Incidence, risk factors and prognostic factors of acute renal failure in patients admitted to an intensive care unit

The objective of the present study was to assess the incidence, risk factors and outcome of patients who develop acute renal failure (ARF) in intensive care units. In this prospective observational study, 221 patients with a 48-h minimum stay, 18-year-old minimum age and absence of overt acute or chronic renal failure were included. Exclusion criteria were organ donors and renal transplantation patients. ARF was defined as a creatinine level above 1.5 mg/dL. Statistics were performed using Pearsons' chi2 test, Student t-test, and Wilcoxon test. Multivariate analysis was run using all variables with P < 0.1 in the univariate analysis. ARF developed in 19.0% of the patients, with 76.19% resulting in death. Main risk factors (univariate analysis) were: higher intra-operative hydration and bleeding, higher death risk by APACHE II score, logist organ dysfunction system on the first day, mechanical ventilation, shock due to systemic inflammatory response syndrome (SIRS)/sepsis, noradrenaline use, and plasma creatinine and urea levels on admission. Heart rate on admission (OR = 1.023 (1.002-1.044)), male gender (OR = 4.275 (1.340-13642)), shock due to SIRS/sepsis (OR = 8.590 (2.710-27.229)), higher intra-operative hydration (OR = 1.002 (1.000-1004)), and plasma urea on admission (OR = 1.012 (0.980-1044)) remained significant (multivariate analysis). The mortality risk factors (univariate analysis) were shock due to SIRS/sepsis, mechanical ventilation, blood stream infection, potassium and bicarbonate levels. Only potassium levels remained significant (P = 0.037). In conclusion, ARF has a high incidence, morbidity and mortality when it occurs in intensive care unit. There is a very close association with hemodynamic status and multiple organ dysfunction.

Vascular dysfunction by myofibroblast activation in patients with idiopathic pulmonary fibrosis and prognostic significance

In this study, we demonstrated the importance of telomerase protein expression and determined the relationships among telomerase, endothelin-1 (ET-1) and myofibroblasts during early and late remodeling of parenchymal and vascular areas in usual interstitial pneumonia (UIP) using 27 surgical lung biopsies from patients with idiopathic pulmonary fibrosis (IPF). Telomerase+, myofibroblasts α-SMA+, smooth muscle cells caldesmon+, endothelium ET-1+ cellularity, and fibrosis severity were evaluated in 30 fields covering normal lung parenchyma, minimal fibrosis (fibroblastic foci), severe (mural) fibrosis, and vascular areas of UIP by the point-counting technique and a semiquantitative score. The impact of these markers was determined in pulmonary functional tests and follow-up until death from IPF. Telomerase and ET-1 expression was significantly increased in normal and vascular areas compared to areas of fibroblast foci. Telomerase and ET-1 expression was inversely correlated with minimal fibrosis in areas of fibroblast foci and directly associated with severe fibrosis in vascular areas. Telomerase activity in minimal fibrosis areas was directly associated with diffusing capacity of the lung for oxygen/alveolar volume and ET-1 expression and indirectly associated with diffusing capacity of the lungs for carbon monoxide and severe fibrosis in vascular areas. Cox proportional hazards regression revealed a low risk of death for females with minimal fibrosis displaying high telomerase and ET-1 expression in normal areas. Vascular dysfunction by telomerase/ET-1 expression was found earlier than vascular remodeling by myofibroblast activation in UIP with impact on IPF evolution, suggesting that strategies aimed at preventing the effect of these mediators may have a greater impact on patient outcome.

p16 (INK4a) has clinicopathological and prognostic impact on oropharynx and larynx squamous cell carcinoma

CDKN2A encodes proteins such as p16 (INK4a), which negatively regulate the cell-cycle. Molecular genetic studies have revealed that deletions in CDKN2A occur frequently in cancer. Although p16 (INK4a) may be involved in tumor progression, the clinical impact and prognostic implications in head and neck squamous cell carcinoma (HNSCC) are controversial. The objective of this study was to evaluate the frequency of the immunohistochemical expression of p16 (INK4a) in 40 oropharynx and 35 larynx from HNSCC patients treated in a single institution and followed-up at least for 10 years in order to explore potential associations with clinicopathological outcomes and prognostic implications. Forty cases (53.3%) were positive for p16 (INK4a) and this expression was more intense in non-smoking patients (P = 0.050), whose tumors showed negative vascular embolization (P = 0.018), negative lymphatic permeation (P = 0.002), and clear surgical margins (P = 0.050). Importantly, on the basis of negative p16 (INK4a) expression, it was possible to predict a probability of lower survival (P = 0.055) as well as tumors presenting lymph node metastasis (P = 0.050) and capsular rupture (P = 0.0010). Furthermore, increased risk of recurrence was observed in tumors presenting capsular rupture (P = 0.0083). Taken together, the alteration in p16 (INK4a) appears to be a common event in patients with oropharynx and larynx squamous cell carcinoma and the negative expression of this protein correlated with poor prognosis.

Identification and validation of novel prostate cancer biomarkers

Prostate cancer (PCa) has emerged as the most commonly diagnosed lethal cancer in European men. PCa is a heterogeneous cancer that in the majority of the cases is slow growing: consequently, these patients would not need any medical treatment. Currently, the measurement of prostate-specific antigen (PSA) from blood by immunoassay followed by digital rectal examination and a pathological examination of prostate tissue biopsies are the most widely used methods in the diagnosis of PCa. These methods suffer from a lack of sensitivity and specificity that may cause either missed cancers or overtreatment as a consequence of over-diagnosis. Therefore, more reliable biomarkers are needed for a better discrimination between indolent and potentially aggressive cancers. The aim of this thesis was the identification and validation of novel biomarkers for PCa. The mRNA expression level of 14 genes including AMACR, AR, PCA3, SPINK1, TMPRSS2-ERG, KLK3, ACSM1, CACNA1D, DLX1, LMNB1, PLA2G7, RHOU, SPON2, and TDRD1 was measured by a truly quantitative reverse transcription PCR in different prostate tissue samples from men with and without PCa. For the last eight genes the function of the genes in PCa progression was studied by a specific siRNA knockdown in PC-3 and VCaP cells. The results from radical prostatectomy and cystoprostatectomy samples showed statistically significant overexpression for all the target genes, except for KLK3 in men with PCa compared with men without PCa. Statistically significant difference was also observed in low versus high Gleason grade tumors (for PLA2G7), PSA relapse versus no relapse (for SPON2), and low versus high TNM stages (for CACNA1D and DLX1). Functional studies and siRNA silencing results revealed a cytotoxicity effect for the knock-down of DLX1, PLA2G7, and RHOU, and altered tumor cell invasion for PLA2G7, RHOU, ACSM1, and CACNA1D knock-down in 3D conditions. In addition, effects on tumor cell motility were observed after silencing PLA2G7 and RHOU in 2D monolayer cultures. Altogether, these findings indicate the possibility of utilizing these new markers as diagnostic and prognostic markers, and they may also represent therapeutic targets for PCa.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

A Diagnostic and Modeling Study on the Onset of Summer Monsoon over South Asia

This study focuses on the onset of southwest monsoon over Kerala. India Meteorological Department (IMD) has been using a semi-objective method to define monsoon onset. The main objectives of the study are to understand the monsoon onset processes, to simulate monsoon onset in a GCM using as input the atmospheric conditions and Sea Surface Temperature, 10 days earlier to the onset, to develop a method for medium range prediction of the date of onset of southwest monsoon over Kerala and to examine the possibility of objectively defining the date of Monsoon Onset over Kerala (MOK). It gives a broad description of regional monsoon systems and monsoon onsets over Asia and Australia. Asian monsoon includes two separate subsystems, Indain monsoon and East Asian monsoon. It is seen from this study that the duration of the different phases of the onset process are dependent on the period of ISO. Based on the study of the monsoon onset process, modeling studies can be done for better understanding of the ocean-atmosphere interaction especially those associated with the warm pool in the Bay of Bengal and the Arabian Sea.

Boundaries and Topological Algorithms

This thesis develops a model for the topological structure of situations. In this model, the topological structure of space is altered by the presence or absence of boundaries, such as those at the edges of objects. This allows the intuitive meaning of topological concepts such as region connectivity, function continuity, and preservation of topological structure to be modeled using the standard mathematical definitions. The thesis shows that these concepts are important in a wide range of artificial intelligence problems, including low-level vision, high-level vision, natural language semantics, and high-level reasoning.

Application of the entropy technique and genetic algorithms to construction site layout planning of medium-size projects

Genetic algorithms (GAs) have been introduced into site layout planning as reported in a number of studies. In these studies, the objective functions were defined so as to employ the GAs in searching for the optimal site layout. However, few studies have been carried out to investigate the actual closeness of relationships between site facilities; it is these relationships that ultimately govern the site layout. This study has determined that the underlying factors of site layout planning for medium-size projects include work flow, personnel flow, safety and environment, and personal preferences. By finding the weightings on these factors and the corresponding closeness indices between each facility, a closeness relationship has been deduced. Two contemporary mathematical approaches - fuzzy logic theory and an entropy measure - were adopted in finding these results in order to minimize the uncertainty and vagueness of the collected data and improve the quality of the information. GAs were then applied to searching for the optimal site layout in a medium-size government project using the GeneHunter software. The objective function involved minimizing the total travel distance. An optimal layout was obtained within a short time. This reveals that the application of GA to site layout planning is highly promising and efficient.

Parameter estimation based on stacked regression and evolutionary algorithms

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors are used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.