Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Does hepatocellular carcinoma in non-alcoholic steatohepatitis exist in cirrhotic and non-cirrhotic patients?

Non-alcoholic steatohepatitis (NASH) has been associated with hepatocellular carcinoma (HCC) often arising in histologically advanced disease when steatohepatitis is not active (cryptogenic cirrhosis). Our objective was to characterize patients with HCC and active, histologically defined steatohepatitis. Among 394 patients with HCC detected by ultrasound imaging over 8 years and staged by the Barcelona Clinic Liver Cancer (BCLC) criteria, we identified 7 cases (1.7%) with HCC occurring in the setting of active biopsy-proven NASH. All were negative for other liver diseases such as hepatitis C, hepatitis B, autoimmune hepatitis, Wilson disease, and hemochromatosis. The patients (4 males and 3 females, age 63 ± 13 years) were either overweight (4) or obese (3); 57% were diabetic and 28.5% had dyslipidemia. Cirrhosis was present in 6 of 7 patients, but 1 patient had well-differentiated HCC in the setting of NASH without cirrhosis (fibrosis stage 1) based on repeated liver biopsies, the absence of portal hypertension by clinical and radiographic evaluations and by direct surgical inspection. Among the cirrhotic patients, 71.4% were clinically staged as Child A and 14.2% as Child B. Tumor size ranged from 1.0 to 5.2 cm and 5 of 7 patients were classified as early stage; 46% of all nodules were hyper-echoic and 57% were <3 cm. HCC was well differentiated in 1/6 and moderately differentiated in 5/6. Alpha-fetoprotein was <100 ng/mL in all patients. HCC in patients with active steatohepatitis is often multifocal, may precede clinically advanced disease and occurs without diagnostic levels of alpha-fetoprotein. Importantly, HCC may occur in NASH in the absence of cirrhosis. More aggressive screening of NASH patients may be warranted.

249 TP53 mutation has high prevalence and is correlated with larger and poorly differentiated HCC in Brazilian patients

Background: Ser-249 TP53 mutation (249(Ser)) is a molecular evidence for aflatoxin-related carcinogenesis in Hepatocellular Carcinoma (HCC) and it is frequent in some African and Asian regions, but it is unusual in Western countries. HBV has been claimed to add a synergic effect on genesis of this particular mutation with aflatoxin. The aim of this study was to investigate the frequency of 249Ser mutation in HCC from patients in Brazil. Methods: We studied 74 HCC formalin fixed paraffin blocks samples of patients whom underwent surgical resection in Brazil. 249Ser mutation was analyzed by RFLP and DNA sequencing. HBV DNA presence was determined by Real-Time PCR. Results: 249Ser mutation was found in 21/74 (28%) samples while HBV DNA was detected in 13/74 (16%). 249Ser mutation was detected in 21/74 samples by RFLP assay, of which 14 were confirmed by 249Ser mutant-specific PCR, and 12 by nucleic acid sequencing. All HCC cases with p53-249ser mutation displayed also wild-type p53 sequences. Poorly differentiated HCC was more likely to have 249Ser mutation (OR = 2.415, 95% CI = 1.001 - 5.824, p = 0.05). The mean size of 249Ser HCC tumor was 9.4 cm versus 5.5 cm on wild type HCC (p = 0.012). HBV DNA detection was not related to 249Ser mutation. Conclusion: Our results indicate that 249Ser mutation is a HCC important factor of carcinogenesis in Brazil and it is associated to large and poorly differentiated tumors.

Treatment of advanced hepatocellular carcinoma with very low levels of amplitude-modulated electromagnetic fields

BACKGROUND: Therapeutic options for patients with advanced hepatocellular carcinoma (HCC) are limited. There is emerging evidence that the growth of cancer cells may be altered by very low levels of electromagnetic fields modulated at specific frequencies. METHODS: A single-group, open-label, phase I/II study was performed to assess the safety and effectiveness of the intrabuccal administration of very low levels of electromagnetic fields amplitude modulated at HCC-specific frequencies in 41 patients with advanced HCC and limited therapeutic options. Three-daily 60-min outpatient treatments were administered until disease progression or death. Imaging studies were performed every 8 weeks. The primary efficacy end point was progression-free survival >= 6 months. Secondary efficacy end points were progression-free survival and overall survival. RESULTS: Treatment was well tolerated and there were no NCI grade 2, 3 or 4 toxicities. In all, 14 patients (34.1%) had stable disease for more than 6 months. Median progression-free survival was 4.4 months (95% CI 2.1-5.3) and median overall survival was 6.7 months (95% CI 3.0-10.2). There were three partial and one near complete responses. CONCLUSION: Treatment with intrabuccally administered amplitude-modulated electromagnetic fields is safe, well tolerated, and shows evidence of antitumour effects in patients with advanced HCC. British Journal of Cancer (2011) 105, 640-648. doi:10.1038/bjc.2011.292 www.bjcancer.com Published online 9 August 2011 (C) 2011 Cancer Research UK

Nodules Less Than 20 mm and Vascular Invasion are Predictors of Survival in Small Hepatocellular Carcinoma

Background: The aims of this study were to analyze the overall survival of patients with cirrhosis and small hepatocellular carcinoma (HCC) and identify independent pretreatment predictors of survival in Brazil. Methods: Between 1998 and 2003, 74 patients with cirrhosis and small HCC were evaluated. Predictors of survival were identified using the Kaplan-Meier survival curves and the Cox model. Results: The overall survival rates were 80%, 41%, and 17% at 12, 36, and 60 months, respectively. The mean length of follow-up after HCC diagnosis was 23 months (median 22 mo, range: I to 86 mo) for the entire group. Univariate analysis showed that model for endstage liver disease (MELD) score (P = 0.016), Child-Pugh classification (P = 0.007), alpha-fetoprotein level (P = 0.006), number of nodules (P = 0.041), tumor diameter (P = 0.009), and vascular invasion (P < 0.0001) were significant predictors Of Survival. Cox regression analysis identified vascular invasion (relative risk = 14.60, confidence interval 95% = 3.3-64.56, P < 0.001) and tumor size > 20 mm (relative risk = 2.14, confidence interval 95% = 1.07-4.2, P = 0.030) as independent predictors of decreased survival. Treatment of HCC was related to increased overall survival. Conclusions: Identification of HCC smaller than 20 mm is associated with longer survival. Presence of vascular invasion, even in small tumors, maybe associated with poor prognosis. Treatment of small tumors Of LIP to 20 mm diameter is related to increased survival.

A rodent model of NASH with cirrhosis, oval cell proliferation and hepatocellular carcinoma

Background/Aims: Hepatocellular carcinoma (HCC) is a well recognized complication of advanced NASH (non-alcoholic steatohepatitis). We sought to produce a rat model of NASH, cirrhosis and HCC. Methods: Adult Sprague-Dawley rats, weighing 250-300 g, were fed a choline-deficient, high trans-fat diet and exposed to DEN in drinking water. After 16 weeks, the animals underwent liver ultrasound (US), sacrifice and assessment by microscopy, immunohistochemistry and transmission electron microscopy (TEM). Results: US revealed steatosis and focal lesions in 6 of 7. All had steatohepatitis defined as inflammation, advanced fibrosis and ballooning with Mallory-Denk bodies (MDB) with frank cirrhosis in 6. Areas of more severe injury were associated with anti-CK19 positive ductular reaction. HCC, present in all, were macro-trabecullar or solid with polyhedral cells with foci of steatosis and ballooned cells. CK19 was positive in single or solid nests of oval cells and in neoplastic hepatocytes. TEM showed ballooning with small droplet fat, dilated endoplasmic reticulum and MDB in non-neoplastic hepatocytes and small droplet steatosis in some cancer cells. Conclusions: This model replicated many features of NASH including steatohepatitis with ballooning, fibrosis, cirrhosis and hepatocellular carcinoma. Oval cell proliferation was evident and the presence anti-CK 19 positivity in the cancer suggests oval cell origin of the malignancy. (C) 2008 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Arginase-1 A New Immunohistochemical Marker of Hepatocytes and Hepatocellular Neoplasms

The distinction of hepatocellular carcinoma (HCC) from metastatic tumor in the liver often presents a diagnostic challenge that carries significant impact on prognostication and therapy. The number of diagnostically useful immunohistochemical markers of hepatocytes is limited to hepatocyte paraffin antigen (HepPar-1), polyclonal carcinoembryonic antigen, and CD10, with alpha-fetoprotein and glypican-3 labeling HCCs. Arginase-1 (Arg-1) is a binuclear manganese metalloenzyme that catalyzes the hydrolysis of arginine to ornithine and urea. We used immunohistochemistry to compare the sensitivity of Arg-1 to that of HepPar-1 in 151 HCCs. We found that the overall sensitivities of Arg-1 and HepPar-1 are 96.0% and 84.1%, respectively. The sensitivities of Arg-1 in well, moderately, and poorly differentiated HCCs are 100%, 96.2%, and 85.7%, respectively, whereas, in comparison, HepPar-1 demonstrated sensitivities of 100%, 83.0%, and 46.4% for well, moderately, and poorly differentiated tumors, respectively. There were no HCCs in our study that were reactive for HepPar-1 but nonreactive for Arg-1. We also examined Arg-1 expression in nonhepatocellular tumors, including many that are potential mimics of HCC (renal cell carcinomas, neuroendocrine tumors, melanomas, gastric adenocarcinomas, and adrenocortical carcinomas) and found that only 2 non-HCC tumors were reactive for Arg-1. Arg-1 represents a sensitive and specific marker of benign and malignant hepatocytes that may ultimately prove to be a useful diagnostic tool in routine surgical pathology practice.

Treatment of Hepatocellular Carcinoma With Liver Transplantation: A Single-Center Experience From Brazil

Introduction. Orthotopic liver transplantation (OLT) is the treatment of choice of hepatocellular carcinoma (HCC) for patients with cirrhosis, mainly those with early HCC. Herein we have present the clinical characteristics and outcomes of cirrhotic patients with HCC who underwent OLT from cadaveric donors in our institution. Methods. From May 2001 to May 2009, we performed 121 OLT including 24 patients (19.8%) with cirrhosis and HCC within the Milan criteria. In 4 cases, HCC was an incidental finding in the explants. Results. The patients` average age was 55 +/- 10 years, including 82% men. Fifty percent of patients were Child class B or C. The average Model for End Stage Liver Disease for Child A, B, and C categories were 11, 15, and 18, respectively. The HCC diagnosis was made by 2 dynamic images in 16 cases; 1 dynamic image plus alphafetoprotein >400 ng/mL in 4; and 4 by histologic confirmation. Twenty patients received a locoregional treatment before OLT: 6 percutaneous ethanol injection, 9 transarterial chemoembolization, 1 transarterial embolization, and 4 a combination of these modalities. The median follow-up after OLT was 19.7 months (range, 1-51). A vascular invasion was observed in the explant of 1 patient, who developed an HCC recurrence and succumbed at 8 months after OLT. Two further patients, without vascular invasion or satellite tumor displayed tumor recurrences at 7 and 3 months after OLT, and death at 2 and 1 month after the diagnosis. The remaining 25 patients have not shown a tumor recurrence. Conclusion. In the present evaluation, OLT patients with early HCC and no vascular invasion showed satisfactory results and good disease-free survival. Strictly following the Milan criteria for liver transplantation in patients with HCC greatly reduces but does not completely avoid, the chances of tumor recurrence.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Chemoprevention of rat hepatocarcinogenesis with histone deacetylase inhibitors: Efficacy of tributyrin, a butyric acid prodrug

Hepatocellular carcinoma (HCC) ranks in prevalence and mortality among top 10 cancers worldwide. Butyric acid (BA), a member of histone deacetylase inhibitors (HDACi) has been proposed as an anticareinogenic agent. However, its short half-life is a therapeutical limitation. This problem could be circumvented with tributyrin (TB), a proposed BA prodrug. To investigate TB effectiveness for chemoprevention, rats were treated with the compound during initial phases of ""resistant hepatocyte"" model of hepatocarcinogenesis, and cellular and molecular parameters were evaluated. TB inhibited (p < 0.05) development of hepatic preneoplastic lesions (PNL) including persistent ones considered HCC progression sites. TB increased (p < 0.05) PNL remodeling, a process whereby they tend to disappear. TB did not inhibit cell proliferation in PNL, but induced (p < 0.05) apoptosis in remodeling ones. Compared to controls, rats treated with TB presented increased (P < 0.05) hepatic levels of BA indicating its effectiveness as a prodrug. Molecular mechanisms of TB-induced hepatocarcinogenesis chemoprevention were investigated. TB increased (p < 0.05) hepatic nuclear histone H3K9 hyperacetylation specifically in PNL and p21 protein expression, which could be associated with inhibitory HDAC effects. Moreover, it reduced (p < 0.05) the frequency of persistent PNL with aberrant cytoplasmic p53 accumulation, an alteration associated with increased malignancy. Original data observed in our study support the effectiveness of TB as a prodrug of BA and as an HDACi in hepatocarcinogenesis chemoprevention. Besides histone acetylation and p21 restored expression, molecular mechanisms involved with TB anticarcinogenic actions could also be related to modulation of p53 pathways. (C) 2008 Wiley-Liss, Inc.

Immunohistochemical detection of polyductin and co-localization with liver progenitor cell markers during normal and abnormal development of the intrahepatic biliary system and in adult hepatobiliary carcinomas

The longest open reading frame of PKHD1 (polycystic kidney and hepatic disease 1), the autosomal recessive polycystic kidney disease (ARPKD) gene, encodes a single-pass, integral membrane protein named polyductin or fibrocystin. A fusion protein comprising its intracellular C-terminus, FP2, was previously used to raise a polyclonal antiserum shown to detect polyductin in several human tissues, including liver. In the current study, we aimed to investigate by immunohistochemistry the detailed polyductin localization pattern in normal (ductal plate [DP], remodelling ductal plate [RDP], remodelled bile ducts) and abnormal development of the primitive intrahepatic biliary system, known as ductal plate malformation (DPM). This work also included the characterization of polyductin expression profile in various histological forms of neonatal and infantile cholestasis, and in cholangiocellular carcinoma (CCC) and hepatocellular carcinoma (HCC). We detected polyductin expression in the intrahepatic biliary system during the DP and the RDP stages as well as in DPM. No specific staining was found at the stage of remodelled bile ducts. Polyductin was also detected in liver biopsies with neonatal cholestasis, including mainly biliary atresia and neonatal hepatitis with ductular reaction as well as congenital hepatic fibrosis. In addition, polyductin was present in CCC, whereas it was absent in HCC. Polyductin was also co-localized in some DP cells together with oval stem cell markers. These results represent the first systematic study of polyductin expression in human pathologies associated with abnormal development of intrahepatic biliary tree, and support the following conclusions: (i) polyductin expression mirrors developmental properties of the primitive intrahepatic biliary system; (ii) polyductin is re-expressed in pathological conditions associated with DPM and (iii) polyductin might be a potential marker to distinguish CCC from HCC.