An investigation into the suitability of time-of-flight mass spectrometry in forensic toxicology

Liquid chromatography coupled with mass spectrometry is one of the most powerful tools in the toxicologist’s arsenal to detect a wide variety of compounds from many different matrices. However, the huge number of potentially abused substances and new substances especially designed as intoxicants poses a problem in a forensic toxicology setting. Most methods are targeted and designed to cover a very specific drug or group of drugs while many other substances remain undetected. High resolution mass spectrometry, more specifically time-of-flight mass spectrometry, represents an extremely powerful tool in analysing a multitude of compounds not only simultaneously but also retroactively. The data obtained through the time-of-flight instrument contains all compounds made available from sample extraction and chromatography, which can be processed at a later time with an improved library to detect previously unrecognised compounds without having to analyse the respective sample again. The aim of this project was to determine the utility and limitations of time-of-flight mass spectrometry as a general and easily expandable screening method. The resolution of time-of-flight mass spectrometry allows for the separation of compounds with the same nominal mass but distinct exact masses without the need to separate them chromatographically. To simulate the wide variety of potentially encountered drugs in such a general screening method, seven drugs (morphine, cocaine, zolpidem, diazepam, amphetamine, MDEA and THC) were chosen to represent this variety in terms of mass, properties and functional groups. Consequently, several liquid-liquid and solid phase extractions were applied to urine samples to determine the most general suitable and unspecific extraction. Chromatography was optimised by investigating the parameters pH, concentration, organic solvent and gradient of the mobile phase to improve data obtained by the time-of-flight instrument. The resulting method was validated as a qualitative confirmation/identification method. Data processing was automated using the software TargetAnalysis, which provides excellent analyte recognition according to retention time, exact mass and isotope pattern. The recognition of isotope patterns allows excellent recognition of analytes even in interference rich mass spectra and proved to be a good positive indicator. Finally, the validated method was applied to samples received from the A& E Department of Glasgow Royal Infirmary in suspected drug abuse cases and samples received from the Scottish Prison Service, which we received from their own prevalence study targeting drugs of abuse in the prison population. The obtained data was processed with a library established in the course of this work.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Drug of abuse analysis: new methods in herbal and biological matrices for medicinal chemistry and forensic investigation

In this Ph.D. project, original and innovative approaches for the quali-quantitative analysis of abuse substances, as well as therapeutic agents with abuse potential and related compounds were designed, developed and validated for application to different fields such as forensics, clinical and pharmaceutical. All the parameters involved in the developed analytical workflows were properly and accurately optimised, from sample collection to sample pretreatment up to the instrumental analysis. Advanced dried blood microsampling technologies have been developed, able of bringing several advantages to the method as a whole, such as significant reduction of solvent use, feasible storage and transportation conditions and enhancement of analyte stability. At the same time, the use of capillary blood allows to increase subject compliance and overall method applicability by exploiting such innovative technologies. Both biological and non-biological samples involved in this project were subjected to optimised pretreatment techniques developed ad-hoc for each target analyte, making also use of advanced microextraction techniques. Finally, original and advanced instrumental analytical methods have been developed based on high and ultra-high performance liquid chromatography (HPLC,UHPLC) coupled to different detection means (mainly mass spectrometry, but also electrochemical, and spectrophotometric detection for screening purpose), and on attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) for solid-state analysis. Each method has been designed to obtain highly selective, sensitive yet sustainable systems and has been validated according to international guidelines. All the methods developed herein proved to be suitable for the analysis of the compounds under investigation and may be useful tools in medicinal chemistry, pharmaceutical analysis, within clinical studies and forensic investigations.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Structural Connectivity Of The Default Mode Network And Cognition In Alzheimer׳s Disease.

Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy (P=0.01) and streamline count (P=0.029), and higher radial diffusivity (P=0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls (P=0.003 and P=0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities (P=0.048 and P=0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment (P=0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network.

Can We Predict The Clinical Outcomes Of Genitourinary Trauma Patients And Offer Them An Evidence-based Support? The Case For Telemedicine Network.

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Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

Exploring Iris Colour Prediction And Ancestry Inference In Admixed Populations Of South America.

New DNA-based predictive tests for physical characteristics and inference of ancestry are highly informative tools that are being increasingly used in forensic genetic analysis. Two eye colour prediction models: a Bayesian classifier - Snipper and a multinomial logistic regression (MLR) system for the Irisplex assay, have been described for the analysis of unadmixed European populations. Since multiple SNPs in combination contribute in varying degrees to eye colour predictability in Europeans, it is likely that these predictive tests will perform in different ways amongst admixed populations that have European co-ancestry, compared to unadmixed Europeans. In this study we examined 99 individuals from two admixed South American populations comparing eye colour versus ancestry in order to reveal a direct correlation of light eye colour phenotypes with European co-ancestry in admixed individuals. Additionally, eye colour prediction following six prediction models, using varying numbers of SNPs and based on Snipper and MLR, were applied to the study populations. Furthermore, patterns of eye colour prediction have been inferred for a set of publicly available admixed and globally distributed populations from the HGDP-CEPH panel and 1000 Genomes databases with a special emphasis on admixed American populations similar to those of the study samples.

Developmental Rate Of Immatures Of Two Fly Species Of Forensic Importance: Sarcophaga (liopygia) Ruficornis And Microcerella Halli (diptera: Sarcophagidae).

Since insect species are poikilothermic organisms, they generally exhibit different growth patterns depending on the temperature at which they develop. This factor is important in forensic entomology, especially for estimating postmortem interval (PMI) when it is based on the developmental time of the insects reared in decomposing bodies. This study aimed to estimate the rates of development, viability, and survival of immatures of Sarcophaga (Liopygia) ruficornis (Fabricius 1794) and Microcerella halli (Engel 1931) (Diptera: Sarcophagidae) reared in different temperatures: 10, 15, 20, 25, 30, and 35 ± 1 °C. Bovine raw ground meat was offered as food for all experimental groups, each consisting of four replicates, in the proportion of 2 g/larva. To measure the evolution of growth, ten specimens of each group were randomly chosen and weighed every 12 h, from initial feeding larva to pupae, and then discarded. Considering the records of weight gain, survival rates, and stability of growth rates, the range of optimum temperature for the development of S. (L.) ruficornis is between 20 and 35 °C, and that of M. halli is between 20 and 25 °C. For both species, the longest times of development were in the lowest temperatures. The survival rate at extreme temperatures (10 and 35 °C) was lower in both species. Biological data such as the ones obtained in this study are of great importance to achieve a more accurate estimate of the PMI.

Quantum Dots As Biophotonics Tools.

This chapter provides a short review of quantum dots (QDs) physics, applications, and perspectives. The main advantage of QDs over bulk semiconductors is the fact that the size became a control parameter to tailor the optical properties of new materials. Size changes the confinement energy which alters the optical properties of the material, such as absorption, refractive index, and emission bands. Therefore, by using QDs one can make several kinds of optical devices. One of these devices transforms electrons into photons to apply them as active optical components in illumination and displays. Other devices enable the transformation of photons into electrons to produce QDs solar cells or photodetectors. At the biomedical interface, the application of QDs, which is the most important aspect in this book, is based on fluorescence, which essentially transforms photons into photons of different wavelengths. This chapter introduces important parameters for QDs' biophotonic applications such as photostability, excitation and emission profiles, and quantum efficiency. We also present the perspectives for the use of QDs in fluorescence lifetime imaging (FLIM) and Förster resonance energy transfer (FRET), so useful in modern microscopy, and how to take advantage of the usually unwanted blinking effect to perform super-resolution microscopy.

[gait Speed, Grip Strength And Self-rated Health Among The Elderly: Data From The Fibra Campinas Network, São Paulo, Brazil].

The article seeks to investigate patterns of performance and relationships between grip strength, gait speed and self-rated health, and investigate the relationships between them, considering the variables of gender, age and family income. This was conducted in a probabilistic sample of community-dwelling elderly aged 65 and over, members of a population study on frailty. A total of 689 elderly people without cognitive deficit suggestive of dementia underwent tests of gait speed and grip strength. Comparisons between groups were based on low, medium and high speed and strength. Self-related health was assessed using a 5-point scale. The males and the younger elderly individuals scored significantly higher on grip strength and gait speed than the female and oldest did; the richest scored higher than the poorest on grip strength and gait speed; females and men aged over 80 had weaker grip strength and lower gait speed; slow gait speed and low income arose as risk factors for a worse health evaluation. Lower muscular strength affects the self-rated assessment of health because it results in a reduction in functional capacity, especially in the presence of poverty and a lack of compensatory factors.

Going Beyond Seizure Control: Can Pharmacotherapy Help To Restore Cognitive Network Function?

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Whole Cortical And Default Mode Network Mean Functional Connectivity As Potential Biomarkers For Mild Alzheimer's Disease.

The search for an Alzheimer's disease (AD) biomarker is one of the most relevant contemporary research topics due to the high prevalence and social costs of the disease. Functional connectivity (FC) of the default mode network (DMN) is a plausible candidate for such a biomarker. We evaluated 22 patients with mild AD and 26 age- and gender-matched healthy controls. All subjects underwent resting functional magnetic resonance imaging (fMRI) in a 3.0 T scanner. To identify the DMN, seed-based FC of the posterior cingulate was calculated. We also measured the sensitivity/specificity of the method, and verified a correlation with cognitive performance. We found a significant difference between patients with mild AD and controls in average z-scores: DMN, whole cortical positive (WCP) and absolute values. DMN individual values showed a sensitivity of 77.3% and specificity of 70%. DMN and WCP values were correlated to global cognition and episodic memory performance. We showed that individual measures of DMN connectivity could be considered a promising method to differentiate AD, even at an early phase, from normal aging. Further studies with larger numbers of participants, as well as validation of normal values, are needed for more definitive conclusions.

[strategies For Integration Of Health Care Practices And Sanitary Surveillance In The Context Of The Implementation Of Rede Cegonha - A Brazilian Mother And Infant Health Care Network].

Mother and infant mortality has been the scope of analysis throughout the history of public health in Brazil and various strategies to tackle the issue have been proposed to date. The Ministry of Health has been working on this and the Rede Cegonha strategy is the most recent policy in this context. Given the principle of comprehensive health care and the structure of the Unified Health System in care networks, it is necessary to ensure the integration of health care practices, among which are the sanitary surveillance actions (SSA). Considering that the integration of health care practices and SSA can contribute to reduce mother and infant mortality rates, this article is a result of qualitative research that analyzed the integration of these actions in four cities in the State of São Paulo/Brazil: Campinas, Indaiatuba, Jaguariúna and Santa Bárbara D'Oeste. The research was conducted through interviews with SSA and maternal health managers, and the data were evaluated using thematic analysis. The results converge with other studies, identifying the isolation of health care practices and SSA. The insertion of SSA in collectively-managed areas appears to be a potential strategy for health planning and implementation of actions in the context under scrutiny.

[child Protection Network And The Intersector Implementation Of The Circle Of Security As Alternatives To Medication].

To describe the clinical history of a child with aggressive behavior and recurring death-theme speech, and report the experience of the team of authors, who proposed an alternative to medication through the establishment of a protection network and the inter-sector implementation of the circle of security concept. A 5-year-old child has a violent and aggressive behavior at the day-care. The child was diagnosed by the healthcare center with depressive disorder and behavioral disorder, and was medicated with sertraline and risperidone. Side effects were observed, and the medications were discontinued. Despite several actions, such as talks, teamwork, psychological and psychiatric follow-up, the child's behavior remained unchanged. A unique therapeutic project was developed by Universidade Estadual de Campinas' Medical School students in order to establish a connection between the entities responsible for the child's care (daycare center, healthcare center, and family). Thus, the team was able to develop a basic care protection network. The implementation of the inter-sector circle of security, as well as the communication and cooperation among the teams, produced very favorable results in this case. This initiative was shown to be a feasible and effective alternative to the use of medication for this child.