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.

ObjectiveTo predict the in-hospital mortality risk among patients with ischemic stroke combined by dysphagia using interpretable machine learning methods, so as to provide more evidence-based support for the prognosis prediction of patients with ischemic stroke combined by dysphagia. MethodsMedical record of 308 patients diagnosed with ischemic stroke combined by dysphagia in the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) (2.0) in the United States were retrospectively analyzed. Features of the research data were screened based on the least absolute shrinkage and selection operator, and which were randomly divided into a training set and a test set at a ratio of 7∶3. Then ten models, including logistic regression, random forest, K-nearest neighbor, linear discriminant analysis, naive bayes (NB), neural network, quadratic discriminant analysis, recursive partitioning tree, extreme gradient boosting tree, and support vector machine, etc. were constructed. The predictive effect was measured by calculating the area under the curve (AUC) of receiver operating characteristics. In addition, the calibration curve and Brier score were used to evaluate the calibration degree of the model, and the decision curve was drawn to reflect the clinical net benefit. The Shapley additive explanation method was used to analyze the interpretability of the black box model and explore the important decision-making factors. ResultsThe NB model in the test set showed better predictive ability compared with other models (AUC=0.85, 95%CI: 0.83‒0.88). After interpretability analysis, it was found that blood urea nitrogen (BUN), age, sequential organ failure assessment, bicarbonate, chloride, and hypertension were important risk factors for in-hospital mortality in patients with ischemic stroke combined by dysphagia. ConclusionThe comprehensive performance of the NB model is better than that of the other nine models in predicting the risk of in-hospital mortality in patients with ischemic stroke combined by dysphagia. The interpretability of the model can help clinicians better understand the reasons behind the results and take further reasonable intervention measures for risk factors to improve the survival probability of patients.

Haematitum and Limonitum are two iron-containing mineral medicines included in the 2020 edition of the Chinese Pharmacopoeia. They have similar main components and major differences in their property, flavor, channel tropism, and clinical uses. In this study, we investigated the surface properties, mineral composition, mineral dissociation, elemental composition, and iron state of Haematitum and Limonitum to explore their mineralogical differences. Scanning electron microscopy(SEM), specific surface and porosity analyzer, X-ray diffractometer(XRD), X-ray photoelectron spectrometer(XPS), and advanced mineral identification and characterization system(AMICS) were used to analyze the mineralogy of Haematitum and Limonitum. The results showed that Haematitum had an angular surface with granular attachments and a specific surface area of 17.04 m~2·g~(-1). In comparison, Limonitum had a smooth and flat surface with a bundled acicular crystal structure and a specific surface area of 46.29 m~2·g~(-1). Haematitum consists of 31 detectable minerals containing 18 elements, with the major element, iron(44.5% Fe~(2+) and 55.5% Fe~(3+)) distributed in 17 minerals, including hematite, iron oxide, knebelite, siderite, and magnesioferrite. Limonitum consists of 32 detectable minerals containing 17 elements, with the major element, iron(14.5% Fe~(2+) and 85.5% Fe~(3+)) distributed in 19 minerals, including limonite, iron oxide, chlorite, and knebelite. In summary, the elemental composition of Haematitum and Limonitum does not differ greatly, but there are large differences in the mineral composition and iron state. The large specific surface area and strong adsorption capacity of Limonitum may be one of the mechanisms of its anti-diarrheal action. The Fe_2O_3 and illite contained in Haematitum and Limonitum may be the key substances for their hemostasis effects. The mineralogical differences are expected to provide a reference for explaining the scientific connotation of mineral medicine and laying a material foundation for studying its mechanism of action.
Iron/analysis* ; Minerals/chemistry* ; Drugs, Chinese Herbal/chemistry* ; X-Ray Diffraction ; Microscopy, Electron, Scanning ; Photoelectron Spectroscopy

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

This study aims to explore the intervention effects of isorhamnetin(Isor) on acute lung injury(ALI) and its regulatory effects on pyroptosis mediated by the NOD-like receptor family pyrin domain containing 3(NLRP3)/apoptosis-associated speck-like protein containing a CARD(ASC)/cysteine aspartate-specific protease-1(caspase-1) axis. In the in vivo experiments, 60 BALB/c mice were divided into five groups. Except for the control group, the other groups were administered Isor by gavage 1 hour before intratracheal instillation of LPS to induce ALI, and tissues were collected after 12 hours. In the in vitro experiments, RAW264.7 cells were divided into five groups. Except for the control group, the other groups were pretreated with Isor for 2 hours before LPS stimulation and subsequent assessments. Hematoxylin-eosin(HE) staining was used to observe pathological changes in lung tissue, while lung swelling, protein levels in bronchoalveolar lavage fluid(BALF), and myeloperoxidase(MPO) levels in lung tissue were measured. Cell proliferation toxicity and viability were assessed using the cell counting kit-8(CCK-8) method. Enzyme-linked immunosorbent assay(ELISA) was used to detect the levels of interleukin-1β(IL-1β), IL-6, IL-18, and tumor necrosis factor-α(TNF-α). Protein levels of NLRP3, ASC, cleaved caspase-1, and the N-terminal fragment of gasdermin D(GSDMD-N) were evaluated using immunohistochemistry, immunofluorescence, and Western blot. The results showed that in the in vivo experiments, Isor significantly improved pathological damage in lung tissue, reduced lung swelling, protein levels in BALF, MPO levels in lung tissue, and levels of inflammatory cytokines such as IL-1β, IL-6, IL-18, and TNF-α, and inhibited the high expression of the NLRP3/ASC/caspase-1 axis and the pyroptosis core gene GSDMD-N. In the in vitro experiments, the safe dose of Isor was determined through cell proliferation toxicity assays. Isor reduced cell death and inhibited the expression levels of the NLRP3/ASC/caspase-1 axis, GSDMD-N, and inflammatory cytokines. In conclusion, Isor may alleviate ALI by modulating pyroptosis mediated by the NLRP3/ASC/caspase-1 axis.
Animals ; Pyroptosis/drug effects* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Acute Lung Injury/physiopathology* ; Mice ; Mice, Inbred BALB C ; Quercetin/pharmacology* ; Caspase 1/genetics* ; CARD Signaling Adaptor Proteins/genetics* ; Male ; RAW 264.7 Cells ; Humans ; Lung/metabolism*

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

OBJECTIVE: To evaluate the protective effects of gentiopicroside (GPS) against reactive oxygen species (ROS)-induced NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome activation in endothelial cells, aiming to reduce atherosclerosis. METHODS: Eight-week-old male ApoE-deficient mice were randomly divided into 2 groups (n=10 per group): the vehicle group and the GPS treatment group. Both groups were fed a high-fat diet for 16 weeks. GPS (40 mg/kg per day) was administered by oral gavage to the GPS group, while the vehicle group received an equivalent volume of the vehicle solution. At the end of the treatment, blood and aortic tissues were collected for assessments of atherosclerosis, lipid profiles, oxidative stress, and molecular expressions related to NLRP3 inflammasome activation, ROS production, and apoptosis. Additionally, in vitro experiments on human aortic endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) were conducted to evaluate the effects of GPS on NLRP3 inflammasome activation, pyroptosis, apoptosis, and ROS production, specifically examining the role of the sirtuin 1 (SIRT1)/nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. SIRT1 and Nrf2 inhibitors were used to confirm the pathway's role. RESULTS: GPS treatment significantly reduced atherosclerotic lesions in the en face aorta (P<0.01), as well as in the thoracic and abdominal aortic regions, and markedly decreased sinus lesions within the aortic root (P<0.05 or P<0.01). Additionally, GPS reduced oxidative stress markers and proinflammatory cytokines, including interleukin (IL)-1 β and IL-18, in lesion areas (P<0.05, P<0.01). In vitro, GPS inhibited ox-LDL-induced NLRP3 activation, as evidenced by reduced NLRP3 (P<0.01), apoptosis-associated speck-like protein containing a CARD, cleaved-caspase-1, and cleaved-gasdermin D expressions (all P<0.01). GPS also decreased ROS production, apoptosis, and pyroptosis, with the beneficial effects being significantly reversed by SIRT1 or Nrf2 inhibitors. CONCLUSION GPS exerts an antiatherogenic effect by inhibiting ROS-dependent NLRP3 inflammasome activation via the SIRT1/Nrf2 pathway.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Reactive Oxygen Species/metabolism* ; Iridoid Glucosides/therapeutic use* ; NF-E2-Related Factor 2/metabolism* ; Animals ; Atherosclerosis/metabolism* ; Inflammasomes/drug effects* ; Male ; Sirtuin 1/metabolism* ; Signal Transduction/drug effects* ; Humans ; Endothelial Cells/pathology* ; Mice ; Oxidative Stress/drug effects* ; Apoptosis/drug effects* ; Lipoproteins, LDL ; Mice, Inbred C57BL