Associations between end stage renal disease and HLA polymorphisms in the Guangxi Zhuang population.

To investigate the genetic relationship between end stage renal disease (ESRD) and human leukocyte antigen (HLA) alleles in the Guangxi Zhuang population. We performed polymerase chain reaction reversed sequence-specific oligonucleotide (PCR-rSSO) in 325 patients with ESRD and genotyped the HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 loci. The direct counting method was used to determine the frequencies of HLA alleles, and Arlequin software (version 3.5.2.2) was used for haplotypic frequency analyses to compare the included ESRD patients with 350 healthy donors from the Guangxi Zhuang population. In our study, 120 HLA alleles, 284 HLA-A-B-DRB1 haplotypes, and 332 HLA-A-C-B-DRB1-DQB1 haplotypes were detected. We found that only A*11:01-B*15:02-DRB1*12:02 had a positive association with ESRD (P = 0.001, Pc = 0.020, OR = 3.106, 95% CI = 1.497-6.446) after Bonferroni correction; thus, individuals with this haplotype may be susceptible to ESRD. A*11:01-B*15:02-DRB1*12:02 is a potentially valuable haplotype for evaluating the risk of ESRD in the Guangxi Zhuang population.

Zero-shot prompt-based video encoder for surgical gesture recognition.

PURPOSE: In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called zero-shot capability). In this paper we investigate the feasibility of latter option. METHODS: Leveraging the bridge-prompt framework, we prompt-tune a pre-trained vision-text model (CLIP) for gesture recognition in surgical videos. This can utilize extensive outside video data such as text, but also make use of label meta-data and weakly supervised contrastive losses. RESULTS: Our experiments show that prompt-based video encoder outperforms standard encoders in surgical gesture recognition tasks. Notably, it displays strong performance in zero-shot scenarios, where gestures/tasks that were not provided during the encoder training phase are included in the prediction phase. Additionally, we measure the benefit of inclusion text descriptions in the feature extractor training schema. CONCLUSION: Bridge-prompt and similar pre-trained + prompt-tuned video encoder models present significant visual representation for surgical robotics, especially in gesture recognition tasks. Given the diverse range of surgical tasks (gestures), the ability of these models to zero-shot transfer without the need for any task (gesture) specific retraining makes them invaluable.

The HLA-DRB1*04:357 allele first identified in a Chinese transplant recipient.

HLA-DRB1*04:357 differs from HLA-DRB1*04:17:02 by two nucleotides in exon 2.

Limited sampling strategy for predicting area under the concentration-time curve for mycophenolic Acid in Chinese adults receiving mycophenolate mofetil and tacrolimus early after renal transplantation.

BACKGROUND: The objective of the study was to investigate the pharmacokinetics of mycophenolate mofetil (MMF) in Chinese adults early after renal transplantation by an enzyme multiplied immunoassay technique and to establish a limited sampling strategy to predict the area under the concentration-time curve for plasma levels of mycophenolic acid (MPA-AUC). METHODS: Fifty-eight recipients who underwent renal transplantation with an organ donated after cardiac death used a triple immunosuppressant strategy of MMF, tacrolimus, and prednisone. On the seventh day posttransplantation, plasma samples were collected at 0 hours (pre-dose) and at 0.5, 1, 1.5, 2, 4, 6, 8, 10, and 12 hours postdose (C0h, C0.5h, C1h, C1.5h, C2h, C4h, C6h, C8h, C10h, and C12h, respectively). Enzyme multiplied immunoassay technique was used to measure mycophenolic acid concentration, and model equations were generated by multiple stepwise regression analysis to determine MPA-AUC0-12h. RESULTS: The 3-point equation obtained by multiple linear regression analysis was MPA-AUC = 7.951 + 4.04C6h + 1.893C2h + 4.542C10h (adjusted r = 0.863); the 4-point equation was MPA-AUC = 4.272 + 4.074C6h + 1.896C2h + 4.680C10h + 0.859C0.5h (adjusted r = 0.918). The % mean prediction error, % mean absolute error, and % root mean squared prediction error for the best-fit formula using C6h, C2h, C10h, and C0.5h were -0.2%, 8.7%, and 14.2%, respectively. CONCLUSIONS: In Chinese adults receiving MMF and tacrolimus early after renal transplantation, the best equation for predicting MPA-AUC0-12h is 4.272 + 4.074C6h + 1.896C2h + 4.680C10h + 0.859C0.5h.