AIM:To evaluate the effects of periocular injection of triamcinolone acetonide combined with dexamethasone on ocular surface function and tear dynamics in patients with thyroid-associated ophthalmopathy(TAO).METHODS: In this single-center retrospective study, 26 TAO patients(52 eyes)treated between September 2020 and September 2023 received periocular injections of triamcinolone acetonide(20 mg)and dexamethasone(2.5 mg). Clinical parameters, including clinical activity score(CAS), ocular surface disease index(OSDI), Schirmer I test(SⅠt), tear film breakup time(BUT), tear meniscus height(TMH), corneal fluorescein staining(FL), meibomian gland loss, and lipid secretion score, were assessed at baseline, 1 wk, and 1 mo post-injection.RESULTS: There were statistically significant differences in CAS, OSDI, SⅠt, BUT, TMH, FL score, and meibomian gland secretion score before and after injection in the included patients(all P<0.05). At 1 wk after injection, there were differences in CAS, OSDI, SⅠt, BUT, TMH, FL score, and meibomian gland secretion score compared with those before injection(all P<0.0167). At 1 mo after injection, there were differences in CAS, OSDI, SⅠt, BUT, TMH, FL score, and meibomian gland secretion score compared with those at 1 wk after injection(all P<0.0167). At 1 mo after injection, there were no differences in CAS, OSDI, SⅠt, BUT, TMH, FL score, and meibomian gland secretion score compared with those before injection(all P>0.05). There was a difference in meibomian gland dropout score before and after injection in the included patients(P<0.05), but pairwise comparisons showed no differences(P=0.900, 0.306). During the treatment period, 1 patient experienced transient elevation of intraocular pressure(25 mmHg), which was alleviated after control with intraocular pressure-lowering medication, and no cases of secondary glaucoma occurred.CONCLUSION: Periocular injection of triamcinolone acetonide combined with dexamethasone provides short-term improvement in ocular surface symptoms, tear film stability and secretion in TAO patients. However, efficacy diminishes over time and does not reverse structural damage. Long-term maintenance therapy is recommended.

OBJECTIVES: To investigate the mechanism by which transforming growth factor‑β (TGF‑β) regulates major histocompatibility complex class I (MHC-I) expression in hepatocellular carcinoma (HCC) cells and its role in immune evasion of HCC. METHODS: HCC cells treated with TGF‑β alone or in combination with SB-431542 (a TGF-β type I receptor inhibitor) were examined for changes in MHC-I expression using RT-qPCR and Western blotting. A RNA interference experiment was used to explore the role of miR-23a-3p/IRF1 signaling in TGF‑β‑mediated regulation of MHC-I. HCC cells with different treatments were co-cultured with human peripheral blood mononuclear cells (PBMCs), and the changes in HCC cell proliferation was assessed using CCK-8 and colony formation assays. T-cell cytotoxicity in the co-culture systems was assessed with lactate dehydrogenase (LDH) release and JC-1 mitochondrial membrane potential assays, and T-cell activation was evaluated by flow cytometric analysis of CD69 cells and ELISA for TNF-α secretion. RESULTS: TGF‑β treatment significantly suppressed MHC-I expression in HCC cells and reduced T-cell activation, leading to increased tumor cell proliferation and decreased HCC cell death in the co-culture systems. Mechanistically, TGF-β upregulated miR-23a-3p, which directly targeted IRF1 to inhibit MHC-I transcription. Overexpression of miR-23a-3p phenocopied TGF‑β‑induced suppression of IRF1 and MHC-I. CONCLUSIONS We reveal a novel immune escape mechanism of HCC, in which TGF‑β attenuates T cell-mediated antitumor immunity by suppressing MHC-I expression through the miR-23a-3p/IRF1 signaling axis.
Humans ; MicroRNAs/genetics* ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Interferon Regulatory Factor-1/metabolism* ; Transforming Growth Factor beta/metabolism* ; Signal Transduction ; Histocompatibility Antigens Class I/metabolism* ; Cell Line, Tumor ; Tumor Escape ; Coculture Techniques

Objective To develop a risk prediction model for ischemic stroke in symptomatic intracranial atherosclerotic stenosis(ICAS)patients based on high-resolution magnetic resonance imaging(HR-MRI)and arterial spin labeling(ASL)imaging.Methods A total of 142 patients were included and divided into acute ischemic stroke(AIS)and transient ischemic attack(TIA)groups based on stroke occurrence.Clinical risk factors,plaque characteristics,and arterial transit artifact(ATA)presence on ASL images were compared between the two groups.Multivariate logistic regression analysis was performed,incorporating clinical risk factors,plaque characteristics,and double post labeling delay(PLD)ATA presence.The predictive value of different models was compared using receiver operating characteristic(ROC)curve and DeLong tests.Results Hypertension,positive lumen remodeling,plaque enhance-ment rate,1.5 s-ATA presence,and 2.5 s-ATA presence were independent risk factors for AIS(P＜0.05).The combination of HR-MRI and ASL imaging predicted AIS most effectively[area under the curve(AUC)=0.908;95％ confidence interval(CI)0.862-0.954].No significant difference was found between the prediction performances of HR-MRI and ASL(95％CI-0.041-0.082,Z=0.659,P=0.509).Conclusion ASL is more convenient than HR-MRI for predicting ischemic stroke in ICAS patients.A model combining plaque characteristics and ATA presence effectively predicts AIS occurrence.

Objective:To investigate the current status of the implementation of active health management in tumor care in community health service institutions in Shanghai.Methods:This cross-sectional study was conducted in July 2022. A questionnaire was designed based on tumor management practices according to active health principles was designed and distributed to all 247 community health service institutions in 16 administrative districts of Shanghai. Data were collected on tumor screening, establishment of health records for tumor patients, and follow-up practices.Results:Of the 247 questionnaires distributed, 239 were returned, with 228 valid responses (effective response rate: 92.3%). Of the 228 institutions, 72 (31.6%) were located in urban areas, 101 (44.3%) in suburban areas, and 55 (24.1%) in rural areas. Tumor screening was performed by 89.0% (203/228) of the institutions, mainly for colorectal, lung, liver, breast, and gastric cancers. Health records for tumor patients were established by 92.5% (211/228) of institutions. Follow-up of tumor patients was conducted by 99.1% (226/228) of institutions, with 29.6% (67/226) performing 1-2 annual follow-ups. Telephone follow-up was used by 99.1% (224/226) of institutions, while 42.5% (96/226) adjusted the frequency of follow-up based on patients′ Karnofsky Performance Status scores. Targeted health education for patients and families was provided by 84.6% (193/228) of institutions, including exercise guidance (78.8%, 152/193) and psychological support (76.2%, 147/193). No statistically significant differences were observed among urban, suburban, and rural institutions in tumor screening, health record establishment, follow-up frequency, assessment content, follow-up methods, and tumor health education and content (all P>0.05). Conclusions:The coverage of tumor screening in Shanghai′s community health service institutions remains incomplete, the quality of follow-up needs to be improved, and health education programs are not comprehensive. There were no regional differences in tumor screening, health record establishment, follow-up practices, or tumor health education among urban, suburban, and rural populations.

Objective:To evaluate the effect of an obstetric artificial intelligence (AI) assistant combined with a family-centered health education model on maternal self-care ability, comfort status, and spousal caregiving ability.Methods:This prospective, single-center, parallel randomized controlled trial used 1∶1 randomization and was conducted as a superiority trial. Postpartum mothers and their spouses admitted to family-style single rooms at the Third Affiliated Hospital of Guangzhou Medical University between October 2024 and April 2025 were enrolled and randomly assigned to control or intervention groups using a random number table. The control group received conventional health education, while the intervention group received conventional health education plus the AI-assisted family-centered model. Interventions were administered at 2 hours, 6 hours, and 24 hours postpartum, and before discharge. Outcomes included maternal self-care ability, comfort status, and spousal caregiving ability, which were assessed at 2 hours postpartum and before discharge. Data were analyzed using independent and paired t-tests and Chi square tests. Results:Of the 88 mother-spouse dyads initially recruited, four were excluded due to mother-infant separation (e.g., neonatal jaundice), leaving 84 dyads (42 per group). After the intervention, the intervention group showed significantly higher maternal self-care ability scores [(192.81±13.80) vs. (181.00±21.41) scores, t=3.00], higher maternal comfort scores [(104.43±7.52) vs. (96.00±14.29) scores, t=3.38], and better spousal caregiving ability [(6.07±3.13) vs. (9.50±5.02) scores, t=－3.76] compared to the control group (all P<0.05). Conclusion:The obstetric AI assistant combined with a family-centered health education model significantly improved maternal self-care ability and comfort status, as well as spousal caregiving ability.

Objective : To investigate the protective effect of dimethyl fumarate(DMF) on maternal intrahepatic cholestasis(ICP) during pregnancy induced by di(2-ethylhexyl) phthalate(DEHP) exposure and its mechanism. Methods : Thirty-two 8-week-old female institute of cancer research(ICR) mice were randomly divided into 4 groups: Ctrl group, DEHP group, DMF group and DEHP+DMF group. DEHP and DEHP+DMF groups were treated with DEHP(200 mg/kg) by gavage every morning at 9:00 a.m. DMF and DEHP+DMF groups were treated with DMF(150 mg/kg) from day 13 to day 16 of gestation by gavage. After completion of gavage on day 16 of pregnancy, maternal blood, maternal liver, placenta, and amniotic fluid were collected from pregnant mice after a six-hour abrosia. The body weight of the mother rats and the body weight of the fetus rats were sorted and analyzed; the levels of total bile acid(TBA), alkaline phosphatase(ALP), aspartate aminotransferase/alanine aminotransferase(AST/ALT) in serum and TBA in liver, amniotic fluid and placenta were detected by biochemical analyzer; HE staining was used to observe the pathological changes of liver tissue; Quantitative reverse transcription PCR(RT-qPCR) was used to detect the expression levels of tumor necrosis factor-α(TNF-α), interleukin(IL)-6, IL-1, IL-18 and NOD-like receptor thermal protein domain associated protein 3(NLRP3) in the liver; Western blot was used to detect the expression of the nuclear factor KappaB(NF-κB) and NLRP3. Results : Compared with the control group, the body weight of the DEHP-treated dams and pups decreased(P<0.05); the levels of TBA, ALP, AST/ALT in the serum of dams and the levels of TBA in the liver, amniotic fluid, and placenta of dams increased(P<0.05); the histopathological results showed that liver tissue was damaged, bile ducts were deformed, and there was inflammatory cell infiltration around them; the levels of inflammation-related factors TNF-α, IL-6, IL-1, IL-18 and NLRP3 transcription in maternal liver increased(P<0.05); the expression of NF-κB and NLRP3 protein in maternal liver significantly increased( P<0. 05). Compared with the DEHP group,the body weight of both dams and fetuses significantly increased in DEHP + DMF group( P<0. 05); the levels of TBA,ALP,AST/ALT in the serum of dams and amniotic fluid of fetuses decreased( P<0. 05); the degree of liver lesions was improved; the transcription levels of inflammation-related factors TNF-α,IL-6,IL-1,IL-18 and NLRP3 in maternal liver decreased( P<0. 05); the expression of NF-κB and NLRP3 protein in maternal liver significantly decreased( P<0. 05). Conclusion DMF can effectively protect the DEHP exposure to lead to female ICP,and its mechanism may be through inhibiting the NF-κB/NLRP3 pathway and reducing liver inflammation.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

Objective: To investigate the prevalence of sleep quality among the elderly in nursing homes in Changning District, Shanghai Municipality, so as to provide insights into prevention and intervention strategies for improving sleep quality and overall quality of life for the elderly. Methods: The elderly from 25 nursing homes in Changning District were selected using a two-stage sampling method. Basic information including gender, age and types of medication were collected. Sleep quality was assessed using the Athens Insomnia Scale, and depressive symptoms were measured using the Geriatric Depression Scale. Factors affecting sleep quality among the elderly in nursing homes were analyzed using a multivariable logistic regression model. Results: A total of 739 participants were surveyed, including 516 males (69.82%) and 223 females (30.18%). The majority of participants were aged 80 to <90 years (478, 64.68%). Among them, 432 participants (58.46%) had normal sleep, 144 (19.49%) had suspected insomnia, and 163 (22.06%) had insomnia. Multivariable logistic regression analysis showed that older age (OR=1.030, 95%CI: 1.005-1.055), more medication types (OR=1.971, 95%CI: 1.381-2.812), frequent nighttime bathroom visits (OR=2.921, 95%CI: 1.853-4.605) and depressive symptoms (OR=3.295, 95%CI: 2.440-4.449) were associated with a higher risk of insomnia among the elderly in nursing homes. Conclusions Insomnia was reported in 22.06% of the elderly in nursing homes in Changning District. Age, the number of medication types, frequency of nighttime bathroom visits, and depressive symptoms are the main influencing factors for their sleep quality.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*