Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

Objective To screen the mutations of ABCC8 gene in probands of maturity-onset diabetes of the young pedigrees,and investigate it sgenetic and clinical characteristics.Methods Whole exome sequencing were performed to screen ABCC8 mutations in 56 MODY probands who were admitted to Department of Endocrinology and Metabolism,Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from July 2021 to December 2023.The mutations were verified by Sanger sequencing,and all participants were genotyped.Clinical phenotypes of the mutation carriers were compared with non-DM controls within the families.The identified mutations were evaluated by bioinformatic softwires.Then the pharmacogenomic characteristics of the mutation carriers were analyzed.Results Two heterozygous mutations D655V and R825Q were identified in two MODY probands and their families respectively,and the D655V was a novel mutation.Bioinformatics studies showed that both mutations were deleterious and pathogenic.In comparison with non-DM controls in the two families,mutation carriers with diabetes exhibited significantly lower fasting insulin/fasting plasma glucose,two-hour postprandial insulin/two-hour postprandial insulin plasma glucose,homeostatic model assessment-β(P<0.05).Treatment with oral hypoglycemic agents such as metformin or insulin in these mutation carriers resulted in a moderate reduction in plasma glucose levels.However,switching to targeted Sulfonylurea's(SUs)proved to be more effective.Conclusions In this study,the prevalence of MODY12 is 3.6%in these MODY pedigrees.The remarkable hypoglycemic efficacy of SUs suggests that both D655V and R825Q were activating mutations of ABCC8,and maybe the cause of MODY12 characterized by impaired insulin secretion.

A 57-year-old female patient with small cell lung cancer was treated with serplulimab (100 mg by intravenous infusion on the first day, 21 days as a cycle). After the 10th cycle of treatment, the patient suddenly presented drowsiness, shortness of breath, dry mouth, nausea, vomiting, fatigue and other symptoms. Laboratory tests showed fasting blood glucose 38.9 mmol/L, glycosylated hemoglobin 7.9%, serum C peptide 0.2 μg/L, pH value 7.05, anion gap 31 mmol/L, anti-islet cell antibody 36 kU/L, anti-glutamic acid decarboxylase antibody 131 kU/L, urine ketone body (+++). The patient was diagnosed with fulminant type 1 diabetic ketoacidosis, which was considered to be related to serplulimab. The drug was stopped, and insulin, rehydration, correction of acid-base imbalance, and other treatments were given. After 3 days, the patient′s consciousness returned to normal. After 12 days, her breathing was stable, dry mouth, nausea, vomiting and other symptoms were relieved. Laboratory tests showed random blood glucose 13.6 mmol/L, pH value 7.44, anion gap 6 mmol/L, and urine ketone body negative. After 26 days, her random blood glucose was controlled at 10.0-12.0 mmol/L.

Objective To screen the mutations of ABCC8 gene in probands of maturity-onset diabetes of the young pedigrees,and investigate it sgenetic and clinical characteristics.Methods Whole exome sequencing were performed to screen ABCC8 mutations in 56 MODY probands who were admitted to Department of Endocrinology and Metabolism,Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from July 2021 to December 2023.The mutations were verified by Sanger sequencing,and all participants were genotyped.Clinical phenotypes of the mutation carriers were compared with non-DM controls within the families.The identified mutations were evaluated by bioinformatic softwires.Then the pharmacogenomic characteristics of the mutation carriers were analyzed.Results Two heterozygous mutations D655V and R825Q were identified in two MODY probands and their families respectively,and the D655V was a novel mutation.Bioinformatics studies showed that both mutations were deleterious and pathogenic.In comparison with non-DM controls in the two families,mutation carriers with diabetes exhibited significantly lower fasting insulin/fasting plasma glucose,two-hour postprandial insulin/two-hour postprandial insulin plasma glucose,homeostatic model assessment-β(P<0.05).Treatment with oral hypoglycemic agents such as metformin or insulin in these mutation carriers resulted in a moderate reduction in plasma glucose levels.However,switching to targeted Sulfonylurea's(SUs)proved to be more effective.Conclusions In this study,the prevalence of MODY12 is 3.6%in these MODY pedigrees.The remarkable hypoglycemic efficacy of SUs suggests that both D655V and R825Q were activating mutations of ABCC8,and maybe the cause of MODY12 characterized by impaired insulin secretion.

A 57-year-old female patient with small cell lung cancer was treated with serplulimab (100 mg by intravenous infusion on the first day, 21 days as a cycle). After the 10th cycle of treatment, the patient suddenly presented drowsiness, shortness of breath, dry mouth, nausea, vomiting, fatigue and other symptoms. Laboratory tests showed fasting blood glucose 38.9 mmol/L, glycosylated hemoglobin 7.9%, serum C peptide 0.2 μg/L, pH value 7.05, anion gap 31 mmol/L, anti-islet cell antibody 36 kU/L, anti-glutamic acid decarboxylase antibody 131 kU/L, urine ketone body (+++). The patient was diagnosed with fulminant type 1 diabetic ketoacidosis, which was considered to be related to serplulimab. The drug was stopped, and insulin, rehydration, correction of acid-base imbalance, and other treatments were given. After 3 days, the patient′s consciousness returned to normal. After 12 days, her breathing was stable, dry mouth, nausea, vomiting and other symptoms were relieved. Laboratory tests showed random blood glucose 13.6 mmol/L, pH value 7.44, anion gap 6 mmol/L, and urine ketone body negative. After 26 days, her random blood glucose was controlled at 10.0-12.0 mmol/L.

Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

Objective To explore the association between R611H(G/A,rs734312)variation of WFS1 gene and early-onset type 2 diabetes mellitus(T2DM).Methods A total of 181 Chinese patients with early-onset T2DM(T2DM group)and 196 non-diabetic controls(NC group)were enrolled in this study.The rs734312 variation was detected by PCR-direct sequencing.Genotypic and allelic frequencies of rs734312 and clinical variables were compared and analyzed between the two groups.Results Compared with the NC group,the frequencies of AA genotype and A allele in R611H(G→A)variation were significantly elevated in the early-onset T2DM group,AA vs GA+GG(OR 1.720,95%CI 1.100～2.680,P<0.05).A vs G(OR 1.500,95%CI 1.020～2.220,P<0.05).The remarkable differences of frequencies of genotype and allele in rs734312(G/A)were observed between Asians(China,Japan and Korea)and Caucasians(Denmark,Britain,Spain,France and Russia,P<0.01 for each).Compared with AA genotype,fasting and 2 hours postprandial insulin(FIns and 2 hIns)as well as HOMA-β were significantly rise in GG+GA genotype carriers of early-onset T2DM group(P<0.05).Conclusions The a allele of rs734312 in WFS1 may be a risk factor for early-onset T2DM in Chinese population,and the variation might be a potential genetic marker for predicting the islet β-cell dysfunction in early-onset T2DM in Chinese population.