Alpha 1,3-Galactosyltransferase Deficiency in Pigs Increases Sialyltransferase Activities That Potentially Raise Non-Gal Xenoantigenicity

We examined whether deficiency of the GGTA1 gene in pigs altered the expression of several glycosyltransferase genes. Real-time RT-PCR and glycosyltransferase activity showed that 2 sialyltransferases [α2,3-sialyltransferase (α2,3ST) and α2,6-sialyltransferase (α2,6ST)] in the heterozygote GalT KO liver have higher expression levels and activities compared to controls. Enzyme-linked lectin assays indicated that there were also more sialic acid-containing glycoconjugate epitopes in GalT KO livers than in controls. The elevated level of sialic-acid-containing glycoconjugate epitopes was due to the low level of α-Gal in heterozygote GalT KO livers. Furthermore, proteomics analysis showed that heterozygote GalT KO pigs had a higher expression of NAD+-isocitrate dehydrogenase (IDH), which is related to the CMP-N-acetylneuraminic acid hydroxylase (CMAH) enzyme reaction. These findings suggest the deficiency of GGTA1 gene in pigs results in increased production of N-glycolylneuraminic acid (Neu5Gc) due to an increase of α2,6-sialyltransferase and a CMAH cofactor, NAD+-IDH. This indicates that Neu5Gc may be a critical xenoantigen. The deletion of the CMAH gene in the GalT KO background is expected to further prolong xenograft survival

Le teorie sociologiche sulla comunicazione di massa. Dieci lezioni

La communication research ha oramai guadagnato una propria autonomia scientifica e accademica, sostenuta dal riconoscimento della qualità e rilevanza sociale e culturale dell’oggetto di studio. Le comunicazioni di massa sono una realtà affluente della nuova era antropologica, che si manifesta in molteplici aspetti che incidono sulla riproduzione simbolica e materiale dei sistemi sociali. Di fronte all’emergenza di un fenomeno pervasivo e pluriforme, da circa un secolo, gli studiosi si pongono il problema di come darne conto in maniera adeguata. Il libro ricostruisce lo sviluppo dei differenti paradigmi che si sono affermati nel corso del Novecento, orientando i modelli teorici e le attività di ricerca sui media. INDICE 11 - Prefazione. Ciò che è vivo e ciò che è morto nella teoria della comunicazione del Novecento. Per una storiografia della teoria, i principali modelli e le principali scuole di Michele Infante; 31 - Introduzione; 37 - Capitolo I. Le prime riflessioni sugli effetti dei mass media; 63 - Capitolo II. La scoperta delle variabili intervenienti; 85 - Capitolo III. Le reti sociali e il “flusso a due fasi”; 121 - Capitolo IV. L’approccio degli usi e delle gratificazioni; 141 - Capitolo V. La teoria critica vs. l’industria culturale; 171 - Capitolo VI. I Cultural Studies; 197 - Capitolo VII. La teoria dell’agenda setting; 227 - Capitolo VIII. La teoria della spirale del silenzio; 239 - Capitolo IX. La teoria della coltivazione; 257 - Capitolo X. La teoria della dipendenza; 273 - Bibliografia

Changing Molecular Epidemiology of Group B Streptococcus in Korea

The prevalence of group B streptococcus (GBS) among pregnant women and disease burdens in neonates and adults are increasing in Korea. Colonizing isolates, collected by screening pregnant women (n=196), and clinical isolates collected from clinical patients throughout Korea (n=234), were serotyped and screened for antibiotic resistance. Serotype III (29.8%) and V (27.7%) predominated, followed by Ia (17.0%). Antibiotic resistance was higher among clinical than colonizing isolates for erythromycin (35.1% and 26.9%; P=0.10) and for clindamycin (49.4% and 42.1%; P=0.17). erm(B) occurred in 91.9% of erythromycin resistant isolates, and 84.0% of isolates resistant to clindamycin. Only five isolates (4.2%) resistant to erythromycin were susceptible to clindamycin; by contrast, and unique to Korea, 34% of isolates resistant to clindamycin were erythromycin susceptible. Among these 60 erythromycin-susceptible & clindamycin-resistant isolates, 88% was serotype III, and lnu(B) was found in 89% of strains. Four fifths of the serotype V isolates were resistant to both erythromycin and clindamycin. Further characterization of the genetic assembly of these resistance conferring genes, erm(B) and lnu(B), will be useful to establish the clonal lineages of multiple resistance genes carrying strains

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients

Abstract Background Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations. The purpose of our study was to improve survival prediction in patients with NSCLC by incorporating prognostic information from F-18 fluorodeoxyglucose positron emission tomography (FDG PET) images into a traditional survival prediction model using clinical data. Results The multimodal deep learning model showed the best performance, with a C-index and mean absolute error of 0.756 and 399 days under a five-fold cross-validation, respectively, followed by ResNet3D for PET (0.749 and 405 days) and CPH for clinical data (0.747 and 583 days). Conclusion The proposed deep learning-based integrative model combining the two modalities improved the survival prediction in patients with NSCLC

Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

Due to the increase of lung cancer globally, and particularly in Korea, survival analysis for this type of cancer has gained prominence in recent years. For this task, mathematical and traditional machine learning approaches are commonly used by medical doctors. While the deep learning approach has had proven success in computer vision tasks, natural language processing and other AI techniques are also adopted for this task. Due to the privacy issues and management process, data in medicine are difficult to collect, which leads to a paucity of samples. The small number of samples makes it difficult to use deep learning and renders this approach unusable. In this investigation, we propose a network architecture that combines a variational autoencoder (VAE) with the typical DNN architecture to solve the survival analysis task. With a training size of n = 4107, MVAESA achieves a C-index of 0.722 while CoxCC, CoxPH, and CoxTime achieved scores of 0.713, 0.703, and 0.710, respectively. With a small training size of n = 379, MVAESA achieves a C-index of 0.707, compared with 0.689, 0.688 and 0.690 for CoxCC, CoxPH, and CoxTime, respectively. The results show that the combination of a VAE with a target task makes the network more stable and that the network could be trained using a small-sized sample

Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

PURPOSE: Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. METHODS: Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. RESULTS: Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. CONCLUSIONS: Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer

Potential Association between Vaginal Microbiota and Cervical Carcinogenesis in Korean Women: A Cohort Study

Convincing studies demonstrated that vaginal flora is one of the most impactful key components for the well-being of the genital tract in women. Nevertheless, the potential capability of vaginal-derived bacterial communities as biomarkers to monitor cervical carcinogenesis (CC) has yet to be studied actively compared to those of bacterial vaginosis (BV). We hypothesized that vaginal microbiota might be associated with the progression of CC. In this study, we enrolled 23 participants, including healthy controls (HC group; n = 7), patients with cervical intraepithelial neoplasia (CIN) 2 and 3 (CIN group, n = 8), and patients with invasive cervical cancer (CAN group; n = 8). Amplicon sequencing was performed using the Ion Torrent PGM to characterize the vaginal microbiota. Patients with CIN and CAN presented vaginal microbiota dysbiosis compared with HC. The alpha diversity analysis revealed that CC has a trend to be increased in terms of diversity indexes. Moreover, CC was associated with the abundance of specific microbes, of which Lactobacillus and Gardnerella were the most significantly different between HC and CIN, whereas Streptococcus was differentially abundant in CAN compared with CIN. We then evaluated their diagnostic abilities. Testing in terms of diagnostic ability using the three genera revealed considerably high performance with an area under the receiver-operating characteristic curve of 0.982, 0.953, and 0.922. The current study suggests that the presence of Gardnerella and Streptococcus may be involved in the advancment of CC

Potential Association between Vaginal Microbiota and Cervical Carcinogenesis in Korean Women: A Cohort Study

Convincing studies demonstrated that vaginal flora is one of the most impactful key components for the well-being of the genital tract in women. Nevertheless, the potential capability of vaginal-derived bacterial communities as biomarkers to monitor cervical carcinogenesis (CC) has yet to be studied actively compared to those of bacterial vaginosis (BV). We hypothesized that vaginal microbiota might be associated with the progression of CC. In this study, we enrolled 23 participants, including healthy controls (HC group; n = 7), patients with cervical intraepithelial neoplasia (CIN) 2 and 3 (CIN group, n = 8), and patients with invasive cervical cancer (CAN group; n = 8). Amplicon sequencing was performed using the Ion Torrent PGM to characterize the vaginal microbiota. Patients with CIN and CAN presented vaginal microbiota dysbiosis compared with HC. The alpha diversity analysis revealed that CC has a trend to be increased in terms of diversity indexes. Moreover, CC was associated with the abundance of specific microbes, of which Lactobacillus and Gardnerella were the most significantly different between HC and CIN, whereas Streptococcus was differentially abundant in CAN compared with CIN. We then evaluated their diagnostic abilities. Testing in terms of diagnostic ability using the three genera revealed considerably high performance with an area under the receiver-operating characteristic curve of 0.982, 0.953, and 0.922. The current study suggests that the presence of Gardnerella and Streptococcus may be involved in the advancment of CC