Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response.

BACKGROUND: Theranostic approaches-the use of diagnostics for developing targeted therapies-are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. METHODS: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study 'Understanding Society'. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. RESULTS: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. CONCLUSIONS: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.

The Behavioral Factors That Influence Person-Centered Social Care: A Literature Review and Conceptual Framework.

The last decade has seen numerous policy reforms to emplace person-centered social care. Consequently, the public has been given more information, choice, and autonomy to decide how best they want to be cared for later in life. Despite this, adults generally fail to plan or prepare effectively for their future care needs. Understanding the behavioral antecedents of person-centered decision-making is thus critical for addressing key gaps in the provision of quality social care. To this end, we conducted a literature review of the psychological and health sciences with the aim of identifying the aspects that influence person-centered decision-making in social care. Using an established theoretical framework, we distilled nine behavioral factors-knowledge, competency, health, goal clarity, time discounting, familiarity, cognitive biases, cognitive overload, and emotion-associated with "Capability," "Opportunity," "Motivation," and "Behavior" that explained person-centered decision-making in social care. These factors exist to different degrees and change as a person ages, gradually impacting their ability to obtain the care they want. We discuss the role of carers and the promise of shared decision-making and conclude by advocating a shift from personal autonomy to one that is shared with carers in the delivery of quality social care.

Meh, whatever: The effects of indifference expressions on cooperation in social conflict.

Conflicts are inherently emotional, yet parties in conflict may choose to explicitly express indifference. It is unclear, however, whether this represents an effective strategy. Drawing on emotions as social information (EASI) theory, we examined the interpersonal effects of indifference expressions in conflict and the processes that underlie these effects. Study 1 indicated that people believe indifference expressions constitute a neutral emotional signal. However, Study 2 demonstrated experimentally that counterparts' indifference expressions reduce focal negotiators' cooperative intentions through both affective (negative affective reactions) and inferential (decreased expected collaboration) processes when compared to negative (anger, contempt), positive (hope), and neutral (no emotion) expressions. Study 3 revealed negative effects of indifference (vs. neutral) expressions on cooperative intentions, expected collaboration, and heart rate variability as a physiological indicator of affective responding. Results further showed an indirect effect through expected collaboration, but not through affective reactions. Study 4 established the negative effects of indifference expressions on a behavioral measure of cooperation through expected collaboration. Studies 5 and 6 (preregistered) demonstrated that the impact of indifference expressions on cooperative intentions (Study 5) and actual cooperation (Study 6) via counterpart's expected collaboration is reduced when a counterpart explicitly indicates cooperative intentions, reducing the diagnostic value of indifference expressions. Across studies (N = 2,447), multiple expressive modalities of indifference were used, including verbal and nonverbal expressions. Findings demonstrate that explicit expressions of indifference have qualitatively different interpersonal effects than other emotional expressions, including neutral expressions, and cast doubt on the effectiveness of expressing indifference in negotiating social conflict. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Disentangling interoceptive abilities in alexithymia.

In recent years, research on interoceptive abilities (i.e., sensibility, accuracy, and awareness) and their associations with emotional experience has flourished. Yet interoceptive abilities in alexithymia-a personality trait characterized by a difficulty in the cognitive interpretation of emotional arousal, which impacts emotional experience-remain under-investigated, thereby limiting a full understanding of subjective emotional experience processing. Research has proposed two contrasting explanations thus far: in one model, the dimensions of interoceptive sensibility and accuracy in alexithymia would increase; in the other model, they would decrease. Surprisingly, the contribution of interoceptive awareness has been minimally researched. In this study (N = 182), the relationship between participants' level of alexithymia and the three interoceptive dimensions was tested. Our results show that the higher the level of alexithymia is, the higher interoceptive accuracy and sensibility (R2 = 0.29 and R2 = 0.14); conversely, the higher the level of alexithymia is, the lower interoceptive awareness (R2 = 0.36). Moreover, an ROC analysis reveals that interoceptive awareness is the most accurate predictor of alexithymia, yielding over 92% accuracy. Collectively, these results support a coherent understanding of interoceptive abilities in alexithymia, whereby the dissociation of interoceptive accuracy and awareness may explain the underlying psycho-physiological mechanisms of alexithymia. A possible neurocognitive mechanism is discussed which suggests insurgence of psychosomatic disorders in alexithymia and related psychotherapeutic approaches.

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network.

PURPOSE: Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. METHODS: We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model's performance. From the external test dataset, we calculated the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. CONCLUSIONS: Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00166-4.

Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the 'fast decliner' phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis. METHODS: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). RESULTS: Three COPD phenotypes were identified, the most common of which was the 'fast decliner'-characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function-yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. CONCLUSIONS: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.

The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. METHODS: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. RESULTS: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. CONCLUSIONS: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda.

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

Neurofeedback in the workplace: from neurorehabilitation hope to neuroleadership hype?

Brain-computer interface neurofeedback has rapidly become an engaging topic for occupational research at large. Notwithstanding some criticism, research and practice have begun converging on the efficacy of brain-computer interface neurofeedback as a part of holistic interventions in rehabilitation. Yet, its use in vocational contexts has recently blossomed into wider attributes, beyond rehabilitation practice per se, additionally targeting performance enhancements and leadership interventions in healthy individuals. By exploring this emerging scenario, this paper aims to provide an interdisciplinary forum of analysis on the deriving implications for rehabilitation professionals, signaling how these may invite both possible threats for the field and opportunities to engage in novel translational partnerships.

Detection of molecular processes in the intact retina by ATR-FTIR spectromicroscopy.

We used Fourier transform infrared spectromicroscopy in the attenuated total reflection configuration to study biochemical events associated with the response to light of an intact retina. We show that the technique is suitable for the detection in real time of molecular processes occurring in rod outer segments induced by light absorption. Two-dimensional correlation analysis was applied to the identification and interpretation of specific spectral changes associated to the evolution of the system. The technique allows us to observe an extensive protein translocation, which we interpret as arising from the release of transducin from the disk membrane and its redistribution from the outer segment towards the inner segment of rod cells. These results are in full agreement with our current understanding of retinal physiology and validate the technique as a useful tool for the study of complex molecular processes in intact tissue. [figure: see text] Spectral changes in the mid infrared region following exposure of an intact retina to light.