ObjectiveTo evaluate the pregnancy outcomes of assisted reproductive technology （ART） in women with a history of thyroid cancer who retained fertility intentions after completing cancer treatment. MethodsA retrospective analysis was performed on 61 patients with a history of thyroid cancer who underwent in vitro fertilization/intracytoplasmic sperm microinjection and embryo transfer （IVF/ICSI-ET）. These patients were included as the case group. A total of 122 non-cancer patients who received ART during the same period were selected as the control group using 1∶2 matching based on age and oocyte retrieval time. Baseline characteristics， outcomes of the first ART cycle， and cumulative pregnancy outcomes were compared between the two groups. ResultsThere was no significant difference in the basic data， the total amount of gonadotropin （Gn） and the days of use between the case group and the control group （P>0.05）. However， the case group had significantly fewer retrieved oocytes， mature oocytes （MII）， lower fertilization and cleavage rates， and fewer transferable and high-quality embryos， as well as fewer embryos transferred during the first cycle （P < 0.05）. However， there was no significant difference in the rate of first embryo implantation and first clinical pregnancy between the two groups （P>0.05）. In the analysis of cumulative outcomes， the two groups did not show statistically significant differences in the cumulative pregnancy rate， clinical pregnancy rate per transfer cycle， the number of oocyte retrieval cycles required per live birth， the number of embryo transfer cycles required per live birth， and the number of embryos used for each live birth （P>0.05）. However， the cumulative live birth rate was significantly lower in the case group compared to the control group （P=0.005）. ConclusionAfter treatment for thyroid cancer， when ART is used to help pregnant women， the pregnancy outcome is comparable to that of women without tumors. Individualized reproductive management and timely fertility preservation strategies are recommended to optimize reproductive outcomes in this population.

[摘 要] 目的：探讨环状RNA CEMIP（circCEMIP）对膀胱癌UMUC-3细胞增殖、迁移和侵袭的影响及其分子机制。方法：通过TCGA数据库分析circCEMIP在膀胱癌组织中的表达水平，分析其表达与膀胱癌患者临床分期及生存期的关系。采用qPCR法检测circCEMIP在膀胱癌细胞5637、UMUC-3、MGH-U3、J82和T24中的表达。利用RNA干扰技术，分别将si-circCEMIP及其阴性对照（si-NC）、anti-miR-335及其阴性对照（anti-miR-NC）转染UMUC-3细胞，记为si-circCEMIP组、si-NC组、si-circCEMIP + anti-miR-335组和si-circCEMIP + anti-miR-NC组。采用克隆形成实验、划痕愈合实验和Transwell实验分别检测circCEMIP和miR-335表达对UMUC-3细胞增殖、迁移和侵袭能力的影响，双萤光素酶报告基因实验验证circCEMIP与miR-335的靶向关系，WB法检测细胞中VEGF-C信号通路相关蛋白的表达。构建UMUC-3细胞裸鼠皮下移植瘤模型，观察敲低circCEMIP对移植瘤生长的影响。结果：膀胱癌组织中circCEMIP呈高表达（P < 0.01），其表达水平与膀胱癌的临床分期正相关（P < 0.01），circCEMIP高表达患者生存率较低（P < 0.01）。circCEMIP在膀胱癌5637、UMUC-3、MGH-U3、J82和T24细胞中呈高表达（均P < 0.01）。敲低circCEMIP显著降低UMUC-3细胞的增殖、迁移和侵袭能力（均P < 0.01）。circCEMIP可靶向结合miR-335（P < 0.01），敲低circCEMIP能显著上调miR-335表达（P < 0.01）。抑制miR-335表达能逆转敲低circCEMIP对UMUC-3细胞增殖、迁移和侵袭的抑制作用（均P < 0.01）。敲低circCEMIP能明显下调VEGF-C信号通路相关蛋白VEGF-C、MMP-2、MMP-9和β-catenin表达（均P < 0.01），抑制miR-335表达能部分逆转敲低circCEMIP对该通路相关蛋白表达的抑制作用（均P < 0.01）。体内实验证实，敲低circCEMIP能够抑制裸鼠膀胱癌移植瘤的生长（P < 0.01）。结论：敲低circCEMIP通过上调miR-335表达抑制膀胱癌UMUC-3细胞的增殖、迁移和侵袭。

Objective To estimate the lifetime attributable risk (LAR) of thyroid cancer due to radiation in individuals undergoing chest computed tomography (CT) examinations. Methods Scanning parameters were retrospectively collected from DICOM files of 660 individuals who underwent chest CT scans between 2022 and 2024. Individuals were stratified by age and sex. Size-specific dose estimates were calculated using a formula based on the volume CT dose index for each individual. The radiation doses received by the thyroid were estimated. The cancer risk prediction model from the US National Academy of Sciences report on the Biological Effects of Ionizing Radiation was referenced to predict the LAR of thyroid cancer. Results The LAR of thyroid cancer for males/females was 25.170/100 000 and 140.177/100 000 in the ≥ 0 and<5 years of age group, 21.779/100 000 and 102.498/100 000 in the ≥5 and <10 years of age group, 22.987/100 000 and 128.934/100 000 in the ≥10 and <15 years of age group, and 4.979/100 000 and 12.490/100 000 in the ≥15 years of age group. A Spearman correlation analysis showed that age was highly correlated with volume CT dose index, size-specific dose estimates, radiation dose received by the thyroid, and thyroid cancer LAR, with correlation coefficients of 0.887, 0.737, 0.737, and −0.41 (P<0.01), respectively. Sex was correlated with radiation dose received by the thyroid and thyroid cancer LAR, with correlation coefficients of 0.179 and 0.441 (P<0.01), respectively. Conclusion Chest CT scan leads to an increased LAR of thyroid cancer. Appropriate protective measures for the thyroid should be considered during chest CT scan to reduce the impact of radiation on the thyroid.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Objective: To evaluate the effect of community comprehensive management model intervention among patients with dyslipidemia, so as to provide the reference for optimizing community management strategies and improving the target achievement rate for blood lipids among this population. Methods: From May to June 2023, a multi-stage stratified random sampling method was employed to select patients with dyslipidemia from primary healthcare institutions in Jiaxing City, Zhejiang Province. Eligible participants were randomly assigned to either a control group or an intervention group. The control group received routine management, while the intervention group was subjected to a community comprehensive management model in addition to the routine care. Both groups were followed up for 24 months. Data on demographic characteristics, lifestyle behaviors, physical examination indices, and blood biochemical indicators were collected at baseline and after the intervention through questionnaires, physical examinations, and laboratory tests. Changes in obesity rate, central obesity rate, target achievement rates for blood lipids, blood pressure, and blood glucose, as well as lifestyle modifications, were analyzed. Differences between the two groups before and after the intervention were assessed using generalized estimating equations (GEE). Results: The control group consisted of 560 patients, including 303 females (54.11%) and 430 individuals aged ≥65 years (76.79%). The intervention group also included 560 patients, with 300 females (53.57%) and 431 individuals aged ≥65 years (76.96%). Before the intervention, no statistically significant differences were observed between the two groups in terms of gender, age, educational level, history of chronic diseases, and atherosclerotic cardiovascular disease risk stratification (all P>0.05). After 24 months of intervention, interaction effects between group and time were observed for obesity rate, central obesity rate, target achievement rate for blood lipids, target achievement rate for blood glucose, composite target achievement rate, physical activity rate, and medication adherence (all P<0.05). Specifically, the intervention group demonstrated lower rates of obesity and central obesity, and higher target achievement rate of blood lipids, target achievement rate of blood glucose, composite target achievement rate, physical activity rate, and medication adherence compared to the control group. Conclusion The community comprehensive management model contributed to improvements in multiple metabolic parameters (including body weight, waist circumference, blood lipids, and blood glucose) among patients with dyslipidemia, and was associated with increased physical activity rate and medication adherence.

ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.

BackgroundSchizophrenia is a complex， chronic and severe mental disorder， and the pathogenesis of which has not been fully elucidated. The abnormalities in lipid metabolism and circulating metabolomes have already been implicated in the pathophysiology of schizophrenia. However， available studies have mainly focused on a few liposomes and circulating metabolites， failing to systematically reveal the mediating role of circulating metabolomes in the causal relationship between liposomes and schizophrenia. ObjectiveTo uncover mediating role of circulating metabolomes in the causal relationship between liposomes and schizophrenia， thereby providing biomarkers and potential therapeutic targets for the prevention and treatment of schizophrenia. MethodsData from Genome-Wide Association Studies （GWAS） were analyzed， taking data on 179 liposomes as exposure variables， data on 123 circulating metabolites as intermediate variables， and data on schizophrenia as outcome variable. A two-step， two-sample Mendelian randomization analysis was conducted using the inverse-variance weighted （IVW）， MR- Egger， Weighted median， and Weighted mode methods to study the causal relationship of liposomes with schizophrenia and the mediating role of circulating metabolomes in the relationship. ResultsIVW model identified 8 lipids associated with schizophrenia without reverse causality. There were 5 circulating metabolomes strongly associated with schizophrenia. Acetate played a significant mediating role in the causal relationship between phosphatidylinositol （18：0_18：2） and schizophrenia （P=0.023， 95% CI： 0.036~0.532）， accounting for 28.4% of the causal relationship. ConclusionThis study demonstrates a causal relationship between liposomes and schizophrenia， with phosphatidylinositol being a risk factor in the progression of schizophrenia， and acetate playing a mediating role in this process. ［Fund by National Natural Science Foundation of China General Program （number， 82271546）； Shanxi Merit Funding for Overseas Students Sci-Tech Activities Project （number， 20240041）； Shanxi Province Science and Technology Innovation Leading Talent Team Project （number， 202304051001049）； Shanxi Scientific Research Foundation for the Returned Overseas Chinese Scholars （number， 2022-190）； "Six Measures for Health Care Prosperity" Specialized Research Program （number， Y2024008）］

To address the current issue of low performance in predicting the binding affinity between antigenic peptides and specific MHC class II molecules, which fails to meet clinical requirements, we proposed T5MHCII, a deep learning-based prediction model for the affinity of MHC II class molecules to peptides. The model employed the knowledge previously acquired from the protein language model ProtT5 to extract the amino acid sequences via a transfer learning approach, thereby generating high-quality characterizations. This knowledge was then integrated with the robust learning abilities of deep learning to develop a novel model with enhanced predictive capabilities. The results of the five-fold cross-validation demonstrated that the model exhibited superior performance compared to NetMHCIIpan-3.2, PUFFIN, DeepMHCII, and RPEMH, with an AUC of 0.893±0.003 and a PCC of 0.780±0.006. The leave-one-out cross-validation (LOOCV) further demonstrated that the model exhibited enhanced generalization capabilities. This study proposes a novel approach to enhance the precision of peptide-MHCII prediction in the context of limited data affinity through the application of deep learning techniques.

Alzheimer’s disease (AD), a progressive neurodegenerative disorder and the leading cause of dementia in the elderly, is characterized by severe cognitive decline, loss of daily living abilities, and neuropsychiatric symptoms. This condition imposes a substantial burden on patients, families, and society. Despite extensive research efforts, the complex pathogenesis of AD, particularly the early mechanisms underlying cognitive dysfunction, remains incompletely understood, posing significant challenges for timely diagnosis and effective therapeutic intervention. Among the various cellular components implicated in AD, GABAergic interneurons have emerged as critical players in the pathological cascade, playing a pivotal role in maintaining neural network integrity and function in key brain regions affected by the disease. GABAergic interneurons represent a heterogeneous population of inhibitory neurons essential for sustaining neural network homeostasis. They achieve this by precisely modulating rhythmic oscillatory activity (e.g., theta and gamma oscillations), which are crucial for cognitive processes such as learning and memory. These interneurons synthesize and release the inhibitory neurotransmitter GABA, exerting potent control over excitatory pyramidal neurons through intricate local circuits. Their primary mechanism involves synaptic inhibition, thereby modulating the excitability and synchrony of neural populations. Emerging evidence highlights the significant involvement of GABAergic interneuron dysfunction in AD pathogenesis. Contrary to earlier assumptions of their resistance to the disease, specific subtypes exhibit vulnerability or altered function early in the disease process. Critically, this impairment is not merely a consequence but appears to be a key driver of network hyperexcitability, a hallmark feature of AD models and potentially a core mechanism underlying cognitive deficits. For instance, parvalbumin-positive (PV⁺) interneurons display biphasic alterations in activity. Both suppressing early hyperactivity or enhancing late activity can rescue cognitive deficits, underscoring their causal role. Somatostatin-positive (SST⁺) neurons are highly sensitive to amyloid β-protein (Aβ) dysfunction. Their functional impairment drives AD progression via a dual pathway: compensatory hyperexcitability promotes Aβ generation, while released SST-14 forms toxic oligomers with Aβ, collectively accelerating neuronal loss and amyloid deposition, forming a vicious cycle. Vasoactive intestinal peptide-positive (VIP⁺) neurons, although potentially spared in number early in the disease, exhibit altered firing properties (e.g., broader spikes, lower frequency), contributing to network dysfunction (e.g., in CA1). Furthermore, VIP release induced by 40 Hz sensory stimulation (GENUS) enhances glymphatic clearance of Aβ, demonstrating a direct link between VIP neuron function and modulation of amyloid pathology. Given their central role in network stability and their demonstrable dysfunction in AD, GABAergic interneurons represent promising therapeutic targets. Current research primarily explores three approaches: increasing interneuron numbers (e.g., improving cortical PV⁺ interneuron counts and behavior in APP/PS1 mice with the antidepressant citalopram; transplanting stem cells differentiated into functional GABAergic neurons to enhance cognition), enhancing neuronal activity (e.g., using low-dose levetiracetam or targeted activation of specific molecules to boost PV⁺ interneuron excitability, restoring neural network γ‑oscillations and memory; non-invasive neuromodulation techniques like 40 Hz repetitive transcranial magnetic stimulation (rTMS), GENUS, and minimally invasive electroacupuncture to improve inhibitory regulation, promote memory, and reduce Aβ), and direct GABA system intervention (clinical and animal studies reveal reduced GABA levels in AD-affected brain regions; early GABA supplementation improves cognition in APP/PS1 mice, suggesting a therapeutic time window). Collectively, these findings establish GABAergic interneuron intervention as a foundational rationale and distinct pathway for AD therapy. In conclusion, GABAergic interneurons, particularly the PV⁺, SST⁺, and VIP⁺ subtypes, play critical and subtype-specific roles in the initiation and progression of AD pathology. Their dysfunction significantly contributes to network hyperexcitability, oscillatory deficits, and cognitive decline. Understanding the heterogeneity in their vulnerability and response mechanisms provides crucial insights into AD pathogenesis. Targeting these interneurons through pharmacological, neuromodulatory, or cellular approaches offers promising avenues for developing novel, potentially disease-modifying therapies.

Objective: To understand the indoor light at night (LAN) exposure intensity during sleep among children and adolescents in Shanghai, so as to provide a basis for exploring potential health risks and formulating effective interventions. Methods: From April to December in 2024, a total of 628 students in grades 4-7 were recruited from three schools in Shanghai. A portable illuminance meter was used to measure LAN for one week, and participants recorded their sleep time. The Kruskal-Wallis H- test was used for comparison between groups, and the error bar chart was used to show the trend and variation range of average LAN exposure intensity in different sleep periods. Results: The indoor LAN exposure intensity of children and adolescents in Shanghai was [2.4(0.8, 5.9)lx] during sleep, and 28.8% of children and adolescents were exposed to indoor LAN≥5 lx. There was no significant differences in indoor LAN exposure intensity between boys [2.4(1.0, 5.9)lx] and girls [2.3(0.7, 5.9)lx] ( Z=-0.86, P > 0.05 ). The indoor LAN exposure intensity of primary school students [2.9(1.1, 6.6)lx] was higher than that of junior high school students [1.0(0.3, 3.1)lx] ( Z =-5.87), and indoor LAN exposure intensity of students in the main urban area [3.2(1.1, 7.8)lx] was higher than that of rural students [1.6(0.5, 4.3)lx] ( Z =-5.23)(both P <0.05). The indoor LAN exposure intensity showed an overall decreasing trend during sleep of children and adolescents ( tau=-0.81, P =0.02), with a slight increase before waking up. Conclusions Indoor LAN exposure intensity among children and adolescents in Shanghai is generally high, especially among primary school students and students living in the main urban area. Health policy and education should be strengthened to reduce the impact of LAN on children and adolescent health.