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General General

Clinical significance of combining salivary mRNAs and carcinoembryonic antigen for ovarian cancer detection.

In Scandinavian journal of clinical and laboratory investigation ; h5-index 22.0

Salivary mRNA biomarkers and serum carcinoembryonic antigen (CEA) have been recognized as promising liquid biopsy methods for detection of multiple cancers. However, current tests normally use solitary type of biomarkers, and are limited by unsatisfactory sensitivity and specificity when applied to differentiate cancer patients from healthy controls. In this study, a combined approach of CEA and salivary mRNA biomarkers was evaluated for discriminatory performance of ovarian cancer patients from healthy controls. We designed our study with two phases: a discovery phase to find and evaluate multiple biomarkers, and an independent validation phase to confirm the applicability of the selected biomarkers. In the discovery phase, a total of 140 ovarian cancer patients and 140 healthy controls were recruited. The CEA level in blood as well as five mRNA biomarkers in saliva (i.e. AGPAT1, B2M, BASP1, IER3 and IL1β) were measured, followed by developing a machine-learning model to differentiate ovarian cancer patients and healthy controls. We found a novel panel of biomarkers, which could differentiate ovarian cancer patients from healthy controls with high sensitivity (89.3%) and high specificity (82.9%). Next, we applied this panel of biomarkers in an independent validation study that consisted of 60 ovarian cancer patients and 60 healthy controls. The ovarian cancer patients were successfully differentiated from healthy controls in the validation phase, with sensitivity reaching 85.0% and specificity reaching 88.3%. To our best knowledge, it is the first time that a combined use of CEA and salivary mRNA biomarkers were applied for non-invasive detection of ovarian cancer.

Yang Jinfang, Xiang Cuiping, Liu Jianmeng

2020-Dec-10

CEA, Liquid biopsy, blood, cancer, mRNA, ovarian, saliva

General General

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls.

In The British journal of clinical psychology

OBJECTIVES : While theoretical models link obsessive-compulsive disorder (OCD) with executive function deficits, empirical findings from the neuropsychological literature remain mixed. These inconsistencies are likely exacerbated by the challenge of high-dimensional data (i.e., many variables per subject), which is common across neuropsychological paradigms and necessitates analytical advances. More unique to OCD is the heterogeneity of symptom presentations, each of which may relate to distinct neuropsychological features. While researchers have traditionally attempted to account for this heterogeneity using a symptom-based approach, an alternative involves focusing on underlying symptom motivations. Although the most studied symptom motivation involves fear of harmful events, 60-70% of patients also experience sensory phenomena, consisting of uncomfortable sensations or perceptions that drive compulsions. Sensory phenomena have received limited attention in the neuropsychological literature, despite evidence that symptoms motivated by these experiences may relate to distinct cognitive processes.

METHODS : Here, we used a supervised machine learning approach to characterize neuropsychological processes in OCD, accounting for sensory phenomena.

RESULTS : Compared to logistic regression and other algorithms, random forest best differentiated healthy controls (n = 59; balanced accuracy = .70), patients with sensory phenomena (n = 29; balanced accuracy = .59), and patients without sensory phenomena (n = 46; balanced accuracy = .62). Decision-making best distinguished between groups based on sensory phenomena, and among the patient subsample, those without sensory phenomena uniquely displayed greater risk sensitivity compared to healthy controls (d = .07, p = .008).

CONCLUSIONS : Results suggest that different cognitive profiles may characterize patients motivated by distinct drives. The superior performance and generalizability of the newer algorithms highlights the utility of considering multiple analytic approaches when faced with complex data.

PRACTITIONER POINTS : Practitioners should be aware that sensory phenomena are common experiences among patients with OCD. OCD patients with sensory phenomena may be distinguished from those without based on neuropsychological processes.

Stamatis Caitlin A, Batistuzzo Marcelo C, Tanamatis Tais, Miguel Euripedes C, Hoexter Marcelo Q, Timpano Kiara R

2020-Dec-10

executive function, machine learning, neuropsychology, obsessive-compulsive disorder, sensory phenomena

General General

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

In Brain : a journal of neurology

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

Shireby Gemma L, Davies Jonathan P, Francis Paul T, Burrage Joe, Walker Emma M, Neilson Grant W A, Dahir Aisha, Thomas Alan J, Love Seth, Smith Rebecca G, Lunnon Katie, Kumari Meena, Schalkwyk Leonard C, Morgan Kevin, Brookes Keeley, Hannon Eilis, Mill Jonathan

2020-Oct-29

DNA methylation, age, brain, clock, cortex

General General

The Impact of Artificial Intelligence on the Chess World.

In JMIR serious games

This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating. Artificial intelligence can address these concerns.

Duca Iliescu Delia Monica

2020-Dec-10

AlphaZero, MuZero, artificial intelligence, cheat detection, chess, coronavirus, games

General General

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.

In Journal of biomolecular structure & dynamics

To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019-May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody development for COVID-19. Out of 31 studies included, 16 studies applied AI for drug repurposing, whereas 10 studies utilized AI for novel drug discovery. Only four studies used AI technology for vaccine development, whereas one study generated stable antibodies against SARS-CoV-2. Approx. 50% of studies exclusively targeted 3CLpro of SARS-CoV-2, and only two studies targeted ACE/TMPSS2 for inhibiting host viral interactions. Around 16% of the identified drugs are in different phases of clinical evaluation against COVID-19. AI has emerged as a promising solution of COVID-19 therapeutics. During this current pandemic, many of the researchers have used AI-based strategies to process large databases in a more customized manner leading to the faster identification of several potential targets, novel/repurposing of drugs and vaccine candidates. A number of these drugs are either approved or are in a late-stage clinical trial and are potentially effective against SARS-CoV2 indicating validity of the methodology. However, as the use of AI-based screening program is currently in budding stage, sole reliance on such algorithms is not advisable at this current point of time and an evidence based approach is warranted to confirm their usefulness against this life-threatening disease. Communicated by Ramaswamy H. Sarma.

Kaushal Karanvir, Sarma Phulan, Rana S V, Medhi Bikash, Naithani Manisha

2020-Dec-10

Artificial intelligence, COVID-19, drug repurposing, novel drug discovery, vaccine development

Surgery Surgery

Artificial intelligence and perioperative medicine.

In Minerva anestesiologica ; h5-index 29.0

Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice that starts from the moment of contemplation of surgery until full recovery. Every perioperative phase (preoperative, intraoperative and postoperative) has to be studied and planned in order to optimize the entire patient management. Perioperative optimization does not only concern a short-term outcome improvement, but it has also a strong impact on long term survival. Clinical cases variability leads to the collection and analysis of a huge amount of different data, coming from multiple sources, making perioperative management standardization very difficult. Artificial Intelligence (AI) can play a primary role in this challenge, helping human mind in perioperative practice planning and decision-making process. AI refers to the ability of a computer system to perform functions and reasoning typical of the human mind; Machine Learning (ML) could play a fundamental role in pre-surgical planning, during intraoperative phase and postoperative management. Perioperative medicine is the cornerstone of surgical patient management and the tools deriving from the application of AI seem very promising as a support in optimizing the management of each individual patient. Despite the increasing help that will derive from the use of AI tools, the uniqueness of the patient and the particularity of each individual clinical case will always keep the role of the human mind central in clinical and perioperative management. The role of the physician, who must analyse the outputs provided by AI by following his own experience and knowledge, remains and will always be essential.

Bignami Elena G, Cozzani Federico, Del Rio Paolo, Bellini Valentina

2020-Dec-10