)undertook the nutritional work.The number of engaging the nutrition full-time work in the units was 0(0,0).Among the nutritional staff,the age in 164 persons was 30-＜40 years old,accounting for 48.38％,180 persons(53.10％)had the primary title,232 persons(68.44％)had the bachelor degree,287 persons(84.66％)had the medical related background,but only 2 persons had the nutritional related profes-sional background.There were 57(48.72％),75(64.10％),77(65.81％)primary medical institutions in carry-ing out the nutrition and health management of pregnant women,children aged 0-6 years old and elderly peo-ple,34 institutions(29.10％)in carrying out nutritional monitoring,and 17 institutions(14.50％)had the clinical nutrition work ability.Compared with the villages and towns,the proportion of urban area primary medical institutions in carrying out the blood routine items in children aged 0-6 years old,hemoglobin,blood routine and urine routine items in elderly people was higher,the number of published popular science works on nutrition was more,and the differences were statistically significant(P＜0.05).Conclusion The nutrition work system of primary medi-cal institutions in Chongqing City is temporarily imperfect,the specialized persons still lack and the nutritional health service level needs to be further strengthened.

Objective: QL1604 is a highly selective, humanized monoclonal antibody against programmed death protein 1. We assessed the efficacy and safety of QL1604 plus chemotherapy as first-line treatment in patients with advanced cervical cancer. Methods: This was a multicenter, open-label, single-arm, phase II study. Patients with advanced cervical cancer and not previously treated with systemic chemotherapy were enrolled to receive QL1604 plus paclitaxel and cisplatin/carboplatin on day 1 of each 21-day cycle for up to 6 cycles, followed by QL1604 maintenance treatment. Results: Forty-six patients were enrolled and the median follow-up duration was 16.5 months. An 84.8% of patients had recurrent disease and 13.0% had stage IVB disease. The objective response rate (ORR) per Response Evaluation Criteria in Advanced Solid Tumors (RECIST) v1.1 was 58.7% (27/46). The immune ORR per immune RECIST was 60.9% (28/46).The median duration of response was 9.6 months (95% confidence interval [CI]=5.5–not estimable). The median progression-free survival was 8.1 months (95% CI=5.7–14.0). Fortyfive (97.8%) patients experienced treatment-related adverse events (TRAEs). The most common grade≥3 TRAEs (>30%) were neutrophil count decrease (50.0%), anemia (32.6%), and white blood cell count decrease (30.4%). Conclusion QL1604 plus paclitaxel-cisplatin/carboplatin showed promising antitumor activity and manageable safety profile as first-line treatment in patients with advanced cervical cancer. Programmed cell death protein 1 inhibitor plus chemotherapy may be a potential treatment option for the patient population who have contraindications or can’t tolerate bevacizumab, which needs to be further verified in phase III confirmatory study.