A KRT1 gene mutation related to epidermolytic ichthyosis in a Chinese family

We report a Chinese family with members affected by epidermolytic ichthyosis (EI), caused by KRT gene mutations. The proband was a 14‐year‐old boy who had simultaneous appearance of nephroblastoma and epidermolytic ichthyosis (EI). Both the patient and his mother exhibited the specific clinical and pathological manifestations of EI. We analysed all exons and flanking sequences of the KRT1 and KRT10 genes using PCR, and found that the proband and his mother had a G>C transition at nucleotide position 1432 in exon 7 of KRT1, resulting in an amino acid substitution of glutamate (GAA) to glutamine (CAA) at codon 478 (E478Q). The KRT10 gene had no mutations.     

Porocarcinoma coexisting at a site of Bowen disease in a 63‐year‐old woman

Porocarcinoma is an unusual, locally aggressive and potentially fatal neoplasm. Several cutaneous malignancies have been described in association with porocarcinoma, including squamous cell carcinoma, basal cell carcinoma and tricholemmal carcinoma. Previous reports have indicated that the occurrence of malignant tumours in combination with porocarcinoma is extremely rare, in particular with regard to Bowen disease (BD). We report an uncommon case of porocarcinoma occurring synchronously in a single BD lesion in a 63‐year‐old woman with multiple BD lesions. The clinical and histological findings confirmed this diagnosis.     

Surgical Technique Notes of Arterial Vascular Reconstruction During Kidney Transplantation: Personal Experience and Literature Review

Background

Arterial vascular anomalies in patients undergoing kidney transplantation (KT) are correlated with a higher incidence of early surgical complications, potentially causing graft loss. Arterial reconstruction allows patients to overcome these surgical challenges, thus minimizing the risk of poor outcomes. The aim of the present study is to retrospectively investigate the safety and effectiveness of the multiple arterial reconstruction technique with a Teflon patch in case of an unavailable aortic patch: to do so, surgical complications, graft function, and patient survival were evaluated.

Improvements in epidermal function prevent relapse of psoriasis: a self‐controlled study

While therapeutic approaches for psoriasis are widely available, preventive regimens are lacking. We aimed to determine whether improvements in epidermal function could prevent psoriasis relapse. Two self‐controlled cohort studies were designed, enrolling two cohorts of patients with psoriasis (n = 30 and n = 60) to be treated topically with an in‐house‐prepared emollient or ATOPALM^® cream applied twice daily to one forearm for 20 and 30 days, respectively, while the same sites on the contralateral arm served as the untreated control. Epidermal function on both arms was assessed prior to and at the end of the trials. Delayed relapse on the treated arm was seen in 54.5% and 71% of patients in the first and second cohort, respectively. The time of psoriatic relapse correlated with the extent of abnormalities in baseline epidermal function. These results suggest that improvements in epidermal function with topical emollients can prevent/attenuate the development of psoriasis.     

Graph Convolutional Network Based on Manifold Similarity Learning

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.