Resolution of occult anastomotic stricture with anal dilator: challenges with the conventional diagnostic criteria in low anterior rectal resection patient-a case report.

Background: Anastomotic stricture (AS) is a common complication following rectal cancer surgery with anastomosis, but its diagnosis and management pose significant challenges due to the lack of standardized diagnostic criteria. We present a case highlighting the complexities encountered in diagnosing and managing occult AS post-rectal cancer surgery. Case presentation: A 51-year-old male patient presented with symptoms suggestive of AS following robot-assisted laparoscopic low anterior resection for rectal adenocarcinoma. Despite conventional evaluations, including colonoscopy, digital rectal examination, and radiography, AS was not identified. Following prolonged and ineffective treatment for suspected conditions such as low anterior resection syndrome (LARS), the patient underwent anal dilatation, resulting in significant symptom improvement. Conclusions: This case underscores the challenges associated with diagnosing and managing occult AS following rectal cancer surgery. The absence of standardized diagnostic criteria and reliance on conventional modalities may lead to underdiagnosis and inadequate treatment. A comprehensive diagnostic approach considering intestinal diameter, elasticity, and symptoms related to difficult defecation may enhance diagnostic accuracy. Further research is needed to refine the diagnostic and therapeutic strategies for occult AS.

Frequency compensated diffusion model for real-scene dehazing.

Due to distribution shift, deep learning based methods for image dehazing suffer from performance degradation when applied to real-world hazy images. In this paper, this study considers a dehazing framework based on conditional diffusion models for improved generalization to real haze. First, our work finds that optimizing the training objective of diffusion models, i.e., Gaussian noise vectors, is non-trivial. The spectral bias of deep networks hinders the higher frequency modes in Gaussian vectors from being learned and hence impairs the reconstruction of image details. To tackle this issue, this study designs a network unit, named Frequency Compensation block (FCB), with a bank of filters that jointly emphasize the mid-to-high frequencies of an input signal. Our work demonstrates that diffusion models with FCB achieve significant gains in both perceptual and distortion metrics. Second, to further boost the generalization performance, this study proposed a novel data synthesis pipeline, HazeAug, to augment haze in terms of degree and diversity. Within the framework, a solid baseline for blind dehazing is set up where models are trained on synthetic hazy-clean pairs, and directly generalize to real data. Extensive evaluations on real dehazing datasets demonstrate the superior performance of the proposed dehazing diffusion model in distortion metrics. Compared to recent methods pre-trained on large-scale, high-quality image datasets, our model achieves a significant PSNR improvement of over 1 dB on challenging databases such as Dense-Haze and Nh-Haze.

Dynamic Graph Guided Progressive Partial View-Aligned Clustering.

In recent years, there has been a growing focus on multiview data, driven by its rich complementary and consistent information, which has the potential to significantly enhance the performance of downstream tasks. Although many multiview clustering (MVC) methods have achieved promising results by integrating the information of multiple views to learn the consistent representation or consistent graph, these methods typically require complete and entirely accurate correspondences between multiview data, which is challenging to fulfill in practice leading to the problem of partially view-aligned clustering (PVC). To tackle it, we propose a novel method, called dynamic graph guided progressive partial view-aligned clustering (DGPPVC) in this article. To the best of our knowledge, this could be the first work to employ graph convolutional network (GCN) to address the problem of PVC, which explores GCN with dynamic adjacency matrix to reduce unreliable alignments and locate the feature representation with consistent graph structure. In particular, DGPPVC develops an end-to-end framework that encompasses graph construction, feature representation learning, and alignment relationships learning, in which the three parts mutually influence and benefit each other. Moreover, DGPPVC adopts a novel alignment learning strategy that progresses from simplicity to complexity, enabling the step-by-step acquisition of unknown correspondences between different modalities. By giving priority to simple instance pairs, a variant of Jaccard similarities is designed to identify more reliable and complex alignments progressively. During the gradual learning process of alignment relationships, the graph structure matrix is continually and dynamically optimized, thus acquiring a greater variety of graph information between different views. Experiments on several real-world datasets show our promising performance compared with the state-of-the-art methods in partially view-aligned clustering.

PAX9 polymorphism and susceptibility to sporadic non-syndromic severe anodontia: a case-control study in southwest China

Our research aimed to look into the clinical traits and genetic mutations in sporadic non-syndromic anodontia and to gain insight into the role of mutations of PAX9, MSX1, AXIN2 and EDA in anodontia phenotypes, especially for the PAX9. Material and Methods The female proband and her family members from the ethnic Han families underwent complete oral examinations and received a retrospective review. Venous blood samples were obtained to screen variants in the PAX9, MSX1, AXIN2, and EDA genes. A case-control study was performed on 50 subjects with sporadic tooth agenesis (cases) and 100 healthy controls, which genotyped a PAX9 gene polymorphism (rs4904210). Results Intra-oral and panoramic radiographs revealed that the female proband had anodontia denoted by the complete absence of teeth in both the primary and secondary dentitions, while all her family members maintained normal dentitions. Detected in the female proband were variants of the PAX9 and AXIN2 including A240P (rs4904210) of the PAX9, c.148C>T (rs2240308), c.1365A>G (rs9915936) and c.1386C>T (rs1133683) of the AXIN2. The same variants were present in her unaffected younger brother. The PAX9 variations were in a different state in her parents. Mutations in the MSX1 and EDA genes were not identified. No significant diferences were found in the allele and genotype frequencies of the PAX9 polymorphism between the controls and the subjects with sporadic tooth agenesis. Conclusions These results suggest that the association of A240P with sporadic tooth agenesis still remains obscure, especially for different populations. The genotype/phenotype correlation in congenital anodontia should be verified. .