Objective To analyze the case distribution, trends and dispute causes in initial verification of occupational disease diagnosis (VODD) in Guangzhou City from 2015 to 2024. Methods A total of 1 006 cases applying for initial VODD in Guangzhou City from 2015 to 2024 were selected as the study subjects using the convenience sampling method. Data on their basic information, disease category distribution, acceptance status, and dispute-related characteristics were collected and analyzed. Results Among the 1 006 VODD application cases, 884 completed the verification process, accounting for 87.9%. Cases withdrawn by applicants, suspended appraisals, and non-accepted applications accounted for 8.7%, 2.3%, and 1.1%, respectively. Among the 884 cases that completed verification, the most prevalent occupational diseases were occupational noise-induced hearing loss, occupational tumors (benzene-induced leukemia), occupational pneumoconiosis, and occupational chronic benzene poisoning, accounting for 77.0%. Cases appraised as occupational diseases accounted for 41.5%, and the proportion showed a decreasing trend over the years (P<0.01). The inconsistency rate between VODD conclusions and occupational disease diagnostic conclusions was 4.4%, mainly attributable to insufficient cooperation during clinical examinations and incomplete submission of required materials. Conclusion The initial VODD in Guangzhou City from 2015 to 2024 demonstrated relatively concentrated disease categories and controllable dispute levels. Clinical examination compliance and standardization of material submission were key factors affecting consistency of appraisal conclusions. It is proposed that effective measures be adopted to rectify existing deficiencies, with the aim of further enhancing the standardization and refinement of VODD practice.

Objective To investigate the value of predicting microsatellite instability(MSI)status in endometrial cancer based on whole-tumor apparent diffusion coefficient(ADC)histogram.Methods The data of 131 endometrial cancer patients who underwent preoperative MRI examination and were confirmed by pathology were retrospectively analyzed.According to the pathological immu-nohistochemical results,they were divided into microsatellite stability(MSS)group(103 cases)and MSI group(28 cases).The whole-tumor volume of interest(VOI)was outlined using ITK-SNAP software,and ADC histogram analysis was performed using uAI Research Portal software.The t-test or Mann-Whitney U-test were used to compare the differences between the two groups,and multifactorial logistic regression analysis was used to screen independent predictors for the above parameters with differences.The area under the curve(AUC),sensitivity and specificity were calculated using the receiver operating characteristic(ROC)curve.Results The ADC histogram parameters that were statistically significant between groups were ADC10th,ADC90th,ADCmaximum,ADCmedian,ADCmean,ADCrange,ADCinterquartile range,ADCuniformity,ADCvariance,ADCenergy,ADCentropy,ADCtotal energy,ADCroot mean square,ADCmean absolute deviation,ADCrobust mean absolute deviation,all the above parameters were significantly smaller in the MSI group than in the MSS group.Further multifactorial logistic regression analysis results showed that ADCmedian[odds ratio(OR)=1.019,P=0.020]and ADCroot mean square(OR=0.977,P=0.005)were independent predictors of the MSI status in endometrial cancer.The results of ROC curve showed that the AUC of ADCmedian and ADCroot mean square for predicting MSI status were 0.699 and 0.731,respectively,and the AUC of combining the two parameters to predict MSI status was 0.760,with a sensitivity of 57.14%and a specificity of 86.41%.Conclusion The parameters of ADCmedian and ADCroot mean square based on whole-tumor ADC histogram can be used to predict the MSI status of endometrial cancer,and the combined use of these two parameters helps to improve the efficacy of predicting MSI.

Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).

Objective To investigate the value of predicting microsatellite instability(MSI)status in endometrial cancer based on whole-tumor apparent diffusion coefficient(ADC)histogram.Methods The data of 131 endometrial cancer patients who underwent preoperative MRI examination and were confirmed by pathology were retrospectively analyzed.According to the pathological immu-nohistochemical results,they were divided into microsatellite stability(MSS)group(103 cases)and MSI group(28 cases).The whole-tumor volume of interest(VOI)was outlined using ITK-SNAP software,and ADC histogram analysis was performed using uAI Research Portal software.The t-test or Mann-Whitney U-test were used to compare the differences between the two groups,and multifactorial logistic regression analysis was used to screen independent predictors for the above parameters with differences.The area under the curve(AUC),sensitivity and specificity were calculated using the receiver operating characteristic(ROC)curve.Results The ADC histogram parameters that were statistically significant between groups were ADC10th,ADC90th,ADCmaximum,ADCmedian,ADCmean,ADCrange,ADCinterquartile range,ADCuniformity,ADCvariance,ADCenergy,ADCentropy,ADCtotal energy,ADCroot mean square,ADCmean absolute deviation,ADCrobust mean absolute deviation,all the above parameters were significantly smaller in the MSI group than in the MSS group.Further multifactorial logistic regression analysis results showed that ADCmedian[odds ratio(OR)=1.019,P=0.020]and ADCroot mean square(OR=0.977,P=0.005)were independent predictors of the MSI status in endometrial cancer.The results of ROC curve showed that the AUC of ADCmedian and ADCroot mean square for predicting MSI status were 0.699 and 0.731,respectively,and the AUC of combining the two parameters to predict MSI status was 0.760,with a sensitivity of 57.14%and a specificity of 86.41%.Conclusion The parameters of ADCmedian and ADCroot mean square based on whole-tumor ADC histogram can be used to predict the MSI status of endometrial cancer,and the combined use of these two parameters helps to improve the efficacy of predicting MSI.

Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To investigate the value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer.Methods A total of 178 patients with pathology diagnosis of bladder cancer were retrospectively collected,including 31 cases of muscle invasive bladder cancer(MIBC)and 147 cases of non-muscle invasive bladder cancer(NMIBC).Patients were randomly divided into training group and testing group at a ratio of 7︰3.The range of bladder tumors in T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images were segmented as volume of interest(VOI)by using ITK-SNAP software.Radiomics features were extracted through A.K software.The optimal radiomics features were obtained through radiomics algorithm and least absolute shrinkage and selection operator(LASSO)method.Finally,the logistic regression analysis method and random forest model method were used to construct prediction models.The performance of prediction models was evaluated by the receiver operating characteristic(ROC)curve.Results This study constructed four groups of models containing T2WI prediction model,DWI prediction model,ADC prediction model,and T2WI+DWI+ADC prediction model.The area under the curve(AUC)of T2WI,DWI,and ADC prediction models for identifying MIBC and NMIBC were separately 0.920,0.914,and 0.954 in the training group while those were respectively 0.881,0.773,and 0.871 in the testing group.There was no statistical significance between T2WI,DWI,and ADC prediction models.In training and testing groups,the AUC of T2WI+DWI+ADC prediction model were respectively 0.959 and 0.909,which were higher than the single sequence prediction model.The sensitivity and specificity of the training group were 0.905 and 0.853 and the sensitivity and specificity of the testing group were 0.778 and 0.795.Conclusion MRI radiomics prediction model can effectively differentiate MIBC and NMIBC.The T2WI+DWI+ADC prediction model shows better prediction efficiency.

Objective:To evaluate the efficacy and safety of cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in the treatment of pseudomyxoma peritonei (PMP).Methods:In this descriptive case series study, we retrospective analyzed the records of PMP patients treated with CRS and HIPEC between January 2013 and June 2023 at Affiliated Cancer Hospital and Institute of Guangzhou Medical University. The inclusion criteria were as follows: (1) Aged 18 to 75 years and nonpregnant women. (2) Histologically confirmed diagnosis of pseudomyxoma peritonei. (3) Karnofsky Performance Scale (KPS)>70. (4) The functions of major organs such as the heart, liver, lungs, and kidneys can tolerate major surgery for long periods of time. (5) No evidence of extra-abdominal metastasis. Patients with extensive intra-abdominal adhesions or severe infectious diseases were excluded. The main outcomes were overall survival (OS) and postoperative major complications. The postoperative major complications were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (version 5.0). We used the peritoneal cancer index (PCI) score to quantitatively assess the peritoneal metastases and the completeness of cytoreduction (CCR) score at the end of surgery (CCR-0 and CCR-1 considered to be complete CRS).Results:A total of the 186 PMP patients with a median age of 56 (interquartile range extremes (IQRE), 48-64) years were included, 65 (34.9%) males and 121 (65.1%) females. The median peritoneal cancer index (PCI) score was 28 (20-34). Appendiceal origin accounted for 91.4%. Histological types were low grade in 99 patients (53.2%), high grade in 57 patients (30.6%), and 55 patients (29.6%) received complete cytoreduction (CCR-0/1). The median operative duration was 300 (211-430) minutes for all patients. Treatment-related 30-day mortality was 2.7%; 90-day mortality 4.3%; reoperation 1.6%; and severe morbidity 43.0%. Within the entire series, anemia(27.4%), electrolyte disturbance(11.6%), and hypoalbuminemia(7.5%) were the most frequent major complications (grade 3-4). The incidences of gastrointestinal anastomotic leakage, abdominal bleeding, and abdominal infection were 2.2%, 2.2%, and 4.3%, respectively. After a median follow-up of 38.1 (95%CI:31.2-45.1) months, the 5-year OS was 50.3% (95%CI: 40.7%-59.9%) with a median survival time of 66.1 (95%CI: 43.1-89.1) months. The survival analysis showed that patients with pathological low grade, low PCI, and low CCR score had better survival with statistically significant differences (all P<0.05). Further stratified into complete and incomplete CRS subgroups, the 5-year OS of the CCR-0 and CCR-1 subgroups was 88.9% (95%CI: 68.3%-100.0%) and 77.6% (95%CI: 62.7%-92.5%), respectively; and 42.0% (95%CI: 29.5%-54.5%) in the CCR-2/3 subgroup. Conclusions:CRS and HIPEC may result in a long-term survival benefit for PMP patients with acceptable perioperative morbidity and mortality. This strategy, when complete CRS is possible, could significantly prolong survival for strictly selected patients at experienced centers.

Approximately 140 million people worldwide are homozygous carriers of APOE4 (ε4), a strong genetic risk factor for late onset familial and sporadic Alzheimer's disease (AD), 91% of whom will develop AD at earlier age than heterozygous carriers and noncarriers. Susceptibility to AD could be reduced by targeted editing of APOE4, but a technical basis for controlling the off-target effects of base editors is necessary to develop low-risk personalized gene therapies. Here, we first screened eight cytosine base editor variants at four injection stages (from 1- to 8-cell stage), and found that FNLS-YE1 variant in 8-cell embryos achieved the comparable base conversion rate (up to 100%) with the lowest bystander effects. In particular, 80% of AD-susceptible ε4 allele copies were converted to the AD-neutral ε3 allele in human ε4-carrying embryos. Stringent control measures combined with targeted deep sequencing, whole genome sequencing, and RNA sequencing showed no DNA or RNA off-target events in FNLS-YE1-treated human embryos or their derived stem cells. Furthermore, base editing with FNLS-YE1 showed no effects on embryo development to the blastocyst stage. Finally, we also demonstrated FNLS-YE1 could introduce known protective variants in human embryos to potentially reduce human susceptivity to systemic lupus erythematosus and familial hypercholesterolemia. Our study therefore suggests that base editing with FNLS-YE1 can efficiently and safely introduce known preventive variants in 8-cell human embryos, a potential approach for reducing human susceptibility to AD or other genetic diseases.
Humans ; Apolipoprotein E4/genetics* ; Cytosine ; Mutation ; Blastocyst ; Heterozygote ; Gene Editing ; CRISPR-Cas Systems