Cholangiocarcinoma (CCA) is a malignancy characterized by tumor cells originating in the liver or bile ducts, exhibiting features of cholangiocyte differentiation. It poses a significant clinical challenge due to the limited diagnostic and therapeutic options available. Robust animal models are essential for advancing our understanding of CCA pathogenesis and developing effective treatments. This review provides a comprehensive overview of CCA mouse models, highlighting various approaches, including chemical induction, genetically engineered models, and tumor xenografts. Each model is discussed in terms of its establishment techniques, pathological characteristics, and research significance, with a focus on intrahepatic CCA. Chemical induction models, such as diethylnitrosamine- and azoxymethane-induced models, offer insights into tumorigenesis processes, whereas genetically modified models involving alterations in key genes such as Kirsten rat sarcoma viral oncogene homolog, tumor protein 53, and isocitrate dehydrogenase serve as important tools for studying the molecular mechanisms underlying CCA. Xenograft models, including patient-derived xenografts, bridge the gap between experimental research and clinical applications, allowing for precise therapeutic evaluations. By comparing these models, this review underscores their respective advantages and limitations, paving the way for future studies aiming to optimize and innovate CCA modeling strategies.

Objective To evaluate the results of anorectal dynamics in patients with severe rec-tocele.Methods A retrospective analysis was conducted on the clinical data of 38 patients defini-tively diagnosed with severe rectocele at the pelvic floor center of the anorectal department of Nanjing Hospital of Traditional Chinese Medicine from January 2020 to January 2023.All patients underwent anorectal manometry,and the results of anorectal dynamics were analyzed.Results A total of 15 pa-tients(39.47％)had elevated anal resting pressure(ARP),20(52.63％)had normal ARP,and 3(7.89％)had decreased ARP.Five patients(13.16％)had elevated maximum anal sphincter pressure(MASP),9(23.68％)had normal MASP,and 24(63.16％)had decreased MASP.Nor-mal defecation relaxation reflex was observed in 15 patients(39.47％),and abnormal defecation re-laxation reflex was observed in 23 patients(60.53％).Ten patients(26.32％)had normal rectal defecation pressure,and 28(73.68％)had decreased rectal defecation pressure.Eleven patients(28.95％)had elevated rectal initial sensory threshold(RIST),27(71.05％)had normal RIST.Fifteen patients(39.47％)had elevated rectal defecation sensory threshold,21(55.26％)had normal rectal defecation sensory threshold,and 2(5.26％)had decreased rectal defecation sensory threshold.Three patients(7.89％)had elevated rectal maximum tolerable volume,26(68.42％)had normal rectal maximum tolerable volume,and 9(23.68％)had decreased rectal maximum tolerable vol-ume.ARP was moderately positively correlated with the chronic constipation severity(CSS)score(P=0.007,r=0.429),and abnormal defecation relaxation reflex was moderately negatively correla-ted with the CSS score(P=0.019,r=-0.329).In 3 patients(7.89％),both ARP and MASP were decreased,and both ARP and MASP were elevated in 5 patients(13.16％).Conclusion Pre-operative anorectal dynamics analysis is necessary for patients with severe rectocele to formulate a reasonable individualized surgical plan and postoperative rehabilitation program.

Objective: To investigate the association between plant-based diet and different types of obesity, so as to provide references for obesity prevention. Methods: Residents aged 35-75 years from 33 counties (cities, districts) in Zhejiang Province were selected as study subjects using a multistage stratified random sampling method between April and December 2024. Demographic information and living behaviors were collected using questionnaire surveys. Height, weight and waist circumference were measured, and body mass index (BMI) was calculated. BMI ≥28.0 kg/m2 was defined as obesity, waist circumference ≥90 cm in males or ≥85 cm in females was defined as central obesity, and individual with obesity who also had central obesity was defined as having compound obesity. Food intake over a 3-day period was collected using the consecutive 3-day 24-hour dietary recall method. The plant diet index (PDI), healthful plant diet index (HPDI), and unhealthful plant diet index (UPDI) were calculated, and categorized into quintiles (Q1-Q5) based on their distribution. Association between the PDI, PDI, UPDI and different types of obesity were analyzed using multivariable logistic regression models. Results: A total of 4 882 individuals were surveyed, including 2 233 males (45.74%) and 2 649 females (54.26%). The average age was (55.42±12.14) years. There were 537 individuals of obesity, 1 718 individuals of central obesity, and 500 individuals of compound obesity, with detection rates of 11.00%, 35.19%, and 10.24%, respectively. Multivariable logistic regression analysis showed that, after adjusting for demographic information and living behaviors, compared with Q1 group, HPDI Q5 group showed a 29.6% lower risk of obesity (OR=0.704, 95%CI: 0.525-0.943) and a 32.1% lower risk of compound obesity (OR=0.679, 95%CI: 0.502-0.918). Conversely, the UPDI Q5 group exhibited a 39.5% higher risk of obesity (OR=1.395, 95%CI: 1.032-1.886) and a 39.8% higher risk of compound obesity (OR=1.398, 95%CI: 1.025-1.907). No statistically significant association was found between PDI and obesity, central obesity, and compound obesity (all P>0.05). As HPDI increased, the risks of obesity and compound obesity showed decreasing trends; as UPDI increased, the risks of obesity and compound obesity showed increasing trends (all Ptrend<0.05). Conclusion A healthful plant-based diet is associated with reduced risks of obesity and compound obesity, while an unhealthful plant-based diet is associated with increased risks of obesity and compound obesity.

OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms. METHODS: Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy. RESULTS: The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (P<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (P<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). CONCLUSION This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
Humans ; Neck Pain/physiopathology* ; Acupuncture Therapy ; Female ; Male ; Adult ; Middle Aged ; Machine Learning ; Magnetic Resonance Imaging ; Young Adult ; Brain/physiopathology* ; Acupuncture Points ; Aged

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Objective:By analyzing the genetic risk of triglyceride-glucose index(Tyg)and insulin resistance(IR)for cardiovascular disease(CVD), to elucidate the extent to which the contribution of Tyg to the risk of CVD development is influenced by IR genetic risk.Methods:In this study, we selected data from a cohort of elderly people in the Kunshan community, screened 7, 385 individuals with both clinical and genomic data, and calculated the polygenic risk score of insulin resistance(IRPRS)for each participant based on publicly available IR genome-wide association data, and assessed the effect of genetic risk and Tyg level on the risk of developing CVD using a multivariate Cox proportional risk model.Calculating interactions to assess the effects of genetic risk and Tyg levels on the risk of developing CVD, the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD were assessed using a multivariate Cox proportional risk model, and subgroup analyses were performed for gender to assess the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD by gender.Results:In the univariate Cox model, Q3 and IRPRS with the highest TYG levels were significantly associated with the risk of CVD, respectively( HR=1.59, 95% CI: 1.33-1.89; P<0.001; HR=1.61, 95% CI: 1.18-2.20; P=0.003).After adjusting for multiple confounders, the Q3 Group with the highest TYG level was still significantly associated with the risk of CVD( HR=1.28, 95% CI: 1.05-1.57; P=0.014), the Association of TYG with the risk of CVD did not change significantly( HR=1.29, 95% CI: 1.05-1.57; P=0.014).We conducted a subgroup analysis by sex and found that among older men, 13, the highest levels of TYG and IRPRS were significantly associated with CVD risk, respectively( HR=1.70, 95% CI: 1.31.2.20; P<0.001; HR=1.98, 95% CI: 1.24-3.15; P=0.004).After adding IRPRS to the model, the Association of TYG with the risk of CVD remained unchanged( HR=1.69, 95% CI: 1.31-2.19; P<0.001).After adjusting for various confounders, Tyg remained significantly associated with the risk of CVD( HR=1.39, 95% CI: 1.04-1.88; P=0.028), the results showed that TYG remained significantly associated with the risk of CVD( HR=1.41, 95% CI: 1.05-1.90; P=0.023), and the association did not decrease.No Association of IRPRS with CVD risk was found in older women. Conclusions:IRPRS and TYG are the risk factors of CVD, and diet, exercise, drugs and other external factors on TYG are the main risk factors of CVD.For individuals with high genetic factors, the risk of CVD can still be reduced by lifestyle adjustments such as diet, exercise and drug intervention.

Nuclear medicine plays a pivotal role in diagnosis and treatment of tumors.The development of radionuclide probes with high affinity and specificity is an effective approach to achieve precise diagnosis and treatment of tumors.With high binding affinity,rapid tissue penetration and excellent pharmacokinetic properties,bicyclic peptide-based radionuclide probes demonstrate remarkable advantages in diagnosis and targeted therapy of tumors.The structural characteristics and design strategies of bicyclic peptide-based radionuclide probes were reviewed in this article,mainly focused on research progresses in targeting tumor-associated receptors.

[Objective]To investigate a simple method for the rapid detection and precise analysis of trace triptolide(TP)in vitro.[Methods]Initially,4-carboxy-4'-methyl-2,2'-bipyridine(Me-bpy-COOH)and cis-bis(2,2'-bipyridine)dichlororuthenium(Ⅱ)(cis-Ru(bpy)2CL2)were used as raw materials to synthesize tripyridine ruthenium monocarboxylic acid fluorescent reagent,namely bis(2,2'-bipyridyl)(4-methyl-4'-carboxy-2,2'-bipyridyl)ruthenium(Ⅱ)bishexafluorophosphate[Ru(bpy)2(bpy-COOH)2PF6].Subsequently,the pre-column derivatization reaction conditions of Ru(bpy)2(bpy-COOH)2PF6 with TP was optimized through orthogonal test and a methodological evaluation of the high performance liquid chromatography-fluorescence detection(HPLC-FLD)of triptolide-terpyridine ruthenium derivative(TTRD)was conducted.Taking commercially available Kunxian Capsules as object,the newly established evaluation method was adopted for detection.[Results]A fluorescent derivative of TP,TTRD was obtained and was characterized by proton nuclear magnetic resonance(1H-NMR)and ultra high performance liquid chromatography-mass spectrometry(UPLC-MS).The optimized pre-column derivatization reaction conditions were a 4:1 molar ratio of Ru(bpy)2(bpy-COOH)2PF6 to TP,a reaction time of 2 hours and a reaction temperature of 45℃.This study presented a HPLC-FLD method for the pre-column derivatization of TP.The method demonstrated a good linear relationship within the range of 50 to 1 200 μg·L-1.The TP content in Kunxian Capsules was measured by using this method,yielding a result of 78.78 μg·g-1,which fell within the prescribed range of national drug standards.[Conclusion]The pre-column-derived HPLC-FLD method for TP exhibited good sensitivity,accuracy and reliability,expanding the HPLC detection range for TP.

Objective:To investigate the effect and mechanism of Jianpi Qinghua granule in improving skeletal muscle insulin resistance induced by long-term low-dose cadmium exposure in rats.Methods:A total of 24 SPF-grade healthy 2-month-old Sprague-Dawley rats were assigned to normal control(NC) group, cadmium exposure(Cd) group, and Jianpi Qinghua granule protection(Cd+ JPQHG) group. After 24 weeks, fasting blood glucose and insulin levels were measured, and HOMA-IR was calculated. The intraperitoneal glucose tolerance test and insulin tolerance test were conducted to assess insulin sensitivity. Skeletal muscle tissues were extracted for Western blot analysis to detect levels of insulin signaling pathway-related proteins. Immunofluorescence was used to assess the translocation of glucose transporter 4(GLUT4), and oxidative stress markers were measured.Results:Compared to the NC group, the Cd group showed significant increases in fasting plasma glucose, fasting insulin levels, and HOMA-IR after 24-week exposure. Abnormal glucose and insulin tolerance were also observed in the Cd group. The 12-week intervention with Jianpi Qinghua granule significantly improved glucose metabolism and alleviated the abnormalities in glucose and insulin tolerance. Western blot results indicated that the phosphorylation levels of PI3K and Akt in the skeletal muscle of the Cd group were significantly reduced compared to the NC group, while these levels were significantly elevated in the Cd+ JPQHG group, along with increased translocation of GLUT4 to the cell membrane. Additionally, cadmium exposure significantly increased H 2O 2 and malondialdehyde levels while decreased antioxidant enzyme activity. These oxidative stress indicators improved significantly after Jianpi Qinghua granule intervention( P<0.05). Conclusion:Jianpi Qinghua granule may improve skeletal muscle insulin resistance and glucose metabolism disorders due to long-term low-dose cadmium exposure by reducing oxidative stress, regulating the phosphorylation levels of key proteins in the insulin signaling pathway, and promoting GLUT4 translocation to the cell membrane.

Objective:To investigate the assoication of amino acid metabolism levels with severity of diabetic peripheral neuropathy(DPN) in patients with type 2 diabetes mellitus.Methods:A retrospective analysis was conducted on 118 patients with type 2 diabetes mellitus who were admitted to Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from January to April 2021. Patients were divided into groups according to the Toronto Clinical Scoring System(TCSS), and amino acid profiling was performed. General demographics and biochemical indicators of each group were collected to analyze the relationship between DPN severity and amino acid metabolism.Results:As TCSS scores increased, patients were older and had a longer duration of diabetes. Statistically significant differences in leucine, isoleucine, tryptophan, and phenylalanine levels were observed among the four groups. After adjusting for confounding variables using covariance analysis, when the TCSS score was≥13, the serum phenylalanine level was significantly elevated, and the difference was statistically significant( P<0.05). Multivariate linear regression analysis indicated that TCSS score was an influencing factor for phenylalanine levels( P=0.010). Multivariate logistic regression analysis demonstrated that higher serum phenylalanine levels correlated with higher TCSS scores( OR=1.047, 95% CI 1.011-1.083, P=0.010). Receiver operating characteristic(ROC) curve analysis revealed that serum phenylalanine and the difference between phenylalanine and tryptophan had diagnostic value for severe DPN patients, with areas under the ROC curve of 0.673(95% CI 0.553-0.793, P=0.006) and 0.746(95% CI 0.641-0.852, P<0.001) respectively. Conclusions:The levels of phenylalanine and tryptophan in the serum of patients with type 2 diabetes mellitus are correlated with the severity of DPN. These findings suggest that serum phenylalanine, tryptophan, or their metabolic products may be involved in the pathogenesis and progression of DPN.