Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Detorque evaluation of dental abutment screws after immersion in a fluoridated artificial saliva solution

Purpose: Implant-abutment connections still present failures in the oral cavity due to the loosening of mechanical integrity by detorque and corrosion of the abutment screws. The objective of this study was to evaluate the detorque of dental abutment screws before and after immersion in fluoridated solutions. Materials and Methods: Five commercial implant-abutment assemblies were assessed in this investigation: (C) Conex˜aoR , (E) EmfilsR , (I) INPR , (S) SINR , and (T) Titanium FixR . The implants were embedded in an acrylic resin and then placed in a holding device. The abutments were first connected to the implants and torqued to 20Ncmusing a handheld torque meter. The detorque values of the abutments were evaluated after 10 minutes. After applying a second torque of 20 Ncm, implant-abutment assemblies were withdrawn every 3 hours for 12 hours in a fluoridated solution over a period of 90 days. After that period, detorque of the abutments was examined. Scanning electronicmicroscopy (SEM) associated to energy dispersive spectroscopy (EDS) was applied to inspect the surfaces of abutments. Results: Detorque values of systems C, E, and I immersed in the fluoridated solution were significantly higher than those of the initial detorque. ANOVA demonstrated no significant differences in detorque values between designs S and T. Signs of localized corrosion could not be detected by SEM although chemical analysis by EDS showed the presence of elements involved in corrosive processes. Conclusion: An increase of detorque values recorded on abutments after immersion in fluoridated artificial saliva solutions was noticed in this study. Regarding chemical analysis, such an increase of detorque can result from a corrosion layer formed between metallic surfaces at static contact in the implant-abutment joint during immersion in the fluoridated solutions.

PEEP-ZEEP technique: cardiorespiratory repercussions in mechanically ventilated patients submitted to a coronary artery bypass graft surgery

Abstract Background The PEEP-ZEEP technique is previously described as a lung inflation through a positive pressure enhancement at the end of expiration (PEEP), followed by rapid lung deflation with an abrupt reduction in the PEEP to 0 cmH2O (ZEEP), associated to a manual bilateral thoracic compression. Aim To analyze PEEP-ZEEP technique's repercussions on the cardio-respiratory system in immediate postoperative artery graft bypass patients. Methods 15 patients submitted to a coronary artery bypass graft surgery (CABG) were enrolled prospectively, before, 10 minutes and 30 minutes after the technique. Patients were curarized, intubated, and mechanically ventilated. To perform PEEP-ZEEP technique, saline solution was instilled into their orotracheal tube than the patient was reconnected to the ventilator. Afterwards, the PEEP was increased to 15 cmH2O throughout 5 ventilatory cycles and than the PEEP was rapidly reduced to 0 cmH2O along with manual bilateral thoracic compression. At the end of the procedure, tracheal suction was accomplished. Results The inspiratory peak and plateau pressures increased during the procedure (p < 0.001) compared with other pressures during the assessment periods; however, they were within lung safe limits. The expiratory flow before the procedure were 33 ± 7.87 L/min, increasing significantly during the procedure to 60 ± 6.54 L/min (p < 0.001), diminishing to 35 ± 8.17 L/min at 10 minutes and to 36 ± 8.48 L/min at 30 minutes. Hemodynamic and oxygenation variables were not altered. Conclusion The PEEP-ZEEP technique seems to be safe, without alterations on hemodynamic variables, produces elevated expiratory flow and seems to be an alternative technique for the removal of bronchial secretions in patients submitted to a CABG.

Evaluating methods for the isolation of marine-derived fungal strains and production of bioactive secondary metabolites

In the present investigation we evaluate methods for the isolation and growth of marine-derived fungal strains in artificial media for the production of secondary metabolites. Inoculation of marine macroorganisms fragments in Petri dishes proved to be the most convenient procedure for the isolation of the largest number of strains. Among the growth media used, 3% malt extract showed the best result for strains isolation and growth, and yielded the largest number of strains from marine macroorganisms. The percentage of strains isolated using each of the growth media which yielded cytotoxic and/or antibiotic extracts was in the range of 23-35%, regardless of the growth media used. Further investigation of extracts obtained from different marine-derived fungal strains yielded several bioactive secondary metabolites, among which (E)-4-methoxy-5-(3-methoxybut-1-enyl)-6-methyl-2H-pyran-2-one is a new metabolite isolated from the Penicillium paxilli strain Ma(G)K.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Bottle effects, toxicity and cell viability during incubation experiments to asses community respiration

Máster en Oceanografía

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Cell-free cryopreserved arterial allografts from multiorgan donors: a new strategy to fabricate artificial blood vessels suited for peripheral vascular surgery

Critical lower limb ischemia is a severe disease. A common approach is infrainguinal bypass. Synthetic vascular prosthesis, are good conduits in high-flow low-resistance conditions but have difficulty in their performance as small diameter vessel grafts. A new approach is the use of native decellularized vascular tissues. Cell-free vessels are expected to have improved biocompatibility when compared to synthetic and are optimal natural 3D matrix templates for driving stem cell growth and tissue assembly in vivo. Decellularization of tissues represent a promising field for regenerative medicine, with the aim to develop a methodology to obtain small-diameter allografts to be used as a natural scaffold suited for in vivo cell growth and pseudo-tissue assembly, eliminating failure caused from immune response activation. Material and methods. Umbilical cord-derived mesenchymal cells isolated from human umbilical cord tissue were expanded in advanced DMEM. Immunofluorescence and molecular characterization revealed a stem cell profile. A non-enzymatic protocol, that associate hypotonic shock and low-concentration ionic detergent, was used to decellularize vessel segments. Cells were seeded cell-free scaffolds using a compound of fibrin and thrombin and incubated in DMEM, after 4 days of static culture they were placed for 2 weeks in a flow-bioreactor, mimicking the cardiovascular pulsatile flow. After dynamic culture, samples were processed for histological, biochemical and ultrastructural analysis. Discussion. Histology showed that the dynamic culture cells initiate to penetrate the extracellular matrix scaffold and to produce components of the ECM, as collagen fibres. Sirius Red staining showed layers of immature collagen type III and ultrastructural analysis revealed 30 nm thick collagen fibres, presumably corresponding to the immature collagen. These data confirm the ability of cord-derived cells to adhere and penetrate a natural decellularized tissue and to start to assembly into new tissue. This achievement makes natural 3D matrix templates prospectively valuable candidates for clinical bypass procedures

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

Inverse Static Analysis of Massive Parallel Arrays of Three-State Actuators via Artificial Intelligence

Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

Complex chemical dynamics through engineering-like methods

Most of the problems in modern structural design can be described with a set of equation; solutions of these mathematical models can lead the engineer and designer to get info during the design stage. The same holds true for physical-chemistry; this branch of chemistry uses mathematics and physics in order to explain real chemical phenomena. In this work two extremely different chemical processes will be studied; the dynamic of an artificial molecular motor and the generation and propagation of the nervous signals between excitable cells and tissues like neurons and axons. These two processes, in spite of their chemical and physical differences, can be both described successfully by partial differential equations, that are, respectively the Fokker-Planck equation and the Hodgkin and Huxley model. With the aid of an advanced engineering software these two processes have been modeled and simulated in order to extract a lot of physical informations about them and to predict a lot of properties that can be, in future, extremely useful during the design stage of both molecular motors and devices which rely their actions on the nervous communications between active fibres.

Design methods and geometric modelling of scaffolds for bone tissue engineering

Every year, thousand of surgical treatments are performed in order to fix up or completely substitute, where possible, organs or tissues affected by degenerative diseases. Patients with these kind of illnesses stay long times waiting for a donor that could replace, in a short time, the damaged organ or the tissue. The lack of biological alternates, related to conventional surgical treatments as autografts, allografts, e xenografts, led the researchers belonging to different areas to collaborate to find out innovative solutions. This research brought to a new discipline able to merge molecular biology, biomaterial, engineering, biomechanics and, recently, design and architecture knowledges. This discipline is named Tissue Engineering (TE) and it represents a step forward towards the substitutive or regenerative medicine. One of the major challenge of the TE is to design and develop, using a biomimetic approach, an artificial 3D anatomy scaffold, suitable for cells adhesion that are able to proliferate and differentiate themselves as consequence of the biological and biophysical stimulus offered by the specific tissue to be replaced. Nowadays, powerful instruments allow to perform analysis day by day more accurateand defined on patients that need more precise diagnosis and treatments.Starting from patient specific information provided by TC (Computed Tomography) microCT and MRI(Magnetic Resonance Imaging), an image-based approach can be performed in order to reconstruct the site to be replaced. With the aid of the recent Additive Manufacturing techniques that allow to print tridimensional objects with sub millimetric precision, it is now possible to practice an almost complete control of the parametrical characteristics of the scaffold: this is the way to achieve a correct cellular regeneration. In this work, we focalize the attention on a branch of TE known as Bone TE, whose the bone is main subject. Bone TE combines osteoconductive and morphological aspects of the scaffold, whose main properties are pore diameter, structure porosity and interconnectivity. The realization of the ideal values of these parameters represents the main goal of this work: here we'll a create simple and interactive biomimetic design process based on 3D CAD modeling and generative algorithmsthat provide a way to control the main properties and to create a structure morphologically similar to the cancellous bone. Two different typologies of scaffold will be compared: the first is based on Triply Periodic MinimalSurface (T.P.M.S.) whose basic crystalline geometries are nowadays used for Bone TE scaffolding; the second is based on using Voronoi's diagrams and they are more often used in the design of decorations and jewellery for their capacity to decompose and tasselate a volumetric space using an heterogeneous spatial distribution (often frequent in nature). In this work, we will show how to manipulate the main properties (pore diameter, structure porosity and interconnectivity) of the design TE oriented scaffolding using the implementation of generative algorithms: "bringing back the nature to the nature".

Introduction to Reservoir Computing Methods

Il documento tratta la famiglia di metodologie di allenamento e sfruttamento delle reti neurali ricorrenti nota sotto il nome di Reservoir Computing. Viene affrontata un'introduzione sul Machine Learning in generale per fornire tutti gli strumenti necessari a comprendere l'argomento. Successivamente, vengono dati dettagli implementativi ed analisi dei vantaggi e punti deboli dei vari approcci, il tutto con supporto di codice ed immagini esplicative. Nel finale vengono tratte conclusioni sugli approcci, su quanto migliorabile e sulle applicazioni pratiche.

Development of in vitro methods to test acetabular prosthetic reconstructions (messa a punto di metodi in vitro per la caratterizzazione biomeccanica di ricostruzioni acetabolari)

The revision hip arthroplasty is a surgical procedure, consisting in the reconstruction of the hip joint through the replacement of the damaged hip prosthesis. Several factors may give raise to the failure of the artificial device: aseptic loosening, infection and dislocation represent the principal causes of failure worldwide. The main effect is the raise of bone defects in the region closest to the prosthesis that weaken the bone structure for the biological fixation of the new artificial hip. For this reason bone reconstruction is necessary before the surgical revision operation. This work is born by the necessity to test the effects of bone reconstruction due to particular bone defects in the acetabulum, after the hip prosthesis revision. In order to perform biomechanical in vitro tests on hip prosthesis implanted in human pelvis or hemipelvis a practical definition of a reference frame for these kind of bone specimens is required. The aim of the current study is to create a repeatable protocol to align hemipelvic samples in the testing machine, that relies on a reference system based on anatomical landmarks on the human pelvis. In chapter 1 a general overview of the human pelvic bone is presented: anatomy, bone structure, loads and the principal devices for hip joint replacement. The purpose of chapters 2 is to identify the most common causes of the revision hip arthroplasty, analysing data from the most reliable orthopaedic registries in the world. Chapter 3 presents an overview of the most used classifications for acetabular bone defects and fractures and the most common techniques for acetabular and bone reconstruction. After a critical review of the scientific literature about reference frames for human pelvis, in chapter 4, the definition of a new reference frame is proposed. Based on this reference frame, the alignment protocol for the human hemipelvis is presented as well as the statistical analysis that confirm the good repeatability of the method.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.