Efficacy and safety of using premedication with simethicone/Pronase during upper gastrointestinal endoscopy examination with sedation: A single center, prospective, single blinded, randomized controlled trial.

BACKGROUND AND AIM: To investigate the efficacy and safety of premedication with simethicone/Pronase during esophagogastroduodenoscopy (EGD) with sedation. METHODS: Six hundred and ten patients were randomly allocated to two groups based on type of premedication given. Premedication used in the control group was 10 mL lidocaine hydrochloride mucilage (LHM, N = 314) and premedication used in the intervention group was 80 mL simethicone/Pronase solution plus 10 mL lidocaine hydrochloride mucilage (SP/LHM, N = 296). EGD was done under sedation. Visibility scores, number of mucosal areas that needed cleansing, water consumption for cleansing, time taken for examination, diminutive lesions, pathological diagnosis, patients' gag reflex and oxygenation (pulse oximetry) were recorded. RESULTS: SP/LHM has significantly lower total visibility score than LHM (7.978 ± 1.526 vs 6.348 ± 1.097, P < 0.01). During the procedure, number of intragastric areas that needed cleansing and amount of water consumed were significantly less in the SP/LHM than in the LHM group (P < 0.01). In SP/LHM (P = 0.01), endoscopy procedure duration was significantly longer. Although there was no significant difference in rate of detection of diminutive lesions between LHM and SP/LHM, the endoscopist carried out more biopsies in SP/LHM. This led to a higher rate of diagnosis of atrophic gastritis (P = 0.014) and intestinal metaplasia (P = 0.024). There was no significant difference in gag reflex (P = 0.604) and oxygenation during the endoscopy procedure for either group of patients. CONCLUSION: Routine use of premedication with simethicone/Pronase should be recommended during EGD with sedation.

Revealing the Dark Side of Non-Local Attention in Single Image Super-Resolution.

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution image from its corresponding low-resolution input. A common technique to enhance the reconstruction quality is Non-Local Attention (NLA), which leverages self-similar texture patterns in images. However, we have made a novel finding that challenges the prevailing wisdom. Our research reveals that NLA can be detrimental to SISR and even produce severely distorted textures. For example, when dealing with severely degrade textures, NLA may generate unrealistic results due to the inconsistency of non-local texture patterns. This problem is overlooked by existing works, which only measure the average reconstruction quality of the whole image, without considering the potential risks of using NLA. To address this issue, we propose a new perspective for evaluating the reconstruction quality of NLA, by focusing on the sub-pixel level that matches the pixel-wise fusion manner of NLA. From this perspective, we provide the approximate reconstruction performance upper bound of NLA, which guides us to design a concise yet effective Texture-Fidelity Strategy (TFS) to mitigate the degradation caused by NLA. Moreover, the proposed TFS can be conveniently integrated into existing NLA-based SISR models as a general building block. Based on the TFS, we develop a Deep Texture-Fidelity Network (DTFN), which achieves state-of-the-art performance for SISR. Our code and a pre-trained DTFN are available on GitHub for verification.

High-Similarity-Pass Attention for Single Image Super-Resolution.

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

Coexisting Infantile Hepatic Hemangioma and Hepatoblastoma in a Neonate: A Case Report.

Infantile hepatic hemangioma and hepatoblastoma are the most common benign and malignant tumors of the liver in the neonatal and early childhood periods, respectively. However, the simultaneous occurrence of these 2 tumors in the same liver lesion is very rare. We report a case of a newborn infant diagnosed with a liver mass by ultrasound examination 4 days after birth. Serum alpha-fetoprotein (AFP) was elevated for his age (32,881.7 ng/mL). The liver mass was resected. Macroscopically, an externally protruding mass measuring 6 × 4 × 3.5 cm was identified. Microscopically, we observed the coexistence of infantile hepatic hemangioma and epithelial hepatoblastoma components within the tumor. The infantile hepatic hemangioma component was composed of multiple small vascular channels lined by endothelial cells. In the hepatoblastoma component, tumor cells were arranged in a 2- to 3-cell-thick trabecular formation. Immunohistochemistry indicated that the tumor cells in the infantile hepatic hemangioma component expressed CD34, CD31, FLI1, and ERG, and those in the hepatoblastoma component expressed hepatocyte, keratin AE1/AE3 and keratin 8, glypican 3, glutamine synthetase, and AFP. Pathological examination confirmed the presence of an infantile hepatic hemangioma combined with epithelial hepatoblastoma (fetal type). The boy did not undergo chemotherapy after the operation. Regular follow-up through serum AFP levels and liver ultrasound for 16 months to date show that the serum AFP levels decreased continuously to normal levels, with no signs of tumor recurrence or metastasis. The coexistence of infantile hepatic hemangioma and hepatoblastoma is rare. Hepatoblastoma should be considered in neonates with liver tumors and elevated AFP.

Global Learnable Attention for Single Image Super-Resolution.

Self-similarity is valuable to the exploration of non-local textures in single image super-resolution (SISR). Researchers usually assume that the importance of non-local textures is positively related to their similarity scores. In this paper, we surprisingly found that when repairing severely damaged query textures, some non-local textures with low-similarity which are closer to the target can provide more accurate and richer details than the high-similarity ones. In these cases, low-similarity does not mean inferior but is usually caused by different scales or orientations. Utilizing this finding, we proposed a Global Learnable Attention (GLA) to adaptively modify similarity scores of non-local textures during training instead of only using a fixed similarity scoring function such as the dot product. The proposed GLA can explore non-local textures with low-similarity but more accurate details to repair severely damaged textures. Furthermore, we propose to adopt Super-Bit Locality-Sensitive Hashing (SB-LSH) as a preprocessing method for our GLA. With the SB-LSH, the computational complexity of our GLA is reduced from quadratic to asymptotic linear with respect to the image size. In addition, the proposed GLA can be integrated into existing deep SISR models as an efficient general building block. Based on the GLA, we constructed a Deep Learnable Similarity Network (DLSN), which achieves state-of-the-art performance for SISR tasks of different degradation types (e.g., blur and noise). Our code and a pre-trained DLSN have been uploaded to GitHub† for validation.

[Clinical and pathologic features of tubulocystic carcinoma of kidney].

OBJECTIVE: To investigate clinicopathological features and differential diagnosis of tubulocystic carcinoma of the kidney. METHODS: The clinical features, histological and immunohistochemical findings were analyzed in 3 cases of tubulocystic carcinoma of the kidney, along with review of the related literatures. RESULTS: Three patients were males with a mean age of 59 years old (range from 44 to 71 years). All presented with no symptom and their tumors were found during routine examination. The tumor size ranged from 1.5 to 5.0 cm in greatest dimension. The tumors were grossly well-circumscribed without capsules and exhibited a spongy cut surface. Microscopically, all three tumors were composed of tubules and cysts of varying sizes separated by thin fibrous septa. The epithelial lining cells were flat, cuboidal and columnar, with often a hobnail-like appearance characterized by abundant eosinophilic cytoplasm with prominent nucleoli. Two cases showed focal clear cytoplasm. One of the three cases coexisted with a papillary renal cell carcinoma. Immunohistochemically, all 3 cases showed positivity for pan-CK, vimentin, CK19, CD10, P504S, and focal positivity for 34ßE12. Two cases showed focal positivity for CK7. CONCLUSIONS: Tubulocystic carcinoma of the kidney is a rare kidney neoplasm and occurs predominantly in males. The tumor is characterized by gross spongy appearance and microscopic cysts and tubules often lined by hobnail-like cells and separated by thin fibrotic stroma. The differential diagnosis mainly includes other lesions of the kidney that have a multicystic growth pattern.

[Clinicopathologic analysis of intestinal follicular lymphoma first presenting with gastrointestinal symptoms].

OBJECTIVE: To study the clinical features, endoscopic findings, pathologic diagnosis and treatment options of intestinal follicular lymphoma first presenting with gastrointestinal symptoms. METHODS: The clinical features, pathologic findings and follow-up data were retrospectively studied in 9 cases of intestinal follicular lymphoma. Immunohistochemical study for CD3, CD5, CD20, CD21, Ki-67, bcl-2, bcl-6, CD10 and cyclin D1 was carried out. RESULTS: Seven of the 9 patients were females and two were males. The age of patients ranged from 5 to 60 years (mean = 44 years). The clinical manifestations included abdominal pain (5 cases), blood in stool (3 cases) and abdominal distension (1 case). The commonest site of involvement was ileocecal region (6/9). Endoscopic examination had been carried out in 6 patients and all showed the presence of multiple polyps. Five cases had undergone endoscopic biopsy. Histologic examination of the endoscopic biopsies showed lymphoma cells located mainly in mucosal layer, forming vague nodules with ill-defined boundaries. Plasma cells and eosinophils were not conspicuous. Immunohistochemically, the tumor cells in all cases diffusely expressed CD20, CD10 and bcl-2. The staining for CD3, CD5 and cyclin D1 was negative. Lymphoid cells with weak CD10-positivity were identified in the interfollicular regions. Four cases were treated with surgical resection and chemotherapy. The other 3 cases received chemotherapy only and the remaining cases were treated conservatively. All of them were still alive on follow up. CONCLUSIONS: Primary intestinal follicular lymphoma affects predominantly elderly patients and has a female predilection. The commonest site of involvement is ileocecal region. Endoscopic examination shows polypoid changes. The disease often runs a relatively indolent clinical course. The prognosis is better than that of primary nodal follicular lymphoma.

Bladder paraganglioma after kidney transplantation: A case report.

BACKGROUND: Kidney transplantation is associated with an increased risk of tumors in the urinary bladder. Among all the pathological types of tumors in the bladder, paraganglioma, which arises from extra-adrenal paraganglia and consists of chromaffin cells, is rare. Paragangliomas might cause severe clinical symptoms due to catecholamine hypersecretion or mass compression. Bladder paragangliomas are rare, especially those appearing after kidney transplantation. Here, we report a case of bladder paraganglioma developing after kidney transplantation. CASE SUMMARY: A 63-year-old woman received a kidney transplant 12 years ago and took oral immunosuppressants (cyclosporine, mizoribine, and methylprednisolone) for regular post-transplant treatment. The patient felt no discomfort and she came to the hospital for a routine checkup. A mass located in the bladder was incidentally discovered by computed tomography, and she underwent surgical treatment. A 2 cm × 2 cm invasive mass was found in the trigone of the bladder and the mass was removed. The diagnosis of paraganglioma was confirmed by morphology and immunophenotyping. The patient had a good prognosis and is still alive. CONCLUSION: Paraganglioma can grow in the bladder, which might cause no clinical symptoms. The diagnosis mainly depends on morphology and immunophenotyping. Surgical resection is an important treatment option for such patients.

Online Identification of Nonlinear Systems With Separable Structure.

Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm.

Weighted Generalized Cross-Validation-Based Regularization for Broad Learning System.

The broad learning system (BLS) is an emerging flat network, which has demonstrated its outstanding performance in classification and regression problems. The regularization plays an important role in the performance of the BLS. In real applications, since the BLS network is usually expanded dynamically, a predetermined regularization parameter may reduce the performance of the network. Using a fixed regularization in some cases, the classification accuracy of the BLS decreases dramatically when we expand the network. To alleviate this problem, we propose a method that automatically finds appropriate regularization parameters for different datasets, which is based on the weighted generalized cross-validation (WGCV). The experimental results indicate that the WGCV method improves the performance of the BLS, and alleviates the accuracy decrease of the incremental learning algorithm.

Coexistent Kaposi sarcoma and post-transplant lymphoproliferative disorder in the same lymph nodes after pediatric liver transplantation: A case report.

BACKGROUND: Kaposi sarcoma and post-transplant lymphoproliferative disorder have been occasionally reported in post-liver transplant patients. However, the simultaneous occurrence of these two diseases in the same lymph nodes is very rare. CASE SUMMARY: We report the case of a 19-mo-old boy, who presented with intermittent fever and enlarged cervical lymph nodes after liver transplantation. Six cervical lymph nodes were biopsied, and the histopathological examinations revealed multifocal hyperplasia of spindle cells around small blood vessels, extravasated erythrocytes, and heavy infiltration of plasma cells in the cortex and medulla of the lymph nodes. The immunohistochemical analyses of spindle cells revealed positive expression of CD34, CD31, erythroblast transformation-specific-related gene, friend leukemia integration 1, and human herpesvirus-8. The lymphoproliferative lesions expressed CD38, CD138, and multiple myeloma 1. Epstein-Barr encoded RNA in situ hybridization demonstrated Epstein-Barr virus-positive lymphoid cells. Finally, we diagnosed the coexistence of Kaposi sarcoma and post-transplant lymphoproliferative disorder (plasmacytic hyperplasia) in the same lymph nodes. Treatment strategy included anti-CD20 monoclonal antibody (rituximab) and discontinuation of the immunosuppressant therapies. Lymph node biopsies during follow-up examinations revealed lymphoid hyperplasia. CONCLUSION: The rare coexistence of Kaposi sarcoma and post-transplant lymphoproliferative disorder in the same lymph nodes post-liver transplantation possibly associates with immunodeficiency and Epstein-Barr virus and human herpesvirus-8 coinfection.

Frequency Principle in Broad Learning System.

Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.

[Loss of pan-T cell antigens CD2, CD3, CD5 and CD7 in Kikuchi's disease].

OBJECTIVE: To study the possible loss of pan-T cell antigens CD2, CD3, CD5 and CD7 in Kikuchi's disease and to evaluate the role of T cell antigen loss in distinguishing benign from malignant T-cell lymphoid lesions. METHODS: Formalin-fixed and paraffin-embedded tissues of 33 cases of Kikuchi's disease and 15 cases of reactive lymphoid hyperplasia were studied by EliVision immunohistochemical staining for CD2, CD3, CD5 and CD7. RESULTS: Twenty-four of the 33 (72.7%) cases of Kikuchi's disease lost one or more of the pan-T cell antigens, including the loss of CD5 only (13 cases), CD7 only (1 case), CD2 only (1 case), CD2 and CD7 (2 cases), CD5 and CD7 (4 cases), CD2 and CD5 (2 cases), and CD2, CD7 and CD5 (1 case). Amongst these cases, the commonest antigen loss was CD5 (20 cases, 60.6%), followed by CD7 (8 cases, 24.2%) and CD2 (6 cases, 18.2%). Compared with the xanthomatous subtype of Kikuchi's disease, the loss of antigens was more commonly seen in the proliferative and necrotizing subtypes. Analysis of follow-up data showed that the loss of antigens in Kikuchi's disease was not significantly associated with the prognosis. In reactive lymphoid hyperplasia, the expression of CD2, CD3, CD5 and CD7 was seen in all cases with similar intensity, with no obvious pan-T cell antigen loss. CONCLUSION: Loss of one or more pan-T cell antigens in Kikuchi's disease is demonstrated in present study, suggesting that the immunophenotypic pattern is not unique in T cell lymphoma.

Recursive Variable Projection Algorithm for a Class of Separable Nonlinear Models.

In this article, we study the recursive algorithms for a class of separable nonlinear models (SNLMs) in which the parameters can be partitioned into a linear part and a nonlinear part. Such models are very common in machine learning, system identification, and signal processing. Utilizing the special structure of the SNLMs, we propose a recursive variable projection (RVP) algorithm, in which at each recursion, the linear parameters of the model are eliminated, and the nonlinear parameters are updated by the recursive Levenberg-Marquart algorithm. Then, based on the updated nonlinear parameters, the linear parameters are updated by the recursive least-squares algorithm. According to a convergence analysis of the RVP algorithm, the parameter estimation error is mean-square bounded. Numerical examples confirm the satisfactory performance of the proposed algorithm.

Insights Into Algorithms for Separable Nonlinear Least Squares Problems.

Separable nonlinear least squares (SNLLS) problems have attracted interest in a wide range of research fields such as machine learning, computer vision, and signal processing. During the past few decades, several algorithms, including the joint optimization algorithm, alternated least squares (ALS) algorithm, embedded point iterations (EPI) algorithm, and variable projection (VP) algorithms, have been employed for solving SNLLS problems in the literature. The VP approach has been proven to be quite valuable for SNLLS problems and the EPI method has been successful in solving many computer vision tasks. However, no clear explanations about the intrinsic relationships of these algorithms have been provided in the literature. In this paper, we give some insights into these algorithms for SNLLS problems. We derive the relationships among different forms of the VP algorithms, EPI algorithm and ALS algorithm. In addition, the convergence and robustness of some algorithms are investigated. Moreover, the analysis of the VP algorithm generates a negative answer to Kaufman's conjecture. Numerical experiments on the image restoration task, fitting the time series data using the radial basis function network based autoregressive (RBF-AR) model, and bundle adjustment are given to compare the performance of different algorithms.

The Correlation Between Mutant p53 Protein Expression and Cell Atypia in Early Differentiated Gastric Adenocarcinoma.

OBJECTIVE: This study aims to explore the correlation between the expression of mutant p53 protein and cellular atypia in early differentiated gastric adenocarcinoma (DGA). METHODS: A total of 107 cases of early DGA samples resected by endoscopic submucosal dissection (ESD) were collected from the Pathology Department of Beijing Friendship Hospital from January 2018 to December 2019. The EnVision two-step immunohistochemical method was used to detect the expression of mutant p53 protein in these cancer tissues, and the correlation with cell atypia was analyzed. RESULTS: In early DGA tissues, the expression rate of mutant p53 protein was significantly higher than in normal gastric mucosa (P < 0.01). However, the expression of mutant p53 protein was not correlated to age or gender (P > 0.05) but to the location of the tumor, depth of invasion, and degree of differentiation (P < 0.01). The expression of mutant p53 protein was closely correlated to cell atypia. Furthermore, this was weakly positive in low-grade atypical adenocarcinoma but strongly positive or negative in high-grade atypical adenocarcinoma, and there was a significant difference between these two (P < 0.01). CONCLUSION: Mutant p53 protein is highly expressed in early DGA, which can be used as an auxiliary index for the diagnosis of early gastric cancer. The different expression patterns of mutant p53 protein in high-grade and low-grade atypical gastric cancers suggest that these may have different genetic changes.

Basis Function Matrix-Based Flexible Coefficient Autoregressive Models: A Framework for Time Series and Nonlinear System Modeling.

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework.

Current indications for endoscopic submucosal dissection of early gastric cancer.

The development of endoscopic treatment technology has further promoted the minimally invasive treatment of early gastric cancer (EGC). Endoscopic treatment has achieved better therapeutic effects in terms of safety and prognosis and is the preferred treatment method for patients who meet the indications for endoscopic treatment. However, the consequent problem is that some patients receiving endoscopic treatment may undergo non-curative resection, and the principle of follow-up management for non-curative resection patients deserves further attention. In addition, there are still debates on how to improve the accuracy of clinical staging, select a reasonable treatment method for patients who meet the expanded indications for endoscopic treatment, manage patients with positive endoscopic surgical margins, conduct research on function-preserving surgery, and manage the treatment of EGC under the current situation in China. Consequently, we aim to review current indications for endoscopic submucosal dissection of EGC in order to better inform treatment options.

Term Selection for a Class of Separable Nonlinear Models.

In this paper, we consider the term selection problem for a class of separable nonlinear models. The strategy is a two-step process in which the nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods.