Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

OBJECTIVES: To investigate the therapeutic mechanism of Thesium chinense Turcz. (TCT) for antibiotic-associated diarrhea (AAD). METHODS: Network pharmacology, KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD, the key signaling pathways and the binding between the active components in TCT and the core protein targets. In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride, the effects of daily gavage of 1% carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg (n=10) on body weight and diarrhea were observed. HE staining, ELISA, 16S rRNA sequencing, and Western blotting were used to examine pathologies, expression levels of IL-6 and TNF-α, changes in gut microbiota, and protein expressions of EGFR, p-EGFR, PI3K, p-PI3K, Akt, and p-Akt in the colon tissues of the mice. RESULTS: We identified a total of 66 active components of TCT and 68 core targets including EGFR, STAT3 and PIK3CA. KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway. Molecular docking showed that EGFR had the highest binding affinity with coniferin, and the EGFR-coniferin complex maintained a stable conformation at 10 ns, whose stability was also confirmed by Gibbs free energy analysis. In the mouse models of AAD, treatment with TCT significantly improved colonic tissue morphology, decreased colonic levels of TNF-α and IL-6, increased gut microbiota diversity, and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides. TCT treatment also markedly reduced protein expressions of p-EGFR, p-PI3K and p-Akt in the colon tissues of the mice. CONCLUSIONS TCT can alleviate AAD in mice by modulating gut microbiota composition, regulating the EGFR/PI3K/Akt signaling pathway, and reducing TNF‑α and IL-6 expressions.
Animals ; Gastrointestinal Microbiome/drug effects* ; Signal Transduction/drug effects* ; Mice ; ErbB Receptors/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Diarrhea/drug therapy* ; Phosphatidylinositol 3-Kinases/metabolism* ; Anti-Bacterial Agents/adverse effects* ; Drugs, Chinese Herbal/therapeutic use* ; Molecular Docking Simulation

OBJECTIVES: The presence of multi-scale skin lesion regions and image noise interference and limited resources of auxiliary diagnostic equipment affect the accuracy of skin disease detection in skin disease detection tasks. To solve these problems, we propose a highly efficient and lightweight skin disease detection model using an improved RT-DETR model. METHODS: A lightweight FasterNet was introduced as the backbone network and the FasterNetBlock module was parametrically refined. A Convolutional and Attention Fusion Module (CAFM) was used to replace the multi-head self-attention mechanism in the neck network to enhance the ability of the AIFI-CAFM module for capturing global dependencies and local detail information. The DRB-HSFPN feature pyramid network was designed to replace the Cross-Scale Feature Fusion Module (CCFM) to allow the integration of contextual information across different scales to improve the semantic feature expression capacity of the neck network. Finally, combining the advantages of Inner-IoU and EIoU, the Inner-EIoU was used to replace the original loss function GIOU to further enhance the model's inference accuracy and convergence speed. RESULTS: The experimental results on the HAM10000 dataset showed that the improved RT-DETR model, as compared with the original model, had increased mAP@50 and mAP@50:95 by 4.5% and 2.8%, respectively, with a detection speed of 59.1 frames per second (FPS). The improved model had a parameter count of 10.9 M and a computational load of 19.3 GFLOPs, which were reduced by 46.0% and 67.2% compared to those of the original model, validating the effectiveness of the improved model. CONCLUSIONS The proposed SD-DETR model significantly improves the performance of skin disease detection tasks by effectively extracting and integrating multi-scale features while reducing both parameter count and computational load.
Humans ; Skin Diseases/diagnosis* ; Skin/pathology* ; Neural Networks, Computer ; Algorithms

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Objective To develop a machine learning(ML)-based prediction model for assessing the therapeutic effects of resveratrol(RES)on the pathological damage of acute pancreatitis(AP),and to optimize RES administration strategies for AP through validation using an animal model.Methods AAn ML-based prediction model was constructed using published data.Interpretability analysis was applied to identify high-efficacy zones within the parameter space of administration dose and frequency,which was followed by rigorous screening to select the optimal dosing strategy that balanced therapeutic efficacy and experimental feasibility.A total of 32 C57BL/6 mice were randomly assigned to 4 groups(n=8 per group),including a control group(Ctrl),an AP model group induced by caerulein(CER)and referred to as CER-AP,a treatment group receiving RES via intraperitoneal injection(RES i.p.),and a treatment group receiving RES via intragastric gavage(RES i.g.).The Ctrl group received intraperitoneal injection of normal saline.The CER-AP and the treatment groups were induced with 10 intraperitoneal injections of CER at 50 μg/kg.RES was administered to the RES i.p.and RES i.g.groups according to the optimal dose and timing predicted by the ML model.Blood and tissue samples were collected 12 hours after the experiment started.Results The gradient boosting decision tree model,optimized via Hyperopt,yielded the best performance,predicting that the optimal dose and administration frequency were 19.992 mg/kg and 3.828 times,respectively.Accordingly,a regimen of 20 mg/kg RES,administered four times,was used in the animal experiments.Compared with the Ctrl group,the CER-AP group exhibited higher pancreatic pathology scores and elevated levels of serum amylase,lipase,pancreatic myeloperoxidase,and trypsin,with all differences reaching statistical significance(all P<0.05).The administration of 20 mg/kg RES via both intraperitoneal injection and intragastric gavage mitigated pancreatic inflammatory cell infiltration and necrosis,improved the overall pathology score,and reduced serum amylase,lipase,and pancreatic myeloperoxidase levels to varying degrees(all P<0.05).Conclusion A regimen of 20 mg/kg RES administered four times effectively alleviates the severity of CER-induced AP.The therapeutic benefits appear to arise from a multi-target regulatory network that simultaneously suppresses inflammatory cascades,mitigates oxidative stress,and reduces apoptosis,thereby reducing pancreatic tissue damage and systemic inflammatory responses.

Objective To investigate the therapeutic mechanism of Thesium chinense Turcz.(TCT)for antibiotic-associated diarrhea(AAD).Methods Network pharmacology,KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD,the key signaling pathways and the binding between the active components in TCT and the core protein targets.In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride,the effects of daily gavage of 1%carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg(n=10)on body weight and diarrhea were observed.HE staining,ELISA,16S rRNA sequencing,and Western blotting were used to examine pathologies,expression levels of IL-6 and TNF-α,changes in gut microbiota,and protein expressions of EGFR,p-EGFR,PI3K,p-PI3K,Akt,and p-Akt in the colon tissues of the mice.Results We identified a total of 66 active components of TCT and 68 core targets including EGFR,STAT3 and PIK3CA.KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway.Molecular docking showed that EGFR had the highest binding affinity with coniferin,and the EGFR-coniferin complex maintained a stable conformation at 10 ns,whose stability was also confirmed by Gibbs free energy analysis.In the mouse models of AAD,treatment with TCT significantly improved colonic tissue morphology,decreased colonic levels of TNF-α and IL-6,increased gut microbiota diversity,and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides.TCT treatment also markedly reduced protein expressions of p-EGFR,p-PI3K and p-Akt in the colon tissues of the mice.Conclusion TCT can alleviate AAD in mice by modulating gut microbiota composition,regulating the EGFR/PI3K/Akt signaling pathway,and reducing TNF-α and IL-6 expressions.

Objective:To construct a management model of data mining for medical imaging equipment,so as to improve the quality of managing equipment.Methods:A management model of mining data was constructed to manage medical imaging equipment.A total of twenty imaging equipment that were using at Hainan Hospital of the General Hospital of the People's Liberation Army of China from April 2022 to March 2024 were selected.According to different management methods,the conventional management was adopted to manage them during April 2022 to March 2023,and the management model of mining data(model management)was adopted to manage them during April 2023 to March 2024.A self-developed questionnaire was used to conduct a satisfaction survey for imaging physicians,staffs of operating and maintaining equipment,and technicians who using and managing equipment,and patients who received diagnosis and treatment by using equipment.The failure rate and the imaging effect of equipment,the satisfaction scores of the relative staffs who used equipment,and the growth amplitude of operational benefits of equipment between two management methods were compared.Results:A total of 20 failures occurred in imaging equipment that adopted model management.In them,the failure rates of the self-equipment,improper operation and insufficient professional level were respectively 15%,5%and 5%,all of which were lower than those of the conventional management method.The predicted failure rate of model management was 75%,which was higher than that of the conventional management method,and the differences of the above indicators between two methods were statistically significant(x2=6.547,4.392,5.124,6.701,P<0.05).The scores of image clarity,qualification rate,excellent rate,qualification rate of body position,and the total score of imaging effect of adopting model management method were respectively(22.36±2.01),(23.21±1.54),(22.65±1.87),(23.21±1.52)and(91.43±6.77)points,all of which were higher than those of the conventional management method,and the differences were statistically significant(t=10.662,12.727,15.324,16.333,13.742,P<0.05).The satisfaction scores of imaging physicians,staffs of operation and maintenance,technicians and patients who uses management for adopting the model management method were all higher than those of the conventional management method,and the differences were statistically significant(t=13.586,14.249,17.021,11.006,P<0.05).The average values of growth amplitude of cost and benefit of equipment operation of adopting model management method were higher than those of the conventional management method,and the average value of the growth amplitude of the cost of the troubleshooting of model management method was lower than that of the conventional management method,and the differences of them were statistically significant(t=15.057,19.310,18.336,P<0.05).Conclusion:The application of the management model of data mining for medical imaging equipment can provide warning of equipment failures in advance,and reduce the failure rate of equipment,and improve the quality of management and operation of equipment,and enhance the service level of equipment.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

Objective:To construct a management model of data mining for medical imaging equipment,so as to improve the quality of managing equipment.Methods:A management model of mining data was constructed to manage medical imaging equipment.A total of twenty imaging equipment that were using at Hainan Hospital of the General Hospital of the People's Liberation Army of China from April 2022 to March 2024 were selected.According to different management methods,the conventional management was adopted to manage them during April 2022 to March 2023,and the management model of mining data(model management)was adopted to manage them during April 2023 to March 2024.A self-developed questionnaire was used to conduct a satisfaction survey for imaging physicians,staffs of operating and maintaining equipment,and technicians who using and managing equipment,and patients who received diagnosis and treatment by using equipment.The failure rate and the imaging effect of equipment,the satisfaction scores of the relative staffs who used equipment,and the growth amplitude of operational benefits of equipment between two management methods were compared.Results:A total of 20 failures occurred in imaging equipment that adopted model management.In them,the failure rates of the self-equipment,improper operation and insufficient professional level were respectively 15%,5%and 5%,all of which were lower than those of the conventional management method.The predicted failure rate of model management was 75%,which was higher than that of the conventional management method,and the differences of the above indicators between two methods were statistically significant(x2=6.547,4.392,5.124,6.701,P<0.05).The scores of image clarity,qualification rate,excellent rate,qualification rate of body position,and the total score of imaging effect of adopting model management method were respectively(22.36±2.01),(23.21±1.54),(22.65±1.87),(23.21±1.52)and(91.43±6.77)points,all of which were higher than those of the conventional management method,and the differences were statistically significant(t=10.662,12.727,15.324,16.333,13.742,P<0.05).The satisfaction scores of imaging physicians,staffs of operation and maintenance,technicians and patients who uses management for adopting the model management method were all higher than those of the conventional management method,and the differences were statistically significant(t=13.586,14.249,17.021,11.006,P<0.05).The average values of growth amplitude of cost and benefit of equipment operation of adopting model management method were higher than those of the conventional management method,and the average value of the growth amplitude of the cost of the troubleshooting of model management method was lower than that of the conventional management method,and the differences of them were statistically significant(t=15.057,19.310,18.336,P<0.05).Conclusion:The application of the management model of data mining for medical imaging equipment can provide warning of equipment failures in advance,and reduce the failure rate of equipment,and improve the quality of management and operation of equipment,and enhance the service level of equipment.

Objective To analyze the relationship of the volume of 87 brain regions with postnatal age and neurobehavior in full-term neonates.Methods A total of 75 full-term newborns[gestational age(39.38±1.22)weeks;male/female(51/24);postnatal age(11.11±6.67)days]without abnormalities on brain MRI(three-dimensional T1-weighted imaging,3D T1WI)at our hospital between November 2010 and September 2017 were retrospectively included.Based on the template of 87 brain regions,the neonatal brains were divided into 87 brain regions and their volumes were calculated by using V-shape Bottleneck network(VB-Net)deep learning segmentation technique,Pearson partial correlation and regression analysis were used to explore the relationship of the volume of each brain region with postnatal age and neurobehavioral scores.Results After adjusting for gestational age,birth weight,head circumference,body length and sex,66.7％of the regional brain volumes(58/87 brain regions)significantly increased with the postnatal age(correlation coefficient r:0.2-0.7,P＜0.05).The volumes of gray matter in bilateral lentiform nucleus,left caudate nucleus,right occipital lobe,right inferior temporal lobe,and bilateral anterior temporal lobe strongly correlated with the postnatal age(r＞0.50,P＜0.05).The gray matter volume of the right occipital lobe linearly increased with age(slope:100.67),and was positively correlated with behavioral scores(r=0.324,P＜0.01).Conclusion Most of regional brain volumes increase with the postnatal age during the neonatal period,and the fastest growth occurs in primary sensorimotor-related brain regions,presenting the spatial heterogeneity.Partial brain region grows with the development of behavioral ability.