Diagnostic Palpation in Osteopathic Medicine: A Putative Neurocognitive Model of Expertise

This thesis examines the extent to which the development of expertise in diagnostic palpation in osteopathic medicine is associated with changes in cognitive processing. Chapter 2 and Chapter 3 review, respectively, the literature on the role of analytical and non-analytical processing in osteopathic and medical clinical decision making; and the relevant research on the use of vision and haptics and the development of expertise within the context of an osteopathic clinical examination. The two studies reported in Chapter 4 examined the mental representation of knowledge and the role of analogical reasoning in osteopathic clinical decision making. The results reported there demonstrate that the development of expertise in osteopathic medicine is associated with the processes of knowledge encapsulation and script formation. The four studies reported in Chapters 5 and 6 investigate the way in which expert osteopaths use their visual and haptic systems in the diagnosis of somatic dysfunction. The results suggest that ongoing clinical practice enables osteopaths to combine visual and haptic sensory signals in a more efficient manner. Such visuo-haptic sensory integration is likely to be facilitated by top-down processing associated with visual, tactile, and kinaesthetic mental imagery. Taken together, the results of the six studies reported in this thesis indicate that the development of expertise in diagnostic palpation in osteopathic medicine is associated with changes in cognitive processing. Whereas the experts’ diagnostic judgments are heavily influenced by top-down, non-analytical processing; students rely, primarily, on bottom-up sensory processing from vision and haptics. Ongoing training and clinical practice are likely to lead to changes in the clinician’s neurocognitive architecture. This thesis proposes an original model of expertise in diagnostic palpation which has implications for osteopathic education. Students and clinicians should be encouraged to appraise the reliability of different sensory cues in the context of clinical examination, combine sensory data from different channels, and consider using both analytical and nonanalytical reasoning in their decision making. Importantly, they should develop their skills of criticality and their ability to reflect on, and analyse their practice experiences in and on action

Understanding the disease and supporting clinical decisions

Barrett’s esophagus (BE) is a recognized premalignant condition of the distal esophagus that constitutes the major risk factor for the development of esophageal adenocarcinoma (EA). Despite the known low rates of BE progression to EA, the incidence of both has increased profoundly over the last decades and esophageal malignancy remains to be a deadly cancer with high morbidity and mortality unless diagnosed at early stages. Current BE clinical management has revealed unsuccessful in reverting this worrisome epidemiological picture and a major hurdle has been the incapacity to discriminate among BE patients those who have a higher risk of malignant progression. In fact, to this date, besides dysplasia none of the existing clinical and histologic criteria could anticipate malignant progression. It is therefore imperative to find reliable molecular biomarkers to guide medical practice and improve the standard of care for BE patients. The global aim of this thesis was to better understand the pathways underlying BE malignant progression and thereby identify reliable biomarkers relevant for the diagnosis, prognosis and management of BE patients thus contributing to an improved understanding of BE biology and an optimized support for clinical decisions. To accomplish these challenging goals an unbiased and a hypothesis-driven strategies were followed. Through the unbiased approach, a meta-analysis of transcriptome datasets and subsequent experimental validation in a cohort BE patients in follow-up was used to define a gene set associated with BE cancer development and, therefore, identify early biomarkers predictive of BE malignant progression. In silico analysis singled out two genes, CYR61 and TAZ as candidate predictive markers for BE malignant progression and experimental validation using quantitative PCR and immunohistochemistry revealed that both genes are upregulated and overexpressed in non-dysplastic BE index biopsies from progressors years before cancer development when compared with index biopsies from BE patients that did not progressed. We also found that EMT and stemness-related genes were also significantly over represented in BE associated with progression. Together, these results support that CYR61 and TAZ are promising early biomarkers to stratify BE patients according to their cancer risk and suggest a novel mechanist route for BE neoplastic progression. Using an hypothesis-driven approach, we explored when and how centrosome abnormalities arise along BE malignant pathway, from the early premalignant condition stage to metastatic disease, by establishing an accurate method to identify and score centrosomes, at the single-cell level, in patient samples and cell lines. We found that centrosome amplification arises as early as the premalignant condition of patients that progress to malignancy and significantly expands at dysplasia stage, which is dependent of p53 loss of function, being then present along cancer progression, namely in EA and metastasis. So, these finding suggest that centrosome amplification could contribute to BE initiation and malignant progression. Considering that centrosome amplification is specific of patients that progress to cancer, this could be further explored to be translated into useful tools to be used in the clinical setting and potentially improve its diagnosis, prognosis and treatment. Moreover, given widespread occurrence of both p53 mutations and centrosome abnormalities in human tumors, our findings are likely to be extended to other cancers. Collectively, both research avenues suggest the existence of different cellular and molecular abnormalities dictating different pathological propensity for malignant progression in BE, right from the beginning, and this could be further explored to trace a cancer risk profile for every patient and guide medical decisions and improve patient care.O esófago de Barrett (EB) é uma reconhecida condição pré-maligna que surge no esófago distal associada ao refluxo gastroesofágico crónico e constitui o maior factor de risco para o desenvolvimento do adenocarcinoma esofágico (AE). Apesar da taxa de progressão maligna do EB ser baixa, a incidência de ambos tem aumentado drasticamente nas últimas décadas. Como agravante, esta neoplasia em estadios avançados está associada a taxas elevadas de morbilidade e mortalidade a menos que diagnosticada e tratada em estadios iniciais, pelo que todos os doentes com EB são integrados em programas de vigilância com vista à detecção precoce de progressão neoplásica. Contudo, esta prática clínica tem-se mostrado ineficaz para reverter este cenário epidemiológico e um dos factores limitantes relaciona-se com o facto da displasia continuar a ser o único marcador de risco de progressão maligna e não haver marcadores moleculares e clínicos que possam predizer e estratificar o potencial maligno do EB. É assim urgente encontrar biomarcadores sensíveis e específicos capazes de guiar a prática médica, melhorar a relação custo-benefício dos programas de vigilância, mas principalmente a qualidade de vida dos doentes com EB. O objectivo principal desta tese foi descobrir novas vias moleculares subjacentes à progressão maligna do EB de forma a identificar biomarcadores fidedignos passíveis de serem aplicados à clínica apoiando o diagnóstico, prognóstico e manejo destes doentes, e assim contribuir para aprofundar o conhecimento relativo ao processo de cancerigénese desta doença e potencialmente melhorar a abordagem clínica aos doentes com EB. Para tal foram seguidas duas linhas de investigação diferentes. De modo a encontrar biomarcadores com potencial preditivo para progressão maligna em doentes com EB foi realizada uma meta-análise de dados de transcriptomas previamente publicados, seguida de uma validação experimental utilizando amostras de EB de doentes acompanhados no programa de vigilância do Instituto Português de Oncologia Francisco Gentil. Esta estratégia permitiu identificar dois promissores biomarcadores, o CYR61 e o TAZ, capazes de estratificar o risco de progressão maligna do EB, não só pela sua expressão diferencial ser a mais significativa na análise bioinformática mas porque a validação experimental revelou que estes dois genes se encontravam sobreexpressos logo na primeira biopsia (vários anos antes do desenvolvimento de cancro) onde foi feito o diagnóstico de EB dos doentes que progrediram comparativamente com a expressão detetada no EB dos não progressores. Adicionalmente, foram ainda identificados outros genes diferencialmente expressos em EB associados a progressão maligna, cujas funções estão associadas a fenótipos de células estaminais e fenómenos de transição epitélio-mesenquimal (TEM) em cancro. Assim, foi também descoberto um potencial novo processo molecular associado ao desenvolvimento de cancro em EB. Paralelamente, e considerando que alterações numéricas dos centrossomas podem estar presentes ao longo da progressão maligna em EB e assim contribuir para o seu processo de cancerigénese, decidimos explorar quando e como surgem as alterações numéricas dos centrossomas ao longo da progressão do EB, desde a condição prémaligna até às metástases ganglionares, tanto em amostras de doentes como em linhas celulares, usando um método de dupla marcação por imunofluorescência para identificar e quantificar fidedignamente o número de centrossomas por célula. Esta análise revelou a existência de células com centrossomas supranumerários logo na fase de metaplasia dos doentes que progrediram para cancro, e que a sua incidência aumenta significativamente na fase de displasia, a qual é dependente da perda de função do gene supressor tumoral p53, estando também depois presentes ao longo das restantes fases de progressão. Estes resultados sugerem assim que a desregulação dos centrossomas pode contribuir para a iniciação e progressão neoplásica do EB. No futuro será importante aprofundar o contributo dos centrossomas na cancerigénese do EB e tentar perceber qual o seu impacto no diagnóstico, prognóstico e tratamento destes doentes. Uma vez que tanto as alterações numéricas dos centrossomas como a perda de função do p53 são achados prevalentes em cancro, os resultados deste estudo poderão ser relevantes para outros modelos tumorais. Em conjunto, os resultados obtidos sugerem que a propensão maligna não é igual em todos os EB. Logo muito cedo no processo, aqueles cujo risco de virem a desenvolver cancro é maior sofrem alterações moleculares e celulares passíveis de serem detetadas e utilizadas como biomarcadores preditivos estratificando o risco de progressão maligna, e desta forma orientar as decisões clínicas, adequar tempos de vigilância e melhorar a abordagem terapêutica

Efficient interaction with large medical imaging databases

Everyday, a wide quantity of hospitals and medical centers around the world are producing large amounts of imaging content to support clinical decisions, medical research, and education. With the current trend towards Evidence-based medicine, there is an increasing need of strategies that allow pathologists to properly interact with the valuable information such imaging repositories host and extract relevant content for supporting decision making. Unfortunately, current systems are very limited at providing access to content and extracting information from it because of different semantic and computational challenges. This thesis presents a whole pipeline, comprising 3 building blocks, that aims to to improve the way pathologists and systems interact. The first building block consists in an adaptable strategy oriented to ease the access and visualization of histopathology imaging content. The second block explores the extraction of relevant information from such imaging content by exploiting low- and mid-level information obtained from from morphology and architecture of cell nuclei. The third block aims to integrate high-level information from the expert in the process of identifying relevant information in the imaging content. This final block not only attempts to deal with the semantic gap but also to present an alternative to manual annotation, a time consuming and prone-to-error task. Different experiments were carried out and demonstrated that the introduced pipeline not only allows pathologist to navigate and visualize images but also to extract diagnostic and prognostic information that potentially could support clinical decisions.Resumen: Diariamente, gran cantidad de hospitales y centros médicos de todo el mundo producen grandes cantidades de imágenes diagnósticas para respaldar decisiones clínicas y apoyar labores de investigación y educación. Con la tendencia actual hacia la medicina basada en evidencia, existe una creciente necesidad de estrategias que permitan a los médicos patólogos interactuar adecuadamente con la información que albergan dichos repositorios de imágenes y extraer contenido relevante que pueda ser empleado para respaldar la toma de decisiones. Desafortunadamente, los sistemas actuales son muy limitados en cuanto al acceso y extracción de contenido de las imágenes debido a diferentes desafíos semánticos y computacionales. Esta tesis presenta un marco de trabajo completo para patología, el cual se compone de 3 bloques y tiene como objetivo mejorar la forma en que interactúan los patólogos y los sistemas. El primer bloque de construcción consiste en una estrategia adaptable orientada a facilitar el acceso y la visualización del contenido de imágenes histopatológicas. El segundo bloque explora la extracción de información relevante de las imágenes mediante la explotación de información de características visuales y estructurales de la morfología y la arquitectura de los núcleos celulares. El tercer bloque apunta a integrar información de alto nivel del experto en el proceso de identificación de información relevante en las imágenes. Este bloque final no solo intenta lidiar con la brecha semántica, sino que también presenta una alternativa a la anotación manual, una tarea que demanda mucho tiempo y es propensa a errores. Se llevaron a cabo diferentes experimentos que demostraron que el marco de trabajo presentado no solo permite que el patólogo navegue y visualice imágenes, sino que también extraiga información de diagnóstico y pronóstico que potencialmente podría respaldar decisiones clínicas.Doctorad

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis

El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostáticoLa ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels mAlcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing.Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11316

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Diagnosis and Treatment of Parkinson's Disease

Parkinson's disease is diagnosed by history and physical examination and there are no laboratory investigations available to aid the diagnosis of Parkinson's disease. Confirmation of diagnosis of Parkinson's disease thus remains a difficulty. This book brings forth an update of most recent developments made in terms of biomarkers and various imaging techniques with potential use for diagnosing Parkinson's disease. A detailed discussion about the differential diagnosis of Parkinson's disease also follows as Parkinson's disease may be difficult to differentiate from other mimicking conditions at times. As Parkinson's disease affects many systems of human body, a multimodality treatment of this condition is necessary to improve the quality of life of patients. This book provides detailed information on the currently available variety of treatments for Parkinson's disease including pharmacotherapy, physical therapy and surgical treatments of Parkinson's disease. Postoperative care of patients of Parkinson's disease has also been discussed in an organized manner in this text. Clinicians dealing with day to day problems caused by Parkinson's disease as well as other healthcare workers can use beneficial treatment outlines provided in this book

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field