Objective To explore the pathogenesis and prognostic factors of liposarcoma in the head and neck region, and simultaneously analyze the efficacy of different treatment regimens. Methods A retrospective analysis was performed on all patients with primary untreated head and neck liposarcoma who were diagnosed and underwent surgical treatment at our hospital from January 2008 to January 2024. All patients were monitored during follow-up, and their prognoses were analyzed using SPSS software. Results A total of 30 patients were included in the study. Liposarcoma accounted for up to 60% of the cases in the orbit, while the remaining liposarcomas were primarily located in various interspaces of the neck. Dedifferentiated liposarcoma was the most common type, comprising 33%, while myxoid pleomorphic liposarcoma was the rarest at 4%. The tumor pathological type (P<0.001) and Ki67 (P=0.014) significantly affected the tumor control rate. However, an analysis of disease-specific survival rates revealed no significant differences across various factors (all P>0.05). Conclusion The prognosis of head and neck liposarcoma is better compared to that of liposarcomas in other parts of the body. However, myxoid pleomorphic liposarcoma, pleomorphic fat sarcoma, and high Ki67 levels are indicators of poor prognosis. Additionally, postoperative adjuvant radiotherapy does not significantly enhance disease-specific survival rates.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

OBJECTIVE To explore the mechanism by which Gongfa Static Training regulates mitophagy through the PTEN-in-duced kinase 1(PINK1)/Parkin pathway in skeletal muscle insulin resistance in type 2 diabetes mellitus(T2DM).METHODS T2DM mouse model was established using a high-fat diet combined with streptozotocin(STZ)intraperitoneal injection.Mice were ran-domly divided into a model group,a metformin group,an aerobic exercise group,and a Gongfa Static Training group.The intervention effects of Gongfa Static Training were evaluated by measuring fasting blood glucose,Homeostatic Model Assessment of Insulin Resist-ance(HOMA-IR),glycated hemoglobin(HbA1c),lipid metabolism indicators,mitochondrial function in the gastrocnemius muscle,and the expression of PINK1 and Parkin-related genes and proteins.RESULTS Gongfa Static Training significantly reduced fasting blood glucose,HbA1c,and insulin resistance index in T2DM mice,improved lipid metabolism,and enhanced insulin sensitivity.It improved the structure and function of mitochondria in the gastrocnemius muscle by upregulating the mRNA and protein expression of PINK1 and Parkin.CONCLUSION Gongfa Static Training improves mitochondrial function and insulin resistance in the skeletal muscle of T2DM mice by regulating the PINK1/Parkin pathway.

Objective To explore risk factors for cardiac valve calcification (CVC) in maintenance hemodialysis (MHD) patients and evaluate its impact on cardiovascular events and mortality. Methods Retrospective selection of 223 patients with MHD admitted to the Department of Nephrology of Jinshan Hospital, Fudan University from June 30, 2019 to June 30, 2024, and enrollment completed within one week of June 30, 2019. Patients were divided into CVC and non-CVC groups. Baseline data and 5-year follow-up data were collected. The binary logistic regression analysis was performed to explore the risk factors for CVC. Kaplan-Meier survival curve was used to analyze the survival rate of patients. Cox proportional hazard regression model was applied to evaluate the impact of CVC on the survival rates of MHD patients. Results Totally, 223 MHD patients with an average age of (58.4±13.5) years and an average dialysis duration of (64.0±55.4) months were involved. Among them, 136(61.0%) were males, 117(52.5%) were complicated with CVC. Age, dialysis duration, diabetic kidney disease (DKD), the serum corrected total calcium and phosphate, intact parathyroid hormone (iPTH), high-sensitive C-reactive protein (hsCRP), and homocysteine (Hcy) were independent related factors for CVC (P＜0.05). Both all-cause mortality (46.6% vs 28.7%) and cardiovascular mortality (33.3% vs 16.0%) were significantly higher in the CVC group than those in the non-CVC group (P＜0.01). Conclusions Age, dialysis duration, the primary disease, calcium and phosphate, and inflammation- and nutrition-related serum indicators are associated with CVC in MHD patients. CVC significantly increases mortality risk of MHD patients.

OBJECTIVE To investigate the prognostic factors of chondrosarcoma of the larynx,deeply analyze its clinical data,and provide a theoretical basis for better treatment of chondrosarcoma of the larynx.METHODS A retrospective analysis was conducted on the complete clinical data of patients with primary chondrosarcoma of the larynx admitted to the Department of Otolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University from January 2010 to December 2024.RESULTS A total of 15 patients were included,including 11 males and 4 females,with a gender ratio of 11∶4.The average age of onset was 57.3 years,and the average clinical symptom duration was 12.2 months.The tumors were mainly located in the cricoid cartilage in 11 patients,in the arytenoid cartilage in 2 patients,and in the thyroid cartilage in 2 patients.Tumor grading showed that 7 patients were grade I and 8 were grade II.Four patients underwent transoral laser minimally invasive surgery,2 patients underwent partial laryngectomy+tracheotomy,and 9 patients underwent total laryngectomy/cervical lymph node dissection+tracheostomy.The 5-year overall survival rate was 85.7%,the 5-year disease-specific survival rate was 100%,and the 5-year local-regional control rate was 90.9%.Gender,tumor location,tumor grade,Ki-67,tumor size,and whether larynx preservation surgery was performed did not affect the local-regional control rate or disease-specific survival rate.CONCLUSION Laryngeal chondrosarcoma generally has a longer disease history and is difficult to detect.The pathological type is mostly well-differentiated.Regional or distant metastasis is rare,and the long-term survival rate is good.Surgical resection is the preferred treatment option.On the basis of not reducing the tumor control rate,surgery that prioritizes preserving laryngeal function should be given priority,while comprehensive treatment is generally not recommended.

Helicobacter pylori(Hp)is a major pathogen that causes peptic ulcer,mucosa-associated tissue lymphoma and gastric cancer.Adhesion colonization is a prerequisite for the pathogenesis of Hp.After infec-tion,Hp first uses urease to neutralize gastric acid,and then it adapts to the environment through motility and chemotactic swimming of flagella.Finally,Hp adheres to gastric epithelial cells through outer membrane pro-teins.Some outer membrane proteins have the biological effect of transporting virulence factors,mediating in-flammation and assisting Hp to produce pathological changes on human body.This paper reviews the mecha-nism of main colonization factors of Hp.

Objective To investigate the relationship between the levels of serum suppressor of cytokine signaling 3(SOCS3),growth differentiation factor-15(GDF-15)and liver fibrosis in patients with type 2 dia-betes mellitus(T2DM)combined with non-alcoholic fatty liver disease(NAFLD).Methods A total of 320 patients with T2DM combined with NAFLD who were hospitalized in this hospital from May 2023 to May 2024 were selected as the study group,and another 320 patients with simple T2DM admitted during the same period were selected as the control group.The levels of serum SOCS3 and GDF-15 were determined by en-zyme-linked immunosorbent assay(ELISA).Pearson correlation analysis was used to analyze the correlation between SOCS3,GDF-15 levels and liver fibrosis indicators.Logistic regression analysis was used to analyze the factors affecting the degree of liver fibrosis.The receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of serum SOCS3 and GDF-15 for the degree of liver fibrosis in patients.Results Compared with the control group,the levels of serum SOCS3,GDF-15,5,type Ⅲ procollagen peptide,laminin and type Ⅳ col-lagen in the study group were significantly increased(P＜0.05).There were statistically significant differ-ences in the levels of type Ⅲ procollagen peptide,laminin and type Ⅳ collagen between the mild to moderate group and the severe group(P＜0.05).Compared with the mild to moderate group,the levels of serum SOCS3 and GDF-15 in the severe group were significantly increased(P＜0.05).The results of Pearson corre-lation analysis showed that serum SOCS3 and GDF-15 in patients with T2DM combined with NAFLD were positively correlated with type Ⅲ procollagen peptide,laminin,and type Ⅳ collagen(P＜0.05).Serum SOCS3,GDF-1 5,type Ⅲ procollagen peptide,laminin and type Ⅳ collagen are risk factors affecting the degree of liver fibrosis in patients with T2DM combined with NAFLD(P＜0.05).The results of the ROC curve showed that the combined diagnosis of serum SOCS3 and GDF-15 for the degree of liver fibrosis in patients with T2DM complicated with NAFLD had the highest area under the curve(AUC),which was superior to the individual diagnosis of each(both P＜0.05),with a corresponding sensitivity of 69.08％and a specificity of 85.71％.The combined diagnosis of the degree of liver fibrosis in patients with T2DM complicated with NAFLD by serum SOCS3,GDF-15,type Ⅲ procollagen peptide,laminin,and type Ⅳ collagen had the highest AUC,which was superior to the individual diagnosis of each index(all P＜0.001),with a corresponding sensi-tivity of 89.47％and a specificity of 97.02％.Conclusion The levels of serum SOCS3 and GDF-15 are elevated in patients with T2DM combined with NAFLD,.The combined diagnosis of serum SOCS3,GDF-15,type Ⅲ procolla-gen peptide,laminin,and type Ⅳ collagen has a high value in the degree of liver fibrosis in patients.

Objective To explore the mechanism by which electroacupuncture improves ocular surface inflammation in dry eye mice.Methods 30 SPF-grade healthy male ICR mice were randomly divided into a blank group,a model group,a sham electroacupuncture group,a western medicine group and an electroacupuncture group,with 6 mice in each group.Mice in the blank group and other four groups were subcutaneously injected 200 μL of sterile physiological saline and 200 μL of scopolamine hydrobromide(0.5 mg dissolved in 0.2 mL of sterile physiological saline)at 8:00,11:00,14:00,and 17:00 every day for 35 consecutive days,respectively.From the 22nd day,mice in the sham electroacupunc-ture group were given blunt scalp acupuncture intervention at bilateral Jingming and Taiyang points,without subcutaneous penetration.In the western medicine group,fluorometholone eye drops were applied to both eyes of the mice at 8:00,13:00,and 18:00 daily,with 1 drop each time.Mice in the electroacupuncture group were given electroacupuncture in-tervention,with the same acupoint location and acupuncture time as the sham electroacupuncture group.The electroacu-puncture frequency was 2 Hz/20 Hz,the waves were sparse-dense and the intensity was 1 mA,once a day for 15 min.All groups were intervened for 14 days.The corneal fluorescein(FL)staining scores of mice in each group were detected be-fore modeling,after modeling,and after intervention.The corneal tissue morphology was observed under a light micro-scope.Immunohistochemistry staining and quantitative reverse transcription polymerase chain reaction(qRT-PCR)were used to detect the protein and mRNA expression of high mobility group box 1(HMGB1)and receptor for advanced glyca-tion end products(RAGE)in the cornea,respectively.Results The FL scores of mice in model,sham electroacupunc-ture,western medicine,and electroacupuncture groups all significantly increased after modeling and intervention,com-pared with those before modeling(all P＜0.01).The FL scores of mice in electroacupuncture and western medicine groups significantly decreased after intervention,compared with those after modeling(both P＜0.01).Compared with the model group,electroacupuncture and western medicine groups showed a significant drop in FL score after intervention(both P＜0.01).HE staining showed that after intervention,mice in electroacupuncture and western medicine groups had a basically normal number of corneal epithelial layers,no obvious shedding of epithelial cells,and neatly arranged and slightly swollen collagen fibers in the stromal layer.The relative protein expression levels of HMGB1 and RAGE in the corneal tissue of both model and sham electroacupuncture groups were significantly higher than those of the blank group(allP＜0.01).The rela-tive protein expression levels of HMGB1 and RAGE in the corneal tissue of both electroacupuncture and western medicine groups were significantly lower than those of the model group(all P＜0.01).The relative mRNA expression levels of HMGB1 and RAGE in the corneal tissue of both model and sham electroacupuncture groups were significantly higher than those of the blank group(all P＜0.01).The relative mRNA expression levels of HMGB1 and RAGE in the corneal tissue of both electroacupuncture and western medicine groups were significantly lower than those of the model group(all P＜0.01).Conclusion Electroacupuncture mitigates corneal epithelial injury,reduces the expression of HMGB1 in the cor-neal tissue,inhibits the binding of HMGB1 and RAGE,and ultimately alleviates ocular surface inflammation responses of dry eye mice.

Objective This study aims to explore effective approaches for the transfer of medical scientific and techno-logical achievements to promote the development of research-oriented hospitals.Methods The technology transfer achievements of an affiliated hospital of a university in Guangdong Province over the past six years(2018-2023)were statistically analyzed.The challenges faced during the transfer process,the measures taken,and the current achievements were discussed.Results The number of patent authorizations and authorized departments in the hospital has increased year by year.The transfer rate has risen from 0％in 2019 to 6.69％in 2023.Currently,46 projects have been successfully transferred,with a total transfer amount exceeding 30 million yuan,indicating significant effectiveness in the transfer of medical scientific and technological achievements.Conclusion The hospital attaches great importance to and overall manages the transfer process,establishing a sound manage-ment structure,improving incentive and support systems,and regularly conducting special lectures,training,and guidance.These efforts guide researchers to start from clinical problems and ultimately serve clinical diagnosis and treatment,creating a fa-vorable environment for technology transfer,improving the transfer rate,and promoting the development of research-oriented hos-pitals.