Objective To understand the current situation of nutrition literacy of primary and secondary school students in Shijingshan District of Beijing, and analyze its influencing factors, and to put forward targeted suggestions for improving the students’ nutrition literacy and promoting their healthy growth. Methods A multi-stage stratified cluster sampling method was used to select 2480 primary and secondary school students and their parents from 5 primary schools, 3 middle schools and 1 high school in Shijingshan District. The multivariate logistic regression model was used to analyze the factors influencing the attainment rate of nutrition literacy. Results The median score of nutrition literacy of 2480 primary and secondary school students from grades 1 to 12 was 77.86 (in hundred-mark system), the quartile range (IQR) was 16.96, and the attainment rate of nutrition literacy was 42.46%. The cognitive level (45.12%) was higher than the skill level (41.20%) among students from grades 3 to 12. In terms of skills, the attainment rate of food preparation was the lowest, at 30.38%. The scores of nutrition literacy of girls were higher than those of boys, and the scores of primary school students were higher than those of secondary school students. Students with different levels of caregiver’s education, family income, and family food environment had different scores of nutrition literacy, and the differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that the attainment rate of nutrition literacy was closely related to student’s gender and study stage, caregiver’s education level, and family food environment. Conclusion The nutrition literacy of primary and secondary school students in Shijingshan District still needs to be improved, especially in the aspect of skills. Targeted nutrition education should be carried out.

ObjectiveTo analyze the adverse reactions associated with the clinical use of Banxia （Rhizoma Pinelliae）- Wutou （Aconitum） in the same formula， with the aim of providing a reference for the safety of their clinical application. MethodsLiterature on the clinical application of antagonistic herbs "Banxia-Wutou" used in the same formula， published from January 1st， 2014， to June 30th， 2023， was retrieved from databases including CNKI， VIP， Wanfang， SinoMed， PubMed， Cochrane Library， and Embase. A database was established， and information related to adverse reactions was extracted， including descriptions， classifications， specific manifestations， management and outcomes， patients' primary diseases （western medicine diseases and traditional Chinese medicine diagnoses and syndromes）， and medication information （dosage， ratio， administration routes， and dosage forms）. ResultsA total of 79 researches simultaneously used antagonistic herbs Banxia-Wutou in the same formula and reported associated advers reactions. Gastrointestinal adverse reactions were the most common， with 8 studies reporting management of adverse reactions and 3 studies reporting improvement with no intervention. Among the 11 researches， the adverse reaction relieved to extant， while other 69 researches didn't report the managment of adverse reaction and its prognosis. For the primary disease in western medicine system， chronic bronchitis and chronic obstructive pulmonary disease （COPD） were most common， while gastric pain was the most common symptom in traditional Chinese medicine with spleen and kidney deficiency and spleen stomach cold deficiency being the most frequent syndromes. The most common Banxia dosage was 10 g， while for the Wutou， Fuzi （Radix Aconiti Lateralis Praeparata） was predominant with the highest dose at 15 g. The most frequent herbal combination was Banxia-fuzi， with a 1∶1 ratio. The main administration route was oral， and the primary dosage form was decoction. ConclusionGastrointestinal adverse reactions are the most common in the clinical use of Banxia-Wutou antagonistic herb combinations. Research on the safety of "Banxia-Wutou" combinations should focus on respiratory system diseases and spleen-stomach related conditions.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

In the realm of oral implantology, currently, there is no unanimous consensus or authoritative guideline regarding how to conduct implant restoration for cases involving oroantral communication or oroantral fistula. In these cases, the bony plate of the maxillary sinus floor is discontinuous, the oro-sinus mucosa is in communication, and a vertical alveolar bone defect exists. This paper presents a case of a patient with two oroantral communications who successfully underwent implant restoration via simple staged surgeries. Following a 2-year clinical follow-up, the implant remains in good condition, and the patient expresses satisfaction. The objective of this paper is to offer a simple, safe, and readily-promotable new approach for such cases.
Humans ; Dental Implantation, Endosseous/methods* ; Dental Implants ; Maxillary Sinus/surgery* ; Oroantral Fistula/surgery*

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.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Objective:To develop a scientific and practical questionnaire for general practice residents, and to conduct multidimensional and comprehensive evaluation of the quality of general practice teaching clinics.Methods:A preliminary draft of the questionnaire items was formulated based on a literature review and in-depth interviews. The Delphi method was employed to conduct two rounds of consultation with 14 experts. Following revisions, a convenience sampling method was used to invite general practice residents from three standardized residency training bases to test the reliability and validity of the questionnaire.Results:The questionnaire consisted of 23 items, covering the three dimensions of preparation, implementation process, and comprehensive evaluation of the teaching clinics. The response rates for the two rounds of the expert consultation were both 100.00%, with expert authority coefficients of 0.89 and 0.90, respectively. The overall Cronbach's α coefficient of the questionnaire was 0.93, and the correlation coefficients between each item score and the total score were all >0.30. Structural validity analysis revealed that three common factors were extracted from the questionnaire, with a cumulative variance contribution rate of 77.89%. Conclusions:The General Practice Teaching Clinic Quality Evaluation Questionnaire for Residents developed in this study demonstrates high reliability and validity. The questionnaire provides a scientific basis for the standardized assessment of teaching quality in general practice clinics. By incorporating resident feedback on the teaching process, the questionnaire promotes the development of a teaching clinic quality improvement mechanism focused on residents and plays a significant role in enhancing the teaching capabilities of supervising physicians in clinics.

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.