Metabolic associated fatty liver disease （MAFLD） is a common chronic liver disease worldwide， and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis， liver cirrhosis， and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard， it may cause complications such as pain and bleeding as an invasive examination， which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years， noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated， including serological models， imaging techniques， and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3， especially deep learning models based on artificial intelligence， in order to provide ideas for the early screening of MAFLD， as well as a scientific reference for optimizing disease management strategies.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Iron is one of the essential trace elements for the human body,which is crucial for the growth and development of newborns,especially premature infants.It participates in the generation of hemoglobin,affects the activity of various enzymes,and subsequently affects neurometabolism,neurochemistry,neuroanatomy,and gene/protein composition,thereby having a lasting impact on the development of the central nervous system.This article reviews the research progress on the relationship between iron metabolism and neurodevelopment in premature infants in recent years,aiming to provide scientific basis for clinical management and preventive intervention of premature infants.

Aim To investigate the effects of Ixerin Z,a sesquiterpene lactone from Ixeris sonchifolia,on bone-lipid metabolic imbalance in ApoE-/-mice and to elu-cidate its molecular mechanisms.Methods A mouse model of ApoE-/-was induced using a high-fat diet,followed by eight weeks of Ixerin Z administration at doses of 1 and 10 mg·kg-1.Serum markers related to bone-lipid metabolism and inflammatory cytokines were quantified.Bone mineral density,biomechanical prop-erties,bone tissue morphology,and bone microstructure changes were analyzed.Computational molecular doc-king was performed to identify potential target proteins of Ixerin Z,and its regulatory effects on bone-lipid me-tabolism were investigated.Results Treatment with Ixerin Z markedly decreased the serum levels of total cholesterol,triglycerides,TNF-α,and IL-1β in ApoE-/-mice.It significantly improved bone mineral density,enhanced biomechanical strength,restored tra-becular structure,and reduced fat accumulation in bone tissue.Investigations revealed that Ixerin Z activated PPARα,thereby promoting fatty acid β-oxidation in bone tissue,and stimulating the Wnt/β-Catenin signa-ling pathway to facilitate bone formation.Furthermore,Ixerin Z suppressed the OPG/RANKL/NF-κB signaling pathway,leading to reduced bone resorption,independ-ent of PPARα activation.Conclusions Ixerin Z dem-onstrates potent therapeutic effects on bone-lipid meta-bolic imbalance in ApoE-/-mice.The mechanism in-volves activating PPARα to promote fatty acid β-oxida-tion in bone tissue,activating PPARα/Wnt/β-Catenin signaling pathway to promote bone formation,and in-hibiting OPG/RANKL/NF-κB signaling pathway to re-duce bone resorption.

Objective To explore the brain damage of SD rats under different time points of hypobaric hypoxia exposure.Methods A rat high-altitube cerebral edema(HACE)model was constructed by simulating an altitude of 6 000 m in a hypobaric hypoxia animal experimental chamber.Thirty-six SD male rats were randomly divided into the control group and the hypobaric hypoxia exposure 3,7 and 14 d groups,with 9 rats in each group.Except for the control group,the rats in each group were continuously exposed to hypobaric hypoxia for 3,7,and 14 d.At the end of the modeling period,serum was collected by blood sampling via the abdominal aorta,and brain tissue samples were taken.The wet-to-dry ratio(W/D)of brain tissue was calculated,and the levels of relevant oxidative enzymes in serum and brain tissue were measured.The expression levels of hypoxia-inducible factor-1α(HIF-1α)and aquaporin 4(AQP4)mRNAs in brain tissue were detected by real-time fluorescence quantitative polymerase chain reaction.Results The W/D of brain tissues in the control group and the group exposed to hypobaric hypoxia for 3,7 and 14 d were 4.46±0.12,4.98±0.16,5.07±0.18 and 4.95±0.07;the superoxide dismutase contents were(111.86±2.45),(90.73±1.48),(79.64±2.56)and(55.33±1.45)U·g-1;the glutathione contents were(126.91±5.18),(125.26±1.53),(56.20±2.17)and(122.73±1.78)μg·mL-1;the malondialdehyde contents were(230.94±2.00),(362.65±3.28),(407.34±3.47)and(237.50±1.59)nmol·g-1;the relative expression levels of HIF-1 α mRNA were 1.00±0,2.99±0.49,4.72±0.49 and 1.91±0.28;the relative expression levels of AQP4 mRNA were 1.00±0,2.62±0.34,8.38±0.84 and 5.27±0.42,respectively.Statistically significant differences were found between the above indexes in the 3,7 and 14 d of hypobaric hypoxia exposure group compared with the control group(P＜0.05,P＜0.01).Conclusion Different time of hypobaric hypoxia exposure can up-regulate the expression of AQPs proteins in HACE rats and cause the disruption of the blood-brain barrier,and the HACE model constructed in the hypobaric hypoxia chamber with 6 000 m intervention for 7 d was more stable.

Excessive fat accumulation,viral infections and sustained inflammatory responses caused by non-alcoholic and alcoholic factors can contribute to liver inflammation,fibrosis and carcinogenesis,promoting the development of chronic liver disease.Gaining an in-depth understanding of the etiologic factors and underlying mechanisms that lead to chronic liver disease can help identify potential therapeutic targets for targeted therapy.Lactate,as an important substance in hepatic metabolism,has been found to be involved in the process of chronic liver disease through various pathways,and this review will provide a useful reference for the prevention and treatment of chronic liver disease.

Objective:To investigate the related factors of cognitive impairment in middle-aged and old-aged patients with type 2 diabetes mellitus(T2DM).Methods:A total of 970 patients with T2DM(585 middle-aged group and 385 old-aged group)were selected from residents of a large community in Beijing from September to December 2018.The Mini-Mental State Examination(MMSE)was used to assess the cognitive func-tion.Multivariate logistic regression was used to analyze the related factors.Results:The detection rates of cognitive impairment were 12.0％and 13.5％in middle-aged and old-aged patients with T2DM,respectively.Among mid-dle-aged patients with T2DM,work(OR=0.22,95％CI:0.03-0.77)and education at the junior college or un-dergraduate level and above(OR=0.18,95％CI:0.04-0.55)were protective factors for cognitive impair-ment.Myocardial infarction(OR=4.13,95％CI:1.26-13.63)was a risk factor for cognitive impairment.Among old-aged patients with T2DM,drinking tea 1-2 times a week(OR=0.11,95％CI:0.01-0.58)and education at the junior college or undergraduate level and above(OR=0.19,95％CI:0.05-0.54)were protective factors for cognitive impairment.Stroke(OR=3.64,95％CI:1.55-8.39)and good sleep self-assessment(OR=2.75,95％CI:1.13-7.35)were risk factors for cognitive impairment.Conclusion:Cognitive impairment in middle-aged pa-tients with T2DM is related to work,education level and myocardial infarction,and cognitive impairment in old-aged patients with T2DM is related to lifestyle,education level and stroke.

Objective To design a night vision simulation training system to provide pilots with practical training on ground night vision goggles.Methods The system had its hardware composed of a control stick,a throttle lever,rudder pedals,a head-up display(HUD),a control box,a computer,a projector and a screen.The HUD and control box were designed using night vision-compatible lighting technology to simulate the ambient light conditions pilots experienced when flying with night vision goggles.The software of the system consisted of five ones developed with C++programming languge for visual scene simulation,instrument simulation,flight performance simulation,integrated management control and cockpit manage-ment.Night vision images and computer-rendered images of typical scenes with varying brightness levels were collected.Objective image evaluation metrics such as contrast and brightness were used as inputs,while subjective evaluation data served as outputs to construct and train a support vector machine(SVM)model.Totally 30 typical night vision training scenarios were selected based on model validation and optimization to evaluate the system's optical fidelity.Results The average fidelity score for the 30 typical night vision training scenarios was 8.05,indicating that the system could realistically simulate terrain and landscapes under various lighting and weather conditions during night flights and static and dynamic targets in the air,on the ground and at sea.Conclusion The system meets the desired requirements and effectively facilitates night vision training for pilots.[Chinese Medical Equipment Journal,2025,46(3):21-26]

Objective:To develop an automatic diagnostic model for temporomandibular joint osteoarthritis(TMJOA)imaging based on deep learning technology,and to assist clinical diagnosis and improve the efficiency and accuracy of TMJOA diagnosis.Methods:CBCT data of 220 patients were collected,and 2 052 sagittal images were exported.Regions of interest were delineated according to the imaging analysis criteria for temporomandibular joint disorders,and the images were classified into TMJOA-free,TMJOA-uncer-tain and TMJOA.The data were randomly divided into a training set and a validation set according to 8∶2 ratio,and the training set data were used to train a TMJOA detection model based on three lightweight YOLOV5 deep learning frameworks,and the models' performance was evaluated on the validation set.Results:The Yolov5N model demonstrated the best performance,achieving a de-tection accuracy,recall,and precision of 92.5％,90.1％and 85.7％on the validation set,respectively.Conclusion:The auto-matic detection model for TMJOA imaging developed in this study can effectively identify arthritic lesions.Artificial intelligence tools are expected to become a powerful auxiliary tool for the clinical diagnosis of TMJOA.