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.

OBJECTIVE: Psoriasis, a common chronic inflammatory skin condition with genetic underpinnings, is traditionally managed with cupping therapy. Although used historically, the precise mechanical effects and therapeutic mechanisms of cupping in psoriasis remain largely unexamined. This study aimed to evaluate cupping therapy's efficacy for psoriasis and investigate its role in modulating inflammatory responses and cellular metabolism. METHODS: Psoriasis was induced in mice using topical imiquimod (IMQ). The effects of cupping on psoriatic lesions were assessed using the Psoriasis Area and Severity Index score, histology, immunohistochemistry, and immunofluorescence staining. polymerase chain reaction sequencing (RNA-seq) and Western blotting were conducted to examine changes in mRNA expression and the AMP-activated protein kinase (AMPK) signaling pathway. RESULTS: Cupping therapy significantly reduced inflammation, epidermal thickness, and inflammatory cell infiltration in mice with IMQ-induced psoriasis. Immunohistochemistry and immunofluorescence showed lower expression of inflammatory markers and a shift in T-cell populations. RNA-seq and Western blotting indicated that cupping upregulated Piezo1 and activated the AMPK pathway, improving energy metabolism in psoriatic skin. CONCLUSION Cupping therapy reduces epidermal hyperproliferation and inflammation in psoriasis, rebalancing the local immune microenvironment. Mechanistically, cupping promotes calcium influx via Piezo1, activates AMPK signaling, and supports metabolic homeostasis, suggesting therapeutic potential for psoriasis. Please cite this article as: Xi RF, Liu X, Wang Y, Lu HZ, Yuan SJ, Guo DJ, Zhu JY, Li FL, Duan YJ. Mechanosensory activation of Piezo1 via cupping therapy: Harnessing neural networks to modulate AMPK pathway for metabolic restoration in a mouse model of psoriasis. J Integr Med. 2025; 23(6):721-732.
Animals ; Psoriasis/chemically induced* ; Mice ; AMP-Activated Protein Kinases/metabolism* ; Disease Models, Animal ; Cupping Therapy/methods* ; Signal Transduction ; Imiquimod ; Ion Channels/genetics* ; Male ; Mechanotransduction, Cellular

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.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

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 explore the application effectiveness of the large language model DeepSeek in the cultivation of clinical thinking skills for medical laboratory technology students.Methods:A non-randomized controlled study was conducted. In the 2024-2025 academic year, two classes of second-year medical laboratory technology students from Hubei University of Chinese Medicine were selected and divided into a DeepSeek-assisted teaching group (Class A, n=53) and a traditional teaching control group (Class B, n=53), totaling 106 students. Both groups followed a 20-week problem-based learning (PBL) framework with identical teaching content, instructors, and class hours. Class A utilized DeepSeek via the"Learning Pass AI"platform for case diagnosis reasoning, prompt construction training, test plan formulation, and result analysis, while Class B received traditional PBL instruction. Paired t-tests were used to compare pre-and post-teaching scores in clinical thinking skills, AI interaction literacy, and prompt construction in Class A. Independent samples t-tests and chi-square ( χ2) tests were used to evaluate differences in case reasoning scores, etiology analysis accuracy, and teaching satisfaction between groups. Structured questionnaires supplemented the evaluation of model-assisted teaching processes. Results:The comparison of pre-and post-teaching scores in Class A showed that post-teaching scores significantly improved in clinical thinking skills[(4.02±0.45) points vs. (3.09±0.50) points, t=2.23)] and AI interaction literacy [(4.62±0.41) points vs. (3.27±0.54) points, t=2.18]. Compared to Class B, Class A demonstrated superior performance in case reasoning scores [(81.1±3.8) points vs.(74.3±4.2) points, t=8.97], etiology analysis accuracy [94.3% (50/53) vs. 81.1% (43/53), χ2=4.29], and teaching satisfaction [(95.6±3.2)points vs. (82.6±4.8) points, t=11.86] ( P<0.05). The results of questionnaires indicated that during model application, the prompt construction improved in logic [(2.85±0.58) points to (4.25±0.50) points, t=14.23, P<0.01] and innovation [(2.60±0.53) points to (4.05±0.46) points, t=11.57, P<0.05], but question clarity (77.4%, 41/53) and medical terminology accuracy (43.4%, 23/53) remained primary shortcomings. Conclusion:Integrating large language models into AI-teacher collaborative learning pathways can effectively promote students′ autonomous inquiry and clinical reasoning skills, thereby enhancing medical laboratory technology students′ clinical thinking skills.