Objective:To develop a nutritional management protocol for head and neck cancer (HNC) patients undergoing radiotherapy based on evidence-based methodology, and to evaluate its clinical effectiveness.Methods:Relevant literature on nutritional management in radiotherapy for HNC patients was systematically searched. After evidence extraction, a preliminary protocol was drafted and finalized through expert consensus. The finalized protocol included five timepoints during hospitalization, covering six components and 35 nursing and clinical care items. A quasi-experimental design was adopted. Using convenience sampling, 100 HNC patients admitted to Jinhua Municipal Central Hospital from October 2022 to June 2024 were enrolled. Patients treated between October 2022 and July 2023 formed the control group ( n=50), and those treated from September 2023 to June 2024 comprised the intervention group ( n=50). The control group received routine care, while the intervention group was managed with the evidence-based nutrition protocol. Body weight and nutrition-related laboratory indicators were measured before radiotherapy, at week 4, and at the end of week 6. Results:At week 4 of radiotherapy, the intervention group had a higher lymphocyte count than the control group, with statistically significant differences ( P<0.05). At week 6, total serum protein, serum albumin, and lymphocyte counts were all higher in the intervention group, with statistically significant differences ( P<0.05) . Conclusions:The evidence-based nutritional management protocol developed for HNC patients undergoing radiotherapy effectively improves nutritional status. It provides a valuable reference for healthcare professionals in clinical practice.

Diabetic kidney disease （DKD） is a kidney disease caused by hyperglycemia，which is one of the most common microvascular complications of diabetes. Due to the high incidence of diabetes，the incidence of DKD has also increased year by year，and DKD has become a global public health problem. The pathogenesis of DKD is related to mechanisms such as oxidative stress，inflammation，renal fibrosis，and decreased mitophagy activity，which are developed under a variety of complex mechanisms. In traditional Chinese medicine，it is believed that the incidence of DKD is closely related to damp heat. Therefore，it is necessary to grasp the treatment method of clearing heat and removing dampness in clinical medication. Huangkui Capsules （HKC） have the effect of clearing damp heat，detoxifying， and detumescence. Because of its unique curative effect on DKD，HKC is often used in the treatment of DKD. HKC plays a role in the treatment of DKD with a variety of pharmacokinetic and pharmacodynamic processes. In many laboratory studies，it has been found that the specific mechanisms of HKC in the treatment of DKD include increasing mitophagy，reducing mitochondrial damage，reducing renal fibrosis，controlling inflammatory response，and inhibiting oxidative stress，which can achieve the purpose of reducing renal damage and promoting renal function. Some clinical studies have also verified that the application of HKC alone can exert renal protective function through anti-inflammatory，anti-oxidative stress，anti-renal fibrosis effects，as well as reduction of urinary protein. Since DKD is not a single injury of renal function，it is often accompanied by problems in blood pressure，blood lipids，blood circulation，body immunity， and other aspects. Therefore，the combination of HKC with other drugs can often achieve more comprehensive results，improve the advantages of various drugs，and improve the therapeutic effect. The combination of drugs such as antihypertensive，lipid-lowering， vascular circulation improvement，immunity inhibition，and anti-oxidative stress with HKC has achieved good results. In addition，HKC is often used in combination with other Chinese patent medicines in clinics. The application of HKC in the treatment of DKD has made some progress，but there are still many places worthy of further study，and the research on the mechanism of HKC is not comprehensive enough. The research on its long-term effect and safety in clinical application is relatively lacking，and the drug variety is relatively single when combined with certain drugs. These problems deserve further attention. Finally，it is necessary to pay attention to the promotion and application of HKC in clinical practice so that HKC can be better applied in clinical practice and better solve practical problems for patients.

Diabetic kidney disease （DKD） is a kidney disease caused by hyperglycemia，which is one of the most common microvascular complications of diabetes. Due to the high incidence of diabetes，the incidence of DKD has also increased year by year，and DKD has become a global public health problem. The pathogenesis of DKD is related to mechanisms such as oxidative stress，inflammation，renal fibrosis，and decreased mitophagy activity，which are developed under a variety of complex mechanisms. In traditional Chinese medicine，it is believed that the incidence of DKD is closely related to damp heat. Therefore，it is necessary to grasp the treatment method of clearing heat and removing dampness in clinical medication. Huangkui Capsules （HKC） have the effect of clearing damp heat，detoxifying， and detumescence. Because of its unique curative effect on DKD，HKC is often used in the treatment of DKD. HKC plays a role in the treatment of DKD with a variety of pharmacokinetic and pharmacodynamic processes. In many laboratory studies，it has been found that the specific mechanisms of HKC in the treatment of DKD include increasing mitophagy，reducing mitochondrial damage，reducing renal fibrosis，controlling inflammatory response，and inhibiting oxidative stress，which can achieve the purpose of reducing renal damage and promoting renal function. Some clinical studies have also verified that the application of HKC alone can exert renal protective function through anti-inflammatory，anti-oxidative stress，anti-renal fibrosis effects，as well as reduction of urinary protein. Since DKD is not a single injury of renal function，it is often accompanied by problems in blood pressure，blood lipids，blood circulation，body immunity， and other aspects. Therefore，the combination of HKC with other drugs can often achieve more comprehensive results，improve the advantages of various drugs，and improve the therapeutic effect. The combination of drugs such as antihypertensive，lipid-lowering， vascular circulation improvement，immunity inhibition，and anti-oxidative stress with HKC has achieved good results. In addition，HKC is often used in combination with other Chinese patent medicines in clinics. The application of HKC in the treatment of DKD has made some progress，but there are still many places worthy of further study，and the research on the mechanism of HKC is not comprehensive enough. The research on its long-term effect and safety in clinical application is relatively lacking，and the drug variety is relatively single when combined with certain drugs. These problems deserve further attention. Finally，it is necessary to pay attention to the promotion and application of HKC in clinical practice so that HKC can be better applied in clinical practice and better solve practical problems for patients.

Objective:To clarify the morphological relationship between the upper airway and TMJ in patients with normal-angle skeletal Ⅱ and skeletal Ⅰ malocclusion.Methods:30 skeletal class Ⅰ and 22 skeletal class Ⅱ patients with normal-angle were included.CBCT examination was performed,and Mimics 21.0 software was used to conduct 3D reconstruction and measurements of the samples.Data was analyzed by using independent t-test and the Pearson correlation test.Results:12 measurements,including the sagittal diameter of nasopharyngeal segment,the sagittal,coronal diameter,minimum cross-sectional area,the volume of palato-pharyngeal segment and glossopharyngeum segment,the total volume of upper airway,posterior oblique slope of the articular eminence and the length of the condylar showed significant differences between skeletal Ⅱ and skeletal Ⅰ subjects with normal-angle(P＜0.05).The posterior oblique slope of the articular eminence showed a positive correlation with the sagittal diameter of the palatopharyngeal segment,volume and minimum cross-sectional area of glossopharyngeum lingual segment(P＜0.05).The liner ratio showed a negative correction with the coronal diameter of palatopharyngeal and glossapharyngeal segment as well as minimum cross-section area of glos-sapharyngeal segment(P＜0.05).Conclusion:The structure of upper airway is correlated with that of TMJ.Differences in the upper airway are statistically significant between skeletal Ⅱ and skeletal Ⅰ malocclusion with normal-angle(P＜0.05).

Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.

Objective:To clarify the morphological relationship between the upper airway and TMJ in patients with normal-angle skeletal Ⅱ and skeletal Ⅰ malocclusion.Methods:30 skeletal class Ⅰ and 22 skeletal class Ⅱ patients with normal-angle were included.CBCT examination was performed,and Mimics 21.0 software was used to conduct 3D reconstruction and measurements of the samples.Data was analyzed by using independent t-test and the Pearson correlation test.Results:12 measurements,including the sagittal diameter of nasopharyngeal segment,the sagittal,coronal diameter,minimum cross-sectional area,the volume of palato-pharyngeal segment and glossopharyngeum segment,the total volume of upper airway,posterior oblique slope of the articular eminence and the length of the condylar showed significant differences between skeletal Ⅱ and skeletal Ⅰ subjects with normal-angle(P＜0.05).The posterior oblique slope of the articular eminence showed a positive correlation with the sagittal diameter of the palatopharyngeal segment,volume and minimum cross-sectional area of glossopharyngeum lingual segment(P＜0.05).The liner ratio showed a negative correction with the coronal diameter of palatopharyngeal and glossapharyngeal segment as well as minimum cross-section area of glos-sapharyngeal segment(P＜0.05).Conclusion:The structure of upper airway is correlated with that of TMJ.Differences in the upper airway are statistically significant between skeletal Ⅱ and skeletal Ⅰ malocclusion with normal-angle(P＜0.05).

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective:To develop a nutritional management protocol for head and neck cancer (HNC) patients undergoing radiotherapy based on evidence-based methodology, and to evaluate its clinical effectiveness.Methods:Relevant literature on nutritional management in radiotherapy for HNC patients was systematically searched. After evidence extraction, a preliminary protocol was drafted and finalized through expert consensus. The finalized protocol included five timepoints during hospitalization, covering six components and 35 nursing and clinical care items. A quasi-experimental design was adopted. Using convenience sampling, 100 HNC patients admitted to Jinhua Municipal Central Hospital from October 2022 to June 2024 were enrolled. Patients treated between October 2022 and July 2023 formed the control group ( n=50), and those treated from September 2023 to June 2024 comprised the intervention group ( n=50). The control group received routine care, while the intervention group was managed with the evidence-based nutrition protocol. Body weight and nutrition-related laboratory indicators were measured before radiotherapy, at week 4, and at the end of week 6. Results:At week 4 of radiotherapy, the intervention group had a higher lymphocyte count than the control group, with statistically significant differences ( P<0.05). At week 6, total serum protein, serum albumin, and lymphocyte counts were all higher in the intervention group, with statistically significant differences ( P<0.05) . Conclusions:The evidence-based nutritional management protocol developed for HNC patients undergoing radiotherapy effectively improves nutritional status. It provides a valuable reference for healthcare professionals in clinical practice.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.