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.

A 39-year-old male patient with ankylosing spondylitis received adalimumab 40 mg subcutaneously every 2 weeks plus sulfasalazine enteric-coated tablets 0.75 g orally twice daily. Two weeks after the therapy initiation, he received his second adalimumab injection and discontinued sulfasalazine because of skin rashes. Meanwhile, cetirizine 10 mg once daily was given as anti-allergic treatment for 3 days. Six days later, he developed fever and persistent holocranial dull pain. The next day the headache worsened, with one episode of vomiting gastric contents. Cranial magnetic resonance imaging revealed findings consistent with infectious leptomeningeal lesions, and cerebrospinal fluid analysis suggested intracranial infection. Empirical anti-infection therapy with ceftriaxone and ganciclovir, as well as intracranial pressure reduction therapy with 20% mannitol was initiated. Two days later, next-generation sequencing of cerebrospinal fluid identified Neisseria meningitidis as the highly probable pathogen. The therapy regimen was adjusted to ceftriaxone, acyclovir and dexamethasone. After 12 days of treatments, the patient achieved full clinical recovery from meningitis.

Objective To construct mice hepatocellular carcinoma models with different tumor immune microenvironments(TIME)and explore the differences.Methods H22 and hepa1-6 were used to construct subcutaneous transplantation tumor model of C57 mice as homologous hepatocellular carcinoma cell lines(denoted as H22 group and hepal-6 group,each n=8),and the differences of TIME were evaluated.Immunohistochemistry was used to detect and quantify the infiltration of T cells,CD4+T cells,CD8+T cells,regulatory T cells and B cells in TIME.Flow cytometry was performed to detect the differences of composition of immune cell subpopulations in peripheral blood and tumor parenchyma.Gene expression profile characteristics of tumor tissue were analyzed based on high-throughput transcriptome sequencing technology,and enrichment analyses of immune-related signaling pathways were evaluated combined with gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG).Results H22 group showed cold and hepa1-6 group showed hot TIME characteristics.The number of T cells,CD4+T cells and CD8+T cells in tumor tissue of H22 group were all lower,while the proportion of T cells,CD4+T cells and CD8+T cells in peripheral blood were all higher than those of hepa1-6 group(all P＜0.05).Compared with H22 group,up-regulated genes of tumor tissue in hepa1-6 group expressed significantly enriched in tumor immune activation-related signaling pathways.Conclusion H22 and hepa1-6 hepatocellular carcinoma models showed distinct TIME characteristics of cold and hot tumors,respectively,and the amount of immune cells in tumor tissue of the former were significantly lower than those in the latter.

In order to the phage therapy for Clostridium perfringens(Cp),Cp was used as the host bacterium to screen specific phages from sewage collected from a large-scale beef cattle farm in a region of Shandong Province.Transmission electron microscopy was utilized to observe the mor-phological structure of the phages.Their characteristics were analyzed through the optimal multi-plicity of infection(MOI),host range,one-step growth curve,and antibacterial experiments.The re-sults showed that one phage was successfully isolated from 18 sewage samples.The plaque was transparent without a halo,exhibiting a broad host range and no lytic reaction with other tested bacteria.The optimal MOI was 0.1,and the phage entered the burst phase in 30 min.The phage maintained high titers at pH3-13 and remained active after treatment at 60℃ for 60 min,demon-strating good stability.Mice were infected with CP and treated with bacteriophage.Animal experi-ments showed that the isolated bacteriophage had a significant inhibitory effect on CP.The results indicated that the isolated phage,designated vB-CpP-Bp7,was a virulent phage with excellent lytic capability,acid-base stability,and thermal stability,laying a foundation for further application of this phage in phage therapy.

Fetal ultrasound standard plane detection was introduced in terms of its importance and problems encountered.The deep learning techniques applied in fetal standard plane detection were reviewed,including transfer learning,modified basic network,hybrid network and multi-task network.The problems encountered by the deep learning techniques during the application were analyzed,and the future research directions were envisioned.[Chinese Medical Equipment Journal,2025,46(5):91-101]

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

This study investigated the association between a proximal promoter polymorphism of IFNGR1 (interferon-γ receptor α chain, IFNGR-α) and breast cancer susceptibility, as well as the prognostic value of its expression variation in breast cancer patients. A case-control study was conducted at the Sixth Medical Center of PLA General Hospital from June 2020 to June 2022. The study included 182 pathologically confirmed breast cancer patients as the breast cancer group, 177 non-tumor patients with benign breast lesions as the benign breast lesions group, and 229 healthy individuals as the normal control group. 2-3 ml EDTA anticoagulant whole blood samples were collected from all participants, and genomic DNA was extracted and stored for further analysis. Basic patient information was retrieved from the hospital′s electronic medical records by patients′ ID number. The proximal promoter sequence of IFNGR1 was obtained from NCBI, and sequencing primers were designed using Primer Premier 6.0. Sanger sequencing was employed to analyze the IFNGR1 promoter sequence in the three groups, and the results were compared with the Eukaryotic Promoter Database (EPD) sequence using Bioedit software. Statistical analysis was performed on single nucleotide polymorphisms (SNPs) in the IFNGR1 promoter. The TCGA database was utilized to assess the relationship between IFNGR1 expression levels and breast cancer patient survival. The findings revealed that the -56 TG genotype of the IFNGR1 promoter was significantly associated with increased breast cancer risk ( Z=2.73, P<0.05). Notably, IFNGR1 expression was lower in breast cancer group compared to normal control group ( P<0.05). Analysis of the TCGA database indicated that patients with high IFNGR1 expression had longer survival times than those with low expression ( HR=0.87, 95% CI:0.77-0.98, P<0.05). In summary, the IFNGR1 -56 TG genotype is associated with an increased risk of breast cancer, and there is a positive correlation between IFNGR1 expression levels and the survival of breast cancer patients.

OBJECTIVE To explore the expression level of miR-196a in cervical cells infected with high-risk human papillomavirus(HPV)16 and 18.METHODS The Gene Expression Omnibus(GEO)was used to screen for dif-ferentially expressed miRNAs between HPV 16 or 18-positive cervical cancer cells and normal cervical cells.On-line biological software https://kmplot.com/analysis/was utilized to analyze the relationship between the most differentially expressed miRNA and the overall survival of cervical cancer patients.Cervical swab samples positive for HPV 16 or HPV 18,detected by real-time fluorescent quantitative polymerase chain reaction(qPCR)genoty-ping,were collected as the study subjects.Cervical swab samples from the same period of physical examination population that were negative for HPV 16 or HPV 18 by qPCR genotyping served as negative controls.The qRT-PCR method was employed to detect the level of miR-196a in cervical cells,with data processed via the 2-△△Ctmethod.RESULTS Differential analysis of the GSE86100 data revealed that miR-196a expression de-creased in HPV 16 or HPV 18-positive cervical cells(log2FC=-6.60,P＜0.001),while miR-3188 expression significantly increased(log2FC=6.22,P＜0.001).Using online analysis tools https://kmplot.com/analysis,it was found that cervical cancer patients with high miR-196a expression had a shorter overall survival compared to those with low m iR-196a expression(HR=1.87,95％CI:1.17-3.00,P=0.008).H owever,there was no cor-relation between miR-3188 and the overall survival of cervical cancer patients(HR=1.47,95％CI:0.92-2.37,P=0.110).The results of specific qRT-PCR testing showed that the expression levels of miR-196a in cervical cells positive for HPV 16 and HPV 18 were 0.93±0.09 and 0.51±0.07,respectively,which were lower than those in the normal control group(1.89±0.13)(P＜0.05),consistent with the sequencing analysis results CONCLUSIONS Infection of cervical cells with HPV 16 or HPV 18 can lead to decreased expression of miR-196a,and the expres-sion level of miR-196a is negatively correlated with the overall survival of cervical cancer patients.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.