Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

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.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To analyze the technical essentials of single-microport assisted micro-uni-port thoracoscopic surgery, and to investigate its surgical efficacy and promotional value. Methods Clinical data of patients who consecutively underwent radical upper lobectomy in the Department of Thoracic Surgery of Xi'an International Medical Center Hospital from March 2023 to June 2024 were retrospectively analyzed. According to the surgical approach, patients were divided into two groups: the single-microport assisted group (underwent upper lobectomy via single-microport assisted micro-uni-port thoracoscopy) and the traditional uniportal group (underwent traditional uniportal thoracoscopic lobectomy). Clinical outcomes were compared between the two groups. Results A total of 62 patients were enrolled. There were 30 patients in the single-microport assisted group, with a mean age of (57.4±10.8) years, and 32 patients in the traditional uniportal thoracoscopic group, with a mean age of (57.6±8.7) years. The baseline data were comparable between the two groups. All patients in both groups successfully underwent minimally invasive surgery without conversion to thoracotomy. The operative time in the single-microport assisted group was significantly shorter than that in the traditional uniportal group [(146.03±30.79) min vs. (171.41±36.41) min, P=0.004]. However, there were no statistically significant differences between the two groups in intraoperative blood loss, number of dissected lymph nodes, duration of chest tube drainage, postoperative pain score, postoperative hospital stay, hospitalization cost, or incidence of postoperative complications (all P>0.05). Conclusion Single-microport assisted micro-uni-port thoracoscopic surgery for upper lobectomy can maximally integrate the advantages of three-port and uni-port VATS while effectively avoiding their disadvantages. It significantly shortens the operative time without increasing postoperative pain or complications, and represents a more minimally invasive, safer, and more convenient surgical approach.

AIM To establish an HPLC-MS/MS method for the simultaneous residue determination of 11 plant growth regulators(PGRs)in Renshen Guben preparations,and to conduct a risk assessment.METHODS The analysis was performed on a 40 ℃ thermostatic ACQUITY UPLC ? Waters HSS T3 column(2.1 mm×100 mm,1.8 μm),with the mobile phase of acetonitrile-0.1％formic acid(containing 5 mmol/L ammonium formate)flowing at 0.30 mL/min in a gradient elution manner,and electro spray ionization was employed in both positive and negative ion scanning,with multiple reaction monitoring mode.The chronic and acute exposure risk values of the detected PGRs were calculated and assessed based on residue levels,health guidance values,and exposure estimates.RESULTS Eleven PGRs exhibited good linear relationships within their own ranges(R2 ≥ 0.990),whose average recoveries were 70.0％-120.0％,with RSDs all below 12.0％.In both oral liquid and pill forms,mepiquat chloride showed the highest average residue levels,while sodium 5-nitroguaiacolate exhibited the highest acute risk value(0.765 7,0.908 1)and chronic risk value(0.023 1,0.027 0).CONCLUSION Although PGRs residues are detected in Renshen Guben preparations,all levels remained within safe limits.

Objective:To develop a treatment-related quality of life scale for Chinese patients with multiple myeloma (MM) and to test its reliability and validity.Methods:The initial scale was constructed through a literature search, Delphi expert correspondence, and cognitive testing. This study conducted a preliminary survey of 379 patients with MM and a formal survey of 865 patients from the hematology departments of 155 hospitals nationwide from February 2024 to March 2024. The final scale was obtained after conducting item analysis and reliability and validity tests on the initial scale.Results:The constructed scale contains 36 items covering six domains: physiological, psychological, social, treatment side effects, general health, and others. In the preliminary survey, the Cronbach’s alpha coefficient of each item ranged from 0.597 to 0.939, and the test-retest reliability was 0.747 ( P<0.001). Exploratory factor analysis extracted eight common factors with a cumulative variance contribution of 60.058%. In the formal survey, the Cronbach’s alpha coefficient of each item ranged from 0.484 to 0.930, and the test-retest reliability was 0.835 ( P<0.001). Confirmatory factor analysis revealed a comparative fit index of 0.750, a root-mean-square error of approximation of 0.090, and a root-mean-square residual of 0.067. Conclusion:The treatment-related quality of life scale for Chinese patients with MM designed in this study exhibited good reliability and validity, reflecting the impact of treatment on the quality of life of patients. This scale can provide a reference to clinicians for assessing the disease status of patients.

Small animal multimodal biomedical imaging refers to the integration of multiple imaging techniques within the same system or device to acquire comprehensive physiological and pathological information of small animals, such as mice and rats. With the continuous advancement of biomedical research, this cutting-edge technology has attracted extensive attention. Multimodal imaging techniques, based on diverse imaging principles, overcome the limitations of single-modal imaging through information fusion, significantly enhancing the overall system's sensitivity, temporal/spatial resolution, and quantitative accuracy. In the future, the integration of new materials and artificial intelligence will further boost its sensitivity and resolution. Through interdisciplinary innovation, this technology is expected to become the core technology of personalized medicine and expand its applications to drug development, environmental monitoring, and other fields, thus reshaping the landscape of biomedical research and clinical practice. This review summarized the progress on the application and investigation of multimodal biomedical imaging techniques, and discussed its development in the future.
Animals ; Multimodal Imaging/trends* ; Rats ; Mice ; Artificial Intelligence ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed

OBJECTIVES: To evaluate the effectiveness of single-balloon and double-balloon enteroscopy in diagnosing pediatric small bowel diseases and assess the diagnostic efficacy of computed tomography enterography (CTE) for small bowel diseases using enteroscopy as the reference standard. METHODS: Clinical data from 576 children who underwent enteroscopy at Hunan Children's Hospital between January 2017 and December 2023 were retrospectively collected. The children were categorized based on enteroscopy type into the single-balloon enteroscopy (SBE) group (n=457) and double-balloon enteroscopy (DBE) group (n=119), and the clinical data were compared between the two groups. The sensitivity and specificity of CTE for diagnosing small bowel diseases were evaluated using enteroscopy results as the standard. RESULTS: Among the 576 children, small bowel lesions were detected by enteroscopy in 274 children (47.6%).There was no significant difference in lesion detection rates or complication rates between the SBE and DBE groups (P>0.05), but the DBE group had deeper insertion, longer procedure time, and higher complete small bowel examination rate (P<0.05). The complication rate during enteroscopy was 4.3% (25/576), with 18 cases (3.1%) of mild complications and 7 cases (1.2%) of severe complications, which improved with symptomatic treatment, surgical, or endoscopic intervention. Among the 412 children who underwent CTE, the sensitivity and specificity for diagnosing small bowel diseases were 44.4% and 71.3%, respectively. CONCLUSIONS SBE and DBE have similar diagnostic efficacy for pediatric small bowel diseases, but DBE is preferred for suspected deep small bowel lesions and comprehensive small bowel examination. Enteroscopy in children demonstrates relatively good overall safety. CTE demonstrates relatively low sensitivity but comparatively high specificity for diagnosing small bowel diseases.
Retrospective Studies ; Treatment Outcome ; Double-Balloon Enteroscopy/statistics & numerical data* ; Single-Balloon Enteroscopy/statistics & numerical data* ; Humans ; Male ; Female ; Child ; Operative Time ; Tomography, X-Ray Computed/statistics & numerical data* ; Sensitivity and Specificity ; Intestine, Small/surgery* ; Intestinal Diseases/surgery*

Objective To evaluate the feasibility of single-incision plus one-port laparoscopic surgery(SILS+1)for subtotal hysterectomy of large uterus based on the concept of enhanced recovery after surgery.Methods A total of 84 patients undergoing laparoscopic subtotal hysterectomy for large uterus(3.5-4.5 gestational months)in the gynecology department of the 960 Hospital,Joint Logistic Support Force,PLA from August 2022 to July 2023 were selected as the study subjects,and were divided into the SILS+1 group(group A,n=50)and the single-port laparoscopic group(group B,n=34)according to different surgical methods.The indexes of operation time,blood loss,operation success rate,exhaust time and visual analogue pain(VAS)scale were observed.Results The operative time of group A[(110.22±10.36)min]was less than that of group B[(131.53±8.51)min],the intraoperative blood loss of group A[(41.86±24.20)mL]was less than that of group B[(117.65±51.05)mL],and the hospitalization cost of group A[(21 308.78±647.90)yuan]was lower than that of group B[(21 769.50±537.84)yuan].The success rate of operation in group A(100.00%)was higher than that in group B(88.24%),the differences were statistically significant(P＜0.05).There were no significant differences in postoperative exhaust time,pain score,antibiotic use days and hospital stay between group A and group B(P＞0.05).The incidence of surgical complications in group A(4.00%)was lower than that in group B(8.82%),and the difference was not statistically significant(P＞0.05).Conclusion Single-incision plus one-port laparoscopic surgery subtotal hysterectomy for large uterus can significantly shorten the operation time,reduce the amount of blood loss,have a high success rate,and do not increase surgical complications,conforms to the concept of enhanced recovery after surgery,and is safe and feasible.