Spontaneous clearance of serum HCV-RNA after splenectomy in a patient with HCV-related liver cirrhosis and portal hypertension: a case report.

BACKGROUND: Spontaneous clearance of chronic hepatitis C virus (HCV) is rare in adults. A T-lymphocyte response is thought to be involved in HCV-RNA clearance. Splenectomy reportedly has a beneficial effect on T cell immune function in patients with cirrhosis. To the best of our knowledge, the present report is the first to describe spontaneous clearance of serum HCV-RNA within 1 year after splenectomy in a patient with cirrhosis. CASE PRESENTATION: A 55-year-old man with HCV cirrhosis was transferred to our institution with advanced pancytopenia, splenomegaly, and gastric varices. He had a 1-year history of ascites, edema, and general fatigue. The patient had a Child-Pugh score of 8 and serological type 1 HCV; the HCV-RNA level was 4.7 log IU/mL. Contrast-enhanced computed tomography showed gastric varices and marked splenomegaly (estimated spleen volume of 2175 mL). Esophagogastroduodenoscopy revealed enlarged gastric varices with no red color sign, and the varices were larger than those 1 year prior. He was diagnosed with decompensated HCV-related liver cirrhosis and portal hypertension. We considered direct-acting antiviral (DAA) therapy; however, DAA therapy was not approved in Japan for patients with decompensated cirrhosis at that time. Hand-assisted laparoscopic splenectomy was performed to improve the worsening portal hypertension. Further, we planned the initiation of DAA therapy after surgery, when such therapy would become available. DAA therapy was approved 1 year after splenectomy. At that time, we measured the HCV-RNA level before the initiation of DAA therapy; unexpectedly, however, serum HCV-RNA was not detectable, and the virus continued to disappear during the following 4 years. His liver function (total bilirubin, albumin, and prothrombin time) and pancytopenia improved during the 5 years postoperatively. The serum aspartate and alanine aminotransferase levels normalized between 1 and 5 years postoperatively. Esophagogastroduodenoscopy showed no change in the gastric varices during the 5 years after surgery. The patient remained asymptomatic and continued to do well. CONCLUSIONS: We have presented a case of spontaneous clearance of HCV-RNA after splenectomy in a patient with cirrhosis and portal hypertension. Splenectomy may be associated with disappearance of HCV-RNA based on previous reports. More cases should be accumulated and evaluated.

The five-item modified frailty index predicts long-term outcomes in elderly patients undergoing colorectal cancer surgery.

BACKGROUND: Frailty has been globally recognized as a predictor of adverse postoperative outcomes. Frailty assessment using the five-factor modified frailty index (5-mFI) has recently gained traction; however, long-term outcomes are unknown in colorectal cancer (CRC) surgery. This study aimed to investigate whether the 5-mFI predicted long-term survival and cause of death on the basis of frailty severity in elderly patients who underwent CRC surgery and to determine the risk factors for mortality. METHODS: A total of 299 patients underwent CRC surgery with curative intent between January 2013 and December 2017. Patients were divided into three groups by the 5-mFI score: group 1 (5-mFI: 0 or 1; n = 164): no frailty; group 2 (5-mFI: 2; n = 91): moderate frailty; and group 3 (5-mFI: ≥ 3; n = 44): severe frailty. Clinicopathological variables, namely comorbidities, 5-mFI, prognostic nutrition index, operative/postoperative data, and outcome, including cause of death, were compared between the three groups. To identify factors associated with death from CRC- and non-CRC-related causes, univariate and multivariate analyses using a Cox regression model were performed. RESULTS: The immediate postoperative morbidity of patients with Clavien-Dindo grade ≥ III complications (9.1%) in group 3 was not significantly different from that in group 1 (9.1%) or group 2 (14.3%); however, the 30-day mortality rate (4.5%) in group 3 was significantly higher. Long-term disease-free survival was similar between frailty groups, suggesting that CRC surgery provides oncological benefit to patients irrespective of frailty. The 5-year survival rates in groups 1, 2, and 3 were 83.5%, 71.2%, and 47.9%, respectively, showing a significantly lower survival rate as frailty advanced. Sixty percent of the deaths in frail patients were due to respiratory failure and cardiovascular diseases. Multivariate analysis demonstrated that advanced age, higher 5-mFI score, and longer postoperative hospital stay were risk factors for mortality unrelated to CRC. Multivariate analysis also revealed that advanced tumor stage, carcinoembryonic antigen ≥ 5 ng/ml, undifferentiated tumor, and R1 resection were risk factors for CRC-related mortality. CONCLUSIONS: The 5-mFI score can predict postoperative short- and long-term outcomes and risk factors for mortality unrelated to CRC. Additionally, long-term survival was negatively associated with the 5-mFI score.

Regression analysis for predicting the elasticity of liquid crystal elastomers.

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

Colorectal cancer surgery in elderly patients 80 years and older: a comparison with younger age groups.

Background: A reduction in complications and mortality can be observed over the last few decades among elderly patients in the early postoperative period for colorectal cancer (CRC) surgery, but long-term outcomes are largely unknown. This study aimed to investigate the long-term outcomes of elderly patients 80 years and older after CRC surgery in comparison with younger age groups. The influence of clinical, oncological, and physical parameters on outcome were retrospectively analyzed. Methods: A total of 346 patients underwent CRC surgery with curative intent between January 2013 and December 2017. Patients were divided into three age groups: younger than 60 (n=47), between 60 and 79 (n=218), and 80 and older (n=81). Clinicopathological variables including comorbidity, modified frailty index, prognostic nutrition index (PNI), operative/postoperative data, and outcome including cause of death were compared among age groups. To identify factors associated with death from CRC and other causes, univariate and multivariate analyses using the Cox proportional hazards model were performed. Results: Immediate postoperative morbidity of patients with Clavien-Dindo grades of III or greater (16.0%) and the 30-day mortality rate (2.5%) of patients 80 years and older were not statistically different from those of younger age groups. Long-term disease-free survival was also similar among age groups, suggesting CRC surgery provides oncological benefit to patients irrespective of age. Multivariate analysis revealed that R1 resection, advanced tumor stage, carcinoembryonic antigen (CEA) level of >5 ng/mL, undifferentiated tumor, and longer postoperative hospital stay were risk factors for CRC death. Long-term overall survival was significantly reduced in comparison to younger age groups. Seventy percent of deaths in elderly patients during follow-up were primarily from respiratory failure and cardiovascular disease. Multivariate analysis demonstrated that advanced age, frailty, low PNI, and open procedure were risk factors for other causes of mortality. Conclusions: Elderly patients undergoing CRC surgery appeared to enjoy similar oncological benefits as younger age groups. Since both modified frailty index and PNI were correlated with mortality unrelated to CRC, preoperative assessment of these factors can be important for predicting outcome and selecting patients for prehabilitation.

Screening toward the Development of Fingerprints of Atomic Environments Using Bond-Orientational Order Parameters.

A combination of atomic numbers and bond-orientational order parameters is considered a candidate for a simple representation that involves information on both the atomic species and their positional relation. The 504 candidates are applied as the fingerprint of the molecules stored in QM9, a data set of computed geometric, energetic, electronic, and thermodynamic properties for 133 885 stable small organic molecules made up of carbon, hydrogen, oxygen, nitrogen, and fluorine atoms. To screen the fingerprints, a regression analysis of the atomic charges given by Open Babel was performed by supervised machine learning. The regression results indicate that the 60 fingerprints successfully estimate Open Babel charges. The results of the dipole moments, an example of a property expressed by charge and position, also had a high accuracy in comparison with the values computed from Open Babel charges. Therefore, the screened 60 fingerprints have the potential to precisely describe the chemical and structural information on the atomic environment of molecules.

Prediction of steady flows passing fixed cylinders using deep learning.

Considerable attention has been given to deep-learning and machine-learning techniques in an effort to reduce the computational cost of computational fluid dynamics simulation. The present paper addresses the prediction of steady flows passing many fixed cylinders using a deep-learning model and investigates the accuracy of the predicted velocity field. The deep-learning model outputs the x- and y-components of the flow velocity field when the cylinder arrangement is input. The accuracy of the predicted velocity field is investigated, focusing on the velocity profile of the fluid flow and the fluid force acting on the cylinders. The present model accurately predicts the flow when the number of cylinders is equal to or close to that set in the training dataset. The extrapolation of the prediction to a smaller number of cylinders results in error, which can be interpreted as internal friction of the fluid. The results of the fluid force acting on the cylinders suggest that the present deep-learning model has good generalization performance for systems with a larger number of cylinders.

Mining of Effective Local Order Parameters to Classify Ice Polymorphs.

Order parameters make it possible to quantify the degree of structural ordering in a material and thus to apply as the reaction coordinates during the free-energy analysis of phase or structure transitions. Furthermore, order parameters are useful in determining the local structures of molecular groups during transition stages. However, identifying or developing local order parameters (LOPs) that are sensitive for specific materials and phases is a non-trivial task. In this study, the ability of LOPs to classify the solid and liquid structures of water at coexistence or triple points is investigated with the aid of supervised machine learning. The classification accuracy of a total of 179,738,433 combinations of 493 LOPs is automatically and systematically compared for water structures at the ice Ih-Ic-liquid coexistence point and the ice III-V-liquid and ice V-VI-liquid triple points. The optimal sets of two LOPs are found for each point, and sets of three LOPs are suggested for better accuracy.

Multistep nucleation of anisotropic molecules.

Phase transition of anisotropic materials is ubiquitously observed in physics, biology, materials science, and engineering. Nevertheless, how anisotropy of constituent molecules affects the phase transition dynamics is still poorly understood. Here we investigate numerically the phase transition of a simple model system composed of anisotropic molecules, and report on our discovery of multistep nucleation of nuclei with layered positional ordering (smectic ordering), from a fluid-like nematic phase with orientational order only (no positional order). A trinity of molecular dynamics simulation, machine learning, and molecular cluster analysis yielding free energy landscapes unambiguously demonstrates the dynamics of multistep nucleation process involving characteristic metastable clusters that precede supercritical smectic nuclei and cannot be accounted for by the classical nucleation theory. Our work suggests that molecules of simple shape can exhibit rich and complex nucleation processes, and our numerical approach will provide deeper understanding of phase transitions and resulting structures in anisotropic materials such as biological systems and functional materials.

Searching for local order parameters to classify water structures at triple points.

The diversity of ice polymorphs is of interest in condensed-matter physics, engineering, astronomy, and biosphere and climate studies. In particular, their triple points are critical to elucidate the formation of each phase and transitions among phases. However, an approach to distinguish their molecular structures is lacking. When precise molecular geometries are given, order parameters are often computed to quantify the degree of structural ordering and to classify the structures. Many order parameters have been developed for specific or multiple purposes, but their capabilities have not been exhaustively investigated for distinguishing ice polymorphs. Here, 493 order parameters and their combinations are considered for two triple points involving the ice polymorphs ice III-V-liquid and ice V-VI-liquid. Supervised machine learning helps automatic and systematic searching of the parameters. For each triple point, the best set of two order parameters was found that distinguishes three structures with high accuracy. A set of three order parameters is also suggested for better accuracy.

Deep learning-based estimation of Flory-Huggins parameter of A-B block copolymers from cross-sectional images of phase-separated structures.

In this study, deep learning (DL)-based estimation of the Flory-Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25-40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Searching local order parameters to classify water structures of ice Ih, Ic, and liquid.

Identifying molecular structures of water and ice helps reveal the chemical nature of liquid and solid water. Real-space geometrical information on molecular systems can be precisely obtained from molecular simulations, but classifying the resulting structure is a non-trivial task. Order parameters are ordinarily introduced to effectively distinguish different structures. Many order parameters have been developed for various kinds of structures, such as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. Order parameters for water have also been suggested but need further study. There has been no thorough investigation of the classification capability of many existing order parameters. In this work, we investigate the capability of 493 order parameters to classify the three structures of ice: Ih, Ic, and liquid. A total of 159 767 496 combinations of the order parameters are also considered. The investigation is automatically and systematically performed by machine learning. We find the best set of two bond-orientational order parameters, Q4 and Q8, to distinguish the three structures with high accuracy and robustness. A set of three order parameters is also suggested for better accuracy.

Mining of effective local order parameters for classifying crystal structures: A machine learning study.

Determining local structures of molecular systems helps the scientific and technological understanding of the function of materials. Molecular simulations provide microscopic information on molecular systems, but analyzing the resulting local structures is a non-trivial task. Many kinds of order parameters have been developed for detecting such local structures. Bond-orientational order parameters are promising for classifying local structures and have been used to analyze systems with such structures as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. A specific set of order parameters derived from Lechner's definitional equation are widely used to classify complex local structures. However, there has been no thorough investigation of the classification capability of other Lechner parameters, despite their potential to precisely distinguish local structures. In this work, we evaluate the classification capability of 112 species of bond-orientational order parameters including Lechner's definitions. A total of 234 248 combinations of these parameters are also evaluated. The evaluation is systematically and automatically performed using machine learning techniques. To distinguish the four types of local structures, we determine the better set of two order parameters by comparing with a conventional set. A set of three order parameters is also suggested for better accuracy. Therefore, the machine learning scheme in the present study enables the systematic, accurate, and automatic mining of effective order parameters for classifying crystal structures.

Machine learning-aided analysis for complex local structure of liquid crystal polymers.

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

A rare case of symptomatic grossly-visible biliary intraepithelial neoplasia mimicking cholangiocarcinoma.

BACKGROUND: Biliary intraepithelial neoplasia (BilIN) is often distinguished by what it is not: the precancerous lesions are not mass-forming, are not the cause of bile duct obstruction, and are small enough (less than 5 mm long) to evade detection by the naked eye. Here, we describe an atypical case of BilIN resembling cholangiocarcinoma (CC) that was large enough to be identified by diagnostic imaging and presented with obstructive jaundice caused by a hematoma in the common bile duct (CBD). CASE PRESENTATION: A 64-year-old man presented to our hospital with upper abdominal pain and anorexia. Initial laboratory examinations revealed increased total bilirubin and a computed tomography (CT) scan revealed a dilated CBD. Gastroenterologists performed an endoscopic sphincterotomy (EST), which revealed that the cause of obstructive jaundice was a hematoma in the CBD. Enhanced CT scan and magnetic resonance cholangiopancreatography (MRCP) performed after the hematoma was drained showed improved dilation of the CBD and an enhanced wall thickness of bile duct measuring 25 × 10 mm at the union of the cystic and common hepatic ducts. A cholangioscope detected an elevated tumor covered by sludge in the CBD, and we performed an extrahepatic bile duct resection and cholecystectomy. The postoperative course was uneventful and the pathological examination of the resected tumor revealed that although the ulcerated lesion had inflammatory granulation tissue, it did not contain the components of invasive carcinoma. Many consecutive intraepithelial micropapillary lesions spread around the ulcerated lesion, and the epithelial cells showed an increased nucleus-to-cytoplasm ratio, nuclear hyperchromasia, and architectural atypia. The pathological diagnosis was BilIN-1 to -2. Immunohistochemical staining showed that S100P was slightly expressed and MUC5AC was positive, while MUC1 was negative and p53 was not overexpressed. CONCLUSION: We experienced an atypical case of BilIN mimicking CC that presented with obstructive jaundice caused by a hematoma in the CBD. Our case suggested that the occurrence of BilIN can be triggered by factors other than inflammation, and can grow to a size large enough to be detected by image analyses.

Development of Coarse-Grained Liquid-Crystal Polymer Model with Efficient Electrostatic Interaction: Toward Molecular Dynamics Simulations of Electroactive Materials.

Liquid-crystal polymers (LCPs) are well known materials for functional sensor and actuators, because of their high-responsiveness to an electric field. Owing to their complex physical nature, however, the prediction of the functions of LCPs is a challenge. To attack this problem from a molecular point of view, a simulation study is a promising approach. In this work, for future applications of molecular dynamics simulations to problems involving an electric field, we develop an LCP model which consists of coarse-grained mesogenic molecules and smeared charges. For the smearing function of the electrostatic force, the Gauss error function is introduced. This smearing is optimized to attain a reasonable accuracy for phase transition phenomena of liquid crystal while numerical instabilities arising from the singularity of the Coulomb potential are circumvented. For swelling systems, our LCP model exhibits the characteristics of both liquid crystals and unentangled polymer chains; orientational order of the mesogenic units and Rouse-like relaxation dynamics. Our coarse-grained LCP model successfully incorporates electric charges and dipoles and is therefore applicable to problems concerning an electric field.

Liver Transplantation With Older Donors: A Comparison With Younger Donors in a Context of Organ Shortage.

BACKGROUND: Older liver grafts have been considered in the past decade due to organ shortage. The aim was to compare outcomes after liver transplantation with either younger or older donors. METHODS: Patients transplanted in our center between 2004 and 2014 with younger donors (younger than 60 years; n = 253) were compared with older donors (older than 75 years; n = 157). Multiorgan transplantations, split grafts, or non-heart-beating donors were not included. RESULTS: Donors in the older group were mostly women deceased from stroke, and only 3 patients had experienced cardiac arrest. Liver tests were significantly better in the older group than in the younger group. There was no difference regarding cold ischemia time, model for end-stage liver disease score, and steatosis. There was no significant difference regarding primary nonfunction and dysfunction, hepatic artery and biliary complications, and retransplantation rates. Graft survival was not different (65% and 64% in the older and younger groups, P = 0.692). Within the older group, hepatitis C infection, retransplantation, and emergency transplantation were associated with poor graft survival. CONCLUSIONS: Provided normal liver tests and the absence of cardiac arrest in donors, older liver grafts (>75 years) may be safely attributed to non-hepatitis C-infected recipients in the setting of a first and nonurgent transplantation.

A pilot study of operational tolerance with a regulatory T-cell-based cell therapy in living donor liver transplantation.

UNLABELLED: Potent immunosuppressive drugs have significantly improved early patient survival after liver transplantation (LT). However, long-term results remain unsatisfactory because of adverse events that are largely associated with lifelong immunosuppression. To solve this problem, different strategies have been undertaken to induce operational tolerance, for example, maintenance of normal graft function and histology without immunosuppressive therapy, but have achieved limited success. In this pilot study, we aimed to induce tolerance using a novel regulatory T-cell-based cell therapy in living donor LT. Adoptive transfer of an ex vivo-generated regulatory T-cell-enriched cell product was conducted in 10 consecutive adult patients early post-LT. Cells were generated using a 2-week coculture of recipient lymphocytes with irradiated donor cells in the presence of anti-CD80/86 monoclonal antibodies. Immunosuppressive agents were tapered from 6 months, reduced every 3 months, and completely discontinued by 18 months. After the culture, the generated cells displayed cell-number-dependent donor-specific inhibition in the mixed lymphocyte reaction. Infusion of these cells caused no significant adverse events. Currently, all patients are well with normal graft function and histology. Seven patients have completed successful weaning and cessation of immunosuppressive agents. At present, they have been drug free for 16-33 months; 4 patients have been drug free for more than 24 months. The other 3 recipients with autoimmune liver diseases developed mild rejection during weaning and then resumed conventional low-dose immunotherapy. CONCLUSIONS: A cell therapy using an ex vivo-generated regulatory T-cell-enriched cell product is safe and effective for drug minimization and operational tolerance induction in living donor liver recipients with nonimmunological liver diseases. (Hepatology 2016;64:632-643).

Repeat endovascular treatment of recurring hepatic artery stenoses in orthotopic liver transplantation.

Hepatic artery stenosis (HAS) is a complication that impacts the results of orthotopic liver transplantation (OLT). Interventional radiological techniques are important therapeutic options for HAS. The aim of this retrospective study was to evaluate the outcome of repeated radiological treatments in recurring HAS after OLT. Of the 941 patients who underwent OLT at our center from January 1998 to September 2010, 48 (5%) were diagnosed with HAS, 37 (77%) of whom underwent transluminal interventional therapy with the placement of an endovascular stent. Success rate, complications, hepatic artery patency and follow-up were reviewed. After stent placement, artery patency was achieved in all patients. Three patients developed complications, including arterial dissection and hematoma. HAS recurrence was observed in 9 patients (24%), and hepatic artery thrombosis (HAT) occurred in 4 (11%). Radiological interventions were repeated 10 times in 8 patients without complications. At a median follow-up of 66 months (range 10-158), hepatic artery patency was observed in 35 cases (94.6%). The 5-year rates for graft and patient survival were 82.3% and 87.7%, respectively. Restenosis may occur in one-third of patients after endovascular treatment for thrombosis and HAS, but the long-term outcomes of iterative radiological treatment for HAS indicate a high rate of success.