ObjectiveQi deficiency and phlegm dampness syndrome is a common type of clinical traditional Chinese medicine（TCM） syndrome. However， there is no standard， scientific， and accurate report on the construction of animal models of Qi deficiency and phlegm dampness syndrome. This study aims to construct a mouse model of Qi deficiency and phlegm dampness syndrome by using a multi-factor composite modeling method and to evaluate the model. MethodsTwenty-one C57BL/6 mice were randomly divided into three groups with seven mice in each group， which were the normal group， model group， and Shenling Baizhusan （SLBZ） group. The control group was fed with ordinary diet and kept in a normal environment. The model group and SLBZ group were fed with a high-fat diet in a high-humidity environment. Swimming with heavy weights until exhaustion and gavage with cold water or lard were used to establish the mouse model of Qi deficiency and phlegm dampness syndrome. In order to test the syndrome by prescription， mice in the SLBZ group were treated with SLBZ for 14 days after model construction. The exhaustive swimming time， body weight， serum lipid levels， tongue changes， "Qi deficiency and phlegm dampness" assessment scale score， and cecal index of mice in each group were measured. The feces of each group of mice were sent for metagenomics and metabolome sequencing， and the changes in intestinal flora and metabolites were analyzed. ResultsAfter the modeling of Qi deficiency and phlegm dampness syndrome， the exhaustive swimming time of mice was obviously shortened （P<0.01）. The serum total cholesterol， low density lipoprotein cholesterol， and non-high density lipoprotein cholesterol of mice were significantly increased （all P<0.01）. The tongue of mice was significantly different from that of the normal group， and the score of the assessment scale was significantly higher than that of the control group （P<0.01）. Cecal index decreased significantly （P<0.01）. The serum lipid level， tongue image， assessment scale score， and cecal index were reversed in the SLBZ group. Metagenomic and metabolome sequencing results showed that intestinal flora and fecal metabolites were significantly changed in mice with Qi deficiency and phlegm dampness syndrome. Akkermansia_muciniphila， Faecalibaculum_rodentium， Eubacterium_plexicaudatum， Eubacterium sp 14_2， Candida glabrata， Romboutsia_ilealis， Turicibacter sp TS3， and other bacteria had significant changes， and the expressions of intestinal metabolites such as chenodeoxycholic acid， choline， L-phenylalanine betaine， and 2-phenylbutyric acid were significantly changed. Related metabolic pathways such as linoleic acid metabolism， primary bile acid biosynthesis， lysine degradation， arginine biosynthesis， and alpha-linolenic acid metabolism were affected. ConclusionThe Qi deficiency and phlegm dampness model of mice can be constructed by the multi-factor composite modeling method of high-fat diet feeding， high-humidity environment feeding， exhaustive swimming with heavy weight， and intragastric administration with cold water or lard. The blood lipid level， tongue change， score of "Qi deficiency and phlegm dampness assessment scale"， cecal index， and changes in related intestinal flora and metabolites of mice can be used as key indicators for model evaluation.

Renal metastasis from differentiated thyroid cancer (DTC) is uncommon and is hard to be distinguished from primary renal cell carcinoma. There is no consensus on diagnosis and treatment, and decisions about surgery and 131I therapy doses usually depend on experience. To better understand clinical characteristics and current treatment status, this study systematically reviews existing studies, exploring epidemiological features, clinical symptoms, imaging findings (including lesions characteristics across various imaging modalities), therapeutic strategies, and prognosis of DTC renal metastases, providing evidence-based references for clinical practice.

Objective To construct a macrophage-related risk assessment model,explore the impact of macrophages on the survival of patients with lung squamous cell carcinoma(LUSC),and predict immune status.Methods We downloaded the data of macrophages and LUSC from the Molecular Signatures DataBase(MSigDB)and The Cancer Genome Atlas(TCGA)database,respectively,screened for differentially expressed macrophage-related genes,and constructed a risk score model using Cox regression analysis.Based on the median value of the risk score,LUSC in the TCGA database was divided into high-and low-risk groups.Kaplan-Meier analyses,receiver operating characteristic curve analyses,clinical case characteristics,and immune analyses were used to evaluate the prognostic model.Finally,we determined the relationship between anticancer drug sensitivity and the risk score using the Genomics of Drug Sensitivity in Cancer(GDSC).Results A total of 124 macrophage-related genes were identified in LUSC.High-risk patients had shorter overall survival and higher infiltration of immunosuppressive cells.Ruxolitinib,vinorelbine,paclitaxel,and sorafenib seemingly exhibited better efficacy in low-risk patients.The mortality rate of LUSC patients decreasd with the reduction of risk scores,and CSF2 and EDN2 had a significant impact on overall survival.Conclusion In this study,we constructed a macrophage gene risk score model for predicting the prognosis of LUSC.The model genes CSF2 and END2 can be used as potential targets for subsequent studies of LUSC.

Objective:To investigate the influence of the complexity metrics of the multi-leaf collimator (MLC) on the γ-pass rate of volumetric modulated arc therapy (VMAT) plans for nasopharyngeal carcinoma (NPC).Methods:A total of 60 VMAT plans for NPC were selected to measure the γ-pass rate. The MLC data across all control points (CPs) in each VMAT plan were analyzed to calculate the mean field area (MFA), average leaf gap (ALG), small aperture score (SAS), and their corresponding weighted metrics including MFAW, ALGW, and SASW, considering dose weight (W). Pearson′s bivariate correlation analysis was conducted to examine the correlations between MLC complexity metrics and the γ-pass rate. Moreover, the receiver operating characteristic (ROC) analysis was employed to assess the predictive efficacy of MLC complexity metrics on dose verification result.Results:Under the 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria, the MFA in the 60 VMAT plans exhibited a positive correlation with the γ-pass rate ( r=0.82, 0.79, 0.72, P<0.05), and the MFAW was also positively correlated with the γ-pass rate ( r=0.83, 0.81, 0.75, P<0.05). The ALG manifested a positive correlation with the γ-pass rate ( r=0.82, 0.79, 0.74, P<0.05), as did the ALGW ( r=0.83, 0.81, 0.77, P<0.05). The SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), and SAS(3-4 cm) displayed negative correlations with the γ-pass rate ( r= -0.86, -0.82, -0.71, -0.84, -0.82, -0.72, -0.79, -0.79, -0.73, -0.30, -0.35, -0.42, P<0.05), whereas the SAS(4-5 cm), SAS(5-6 cm), and SAS(6-40 cm) showed positive correlations with the γ-pass rate ( r=0.49, 0.45, 0.33, 0.73, 0.71, 0.59, 0.79, 0.79, 0.76, P<0.05). The outcomes of SASW reveal slightly stronger correlations than those of SAS. In terms of correlations among complexity metrics, a positive correlation was observed between MFA and ALG ( r=0.98, P<0.05). ALG was negatively correlated with SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), and SAS(3-4 cm) ( r= -0.95, -0.94, -0.89, -0.39, P<0.05), and positively correlated with SAS(4-5 cm), SAS(5-6 cm), and SAS(6-40 cm) ( r=0.51, 0.77, 0.92, P<0.05). The weighted result mirrored these correlations. The ROC-derived analytical result indicate that MFA, MFAW, ALG, ALGW, SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), SAS(6-40 cm), SASW(0-1 cm), SASW(1-2 cm), SASW(2-3 cm), and SASW(6-40 cm) demonstrated exceptional predictive efficacy for dose verification result [Area under the curve (AUC) > 0.9, P<0.05]. Conclusions:The γ-pass rate of VMAT plans for NPC is strongly correlated with MLC complexity metrics, which demonstrate excellent predictive efficacy for dose verification result. The increased uncertainty in dose calculations and measurements caused by narrow fields generated by the MLC is a significant factor contributing to the reduced γ-pass rate of VMAT plans. This finding is associated with discrepancies in the precision of treatment planning system (TPS) modeling and the accuracy of dose verification tools, providing a reference for similar challenges.

Objective:To investigate the differences in dosimetric parameters for target volumes and organs at risk (OARs), radiation doses to reconstructed tissues, and beam-on time in radiotherapy among helical tomotherapy (HT), volumetric modulated arc therapy (VMAT), and fixed-field intensity-modulated radiotherapy (F_IMRT) following immediate breast reconstruction for breast cancer, thereby providing a reference for the selection of clinical radiotherapy techniques.Methods:This study retrospectively investigated 15 breast cancer patients who underwent radiotherapy following modified radical mastectomy and immediate breast reconstruction at the Liuzhou Worker′s Hospital from August 2018 to July 2023. During target volume delineation, precautions were taken to avoid the reconstructed tissues, which were delineated separately. Customized HT, VMAT, and F_IMRT treatment plans were designed for each patient. The plans were categorized into the HT, VMAT, and F_IMRT groups based on different radiotherapy techniques employed. They were comparatively analyzed through one-way analysis of variance (ANOVA), with multiple comparisons further conducted in the case of significant differences.Results:Statistical analyses reveal significant differences in various parameters of target volumes among the three groups of plans ( F = 38.73, 14.95, 37.01, 48.05, 35.55, 22.56, 34.30, P < 0.05). Pairwise comparisons indicate that the maximum dose ( D2%), minimum dose ( D98%), mean dose ( Dmean), and the proportion of high-dose volumes within the target volume ( V107%and V110%) in both the HT and VMAT groups were significantly better than those in the F_IMRT group. The HT group demonstrated the optimal conformity index (CI), while the VMAT group displayed the superior homogeneity index (HI) compared to the other two groups. In terms of OAR, the V20 of the ipsilateral lung was the lowest in the HT group ( F = 14.31, P < 0.05) and the highest in the F_IMRT group ( F = 14.31, P < 0.05). However, the V5 and Dmean for both the ipsilateral and contralateral lungs in the HT group significantly surpassed those of the other groups ( F = 39.16, 31.91, P < 0.05). The mean dose Dmean ( F = 5.57, P < 0.05) of the contralateral breast was significantly reduced in the VMAT group compared to the other two groups. No statistically significant differences were observed for other OARs, including the heart, spinal cord PRV, thyroid, and humeral head ( P > 0.05). The radiation doses to reconstructed tissues ( Dmax, V53.5, Dmean) ascended in the order of HT, VMAT, and F_IMRT groups ( F = 17.69, 17.53, 15.11, P < 0.05). The HT and F_IMRT groups showed similar beam-on times ( P > 0.05), both exceeding that of the VMAT group by several folds ( F = 28.72, P < 0.05). Conclusions:The comparative analysis indicates that the three radiotherapy techniques exhibit distinct advantages and limitations, with F_IMRT demonstrating the least comprehensive advantage. HT can enhance the conformity of target volumes while reducing the overall radiation doses to reconstructed tissues and the crucial indicator V20 in the ipsilateral lung. VMAT demonstrates the highest treatment efficiency, yielding improved dose uniformity in the target volume and reduced radiation doses to the contralateral breast. It is advisable to prioritize HT or VMAT based on actual clinical conditions.

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

Objective:To investigate the influence of the complexity metrics of the multi-leaf collimator (MLC) on the γ-pass rate of volumetric modulated arc therapy (VMAT) plans for nasopharyngeal carcinoma (NPC).Methods:A total of 60 VMAT plans for NPC were selected to measure the γ-pass rate. The MLC data across all control points (CPs) in each VMAT plan were analyzed to calculate the mean field area (MFA), average leaf gap (ALG), small aperture score (SAS), and their corresponding weighted metrics including MFAW, ALGW, and SASW, considering dose weight (W). Pearson′s bivariate correlation analysis was conducted to examine the correlations between MLC complexity metrics and the γ-pass rate. Moreover, the receiver operating characteristic (ROC) analysis was employed to assess the predictive efficacy of MLC complexity metrics on dose verification result.Results:Under the 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria, the MFA in the 60 VMAT plans exhibited a positive correlation with the γ-pass rate ( r=0.82, 0.79, 0.72, P<0.05), and the MFAW was also positively correlated with the γ-pass rate ( r=0.83, 0.81, 0.75, P<0.05). The ALG manifested a positive correlation with the γ-pass rate ( r=0.82, 0.79, 0.74, P<0.05), as did the ALGW ( r=0.83, 0.81, 0.77, P<0.05). The SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), and SAS(3-4 cm) displayed negative correlations with the γ-pass rate ( r= -0.86, -0.82, -0.71, -0.84, -0.82, -0.72, -0.79, -0.79, -0.73, -0.30, -0.35, -0.42, P<0.05), whereas the SAS(4-5 cm), SAS(5-6 cm), and SAS(6-40 cm) showed positive correlations with the γ-pass rate ( r=0.49, 0.45, 0.33, 0.73, 0.71, 0.59, 0.79, 0.79, 0.76, P<0.05). The outcomes of SASW reveal slightly stronger correlations than those of SAS. In terms of correlations among complexity metrics, a positive correlation was observed between MFA and ALG ( r=0.98, P<0.05). ALG was negatively correlated with SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), and SAS(3-4 cm) ( r= -0.95, -0.94, -0.89, -0.39, P<0.05), and positively correlated with SAS(4-5 cm), SAS(5-6 cm), and SAS(6-40 cm) ( r=0.51, 0.77, 0.92, P<0.05). The weighted result mirrored these correlations. The ROC-derived analytical result indicate that MFA, MFAW, ALG, ALGW, SAS(0-1 cm), SAS(1-2 cm), SAS(2-3 cm), SAS(6-40 cm), SASW(0-1 cm), SASW(1-2 cm), SASW(2-3 cm), and SASW(6-40 cm) demonstrated exceptional predictive efficacy for dose verification result [Area under the curve (AUC) > 0.9, P<0.05]. Conclusions:The γ-pass rate of VMAT plans for NPC is strongly correlated with MLC complexity metrics, which demonstrate excellent predictive efficacy for dose verification result. The increased uncertainty in dose calculations and measurements caused by narrow fields generated by the MLC is a significant factor contributing to the reduced γ-pass rate of VMAT plans. This finding is associated with discrepancies in the precision of treatment planning system (TPS) modeling and the accuracy of dose verification tools, providing a reference for similar challenges.

Objective:To investigate the differences in dosimetric parameters for target volumes and organs at risk (OARs), radiation doses to reconstructed tissues, and beam-on time in radiotherapy among helical tomotherapy (HT), volumetric modulated arc therapy (VMAT), and fixed-field intensity-modulated radiotherapy (F_IMRT) following immediate breast reconstruction for breast cancer, thereby providing a reference for the selection of clinical radiotherapy techniques.Methods:This study retrospectively investigated 15 breast cancer patients who underwent radiotherapy following modified radical mastectomy and immediate breast reconstruction at the Liuzhou Worker′s Hospital from August 2018 to July 2023. During target volume delineation, precautions were taken to avoid the reconstructed tissues, which were delineated separately. Customized HT, VMAT, and F_IMRT treatment plans were designed for each patient. The plans were categorized into the HT, VMAT, and F_IMRT groups based on different radiotherapy techniques employed. They were comparatively analyzed through one-way analysis of variance (ANOVA), with multiple comparisons further conducted in the case of significant differences.Results:Statistical analyses reveal significant differences in various parameters of target volumes among the three groups of plans ( F = 38.73, 14.95, 37.01, 48.05, 35.55, 22.56, 34.30, P < 0.05). Pairwise comparisons indicate that the maximum dose ( D2%), minimum dose ( D98%), mean dose ( Dmean), and the proportion of high-dose volumes within the target volume ( V107%and V110%) in both the HT and VMAT groups were significantly better than those in the F_IMRT group. The HT group demonstrated the optimal conformity index (CI), while the VMAT group displayed the superior homogeneity index (HI) compared to the other two groups. In terms of OAR, the V20 of the ipsilateral lung was the lowest in the HT group ( F = 14.31, P < 0.05) and the highest in the F_IMRT group ( F = 14.31, P < 0.05). However, the V5 and Dmean for both the ipsilateral and contralateral lungs in the HT group significantly surpassed those of the other groups ( F = 39.16, 31.91, P < 0.05). The mean dose Dmean ( F = 5.57, P < 0.05) of the contralateral breast was significantly reduced in the VMAT group compared to the other two groups. No statistically significant differences were observed for other OARs, including the heart, spinal cord PRV, thyroid, and humeral head ( P > 0.05). The radiation doses to reconstructed tissues ( Dmax, V53.5, Dmean) ascended in the order of HT, VMAT, and F_IMRT groups ( F = 17.69, 17.53, 15.11, P < 0.05). The HT and F_IMRT groups showed similar beam-on times ( P > 0.05), both exceeding that of the VMAT group by several folds ( F = 28.72, P < 0.05). Conclusions:The comparative analysis indicates that the three radiotherapy techniques exhibit distinct advantages and limitations, with F_IMRT demonstrating the least comprehensive advantage. HT can enhance the conformity of target volumes while reducing the overall radiation doses to reconstructed tissues and the crucial indicator V20 in the ipsilateral lung. VMAT demonstrates the highest treatment efficiency, yielding improved dose uniformity in the target volume and reduced radiation doses to the contralateral breast. It is advisable to prioritize HT or VMAT based on actual clinical conditions.

Objective To construct a macrophage-related risk assessment model,explore the impact of macrophages on the survival of patients with lung squamous cell carcinoma(LUSC),and predict immune status.Methods We downloaded the data of macrophages and LUSC from the Molecular Signatures DataBase(MSigDB)and The Cancer Genome Atlas(TCGA)database,respectively,screened for differentially expressed macrophage-related genes,and constructed a risk score model using Cox regression analysis.Based on the median value of the risk score,LUSC in the TCGA database was divided into high-and low-risk groups.Kaplan-Meier analyses,receiver operating characteristic curve analyses,clinical case characteristics,and immune analyses were used to evaluate the prognostic model.Finally,we determined the relationship between anticancer drug sensitivity and the risk score using the Genomics of Drug Sensitivity in Cancer(GDSC).Results A total of 124 macrophage-related genes were identified in LUSC.High-risk patients had shorter overall survival and higher infiltration of immunosuppressive cells.Ruxolitinib,vinorelbine,paclitaxel,and sorafenib seemingly exhibited better efficacy in low-risk patients.The mortality rate of LUSC patients decreasd with the reduction of risk scores,and CSF2 and EDN2 had a significant impact on overall survival.Conclusion In this study,we constructed a macrophage gene risk score model for predicting the prognosis of LUSC.The model genes CSF2 and END2 can be used as potential targets for subsequent studies of LUSC.