ObjectiveTo evaluate the predictive value of six surrogate indices of insulin resistance （IR） for cardiovascular disease （CVD） risk in adults with cardiovascular-kidney-metabolic syndrome （CKM） stages 0-3. MethodsRelated data of 6 813 participants from the China health and retirement longitudinal study （CHARLS） were analyzed. Cox proportional hazards models and restricted cubic spline （RCS） analyses were performed to evaluate the associations between six insulin resistance surrogate indices （TyG， TyG-BMI， TyG-WC， TyG-WHtR， TyG-CVAI， CVAI） and CVD incidence. Predictive performance of 6 indices was comprehensively compared from 3 dimensionalities， using receiver operating characteristic （ROC） curves for discriminative ability， net reclassification improvement （NRI） for incremental predictive value， and decision curve analysis （DCA） for clinical utility. ResultsAll six IR surrogate indices significantly predicted CVD in the CKM population. RCS analysis revealed linear associations between each index and CVD incidence. ROC analysis showed that CVAI had the highest AUC （0.628，95%CI： 0.611-0.646）， significantly outperforming the single TyG index （P=0.001 6）. NRI analysis indicated significant reclassification improvement for the event group with CVAI （event NRI=0.042 8）. DCA demonstrated that CVAI achieved the highest net benefit across most thresholds. Subgroup analysis confirmed stronger associations between CVAI and CVD risk in CKM stages 2-3. ConclusionsThese six IR surrogate indices serve as valuable tools for predicting CVD incidence in individuals with CKM stages 0-3. Among them， CVAI exhibits optimal predictive value for future CVD risk in adults with CKM stages 1-3.

Objective:We aimed to investigate the relationship between CYP2D6*10 gene polymorphisms and adverse reactions associated with tamoxifen treatment in breast cancer patients, and assess the value of CYP2D6*10 gene polymorphism testing in guiding the use of medications in endocrine therapy for breast cancer.Methods:177 breast cancer patients with HR-positive and postoperative tamoxifen were admitted to the First Affiliated Hospital of Nanjing Medical University from November 2012 to December 2021. Their clinicopathologic data were collected for follow-up observation of adverse reactions related to tamoxifen treatment. After two years of tamoxifen treatment, finger blood of these patients was taken for CYP2D6 gene polymorphism detection. Moreover, databases including RNAfold, QTLbase, 3DSNP v2.0, RegulomeDB 2.2, and HaploReg v4.2 were used to predict the annotation of proximal and distal interactions of CYP2D6 polymorphic sites between genes and regulatory elements.Results:Genotyping analysis revealed 40 patients (22.6%) with the CC genotype, 79 (44.6%) with the CT genotype, and 58 (32.8%) with the TT genotype. Common adverse reactions to tamoxifen included abnormal liver function (58), fatty liver (81), uterine fibroids (12), and endometrial surgery for endometrial thickening (17). Univariate and multivariate logistic regression analyses showed that there were no significant statistical differences between the CC and CT+TT genotypes in terms of liver damage, new-onset fatty liver, uterine fibroids, or tumor recurrence and metastasis ( P＞0.05). Notably, endometrial thickening was more significant in patients with the CT+TT genotype (4.37±3.82 mm) than in those with the CC genotype (2.43±2.96 mm), with a statistically significant difference between them ( P＜0.01). Bioinformatic analysis suggested that in breast tissues, the CYP2D6*10 polymorphic locus had a significant expression quantitative trait locus (eQTL) effect with CYP2D6, and its genetic variations could affect the binding of CYP2D6 to transcription factors, which might modulate the expression of CYP2D6 through changes in secondary structure and chromatin modifications, etc., and thus affect the tamoxifen drug sensitivity. Further eQTL analysis showed significant correlation between CYP2D6 expression levels with different genotypes of the CYP2D6 rs1065852 polymorphism in breast tissues ( P＜0.01). Conclusion:Tamoxifen remains a primary therapeutic agent for premenopausal HR-positive breast cancer patients, and its efficacy is influenced by polymorphisms in the CYP2D6*10. It is recommended that for breast cancer patients carrying the CYP2D6 CT and TT genotypes, endometrial monitoring should be strengthened during treatment with tamoxifen, and the medication should be adjusted in a timely manner.

Objective:To develop radiomics score (RS) based on 18F-FDG PET/CT, and construct the machine learning model combining clinical and other relevant factors for personalized prediction of 2-year event-free survival (2-EFS) in patients with diffuse large B-cell lymphoma (DLBCL), and to perform interpretability analysis of the model. Methods:A total of 91 patients (49 males, 42 females; age (57.8±12.8) years) with pathologically confirmed DLBCL from December 2017 to December 2020 at the First Hospital of Shanxi Medical University were retrospectively analyzed. According to the ratio of 7∶3, patients were randomly divided into training set ( n=63) and test set ( n=28), and divided into non-progression group and progression group according to the follow-up results. The whole-body PET semi-quantitative parameters were calculated from the PET/CT images before treatment, and 328 radiomics features were extracted from the largest target lesions of patients. The least absolute shrinkage and selection operator (LASSO) was used to develop the RS. Clinical and PET characteristic difference analysis was performed through χ2 test and Mann-Whitney U test. Extreme gradient boosting (XGBoost) models were constructed based on clinical, PET radiomics features and RS, and the prediction efficiency of each model was evaluated by ROC AUC. The model interpretability was analyzed by using shapely additive explanation (SHAP). Results:Of all patients, 32 had disease progression and 59 did not. There were no significant differences in baseline characteristics between the training set and the test set ( χ2 values: 0.06-1.84, U values: 665.00-763.00, all P>0.05). The comparison between the progression group and non-progression group in the training set showed statistical differences in the international prognostic index (IPI) score ( χ2=4.87, P=0.027), myelocytomatosis viral oncogene (MYC) protein expression ( χ2=4.29, P=0.038), and metabolic tumor volume (MTV; U=307.00, P=0.038). Seven radiomics features were screened by LASSO. Among XGBoost models with different feature combinations, IPI score, MYC protein expression, MTV combined with RS had the highest predictive efficiency (training set: AUC=0.73; test set: AUC=0.70). Through SHAP analysis, RS was the most predictive feature in the optimal model. Conclusion:The machine learning integrated model of IPI score, MYC protein expression and MTV combined with RS can effectively predict the prognosis of DLBCL patients, and baseline 18F-FDG PET/CT radiomics can be used as a potential means to evaluate the prognosis of DLBCL patients.

Objective:To investigate the steady-state mechanism of interconversion between the SMN1 and SMN2 genes in a normal population. Methods:Fluorescence PCR capillary electrophoresis was employed to assess gene conversion and copy number variation of SMN1 and SMN2 in a cohort of 1, 133 healthy individuals (including 256 males and 877 females) recruited between 2019 and 2023. This study was approved by the Ethics Committee of Henan Provincial People′s Hospital (Ethics No.: 2019-134). Results:No significant gender difference was observed in the single copy carrying rate of SMN1. The probability of conversion from SMN1 to SMN2 was determined to be 3.2% for females, 2.7% for males, and 3.1% for the overall population. The probability of conversion from SMN2 to SMN1 was found to be 5.5% for females, 6.3% for males, and 5.6% for the overall population. No statistically significant difference was found in the conversion probability between different genders ( P>0.05). Among the 99 cases of gene conversion, the SMN1 gene predominantly exhibited a copy number of 2 (97.0%), with the remainder having 3 copies (3%). The SMN2 gene primarily showed a copy number of 2 (72.7%), with the rest having 1 copy (27.3%). Conclusion:Gene conversion tends to normalize the copy numbers of both SMN1 and SMN2 genes towards 2. However, SMN1 exhibited a higher priority over SMN2, causing the copy numbers approaching two.

Objective:To investigate the association between serum uric acid trajectories and the risk of renal function decline and diabetic kidney disease(DKD) incidence in elderly patients with type 2 diabetes mellitus.Methods:This retrospective cohort study included 5 037 elderly patients with type 2 diabetes mellitus aged 60 years and above who underwent at least three health examinations between 2019 and 2023, with 2019 as the baseline. Latent growth mixture modeling(LGMM) was employed to identify distinct serum uric acid trajectories. Renal function changes and DKD incidence were followed from 2020 to 2023. Binary logistic regression models were used to assess the association between serum uric acid trajectories and the risks of renal function decline and DKD.Results:Two distinct serum uric acid trajectory groups were identified based on model selection criteria: A stable group( n=4 485, 89.04%) and an inverted U-shaped group( n=552, 10.96%). After adjusting for potential confounders, compared with the stable trajectory group, the inverted U-shaped group showed a significantly increased risk of estimated glomerular filtration rate(eGFR) <60 mL·min -1·(1.73 m 2) -1, ≥25% decline in eGFR, doubling of serum creatinine, and DKD events, with OR(95% CI) of 1.99(1.28-3.09), 2.27(1.65-3.13), 1.52(1.09-3.02), and 1.52(1.27-1.82), respectively(all P<0.05). In addition, multivariate analysis indicated that elevated baseline serum uric acid levels were also associated with an increased risk of adverse renal outcomes and DKD incidence; However, the magnitude of the associations was lower than that observed for serum uric acid trajectory groups. Conclusions:An inverted U-shaped serum uric acid trajectory is significantly associated with an increased risk of renal function progression and DKD in elderly patients with type 2 diabetes mellitus. These findings highlight the importance of long-term dynamic monitoring of serum uric acid levels to facilitate early identification and intervention for high-risk individuals.

Objective:To analyze the conventional ultrasound（CUS）and contrast-enhanced ultrasound（CEUS）features of hepatic lymphoma，and to investigate the value of CEUS in the diagnosis of hepatic lymphoma.Methods:The images of 39 patients（39 lesions）with hepatic lymphoma pathologically confirmed by surgery and puncture from March 2012 to July 2024 at Zhongshan Hospital，Fudan University were retrospectively analyzed. Evaluations of CUS included the echogenicity，morphology，color Doppler flow imaging（CDFI）situation，evaluations of CEUS included enhancement type，enhancement degree compared to the peripheral normal liver parenchyma and time to enhancement.Results:In the 39 lesions，hypoechoic lesions were detected in 31（79.49%，31/39）patients on CUS. CDFI detected linear or branched color flow signals inside the lesions in 21（53.85%，21/39）lesions，and peripheral color flow signals around the lesions in 3 lesions，while arterial flow signals were detected in 16 of them，with a resistance index of 0.50～0.77（0.67 ± 0.02）. In addition，signs of non-compacted normal blood vessels passing through the lesions were detected in 5 lesions. After injection of contrast medium，39 lesions showed different degrees of enhancement，mainly showed entirety homogeneous enhancement，during the arterial phase of CEUS，34（87.18%，34/39）lesions showed fast enhancement，and when the enhancement reached the peak，26（66.67%，26/39）lesions revealed hyper-enhancement，showing “fast progression”. There were 38（97.44%，38/39）lesions in the portal and delayed phases showed “fast forward”.Conclusions:CUS and CEUS can provide some value in the diagnosis and differential diagnosis of hepatic lymphoma.

Silicone implant leakage and capsular calcification are potential complications following breast augmentation with silicone implants. This article presents a case of silicone implant leakage accompanied by capsular calcification following breast augmentation surgery. A 51-year-old female presented with self-reported breast hardening over 21 years after undergoing bilateral silicone implant augmentation mammoplasty and sought medical attention at the Department of Plastic Surgery, the First Affiliated Hospital of Dalian Medical University in 2019. Physical examination revealed eggshell-like hardening of the implants with Baker grade Ⅱ capsular contracture. CT scans demonstrated capsular calcification, leading to bilateral explantation and capsulectomy. Intraoperative findings showed calcified capsules with gelatinous material adherent to the surface. Postoperative compositional analysis confirmed calcium phosphate deposits in the calcified lesions. During the 6-year follow-up, the patient remained asymptomatic without breast pain, new masses, or impaired upper limb mobility. The incisions healed well with no sensory abnormalities. Literature review suggested that implant leakage may result from either long-term mechanical irritation secondary to capsular calcification or occult silicone leakage.

OBJECTIVE: To investigate the steady-state mechanism of interconversion between the SMN1 and SMN2 genes in a normal population. METHODS: Fluorescence PCR capillary electrophoresis was employed to assess gene conversion and copy number variation of SMN1 and SMN2 in a cohort of 1,133 healthy individuals (including 256 males and 877 females) recruited between 2019 and 2023. This study was approved by the Ethics Committee of Henan Provincial People's Hospital (Ethics No.: 2019-134). RESULTS: No significant gender difference was observed in the single copy carrying rate of SMN1. The probability of conversion from SMN1 to SMN2 was determined to be 3.2% for females, 2.7% for males, and 3.1% for the overall population. The probability of conversion from SMN2 to SMN1 was found to be 5.5% for females, 6.3% for males, and 5.6% for the overall population. No statistically significant difference was found in the conversion probability between different genders (P > 0.05). Among the 99 cases of gene conversion, the SMN1 gene predominantly exhibited a copy number of 2 (97.0%), with the remainder having 3 copies (3%). The SMN2 gene primarily showed a copy number of 2 (72.7%), with the rest having 1 copy (27.3%). CONCLUSION Gene conversion tends to normalize the copy numbers of both SMN1 and SMN2 genes towards 2. However, SMN1 exhibited a higher priority over SMN2, causing the copy numbers approaching two.
Humans ; Male ; Female ; Survival of Motor Neuron 2 Protein/genetics* ; Survival of Motor Neuron 1 Protein/genetics* ; Gene Conversion ; DNA Copy Number Variations/genetics* ; Adult ; Middle Aged ; Gene Dosage

Silicone implant leakage and capsular calcification are potential complications following breast augmentation with silicone implants. This article presents a case of silicone implant leakage accompanied by capsular calcification following breast augmentation surgery. A 51-year-old female presented with self-reported breast hardening over 21 years after undergoing bilateral silicone implant augmentation mammoplasty and sought medical attention at the Department of Plastic Surgery, the First Affiliated Hospital of Dalian Medical University in 2019. Physical examination revealed eggshell-like hardening of the implants with Baker grade Ⅱ capsular contracture. CT scans demonstrated capsular calcification, leading to bilateral explantation and capsulectomy. Intraoperative findings showed calcified capsules with gelatinous material adherent to the surface. Postoperative compositional analysis confirmed calcium phosphate deposits in the calcified lesions. During the 6-year follow-up, the patient remained asymptomatic without breast pain, new masses, or impaired upper limb mobility. The incisions healed well with no sensory abnormalities. Literature review suggested that implant leakage may result from either long-term mechanical irritation secondary to capsular calcification or occult silicone leakage.

Objective:To develop radiomics score (RS) based on 18F-FDG PET/CT, and construct the machine learning model combining clinical and other relevant factors for personalized prediction of 2-year event-free survival (2-EFS) in patients with diffuse large B-cell lymphoma (DLBCL), and to perform interpretability analysis of the model. Methods:A total of 91 patients (49 males, 42 females; age (57.8±12.8) years) with pathologically confirmed DLBCL from December 2017 to December 2020 at the First Hospital of Shanxi Medical University were retrospectively analyzed. According to the ratio of 7∶3, patients were randomly divided into training set ( n=63) and test set ( n=28), and divided into non-progression group and progression group according to the follow-up results. The whole-body PET semi-quantitative parameters were calculated from the PET/CT images before treatment, and 328 radiomics features were extracted from the largest target lesions of patients. The least absolute shrinkage and selection operator (LASSO) was used to develop the RS. Clinical and PET characteristic difference analysis was performed through χ2 test and Mann-Whitney U test. Extreme gradient boosting (XGBoost) models were constructed based on clinical, PET radiomics features and RS, and the prediction efficiency of each model was evaluated by ROC AUC. The model interpretability was analyzed by using shapely additive explanation (SHAP). Results:Of all patients, 32 had disease progression and 59 did not. There were no significant differences in baseline characteristics between the training set and the test set ( χ2 values: 0.06-1.84, U values: 665.00-763.00, all P>0.05). The comparison between the progression group and non-progression group in the training set showed statistical differences in the international prognostic index (IPI) score ( χ2=4.87, P=0.027), myelocytomatosis viral oncogene (MYC) protein expression ( χ2=4.29, P=0.038), and metabolic tumor volume (MTV; U=307.00, P=0.038). Seven radiomics features were screened by LASSO. Among XGBoost models with different feature combinations, IPI score, MYC protein expression, MTV combined with RS had the highest predictive efficiency (training set: AUC=0.73; test set: AUC=0.70). Through SHAP analysis, RS was the most predictive feature in the optimal model. Conclusion:The machine learning integrated model of IPI score, MYC protein expression and MTV combined with RS can effectively predict the prognosis of DLBCL patients, and baseline 18F-FDG PET/CT radiomics can be used as a potential means to evaluate the prognosis of DLBCL patients.