Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

Objective:To apply deep neural networks to predict high-precision dose distribution in volume modulated arc therapy (VMAT) plans for non-small cell lung cancer (NSCLC) patients.Methods:This study developed a U-shaped network called MHA-resunet, which incorporated a large kernel dilated convolution module and a multi-head attention module. The model was trained from 151 VMAT plans of NSCLC patients. CT images, planning target volume (PTV) and organs at risk (OARs) were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks′performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics.Results:The MAE between the dose distribution predicted by MHA-resunet network and the manually planned dose distribution within the PTV area was 1.51 Gy, and the D98 and D95 errors in the PTV area were both < 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-resunet was the smallest in the PTV area and in OARs except for the heart. Conclusions:The proposed MHA-resunet network improves the receptive field to learn the relative spatial relationship between the PTV area and the OARs, enabling accurate prediction of dose distribution in NSCLC patients undergoing VMAT radiotherapy.

Objective To propose a deep learning neural network approach for transforming megavoltage computed tomography(MVCT)images of cervical cancer into pseudo kilovoltage computed tomography(kVCT)images with high signal-to-noise ratio and contrast-to-noise ratio,thus providing three-dimensional anatomical images and localization information required for adaptive radiotherapy of cervical cancer,and guiding the accelerator to achieve precise treatment.Methods The MVCT and kVCT images of 54 patients treated with cervical cancer radiotherapy were collected,with 44 cases randomly selected as the training set,and the remaining 10 cases as the test set.A cyclic generative adversarial network with gating mechanism and multi-channel data input was used to synthesize pseudo-kVCT images from MVCT images.The network training results were evaluated with imaging quality evaluation parameters,such as mean absolute error(MAE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM).Results The MAE,PSNR,and SSIM of MVCT imagesvspseudo-kVCT(5:5)images were(24.9±0.7)HUvs(17.8±0.3)HU,(29.8±0.2)dBvs(30.7±0.2)dB,and 0.841±0.007 vs 0.898±0.003,respectively.Conclusion The generated pseudo-kVCT images have advantages in noise reduction and contrast enhancement,and can reduce the need for additional MV-kVCT electron density calibration in dose calculations.The dose calculation ability of pseudo-kVCT is comparable to that of MVCT,providing a possibility for the application of pseudo-kVCT images in image-guided adaptive radiotherapy.

【Objective】 To systematically evaluate the efficacy and safety of targeted drugs in the treatment of metastatic non-clear cell renal cell carcinoma (nccRCC) and to provide guidance for clinical treatment. 【Methods】 All observational studies and randomized controlled trials (RCTs) of nccRCC treated with targeted drugs were retrieved from the PubMed, Embase, the Cochrane Library and Web of Science. Three independent investigators screened the literature, extracted data and evaluated the quality of literature. The RCTs were evaluated using the Cochrane Handbook. One research with insufficient outcome data (follow-up bias) was assessed as high risk, and the other studies showed low or uncertain risk. The non-RCTs were evaluated with the JBI Quality Assessment Tool, and all studies displayed a low risk of bias. The data were analyzed with Stata 17.0 software. 【Results】 A total of 16 studies involving 989 patients were included. Meta-analysis showed that the objective response rate (ORR) was 12.6% (95%CI:8.1%-17.9%), the total disease control rate (DCR) was 65.3% (95%CI:58.3%-72.1%), the total median progression-free survival (PFS) was 5.80 (95%CI:4.69-6.91) months, and the median overall survival (OS) was 15.93 (95%CI:12.17-19.68) months. In subgroup analysis, the total ORR of patients with metastatic nccRCC treated with sunitinib and cabozantinib were 11.7% (95%CI:6.5%-18.0%) and 17.2% (95%CI:8.4%-28.2%), respectively. The total ORR of patients with papillary renal cell carcinoma was 9.1% (95%CI:2.4%-18.9%). 【Conclusion】 Targeted drugs have a significant effect on patients with metastatic nccRCC, but adverse reactions may occur. Targeted drugs have poor effects on metastatic papillary renal cell carcinoma, and cabozantinib may have greater survival benefits.

IL-34 can promote osteoclast differentiation and activation, which may contribute to steroidinduced osteonecrosis of the femoral head (ONFH). Animal model was constructed in both BALB/c and IL-34 deficient mice to detect the relative expression of inflammation cytokines. Micro-CT was utilized to reveal the internal structure. In vitro differentiated osteoclast was induced by culturing bone marrow-derived macrophages with IL-34 conditioned medium or M-CSF. The relative expression of pro-inflammation cytokines, osteoclast marker genes, and relevant pathways molecules was detected with quantitative real-time RT-PCR, ELISA, and Western blot. Up-regulated IL-34 expression could be detected in the serum of ONFH patients and femoral heads of ONFH mice. IL-34 deficient mice showed the resistance to ONFH induction with the up-regulated trabecular number, trabecular thickness, bone value fraction, and down-regulated trabecular separation. On the other hand, inflammatory cytokines, such as TNF-α, IFN-γ, IL-6, IL-12, IL-2, and IL-17A, showed diminished expression in IL-34 deficient ONFH induced mice. IL-34 alone or works in coordination with M-CSF to promote osteoclastogenesis and activate ERK, STAT3, and non-canonical NF-κB pathways. These data demonstrate that IL-34 can promote the differentiation of osteoclast through ERK, STAT3, and non-canonical NF-κB pathways to aggravate steroid-induced ONFH, and IL-34 can be considered as a treatment target.