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.

Hyperlipidemia is a common metabolic disorder driven by dysregulated lipid metabolism， whose pathological progression involves multifaceted interactions across various signaling pathways， including disrupted cholesterol homeostasis， insulin resistance， chronic inflammation， and oxidative stress. Although existing clinical medications such as statins can effectively reduce low-density lipoprotein cholesterol through single-target inhibition， significant limitations remain in comprehensively improving reverse cholesterol transport， metabolic inflammation， and coordinated regulation of glucose and lipid metabolism. Curcumin， the primary polyphenolic active compound derived from the traditional medicinal herb turmeric， exhibits multi-dimensional pharmacological effects including modulation of lipid metabolism， improvement of insulin sensitivity， suppression of inflammatory responses， and enhancement of antioxidant defense. This review systematically integrates the multi-target regulatory network of curcumin， elucidating its key mechanisms： Balancing the low density lipoprotein receptor/proprotein convertase subtilisin/kexin type 9 （LDLR/PCSK9） signaling pathway to promote reverse cholesterol transport， activating peroxisome proliferator-activated receptor α/γ （PPARα/γ） signaling pathways to coordinate glucolipid metabolic homeostasis， remodeling the gut microbiota-bile acid axis， inhibiting nuclear factor kappa-B （NF-κB）-mediated inflammatory responses， and activating the nuclear factor erythroid 2-related factor 2/antioxidant response element （Nrf2/ARE） antioxidant signaling pathway. Furthermore， supported by clinical research data， the review validates curcumin's therapeutic potential in reducing total cholesterol （TC） and triglyceride （TG） levels， increasing high-density lipoprotein cholesterol （HDL-C）， as well as improving vascular endothelial function and insulin sensitivity. The study highlights the multi-target regulatory properties of curcumin and discusses future clinical applications based on nano-delivery systems and personalized treatment strategies， providing a theoretical basis and novel insights for the prevention and treatment of hyperlipidemia and related metabolic disorders.

OBJECTIVES: To explore the efficacy of DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy for management of cancer pain and provide reference for its standardized clinical application. Methods and. RESULTS: Recommendations were formulated based on literature review and expert group discussion, and consensus was reached following expert consultation. The consensus recommendations are comprehensive, covering the entire treatment procedures from preoperative assessment and preparation, surgical operation process, postoperative management and traditional Chinese medicine treatment to individualized treatment planning. The study results showed that the treatment plans combining traditional Chinese with Western medicine effectively alleviated cancer pain, reduced the use of opioid drugs, and significantly improved the quality of life and enhanced immune function of the patients. Postoperative follow-up suggested good treatment tolerance among the patients without serious complications. CONCLUSIONS The formulated consensus is comprehensive and can provide reference for clinicians to use DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy. The combined treatment has a high clinical value with a good safety profile for management of cancer pain.
Humans ; Medicine, Chinese Traditional ; Cancer Pain/therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Drug Delivery Systems ; Pain Management/methods* ; China

Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca²⁺ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a^-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a^-/- OPCs in vitro and myelination in Tmem63a^-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca²⁺ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals ; Cell Differentiation/physiology* ; Oligodendroglia/metabolism* ; Mice, Knockout ; Mice ; Male ; Myelin Sheath/metabolism* ; Humans ; Child ; Cells, Cultured ; Oligodendrocyte Precursor Cells/metabolism*

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

CD20 is a surface marker protein of B-cell lymphoma, and its extracellular region is the target of specific antibodies and drugs. To obtain a cheap and easily modified specific preparation targeting CD20, we optimized the gene of CD20 extracellular region according to codon degeneracy to facilitate its expression in Escherichia coli. The optimized gene was cloned into pGEX-4T-1 vector, and the recombinant vector was transformed into E. coli BL21(DE3) for expression. The purified protein was identified by SDS-PAGE and Western blotting. Systematic evolution of ligands by exponential enrichment (SELEX) was employed to screen the ssDNA aptamer that specifically binds to the fusion protein, and the affinity of the aptamer to CD20 was detected by flow cytometry. Then, the cytotoxicity test was carried out to examine the inhibitory effect of the aptamer on B lymphoma cells. In this study, we established the prokaryotic expression method of CD20 and obtained the aptamer specifically binding to the extracellular region of CD20, which laid a foundation for the development of therapeutic drugs targeting CD20.
Humans ; Escherichia coli/metabolism* ; SELEX Aptamer Technique/methods* ; Aptamers, Nucleotide/genetics* ; Antigens, CD20/metabolism* ; Ligands

Objective : To establish a method for measuring major organic acids in the tricarboxylic acid cycle using a high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS) system, and to investigate the changes in six tricarboxylic acid cycle organic acids(fumaric acid, malic acid, succinic acid, α-ketoglutaric acid, cis-aconitic acid, and citric acid) in the serum, liver, and placenta of mice exposed to di(2-ethylhexyl) phthalate(DEHP) during pregnancy. Methods : The serum, liver and placental samples from pregnant mice were processed and eluted through a Waters ACQUITY UPLC BEH Amide Column(130 Å, 1.7 μm, 2.1 mm × 150 mm) using a gradient elution program. Mobile phase A comprised an aqueous solution of 10 mmol/L ammonium acetate and 5 μmol/L methanephosphonic acid, while mobile phase B consisted of a 90% acetonitrile aqueous solution containing 10 mmol/L ammonium acetate and 5 μmol/L methanephosphonic acid, with a flow rate maintained at 0.35 ml/min. The mass spectrometry detection system utilized an electrospray ionization technique with negative ion mode for multiple reaction monitoring. Results : The correlation coefficients of the standard curves for the six tricarboxylic acid cycle organic acid metabolites were all above 0.996 within the quantitative range. The method's accuracy ranged from 97.14% to 108.26%, with inter-day and intra-day precision relative standard deviation between 1.35% and 6.73%. The matrix effect was between 93.29% and 107.47%, and the extraction recovery rate ranged from 94.82% to 112.57%. Analysis of six tricarboxylic acid cycle organic acids in the liver, serum, and placenta of DEHP-exposed mice during pregnancy showed significant reductions in fumaric acid, malic acid, α-ketoglutaric acid, cis-aconitic acid, and citric acid compared to the control group(P<0.05). Conclusion The HPLC-MS/MS method established in this study for detecting six tricarboxylic acid cycle organic acids in the serum, liver, and placenta of DEHP-exposed pregnant mice is stable, highly sensitive and selective. Prenatal DEHP exposure induced alterations in the levels of tricarboxylic acid(TCA) cycle organic acid metabolites in the liver, serum, and placenta of mice, suggesting that DEHP exposure during pregnancy may interfere with mitochondrial TCA cycle processes. These findings indicate potential value in the diagnosis and treatment of diseases associated with prenatal DEHP exposure.

Progesterone receptor membrane component 1(PGRMC1)is closely related to hormone therapy which belongs to the membrane-associated progesterone receptor(MAPR)family.A large number of in vitro experiments,in vivo animal experiments,clinical samples of breast cancer patients and blood studies showed that all synthetic progesterone(excluding natural progesterone and dydrogesterone)can promote the rapid proliferation of breast cancer cells overexpressing PGRMC1.In patients with breast cancer,PGRMC1 is significantly negatively correlated with tumor grade and prognosis,and PGRMC1 level in blood is positively correlated with PGRMC1 expression in breast cancer tissues,and PGRMC1 is superior to traditional tumor markers such as carcinoembryonic antigen(CEA),carbohydrate antigen(CA125),and CA153 in predicting early breast cancer.Therefore,PGRMC1 may serve as a predictive marker for identifying an elevated risk of breast cancer associated with menopausal hormone replacement therapy.

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.