The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue's pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.
Humans ; Spectrum Analysis, Raman/methods* ; Tongue Neoplasms/diagnostic imaging* ; Carcinoma, Squamous Cell/diagnostic imaging* ; Artificial Intelligence ; Margins of Excision

Gongju(Chrysanthemum morifolium) is one of the five major medicinal Chrysanthemum varieties included in the Chinese Pharmacopoeia. In recent years, its cultivation areas have changed significantly, resulting in mixed quality of the medicinal herbs. In this study, Gongju cultivated in Anhui, Yunnan, Chongqing, and other places were selected as research objects. Hyperspectral data were collected in the visible-near-infrared(VNIR) and short-wave infrared(SWIR) bands using different modes, such as corolla facing up(A) and flower base facing up(B). After pre-processing the hyperspectral data using five methods, including multiplicative scatter correction(MSC), Savitzky-Golay smoothing(SG), first derivative(D1), second derivative(D2), and standard normal variate(SNV), partial least squares discriminant analysis(PLSDA), random forest(RF), and support vector machine(SVM) were used to establish origin identification models of Gongju at the two geographical scales of the province and the city-county in Anhui province. The accuracy of the prediction results was used as an evaluation index to select the optimal models, and the classification performance of the models was evaluated by confusion matrix. The results showed that the flower base facing up(B) collection model combined with second derivative pretreatment and RF method was the best model for both geographical scale identification models. The modeling effect of the full-band(VNIR + SWIR) was slightly better than that of the single band, with the accuracy of the prediction set in the province and city-county regions reaching 99.69% and 99.40%, respectively. The competitive adaptive reweighted sampling algorithm(CARS), successive projections algorithm(SPA), and variable iterative space shrinkage approach(VISSA) were further used to screen the feature wavelength modeling. The number of feature wavelengths screened by CARS was fewer, and the prediction set accuracy of the two geographical scales models after optimization could reach 99.56% and 98.65%, which was basically comparable to the full-band model. However, the removal of redundant variables could greatly reduce the complexity of the model. The hyperspectral technology combined with the chemometrics model established in this study can achieve the origin identification of Gongju at different geographical scales, providing a theoretical basis and technical reference for the construction of a rapid detection system for Gongju origin and the development of exclusive miniaturized instrumentation and equipment systems.
Chrysanthemum/growth & development* ; China ; Support Vector Machine ; Geography ; Discriminant Analysis ; Spectroscopy, Near-Infrared/methods* ; Spectrum Analysis/methods* ; Drugs, Chinese Herbal/analysis* ; Least-Squares Analysis

Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Electroencephalography/methods* ; Algorithms ; Spectrum Analysis

In order to evaluate the composition and distribution characteristics of inorganic elements in Laminaria japonica, this study employed inductively coupled plasma mass spectrometry(ICP-MS) to detect the inorganic elements and used high performance liquid chromatography tandem ICP-MS(HPLC-ICP-MS) to determine the content of different arsenic species in L. japonica from diffe-rent origins. Micro X-ray fluorescence(Micro-XRF) was used to determine micro-area distribution of inorganic elements in L. japonica. The results showed that the average content of Mn, Fe, Sr, and Al was high, and that of As and Cr exceeded the limits of the national food safety standard. According to the results of HPLC-ICP-MS, arsenobetaine(AsB) was the main species of As contained in L. japonica. The more toxic inorganic arsenic accounts for a small proportion, whereas its content was 1-4 times of the limit in the national food safety standard. The results of Micro-XRF showed that As, Pb, Fe, Cu, Mn, and Ni were mainly distributed on the surface of L. japonica. Among them, As and Pb had a clear tendency to diffuse from the surface to the inside. The results of the study can provide a basis for the processing as well as the medicinal and edible safety evaluation of L. japonica.
Arsenic/analysis* ; Chromatography, High Pressure Liquid/methods* ; Laminaria ; Mass Spectrometry/methods* ; Spectrum Analysis ; Trace Elements/analysis*

Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.
Humans ; Male ; Microscopy/methods* ; Radiopharmaceuticals ; Spectrum Analysis, Raman/methods* ; Technology ; Urologic Neoplasms/diagnosis*

Hyperspectral imaging technology can obtain the spatial and spectral three-dimensional imaging of substances simultaneously, and obtain the unique continuous characteristic spectrum of substances in a wide spectrum range at a certain spatial resolution, which has outstanding advantages in the fine classification and identification of biological substances. With the development of hyperspectral imaging technology, a large amount of data has been accumulated in the exploration of data acquisition, image processing and material inspection. As a new technology means, hyperspectral imaging technology has its unique advantages and wide application prospects. It can be combined with the common biological physical evidence of blood (stains), saliva, semen, sweat, hair, nails, bones, etc., to achieve rapid separation, inspection and identification of substances. This paper introduces the basic theory of hyperspectral imaging technology and its application in common biological evidence examination research and analyzes the feasibility and development of biological evidence testing and identification, in order to provide a theoretical basis for the development of new technology and promote hyperspectral imaging technology in related biological examination, to better serve the forensic practice.
Spectrum Analysis/methods* ; Hyperspectral Imaging ; Forensic Medicine ; Blood Stains ; Technology

METHODS: Eight patients awaiting heart transplantation due to end-stage coronary heart disease underwent coronary CT angiography (CCTA) spectroscopy prior to heart transplantation; coronary artery pathological analysis was performed for all patients. Lipid-core plaques were defined pathologically as manifesting a lipid core diameter > 200 µm, a circumference > 60 degrees, and a cap thickness < 450 µm. The percentage distributions of CT pixel attenuation ≤ 20, 30, 40, and 50 HU were calculated using quantitative histogram analysis.RESULTS: A total of 271 transverse sections were co-registered between CCTA and pathological analysis. Overall, 26 lipid cores and 16 fibrous plaques were identified by pathological analysis. There was no significant difference in median CT attenuation between the lipid and fibrous plaques (51 HU [interquartile range, 46–63] vs. 57 HU [interquartile range, 50–64], p = 0.659). The median percentage of CT pixel attenuation ≤ 30 HU accounted for 11% (5–17) of lipid-core plaques and 0% (0–2) of fibrous plaques (p < 0.001). The sensitivity and specificity of the method for diagnosing lipid plaques by the average CT pixel attenuation ≤ 30 HU were 80.8% and 87.5%, respectively. The area under the receiver operator characteristics curve was 0.898 (95% confidence interval: 0.765–0.970; 3.0% was the best cut-off value). The diagnostic performance was significantly higher than those of the average pixel CT attenuation percentages ≤ 20, 40, and 50 HU and the mean CT attenuation (p < 0.05).CONCLUSION: In in vivo conditions, with the pathological lipid core as the gold standard, quantification of the percentage of average CT pixel attenuation ≤ 30 HU in the histogram can be useful for accurate identification of lipid plaques.]]>
Angiography ; Coronary Disease ; Coronary Vessels ; Diagnosis ; Heart Transplantation ; Humans ; Methods ; Sensitivity and Specificity ; Spectrum Analysis

methods, including biochemical assays, immunoassays, and molecular diagnostics. Despite its strong advantage as an analytical method, many laboratory physicians and clinical laboratories are unwilling to introduce it. Fundamental elements, such as instruments, reagents, facilities, skilled human resources are required to implement mass spectrometry. This review contains considerations for the introduction of liquid chromatography-mass spectrometry to support the clinical laboratories interested in or planning to implement mass spectrometry.]]>
Humans ; Immunoassay ; Indicators and Reagents ; Mass Spectrometry ; Methods ; Pathology, Molecular ; Spectrum Analysis

PURPOSE: Chronic rhinosinusitis (CRS) is a complex immunological condition, and novel experimental modalities are required to explore various clinical and pathophysiological endotypes; mere evaluation of nasal polyp (NP) status is inadequate. Therefore, we collected patient nasal secretions on filter paper and characterized the proteomes. METHODS: We performed liquid chromatography-mass spectrometry (MS)/MS in the data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes. Nasal secretions were collected from 10 controls, 10 CRS without NPs (CRSsNP) and 10 CRS with NPs (CRSwNP). We performed Orbitrap MS-based proteomic analysis in the DDA (5 controls, 5 CRSsNP and 5 CRSwNP) and the DIA (5 controls, 5 CRSsNP and 5 CRSwNP) modes, followed by a statistical analysis and a hierarchical clustering to identify differentially expressed proteins in the 3 groups. RESULTS: We identified 2,020 proteins in nasal secretions. Canonical pathway analysis and gene ontology (GO) evaluation revealed that interleukin (IL)-7, IL-9, IL-17A and IL-22 signaling and neutrophil-mediated immune responses like neutrophil degranulation and activation were significantly increased in CRSwNP compared to control. The GO terms related to the iron ion metabolism that may be associated with CRS and NP development. CONCLUSIONS: Collection of nasal secretions on the filter paper is a practical and non-invasive method for in-depth study of nasal proteomics. Our proteomic signatures also support that Asian NPs could be characterized as non-eosinophilic inflammation features. Therefore, the proteomic profiling of nasal secretions from CRS patients may enhance our understanding of CRS endotypes.
Asian Continental Ancestry Group ; Gene Ontology ; Humans ; Inflammation ; Interleukin-17 ; Interleukin-9 ; Interleukins ; Iron ; Metabolism ; Methods ; Nasal Polyps ; Neutrophils ; Proteome ; Proteomics ; Sinusitis ; Spectrum Analysis

BACKGROUND: Chronic kidney disease (CKD) is associated with fluid retention, which increases total body water (TBW) and leads to changes in intracellular water (ICW) and extracellular water (ECW). This complicates accurate assessments of body composition. Analysis of bioelectrical impedance may improve the accuracy of evaluation in CKD patients and multiple machines and technologies are available. We compared body composition by bioimpedance spectroscopy (BIS) against multi-frequency bioimpedance analysis (BIA) in a multi-ethnic Asian population of stable, non-dialysis CKD patients. METHODS: We recruited 98 stable CKD patients comprising 54.1% men and 70.4% Chinese, 9.2% Malay, 13.3% Indian, and 8.2% other ethnicities. Stability was defined as no variation in serum creatinine > 20% over three months. Patients underwent BIS analyses using a Fresenius body composition monitor, while BIA analyses employed a Bodystat Quadscan 4000. RESULTS: Mean TBW values by BIS and BIA were 33.6 ± 7.2 L and 38.3 ± 7.4 L; mean ECW values were 15.8 ± 3.2 L and 16.9 ± 2.7 L; and mean ICW values were 17.9 ± 4.3 L and 21.0 ± 4.9 L, respectively. Mean differences for TBW were 4.6 ± 1.9 L (P < 0.001), for ECW they were 1.2 ± 0.5 L (P < 0.001), and for ICW they were 3.2 ±1.8 L (P < 0.001). BIA and BIS measurements were highly correlated: TBW r = 0.970, ECW r = 0.994, and ICW r = 0.926. Compared with BIA, BIS assessments of fluid overload appeared to be more associated with biochemical and clinical indicators. CONCLUSION: Although both BIA and BIS can be used for body water assessment, clinicians should be aware of biases that exist between bioimpedance techniques. The values of body water assessments in our study were higher in BIA than in BIS. Ethnicity, sex, body mass index, and estimated glomerular filtration rate were associated with these biases.
Adult ; Asian Continental Ancestry Group ; Bias (Epidemiology) ; Body Composition ; Body Mass Index ; Body Water ; Creatinine ; Electric Impedance ; Glomerular Filtration Rate ; Humans ; Kidney Diseases ; Male ; Methods ; Nutrition Assessment ; Renal Insufficiency, Chronic ; Spectrum Analysis ; Water