ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

Background The affinity between placental transporters and perfluoroalkyl substances (PFAS) could affect the placental transport and toxicity of PFAS, while the study on the interaction between PFAS and placental transporters is limited. Objective To explore interactions between PFAS and placental transporters using molecular docking, and to provide a theoretical basis for PFAS toxicity prediction and fetal health risk assessment. Methods Fifteen PFAS compounds, each conformationally sampled and energy-minimized, and 16 placental transporters, represented by their 3D structures, were imported into a molecular docking software (MOE 20140901). For each PFAS, 30 distinct conformations were generated and docked into the active pockets of the transporters using a semi-flexible docking mode. Docking poses were primarily scored and ranked based on their calculated binding free energy (ΔG, kcal·mol⁻¹), with additional consideration given to hydrogen bonding interactions and the ligand's root mean square deviation (RMSD) at the binding site; the top 20 poses for each complex were subsequently output. Optimal binding configurations were identified as those exhibiting a relatively low binding free energy (ΔG ranging from −3 to −10 kcal·mol⁻¹), well-defined hydrogen bonds, and an RMSD ≤ 2.0 Å. The binding capabilities of the PFAS to the placental transporters were then evaluated based on these optimal docking results. Results The PFAS could bind to the placental transporters, with structural specificity. For example, the binding capabilities increased as the carbon chain length of PFAS increased, and it was also higher for PFOS alternatives than for PFOS. Besides, the binding capabilities of sulfonic PFAS with the same carbon chain length was also stronger than that of carboxylic PFAS. For example, the binding capabilities of PFOS (C8) to 15 placental transporters was higher than that of PFOA (C8), except for glucose transporter 1 (PFOS vs. PFOA: −4.14 vs. −4.14). Further, PFAS might be bound to the placental transporter through hydrogen, ionic, and hydrophobic interactions. Conclusion PFAS are able to bind the placental transporters, and its toxicity and exposure risk can’t be ignored.

In the context of rapid global aging, the number of vulnerable elderly individuals who are sensitive to climate change and air pollution is increasing rapidly, potentially augmenting the burden of related diseases. The intrinsic capacity (IC) of the elderly refers to the comprehensive ability of individuals in physical, cognitive, and mental health aspects, typically encompassing 5 dimensions: cognitive status, motor ability, mental health, sensory function, and vitality. This article reviewed the advancements in epidemiological research on the effects of air pollution and climate change (including meteorological factors) on the overall intrinsic capacity of the elderly and its various dimensions. The results indicated that pollutants such as fine particulate matter (PM_2.5) and ozone (O₃) are most significantly associated with the decline in the cognitive function and vitality dimensions, and extreme meteorological events like high temperatures are also related to the functional deterioration of each dimension of IC. Nevertheless, the current studies mostly focus on the impact of atmospheric environmental factors on a specific dimension of IC rather than on overall IC, and research on the combined exposure to multiple atmospheric factors is even rarer, and the exploration of associated mechanisms is insufficient. Future research should enhance the investigation of the influence and mechanism of the combined exposure to air pollution and climate change on the dynamic changes of IC, and promote multi-center research and transnational cooperation. This review is conducive to clarifying the potential impact of atmospheric environmental factors on the IC of the elderly, providing a scientific basis for formulating health intervention policies to address climate change and air pollution.

ObjectiveFat infiltration has been shown to be closely related to muscle mass loss and a variety of muscle diseases. This study proposes a method based on phase-angle electrical impedance tomography (ΦEIT) to visualize the electrical characteristic response caused by muscle fat infiltration, aiming to provide a new technical means for early non-invasive detection of muscle mass deterioration. MethodsThis study was divided into two parts. First, a laboratory pork model was constructed to simulate different degrees of fat infiltration by injecting1 ml or 2 ml of emulsified fat solution into different muscle compartments, and the phase angle images were reconstructed using ΦEIT. Second, a human experiment was conducted to recruit healthy subjects (n=8) from two age groups (20-25 years old and 26-30 years old). The fat content percentage η_fat of the left and right legs was measured by bioelectrical impedance analysis (BIA), and the phase angle images of the left and right calves were reconstructed using ΦEIT. The relationship between the global average phase angle Φ_M and the spatial average phase angle Φ_Mi of each muscle compartment and fat infiltration was further analyzed. ResultsIn the laboratory pork model, the grayscale value of the image increased with the increase of η_fat and Φ_M showed a downward trend. The results of human experiments showed that at the same fat content percentage, the Φ_M of the 26-30-year-old group was about 20%-35% lower than that of the 20-25-year-old group. The fat content percentage was significantly negatively correlated with Φ_M. In addition, the M₂ (soleus) compartment was most sensitive to fat infiltration, and the spatial average phase angles of the M₂ (soleus), M₃ (tibialis posterior and flexor digitorum longus), and M₄ (tibialis anterior, extensor digitorum longus, and peroneus longus) compartments all showed significant inter-group differences. ConclusionΦEIT imaging can effectively distinguish different degrees of fat infiltration, especially in deep, small or specially located muscles, showing high sensitivity, demonstrating the potential application of this method in local muscle mass monitoring and early non-invasive diagnosis.

ObjectiveTo investigate the dynamic expression profiles and potential regulatory mechanisms of long non-coding RNAs （lncRNAs） in male reproductive system aging. MethodsA naturally aging C57BL/6 mouse model was used and 4 mice were selected each at 3， 15， and 21 months of age. RNA was extracted from seven regions of the male reproductive tract （testis， efferent duct， initial segment of epididymis， caput epididymis， corpus epididymis， cauda epididymis， and vas deferens）， followed by RNA sequencing and bioinformatics analysis. ResultsRegion-specific dynamic expression profiles of lncRNAs were constructed in the testis， epididymis （efferent duct， initial segment， caput， corpus， and cauda）， and vas deferens of male mice. Combined with gene functional enrichment analysis， the functional associations of lncRNAs were elucidated in reproductive system aging. The differentially expressed lncRNAs in the aging testis were primarily involved in hormone biosynthesis and extracellular matrix organization， while those in the initial segment of the epididymis were closely related to cell recognition and epithelial cell migration. A comprehensive lncRNA expression atlas associated with male reproductive aging was established. ConclusionLncRNAs may participate in male reproductive aging through the regulation of the reproductive microenvironment， which provides key molecular targets and a research foundation for understanding age-related fertility decline.

Objective: To explore the associations of moderate to vigorous intensity physical activity (MVPA) and sedentary behavior (SB) among primary and secondary school students and their parents, so as to provide a scientific basis for formulating targeted physical activity promotion strategies for children and adolescents. Methods: From 2021 to 2022, basic information and 24 h movement behaviors of 2 484 pairs of students and their parents were collected from five primary and secondary schools in Haizhu District, Guangzhou City, with a convenient sampling combining with cluster sampling method. Component regression models were constructed to analyze the relationship between parental MVPA, SB and primary and secondary school students MVPA and SB, and a component isochronous substitution model was used to explore the effects of mutual substitution between parental MVPA, residual components (time use components other than SB during the 24 h period), and SB on the behavioral activities of MVPA and SB in primary and secondary school students. Results: Parental MVPA and SB of students in grade 1 to 3 were positively correlated with both students MVPA and SB ( β=0.06, 0.12, P <0.01). The component isochronous substitution model showed that substituting 10 and 20 minutes of MVPA for SB by parents in grade 1 to 3 was associated with an increase in MVPA of students, and substituting 10 and 20 minutes of residual ingredients for SB was associated with a decrease in SB of students, with mean changes of 0.8 (95% CI =0.4-1.2) and 1.4 (95% CI =0.7-2.2) and -1.4 (95% CI =-1.7 to -1.1) and -2.9 (95% CI =-3.4 to -2.3)( P <0.05). No statistically significant associations were observed between parents of students in grades 4 to 6 and 7 to 9 and students physical activity and sedentary behaviour ( P >0.05). Conclusions Parents of students in grades 1 to 3 increases MVPA and decrease SB are beneficial to increase MVPA and decrease SB of students. Parents could promote physical activity among primary and secondary school students, and the intervention gateway should be advanced, with the low grades as the optimal intervention period.

This study aimed to investigate the therapeutic effect of Yindan Pinggan capsules (YDPG) on intrahepatic cholestasis (IHC) through animal experiments, while utilizing network pharmacology and molecular docking techniques to explore its potential mechanisms. Initially, the therapeutic effect of YDPG on an α-naphthylisothiocyanate (ANIT)-induced IHC mouse model was assessed through liver function tests, routine blood tests, and liver pathology analysis. Subsequently, network pharmacology tools were employed to predict the active components, core targets, and signaling pathways of YDPG. Molecular docking technology was employed to verify the binding activity of key active components of YDPG with core targets, followed by protein immunoblotting to validate the key targets. Results showed that YDPG significantly improved liver function abnormalities and hepatocyte damage in IHC mice. Network pharmacology analysis revealed that 94 active components in YDPG were associated with 396 targets for the treatment of IHC, and were significantly enriched in pathways such as the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway, lipid metabolism, and bile secretion. Molecular docking results showed good binding activity between key active components of YDPG and core targets of the PI3K-AKT signaling pathway. Further protein immunoblotting confirmed that YDPG could reduce the phosphorylation levels of PI3K and AKT proteins, core targets of the PI3K-AKT pathway in liver tissue. These findings suggest that YDPG may alleviate biological processes such as oxidative stress and inflammatory responses by regulating the PI3K-AKT signaling pathway, thereby improving liver damage in IHC mice and exerting a therapeutic effect on IHC. This experiment has been approved by the Animal Experiment Ethics Committee of Jinan University (ethical approval number: IACUC-20241011-09).

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

The application of artificial intelligence(AI)in the medical field, particularly for predicting, diagnosing and treating retinal detachment(RD), has made remarkable achievements. This paper reviews the advancements in AI applications for RD across multiple dimensions, including predicting RD incidence, assessing surgical success rates, forecasting postoperative visual outcomes, and evaluating recurrence rates. In diagnostic support, AI technology has demonstrated significant value, especially in ophthalmic imaging, with applications in the intelligent analysis of ultra-wide-angle fundus photography, optical coherence tomography(OCT), ophthalmologic ultrasound images, and AI chatbots models. Furthermore, AI has proven uniquely beneficial in surgical decision-making, robotic-assisted surgical systems, and the assessment of surgical complications. This paper provides a comprehensive overview of the current state of AI applications in RD, underscoring its potential to address numerous challenges in clinical practice. It also explores existing limitations and offers insights into future directions for development in this field.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.