A method was developed for determination of dilauryl thiodipropionate(DLTDP)in fried foods by coupling solid-phase extraction(SPE)pretreatment with reverse-phase liquid chromatography-tandem mass spectrometry(RPLC-MS/MS)detection.Samples were extracted with n-hexane as the solvent,purified using a neutral alumina SPE cartridge,and finally analyzed by RPLC-MS/MS.Quantitative analysis was performed using matrix-matched calibration curves combined with an external standard method under optimal experimental conditions.The results showed that DLTDP exhibited good linearity in the range of 2.0-50.0 μg/L,with a correlation coefficient(R2)≥0.999.The limit of detection(LOD)and the limit of quantification(LOQ)of the method were 0.15 mg/kg and 0.5 mg/kg,respectively.The mean recoveries at three fortification levels(0.5,1.0,and 200 mg/kg)in different samples ranged from 84.8%to 96.8%,with the relative standard deviations(RSDs)all less than 8.0%.The developed method was highly sensitive,accurate and reliable,and easy to operate,making it well suited for the routine quantitative analysis of DLTDP in fried foods.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Aim To study the effect of anti-PD-L1 monoclonal antibody on high-fat diet-induced athero-sclerosis in ApoE-/-mice.Methods Twenty-four ApoE-/-mice were randomly divided into the normal group,high-fat group,and high-fat+anti-PD-L1 mAb group.After 70 days,the blood samples were harves-ted.Blood vessels(aortic root to abdominal aorta)and liver from each groups were stained with Oil Red O.Hematoxylin-eosin staining(HE)was employed to vis-ualize structural changes in liver.Enzyme-linked im-munosorbent assay(ELISA)was applied to detect the serum levels of total cholesterol(CHO),triglyceride(TG),high-density lipoprotein(HDL-c),low-density lipoprotein(LDL-c)and inflammatory factors(IFN-γ,TNF-α,IL-1 β).Flow cytometry was used to detect the proportion of lymphocytes(CD4 and CD8).RT-PCR was utilized to assess the expressions of IFN-γ,TNF-α,IL-1 β,CD4 and CD8 in liver.Results Compared with the high-fat group,the treatment with anti-PD-L1 monoclonal antibody promoted vascular wall and liver lipid accumulation,and also up-regulated serum and liver content of cholesterol(CHO),triglyceride(TG)and high-density lipoprotein(HDL-c).Treatment with anti-PD-L1 monoclonal antibody up-regulated the con-tent of alanine aminotransferase(GPT)and aspartate aminotransferase(GOT)in serum and liver,but not al-kaline phosphatase(AKP).ELISA test indicated that treatment with anti-PD-L1 monoclonal antibody stimu-lated the serum level of IFN-γ,TNF-α and IL-1 β.Fur-thermore,the mRNA level of IFN-γ,TNF-α and IL-1 βin liver was also up-regulated after treatment with anti-PD-L1 monoclonal antibody.With flow cytometry,we observed that treatment with anti-PD-L1 monoclonal antibody promoted hepatic CD8+T and CD8+IFN-γ+T cell activation,but had no effect on CD4+IFN-γ+T cell activation under high-fat feeding conditions.Con-clusions Anti-PD-L1 monoclonal antibody adminis-tered under high-fat feeding conditions can damage liv-er function and aggravate atherosclerosis by activating liver CD8+IFN-γ+T cells.

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

Transmission X-ray tubes are relatively new devices characterized by portability,suitability for miniaturization,and low requirements for shielding,making them ideal radiation sources for kilovoltage X-ray therapy.However,their application in radiotherapy remains underexplored.An electron target model of a transmission X-ray tube is developed using the Monte Carlo toolkit TOPAS 3.8.1.The study investigates the effect of tungsten target thickness on X-ray output efficiency,finding that a tube voltage of 50 kV and a tungsten thickness of 1.4 μm yields the highest emission efficiency.Based on the energy spectrum at this optimal efficiency,polynomial fitting approach is applied to determine the corresponding aluminum filter thickness for mean energies ranging from 20 keV to 35 keV,achieving a mean fitting error of 0.91%.Next,the study simulates dose deposition in a water phantom for spectra with different mean energies and various source-to-surface distances,and plots percent-depth-dose curves,relative normalized dose-depth curves,and relative normalized dose histograms under each treatment condition.Finally,the simulated results are compared with experimental data from the intraoperative radiotherapy system Intrabeam and the superficial X-ray therapy unit SRT-100,obtaining average relative errors of 3.71%and 4.38%,respectively.These findings provide a theoretical foundation for further optimization of transmission X-ray tubes in kilovoltage radiotherapy.

Objective:To explore the dosimetry optimization strategy based on filter thickness and shape selection for the bulb superficial X-ray radiotherapy unit.Methods:Monte Carlo code TOPAS was used to model tubular equipment, and the dose distribution from six X-ray energies (50-150 kV) and five conventional aluminum filters (0.5-3.0 mm) with different thickness were simulated in the water model. The percentage depth dose (PDD) curve along the central axis, the center-axis profile dose at different depths, and the lateral dose distribution were analyzed. The dose distribution of three different designs of aluminum filters (conventional cylindrical, conical and oblique cylindrical filters) was compared to evaluate the effect of dosimetric optimization of different filter shapes.Results:Under the same energy, increasing the thickness of the filter can optimize the superficial skin dose, and the optimization effect of depth dose uniformity can be increased by 26% at a depth of 5.5 mm at 70 kV energy. The raised, flat and inclined dose distribution modes can be achieved by using conventional cylindrical, conical and inclined aluminum filters.Conclusions:By selecting the appropriate X-ray energy and filter thickness, an ideal dose distribution matching the tumor depth can be achieved. The application of personalized filters is also of great significance for diverse target areas.

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

Lung cancer poses a serious threat to global public health security.Chemotherapy,as the main strategy for cancer treatment,faces challenges such as high toxicity and drug resistance.Anticancer peptides have the potential of being developed into new anticancer drugs due to their advantages of broad-spectrum anticancer activity,rapid action,and difficulty in generating drug resistance,but they also face shortcomings such as weak activity and strong toxic side effects.The weakly acidic microenvironment of tumors(pH 6.5-6.8)provides a good idea for the design of anticancer peptides of high-efficiency and low-toxicity.Previously,we designed the acid-sensitive antibacterial peptide pHly-1 using the wolf spider(Lycosa singoriensis)toxin Lycosin-Ⅰ as a template.In this study,we found that pHly-1 also had acid-sensitive anticancer activity.Further alanine scanning analysis of pHly-1 was carried out,and we ob-tained a mutant pHTP-2 with better acid sensitivity,whose IC50(half maximal inhibitory concentration)against A549 cells was 15.68 μmol/L at pH 6.6 and was greater than 100 μmol/L at pH 7.4.At pH 6.6,pHTP-2 could act on various lung cancer cell lines and induce the death of A549 cells by rapid ly-sis;at pH 7.4,500 μmol/L pHTP-2 had weak toxicity to red blood cells(the hemolysis rate was ap-proximately 38％)and primary myocardial cells(the inhibition rate was 49.7％,with P＜0.05).Analy-sis of its charge,particle size,morphology,and secondary structure showed that at pH 6.6,the histidine in the sequence of pHTP-2 was protonated,increasing the positive charge(P＜0.01),decreasing the hy-drated particle size(P＜0.05)and forming an α-helical structure to induce membrane lysis of A549 cells.At pH 7.4,it was deprotonated,the positive charge decreases,a β-sheet structure was formed and self-aggregation occurred,limiting its effect on the A549 cell membrane and showing weak activity.In summary,pHTP-2 could respond to the weakly acidic microenvironment of tumors to exert selective cyto-toxic activity,effectively overcoming the shortcomings of anticancer peptides such as low efficiency and high toxicity.Our findings suggest that it is a high-quality lead molecule for anticancer drugs.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*