Comparative proteomics analysis of samples from hepatic cystic echinococcosis patients using data-independent acquisition approach.

Cystic echinococcosis is a zoonotic disease resulting from infection caused by the larval stage of Echinococcus granulosus. This study aimed to assess the specific proteins that are potential candidates for the development of a vaccine against E. granulosus. The data-independent acquisition approach was employed to identify differentially expressed proteins (DEPs) in E. granulosus samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify several noteworthy proteins. Results: The DEPs in E. granulosus samples were identified (245 pericystic wall vs. parasite-free yellowish granuloma (PYG, 1725 PY vs. PYG, 2274 PN vs. PYG). Further examination of these distinct proteins revealed their predominant enrichment in metabolic pathways, amyotrophic lateral sclerosis, and neurodegeneration-associated pathways. Notably, among these DEPs, SH3BGRL, MST1, TAGLN2, FABP5, UBE2V2, and RARRES2 exhibited significantly higher expression levels in the PYG group compared with the PY group (P < 0.05). The findings may contribute to the understanding of the pathological mechanisms underlying echinococcosis, providing valuable insights into the development of more effective diagnostic tools, treatment modalities, and preventive strategies. SIGNIFICANCE: CE is a major public health hazard in the western regions of China, Central Asia, South America, the Mediterranean countries, and eastern Africa. Echinococcus granulosus is responsible for zoonotic disease through infection Our analysis focuses on the proteins in various samples by data-dependent acquisition (DIA) for proteomic analysis. The importance of this research is to develop new strategies and targets to protect against E. granulosus infections in humans.

The Early Initiation of Perioperative Anticoagulation Therapy in Cirrhotic Patients with Portal Hypertension After Laparoscopic Splenectomy Plus Esophagogastric Devascularization: A 10-Year Single-Center Experience.

Background: Portal veinous system thrombosis (PVST) is a common complication after laparoscopic splenectomy plus esophagogastric devascularization (LSED). Anticoagulation therapy was still in debate in LSED perioperation. This study aimed to determine the postoperative risk factors of PVST, the efficacy and safety of the anticoagulation therapy after LSED procedure, and the potential impact of anticoagulation on patients' liver function and overall survival (OS). Methods: Three hundred patients who underwent LSED were retrospectively enrolled and analyzed in the study. The characteristics of patients, perioperative parameters, risk of PVST, and long-term follow-up data were recorded and analyzed. Results: One hundred eighty six of 300 patients received perioperative anticoagulation therapy (p-AT) postoperation, while 114 patients did not receive p-AT (non-p-AT). The non-p-AT group had more intraoperative blood loss, PVST, longer abdominal drain days, and postoperative hospital stays separately compared with the p-AT group. No significant difference of postoperative complications was found between the two groups. In the 1-year follow-up postoperation, both groups had great improvement in hypersplenism and the portal vein hemodynamics. During the 10-year follow-up period, the incidence of PVST was significantly higher in the non-p-AT group than in the p-AT group. Anticoagulation therapy, sex, variceal hemorrhage history, portal vein diameter, and portal vein velocity were the independent prognostic factors determined by the Cox regression analysis for PVST. The thrombosis-free survival rate (P = .002), recurrent hemorrhage-free survival rate (P < .01), and the OS rate (P < .01) were significantly lower in the non-p-AT group than in the p-AT group. Conclusions: The initiate use of anticoagulation therapy in postoperation of LSED is effective and safe in cirrhotic patients. The anticoagulation therapy after LSED will help decrease portal vein thrombosis, the rebleeding rate, and extend the OS of the patients, especially among women, with variceal hemorrhage history, high portal vein diameter, and low portal vein velocity.

Effects of hypersplenism on the outcome of hepatectomy in hepatocellular carcinoma with hepatitis B virus related portal hypertension.

Background: Although hepatectomy plus splenectomy is not regularly recommended for hepatocellular carcinoma (HCC) with portal hypertension related hypersplenism due to the high risk accompanied with surgical procedures for now. Many researchers still believe that hypersplenism is a controversial adverse prognostic factor for HCC patients. Thus, the primary objective of the study was to determine the effects of hypersplenism on the prognosis of these patients during and after hepatectomy. Methods: A total of 335 patients with HBV-related HCC who underwent surgical resection as primary intervention were included in this study and categorized into three groups. Group A consisted of 226 patients without hypersplenism, Group B included 77 patients with mild hypersplenism, and Group C contained 32 patients with severe hypersplenism. The influence of hypersplenism on the outcome during the perioperative and long-term follow-up periods was analyzed. The independent factors were identified using the Cox proportional hazards regression model. Results: The presence of hypersplenism is associated with longer hospital stays, more postoperative blood transfusions, and higher complication rates. The overall survival (OS, P = 0.020) and disease-free survival (DFS, P = 0.005) were significantly decreased in Group B compared to those in Group A. Additionally, the OS (P = 0.014) and DFS (P = 0.005) were reduced in Group C compared to those in Group B. Severe hypersplenism was a significant independent prognostic variable for both OS and DFS. Conclusion: Severe hypersplenism prolonged the hospital stay, increased the rate of postoperative blood transfusion, and elevated the incidence of complications. Furthermore, hypersplenism predicted lower overall and disease-free survivals.

Downregulation of miR-20b-5p Contributes to the Progression of Liver Fibrosis via the STAT3 Signaling Pathway In Vivo and In Vitro.

BACKGROUND: Activated hepatic stellate cells (HSCs) are primarily involved in liver fibrosis and portal hypertension (PHT). We aimed to investigate the effect of miR-20b-5p on HSCs, liver fibrosis, and PHT. METHODS: MiR-20b-5p expression in HSCs and in mouse liver fibrosis was determined by qPCR. Further, the effects of miR-20b-5p mimic on HSCs migration, proliferation, and apoptosis were investigated in vitro. A dual-luciferase reporter assay was performed to confirm the direct interaction between miR-20b-5p and STAT3. In vivo, mouse liver fibrosis was established by common bile duct ligation and intervened by agomiR-20b-5p. Sirius red staining and hydroxyproline content were used to evaluate collagen deposition. The α-SMA expression in the liver was detected by IHC and Western blotting. The STAT3 signaling pathway and its downregulated cytokines as well as portal pressure and angiogenesis were explored. RESULTS: MiR-20b-5p was significantly downregulated during HSCs activation and in mouse liver fibrosis. The functional analyses demonstrated that miR-20b-5p inhibited cell proliferation, activation, and promoted apoptosis in HSCs in vitro. Moreover, miR-20b-5p regulated STAT3 expression by binding to the 3'UTR of its miRNA directly. Overexpression of miR-20b-5p facilitated HSC activation and proliferation by inhibiting the STAT3 signaling pathway. MiR-20b-5p overexpression suppressed the STAT3 and its downstream cytokines and ameliorated liver fibrosis in mice. The intra- and inter-hepatic angiogenesis were also effectively inhibited. The inhibition of liver fibrosis and neoangiogenesis contributed to the decrease of portal pressure. CONCLUSIONS: MiR-20b-5p plays an important role in the fibrosis and angiogenesis of liver fibrosis by targeting the STAT3 signaling pathway.

Photometric-Stereo-Based Defect Detection System for Metal Parts.

Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts' geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.

Laparoscopic splenectomy and devascularization for massive splenomegaly in portal hypertensive patients: a retrospective study of a single surgical team's experience with 6-year follow-up data.

Background: In China, laparoscopic splenectomy and esophagogastric devascularization (LSED) are effective and safe tools that are used to treat esophageal-fundic variceal bleeding with portal hypertension (PHT) with minimal trauma; however, due to the increased difficulty of operation, their application in massive splenomegaly (MS) remains limited. This study sought to determine the efficacy and safety of LSED in treating MS patients with PHT. Methods: The data of 124 patients who underwent LSED by a single surgical team at our department from January 2015 to December 2020 were retrospectively analyzed. The characteristics of the patients, perioperative parameters, and long-term follow-up data were examined. Results: A total of 61 MS and 63 mild-to-moderate splenomegaly (MMS) patients underwent LSED. Much larger spleen and significant lower of white blood cells and platelets was found in MS group compared the MMS group preoperation (P<0.05). The MS group had a significantly longer operation time (P=0.009), more blood loss (P=0.003), and more abdominal drainage days (P=0.017) than the MMS group. Four patients in the MS group and 0 patients in the MMS group were converted to open surgery. No significant difference was found between the 2 groups in terms of postoperative complications. Nine patients in the MMS group and 10 in the MMS group experienced recurrent bleeding in the follow-up period, but no significant differences were observed in terms of recurrent bleeding and overall survival (OS) between the 2 groups. Conclusions: LSED can be used to treat MS patients with PHT under careful perioperative management. For experienced surgeons, LSED is a safe, feasible, and minimally invasive procedure with satisfactory long-term outcomes that can be used to treat MS patients with PHT. Keywords: Laparoscopic splenectomy (LS); massive splenomegaly (MS); devascularization; portal hypertension (PHT).

Computational framework for steady-state NLOS localization under changing ambient illumination conditions.

Non-line-of-sight (NLOS) imaging of hidden objects is a challenging yet vital task, facilitating important applications such as rescue operations, medical imaging, and autonomous driving. In this paper, we attempt to develop a computational steady-state NLOS localization framework that works accurately and robustly under various illumination conditions. For this purpose, we build a physical NLOS image acquisition hardware system and a corresponding virtual setup to obtain real-captured and simulated steady-state NLOS images under different ambient illuminations. Then, we utilize the captured NLOS images to train/fine-tune a multi-task convolutional neural network (CNN) architecture to perform simultaneous background illumination correction and NLOS object localization. Evaluation results on both stimulated and real-captured NLOS images demonstrate that the proposed method can effectively suppress severe disturbance caused by the variation of ambient light, significantly improving the accuracy and stability of steady-state NLOS localization using consumer-grade RGB cameras. The proposed method potentially paves the way to develop practical steady-state NLOS imaging solutions for around-the-clock and all-weather operations.

Data-driven framework for high-accuracy color restoration of RGBN multispectral filter array sensors under extremely low-light conditions.

RGBN multispectral filter array provides a cost-effective and one-shot acquisition solution to capture well-aligned RGB and near-infrared (NIR) images which are useful for various optical applications. However, signal responses of the R, G, B channels are inevitably distorted by the undesirable spectral crosstalk of the NIR bands, thus the captured RGB images are adversely desaturated. In this paper, we present a data-driven framework for effective spectral crosstalk compensation of RGBN multispectral filter array sensors. We set up a multispectral image acquisition system to capture RGB and NIR image pairs under various illuminations which are subsequently utilized to train a multi-task convolutional neural network (CNN) architecture to perform simultaneous noise reduction and color restoration. Moreover, we present a technique for generating high-quality reference images and a task-specific joint loss function to facilitate the training of the proposed CNN model. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art color restoration solutions and achieving more accurate color restoration results for desaturated and noisy RGB images captured under extremely low-light conditions.

Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels.

Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depth-of-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.

The value of combined vein resection in pancreaticoduodenectomy for pancreatic head carcinoma: a meta-analysis.

BACKGROUND: Although pancreaticoduodenectomy with vein resection (PDVR) is widely performed in selected patients with indications, its benefits remain controversial. In this meta-analysis, we evaluate the safety and efficacy of PDVR in comparison to standard pancreaticoduodenectomy (PD). METHODS: We searched PubMed, Embase, and Cochrane as well as the Chinese National Knowledge Infrastructure, Weipu, and Wanfang databases for studies that evaluate the value of PVDR. The data of the patients who underwent PD or PDVR were analyzed using Review Manager and STATA software. RESULTS: In comparison with the PD group, the PDVR group had a lower R0 resection rate and higher rates of complications such as biliary fistula, reoperation rate, delayed gastric emptying, cardiopulmonary abnormalities, hemorrhage, in-hospital mortality, 30-day mortality. The blood loss, duration of operation, total hospital stay is higher in PDVR group. CONCLUSIONS: Compared to standard PD, PDVR was associated with a greater risk of some specific complications and increase the mortality rate, total hospital stay time, combine with vein resection have a lower R0 resection rate. Therefore, combine with vascular resection for pancreatic cancer needs to be carefully selected by the surgeon.

On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning.

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10 ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion.

Exploiting fusion architectures for multispectral pedestrian detection and segmentation.

Recent research has demonstrated that the fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g., daytime and nighttime). In this paper, we investigate a number of fusion architectures in an attempt to identify the optimal way of incorporating multispectral information for joint semantic segmentation and pedestrian detection. We made two important findings: (1) the sum fusion strategy, which computes the sum of two feature maps at the same spatial locations, delivers the best performance of multispectral detection, while the most commonly used concatenation fusion surprisingly performs the worst; and (2) two-stream semantic segmentation without multispectral fusion is the most effective scheme to infuse semantic information as supervision for learning human-related features. Based on these studies, we present a unified multispectral fusion framework for joint training of semantic segmentation and target detection that outperforms state-of-the-art multispectral pedestrian detectors by a large margin on the KAIST benchmark dataset.

Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: a deep-learning approach.

Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focal-plane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation. Specifically, a comprehensive column FPN model is utilized to depict the nonlinear characteristics of column amplifiers in the readout circuit of FPA. A large number of high-fidelity training images are simulated based on this model and the end-to-end residual deep network is capable of learning the intrinsic difference between undesirable FPN and original image details. Therefore, column FPN can be accurately estimated and further subtracted from the raw infrared images to obtain NUC results. Comparative results with state-of-the-art single-image-based NUC methods, using real-captured noisy infrared images, demonstrate that our proposed deep-learning-based approach delivers better performances of FPN removal, detail preservation, and artifact suppression.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.

A robust tolerance design method based on process capability.

This paper presents a method for robust tolerance design in terms of Process Capability Indices (PCI). The component tolerance and the suitable manufacturing processes can be selected based on the real manufacturing context. The robustness of design feasibility under the effect of uncertainties is also discussed. A comparison between the results obtained by the proposed model and other methods indicates that robust and reliable tolerance can be obtained.