BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

Objective: To explore the association between daytime outdoor physical activity (OPA) and oral health indicators among primary school students, aiming to provide evidence for developing oral health intervention strategies based on natural exposure. Methods: In October 2023, based on the "Tianchang Children s Light Exposure and Growth Development Cohort", 799 second and third grades children were recruited from two primary schools in Tianchang, Chuzhou City, Anhui Province. Physical activity intensity and light exposure were objectively monitored for 24 hours over 5 consecutive days using triaxial accelerometers synchronized with portable illuminance meters. Standardized oral examinations were performed to record dental caries, gingivitis, and malocclusion. Demographics, lifestyle variables, and household socioeconomic data were collected via questionnaires. Multiple linear regression (for the number of carious teeth) and Logistic regression (for gingivitis risk) were used to analyze the relationship between daytime outdoor moderate to vigorous physical activity (MVPA) and oral health outcomes. Results: The average daytime outdoor MVPA was (0.76±0.35)h, with (0.95±0.40)h on weekdays and (0.49±0.47)h on weekends. The detection rates for dental caries, gingivitis, and malocclusion were 31.0%, 4.6%, and 59.7%, respectively. Compared with children with good oral health, the duration of outdoor MVPA on school days was reduced in children with caries or gingivitis ( Z =-11.4, -5.01, both P <0.01). Multiple regression analysis showed that after adjusting for factors such as gender, age, body mass index, oral hygiene behaviors, an increase in daytime outdoor MVPA duration was associated with a decrease in the number of dental caries ( β=-0.64, 95%CI =-0.93 to -0.35) and a reduced risk of gingivitis ( OR= 0.58 , 95%CI =0.34-0.98) in primary school students (both P <0.05). The association was primarily observed on school days ( β=-0.72, 95%CI = -1.07 to -0.37; OR=0.42, 95%CI =0.21-0.85) (both P < 0.05). Conclusions Daytime outdoor MVPA on weekdays is significantly associated with a lower number of carious teeth and a reduced risk of gingivitis in primary school students. Increasing daytime outdoor activities on weekdays may serve as a promising and potential strategy for promoting children s oral health.

Objective: To analyze the epidemiological characteristics of varicella among nursery children in Shenzhen from 2015 to 2024, and to evaluate the impact of optimizing varicella vaccine (VarV) immunization strategies on varicella incidence. Methods: Varicella incidence data for nursery children in Shenzhen from 2015 to 2024 were obtained from the China Disease Prevention and Control Information System. The study period was divided into three phases:one dose self pay VarV (January 2015 to October 2017), two dose self pay VarV (November 2017 to October 2019), and two dose free VarV (November 2019 to December 2024). Interrupted time series (ITS) analysis was conducted to assess changes in the level and trend of varicella incidence associated with each phase of policy implementation. Results: A total of 27 517 varicella cases was reported among nursery children from 2015 to 2024, with an average annual incidence of 514.01/100 000. During the same period, 136 clustered outbreaks were reported in nursery institutions, involving a cumulative total of 1 091 cases. ITS analysis showed that during the self pay 1 dose stage, the varicella incidence among nursery children showed an upward trend, with an average monthly increase of 2.58/100 000 (95% CI =2.21/ 100 000 -2.95/100 000, P <0.01). After the implementation of the self pay 2 dose strategy, the incidence decreased, with a change in incidence of -26.12/100 000 (95% CI =-37.30/100 000 to -14.94/100 000) and a change in slope of -2.65/100 000 (95% CI = -3.38/100 000 to -1.93/100 000)(all P <0.01). After the implementation of the free 2 dose strategy, the incidence decreased further, with a change in incidence of -40.03/100 000 (95% CI =-50.39/100 000 to -29.66/100 000, P <0.01) and a change in slope of -0.56/100 000 (95% CI =-1.20/100 000-0.08/100 000, P =0.09). Conclusion The gradual optimization of the VarV vaccination strategy in Shenzhen from self pay 1 dose to free 2 dose has significantly reduced the varicella incidence among nursery children, demonstrating good short term control and long term intervention effectiveness.

Objective To analyze the genetic characteristics and variations of influenza A (H3N2) viruses in Ma'anshan from 2022 to 2024, and to provide a scientific basis for local influenza prevention and control. Methods From April 2022 to March 2024, influenza-like illness (ILI) specimens were collected from three national influenza surveillance sentinel hospitals in Ma’anshan. Samples positive for influenza by real-time PCR were subjected to virus culture and identification. A total of 40 representative A/H3N2 strains with hemagglutination titers ≥8 were selected for whole-genome sequencing. Genetic evolution, homology, amino acid variations, and glycosylation sites were analyzed. Results All H3N2 representative strains from the 2022–2023 influenza season belonged to clade 3C.2a1b.2a.1a.1, while those from the 2023–2024 season fell into clade 3C.2a1b.2a.2a.3a.1. The nucleotide and amino acid sequence similarities of HA and NA between the 40 representative strains and the vaccine strain A/Darwin/6/2021 were all above 97.35%. Compared with the vaccine strain, amino acid mutations were identified in antigenic sites A, B, C, and E, as well as in receptor-binding sites of the HA protein. An I222V substitution was detected in the NA protein. The HA protein contained four additional glycosylation sites compared to the vaccine strain, while the glycosylation pattern of the NA protein remained consistent. Conclusion No antigenic drift was observed in the influenza A/H3N2 viruses in Ma'anshan City from 2022 to 2024, but genetic changes such as branching variations, key amino acid substitutions, and an increase in HA glycosylation sites were observed. These findings underscore the importance of sustained molecular surveillance of local influenza viruses.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

OBJECTIVE To establish a method for simultaneous determination of plasma concentration of lamotrigine（LTG）， levetiracetam（LEV） and perampanel（PER） in children with epilepsy and apply this method in clinical practice. METHODS Plasma proteins were precipitated with acetonitrile. Using PER-D 5 as internal standard， ultra-high performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） method was adopted. The determination was performed on ACQUITY UPLC HSS T3 C 18 column with mobile phase consisted of 0.1% formic acid with 5 mmol/L ammonium acetate-acetonitrile （gradient elution） at the flow rate of 0.3 mL/min. The column temperature was 40 ℃， and sample size was 5 μL. The analysis time was 5 min. The electrospray ionization source and multiple reaction monitoring mode were used for positive ion scanning. The ion pairs used for quantitative analysis of LTG， LEV， PER and internal standard were m / z 255.9→144.9， m / z 171.1→126.1， m / z 350.1→219.0 and m / z 354.9→220.2， respectively. The steady-state trough concentrations of the aforementioned drugs in the plasma of 14 pediatric epilepsy patients receiving combination therapy were determined using the same UPLC-MS/MS method as above. RESULTS The linear ranges of LTG， LEV and PER were 0.15-24 μg/mL （ R 2 ＞0.993）， 0.312 5-50 μg/mL （ R 2 ＞0.997） and 6.25-1 000 ng/mL （ R 2 ＞0.997）， respectively. The lower limits of quantification were 0.15 μg/mL， 0.312 5 μg/mL and 6.25 ng/mL， respectively. RSDs of intraday and interday precision tests of the three drugs were no more than 9.83%， and the accuracies （relative errors） were between －9.33% and 13.72%（ n ＝6 or n ＝18）； the average extraction recovery rates were 86.4%-97.9%， and the average matrix effects were 86.9%-110.0% （ n ＝6）. The absolute values of the relative errors in the stability tests were all below 15%. The steady-state trough concentrations of LTG， LEV and PER were （5.64±4.03）μg/mL， （10.67±8.78）μg/mL and（450.20±251.27）ng/mL， respectively； the rates of achieving target trough concentrations were 71.4%， 37.5% and 84.6%， respectively. CONCLUSIONS The established UPLC-MS/MS method is specific， rapid and suitable for the plasma concentration monitoring in epileptic children receiving combination therapy.

ObjectiveTo investigate the possible mechanism of Jishe Qushi Capsule （脊蛇祛湿胶囊， JQC） in treating rheumatoid arthritis （RA） from the perspective of macrophage polarization mediated by neutrophil extracellular traps （NETs）. MethodsTwenty-four female SD rats were randomly divided into four groups， blank control group， model group， JQC group， and peptidylarginine deiminase 4 （PAD4） inhibitor group with 6 rats in each group. All groups but the blank control group were subjected to the induction of collagen-induced arthritis （CIA）. After successful model establishment， rats in the JQC group received intragastric administration of JQC 1.47 g/kg daily； rats in the PAD4 inhibitor group received intraperitoneal injections of the PAD4 inhibitor 4 mg/kg weekly. Rats in the blank， model， and PAD4 inhibitor groups received 2 ml of pure water daily by gavage. All treatments lasted 4 weeks. Joint lesions of each group were assessed on day 7， 14， 21， 28， and 35 after model establishment， and arthritis index （AI） scores were recorded. At 24 h after the final administration， histopathology of knee joints， including HE staining， safranin O-fast green staining， and TRAP staining， was performed. Flow cytometry was used to detect the counts of M1 and M2 macrophages in peripheral blood. ELISA was used to determine serum levels of TRACP， NETs， TNF-α， IL-1β， and iNOS. Western Blotting and qRT-PCR were used to measure MPO， NE， RANKL， OPG， and p65 protein and mRNA expression in knee cartilage tissue. ResultsCompared with the blank control group， the model group showed increased AI scores （P＜0.05）， marked synovial inflammatory infiltration， angiogenesis， and bone-cartilage destruction， increased TRAP-positive osteoclasts， increased M1 macrophages and decreased M2 macrophages， elevated serum TRACP， NETs， TNF-α， IL-1β， and iNOS （P＜0.05）， elevated MPO， NE， RANKL， and p65 protein/mRNA expression and decreased OPG protein/mRNA expression in knee cartilage tissue （P＜0.05）. Compared with the model group， the JQC group exhibited improved synovial inflammation， angiogenesis， and bone-cartilage damage， reduced AI scores on day 21， 28， and 35， decreased osteoclast counts， decreased M1 macrophages and increased M2 macrophages， reduced serum TRACP， NETs， TNF-α， IL-1β， and iNOS （P＜0.05）， decreased MPO， NE， RANKL， and p65 protein/mRNA expression and increased OPG expression （P＜0.05）. Compared with the PAD4 inhibitor group， the JQC group showed significantly lower AI scores， reduced M1 macrophages， increased M2 macrophages （P＜0.05）， reduced serum TRACP， TNF-α， IL-1β， and iNOS， decreased MPO， RANKL， and p65 expression， and increased OPG levels （P＜0.05）. ConclusionThe therapeutic mechanism of JQC for RA may involve inhibition of NETs formation， downregulation of the RANKL/NF-κB signaling pathway， and regulation of macrophage M1/M2 polarization imbalance， thereby suppressing osteoclastogenesis and inflammatory bone destruction.

ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.