Objective To analyze personal external exposure dose monitoring results of pet hospital radiation workers in Beijing from 2022 to 2024, and to provide scientific evidence for developing effective monitoring protocols, standardizing radiological practices in pet hospitals, and protecting the health of both workers and the public. Methods Personal dose equivalents, H_p(10), were measured using GR-200A thermoluminescent dosimeters. Monitoring included radiation workers at 286 pet hospitals across all 16 districts of Beijing from 2022 to 2024. Each monitoring cycle lasted no more than 90 days. Annual effective doses were calculated by summing the results of four consecutive quarterly cycles. Data were processed and analyzed using Access and TLPS 6.41 software. All evaluations complied with national laws, regulations, and technical standards. Results A total of 1,579 person-time measurements were recorded. Only 51.87% of workers completed the full four-cycle annual monitoring schedule. Over the study period, the mean annual effective dose was 0.29 mSv, and the median dose was 0.14 mSv. The collective effective dose was 238.34 man-mSv. Notably, 94.26% of workers had annual effective doses below 1.0 mSv, and only 0.12% had doses at or above 5 mSv. Dose levels showed a statistically significant downward trend (P<0.05). Both independent and chain-branded clinics had a median annual effective dose of 0.14 mSv. In independent clinics, the proportion of workers with doses below 1.0 mSv increased from 80.77% in 2022 to 98.82% in 2024. In chain-branded clinics, this proportion rose from 96.88% to 98.11% over the same period. Conclusion Radiation exposure among pet hospital workers in Beijing remains at low levels, well below regulatory limits, and shows an overall declining trend. The disparity in personal doses between independent and chain-branded pet hospitals has diminished over time. Nevertheless, the industry still faces challenges such as high staff turnover and low monitoring completion rates. Expanding monitoring coverage and harmonizing protection standards would provide a scientific basis for precise, nationwide regulation.

Objective This study compared organ doses and effective doses in typical adult patients during dental CBCT examinations using a head-and-neck anthropomorphic phantom and a whole-body phantom. It aimed to determine an appropriate method for evaluating effective dose in dental CBCT. Methods A domestically manufactured dental CBCT unit was used to scan an adult male anthropomorphic phantom under simulated clinical conditions. Thermoluminescent dosimeters were placed on organs and tissues of a whole-body anthropomorphic phantom. Organ equivalent doses were measured, and effective doses were calculated using two approaches: one based on all organ doses from the whole-body phantom, and the other based only on head-and-neck organ doses. Entrance surface air kerma (K_a,e) was also measured at the lens of the eye, thyroid, thymus, and gonads. Results The effective dose estimated with the whole-body phantom was 382.03 μSv. The effective dose estimated with the head-and-neck phantom was 354.03 μSv. The radiation dose to head-and-neck organs and tissues accounted for 92.7% of the total effective dose. The highest K_a,e values were found in the parotid glands, followed by the lens of the eye. Conclusion During dental CBCT scanning, the contribution of organ below the head and neck to the total effective dose is limited. To simplify dose assessment procedures, the effective dose for the patient can be evaluated using only a head-and-neck phantom.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.

Peroxisome proliferator-activated receptor gamma(PPAR-γ)is a member of the ligand-activated nuclear tran-scription factor superfamily.Activated PPAR-γ is involved in the regulation of many central nervous system(CNS)events,and is involved in cholesterol metabolism by inducing or inhibi-ting a series of gene pathways,thereby inhibiting the deposition of β-amyloid protein(Aβ).It plays an important neuroprotec-tive role in Alzheimer's disease(AD),improves memory and cognition in AD,and is a potential target for AD.Drug develop-ment aimed at restoring cholesterol homeostasis may be a poten-tial strategy to counteract AD.By analyzing the distribution and structure of PPAR-γ,focusing on the biological correlation be-tween PPAR-γ-mediated cholesterol metabolism and AD,this paper describes the mechanism regulation of PPAR-γ on key proteins,genes and their corresponding molecules,providing a new reference for the treatment of AD.

Objective To investigate the population characteristics,distribution of traditional Chinese medicine(TCM)syndromes and influencing factors of long-term prognosis of diarrhea-predominant irritable bowel syndrome(IBS-D),and to provide evidence for the formulation of intervention program for IBS-D patients.Methods A total of 124 patients with IBS-D admitted to the medical institutions of the project team members from July 2020 to August 2022 were selected.According to the scoring results of IBS Quality of Life Measure(IBS-QOL),the patients were divided into the good prognosis group(81 cases)and the poor prognosis group(43 cases).The distribution of TCM syndromes in patients with IBS-D was explored,and the difference of IBS-QOL scores of the patients between good prognosis group and poor prognosis group was compared.Univariate logistic regression analysis and multivariate logistic regression analysis were used to determine the main risk factors for poor prognosis in patients with IBS-D.Results(1)The analysis of population characteristics showed that there was no significant difference in the proportion of male and female patients with IBS-D.The patients with IBS-D were usually middle-aged,and had a large interval span of the course of disease.The severity of their symptoms was mostly moderate.All of the patients with IBS-D had various degrees of anxiety and depression,and had nutritional imbalance.(2)The distribution of TCM syndromes in the patients with IBS-D were shown as the following:78 cases were identified as liver depression and spleen deficiency type,accounting for 62.90％;26 cases were identified as spleen-qi deficiency type,accounting for 20.97％;20 cases were identified as spleen and kidney yang deficiency type,accounting for 16.13％.(3)Analysis of IBS-QOL score showed that compared with the good prognosis group,the items scores of negative emotion,physical function,behavioral disorder,health status,being fastidious about food,social function,sexual behavior and interpersonal relationship of IBS-QOL in the poor prognosis group were significantly lowered(P＜0.01).(4)The univariate analysis showed that the risk of poor prognosis in patients with IBS-D would be increased by the factors of age,education level,course of disease,severity of symptoms,anxiety state,depression state,TCM syndrome types,Acute Physiology and Chronic Health Evaluation scoring system Ⅱ(APACHE 11)score,complication of neurological diseases,hemoglobin level,albumin level and total protein level(P＜0.01).(5)The multivariate Logistic regression analysis showed that the risk factors for poor prognosis of IBS-D patients involved age,education level below junior high school,the severity of symptoms being severe,Self-Rating Anxiety Scale(SAS)score,Self-Rating Depression Scale(SDS)score,TCM syndrome being liver depression and spleen deficiency type,hemoglobin level,albumin level and total protein level(P＜0.01).Conclusion Most of IBS-D patients exert long-term poor prognosis,and their long-term prognosis is affected by the factors of age,education level,severity of symptoms,anxiety and depression state,nutritional imbalance and TCM syndrome being liver depression and spleen deficiency type.The identification of the risk factors of poor prognosis will provide evidence for the formulation and adjustment of clinical intervention programs.

Objective:To investigate the current situation of diet management in patients with colorectal cancer after stent-based diverting technique, and to provide basis for formulating relevant nursing intervention strategies.Methods:Objective sampling method was used to conduct semi-structured interviews on 15 patients who underwent stent-based diverting technique for colorectal cancer and had the bypass tube removed from Sir Run Run Shaw Hospital Affiliated to Medical College of Zhejiang University from March to July 2023. The interview outline was established based on information-motivation-behavioral skills(IMB) model, and the data were analyzed, summarized and extracted by Colaizzi 7-step analysis method.Results:There were 10 males and 5 females, aged 34-76 years old. According to the three elements of the IMB model, the current situation of diet management was summarized into nine themes. The information included the difficulty in obtaining effective diet guidance information, the lack of specific diet guidance content, the need for individualized diet information guidance mode, and the poor continuity of information exchange after discharge. The motivations included ignoring the importance of diet management, dislike the taste of oral nutritional preparations, and weak support from family members. Behavioral skills include inadequate tube care skills and lack of oral nutrition preparation skills.Conclusions:There are many problems in the diet management of patients after colorectal cancer stent-based diverting technique. Medical staff should optimize the diet education information of colorectal cancer patients after surgery, provide multi-level, multi-time and multi-form continuous care, mobilize the active participation of family members, improve the motivation of patients′ diet management, refine the nursing process of the bypass tube, strengthen the application guidance of oral nutrition preparation skills, and improve patients′ diet management ability.

Objective To construct an artificial intelligence assisted diagnosis model based on deep learning for dynamically recognizing gastric lesions and their locations under gastroscopy in real time,and to evaluate its ability to detect and recognize gastric lesions and their locations.Methods The gastroscopy videos of 104 patients in our hospital was retrospectively analyzed,and the video frames were manually annotated.The annotated picture frames of lesion category were divided into the training set and the validation set according to the ratio of 8∶2,and the annotated picture frames of location category were divided into the training set and the validation set according to the patient sources at the ratio of 8∶2.These sets were utilized for training and validating the respective models.YoloV4 model was used for the training of lesion recognition,and ResNet152 model was used for the training of location recognition.The accuracy,sensitivity,specificity,positive predictive value,negative predictive value and location recognition accuracy of the auxiliary diagnostic model were evaluated.Results A total of 68 351 image frames were annotated,with 54 872 frames used as the training set,including 41 692 frames for lesion categories and 13 180 frames for location categories.The validation set consisted of 13 479 frames,comprising 10 422 frames for lesion categories and 3 057 frames for location categories.The lesion recognition model achieved an overall accuracy of 98.8%,with a sensitivity of 96.6%,specificity of 99.3%,positive predictive value of 96.3%,and negative predictive value of 99.3% in validation set.Meanwhile,the location recognition model demonstrated an top-5 accuracy of 87.1% .Conclusion The artificial intelligence assisted diagnosis model based on deep learning for real-time dynamic recognition of gastric lesions and their locations under gastroscopy has good ability in lesion detection and location recognition,and has great clinical application prospects.

Nonalcoholic fatty liver disease （NAFLD） is a multisystem disease associated with obesity， insulin resistance， and dyslipidemia and has a complex pathogenesis. Studies have shown that gut microbiota dysbiosis is closely associated with the onset of NAFLD， and traditional Chinese medicine treatment can improve the laboratory markers and clinical symptoms of NAFLD patients by regulating intestinal microbiota and its metabolites. This article elaborates on the association between NAFLD and gut microbiota， the involvement of gut microbiota dysbiosis in the pathogenesis of NAFLD， and the possible mechanism of traditional Chinese medicine treatment in improving NAFLD from the perspective of gut microbiota， in order to provide new ideas for the treatment of NAFLD.