Renal cell carcinoma of native kidney in Chinese renal transplant recipients: a report of 12 cases and a review of the literature

Objectives To present and discuss the epidemiological and clinical aspects, as well as therapeutic options and outcome of de novo renal cell carcinoma (RCC) of the native kidneys in a series of Chinese renal transplant recipients. Patients and Methods A retrospective, cohort study examining all renal transplant recipients with the diagnosis of RCC of native kidney followed up in two major regional hospitals in Hong Kong between January 2000 and December 2009. Clinical data includedage, gender, cause of renal failure, symptoms at presentation, duration of transplantation, immunosuppressive therapy, and history of acquired cystic kidney disease (ACKD). Laboratory, radiographic, operative, and pathology reports were used to assess the tumor extent. Results Among the 1,003 renal transplant recipients recruited, 12 transplant recipients had a nephrectomy for a total of 13 RCC. The prevalence of de novo RCC was 1.3%. The mean age at diagnosis of RCC was 48.4 years, and the median time from transplantation to diagnosis was 6.1 years. ACKD was found in 6 (50%) of the patients. All patients except one were asymptomatic. pT1 disease was found in ten patients with a mean tumor size of 3.2 cm. All patients were treated successfully with radical nephrectomy. After a median follow-up of 38 months, two patients (16.7%) died. One died of sepsis, and the other died of metastatic carcinoma. Conclusions With increasing data showing a better prognosis if RCC is detected early by screening, it is time to consider screening all kidney transplant recipients for ACKD and RCC. © The Author(s) 2011. This article is published with open access at Springerlink.com.published_or_final_versionSpringer Open Choice, 21 Feb 201

A clinicopathological study of non-functioning pituitary neuroendocrine tumours using the World Health Organization 2022 classification

BackgroundThe 2022 World Health Organization (WHO) classification of pituitary neuroendocrine tumour (PitNET) supersedes the previous one in 2017 and further consolidates the role of transcription factors (TF) in the diagnosis of PitNET. Here, we investigated the clinical utility of the 2022 WHO classification, as compared to that of 2017, in a cohort of patients with non-functioning PitNET (NF-PitNET).MethodsA total of 113 NF-PitNET patients who underwent resection between 2010 and 2021, and had follow-up at Queen Mary Hospital, Hong Kong, were recruited. Surgical specimens were re-stained for the three TF: steroidogenic factor (SF-1), T-box family member TBX19 (TPIT) and POU class 1 homeobox 1 (Pit-1). The associations of different NF-PitNET subtypes with tumour-related outcomes were evaluated by logistic and Cox regression analyses.ResultsBased on the 2022 WHO classification, the majority of NF-PitNET was SF-1-lineage tumours (58.4%), followed by TPIT-lineage tumours (18.6%), tumours with no distinct lineage (16.8%) and Pit-1-lineage tumours (6.2%). Despite fewer entities than the 2017 classification, significant differences in disease-free survival were present amongst these four subtypes (Log-rank test p=0.003), specifically between SF-1-lineage PitNET and PitNET without distinct lineage (Log-rank test p<0.001). In multivariable Cox regression analysis, the subtype of PitNET without distinct lineage (HR 3.02, 95% CI 1.28-7.16, p=0.012), together with tumour volume (HR 1.04, 95% CI 1.01-1.07, p=0.017), were independent predictors of a composite of residual or recurrent disease.ConclusionThe 2022 WHO classification of PitNET is a clinically useful TF and lineage-based system for subtyping NF-PitNET with different tumour behaviour and prognosis

Meta-analysis Followed by Replication Identifies Loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as Associated with Systemic Lupus Erythematosus in Asians

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic involvement and ethnic differences. Susceptibility genes identified so far only explain a small portion of the genetic heritability of SLE, suggesting that many more loci are yet to be uncovered for this disease. In this study, we performed a meta-analysis of genome-wide association studies on SLE in Chinese Han populations and followed up the findings by replication in four additional Asian cohorts with a total of 5,365 cases and 10,054 corresponding controls. We identified genetic variants in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with the disease. These findings point to potential roles of cell-cycle regulation, autophagy, and DNA demethylation in SLE pathogenesis. For the region involving TET3 and that involving CDKN1B, multiple independent SNPs were identified, highlighting a phenomenon that might partially explain the missing heritability of complex diseases

Mixed precision comparison in reconfigurable systems

Customisable data formats provide an opportunity for exploring trade-offs in accuracy and performance of reconfigurable systems. This paper introduces a novel methodology for mixed-precision comparison, which improves comparison performance by using reduced-precision data paths while maintaining accuracy by using high-precision data paths. Our methodology adopts reduced-precision data-paths for preliminary comparison, and high-precision data-paths when the accuracy for preliminary comparison is insufficient. We develop an analytical model for performance estimation of the proposed mixed-precision methodology. Optimisation based on integer linear programming is employed for determining the optimal precision and resource allocation for each of the data paths. The effectiveness of our approach is evaluated using a common collision detection problem. Performance gains of 4 to 7.3 times are obtained over baseline fixed-precision designs for the same FPGAs. With the help of the proposed mixed-precision methodology, our FPGA designs are 15.4 to 16.7 times faster than software running on multi-core CPUs with the same technology. © 2011 IEEE.link_to_subscribed_fulltex