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Public Health Public Health

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Human voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population.

OBJECTIVE : This study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI.

METHODS : This study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated.

RESULTS : The study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755.

CONCLUSIONS : Multiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring.

Ding Huitong, Mandapati Amiya, Karjadi Cody, Ang Ting Fang Alvin, Lu Sophia, Miao Xiao, Glass James, Au Rhoda, Lin Honghuang

2022-Dec-22

association, digital voice, mild cognitive impairment, neuropsychological test, prediction

General General

Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties.

In Marine drugs ; h5-index 62.0

Complex pathological diseases, such as cancer, infection, and Alzheimer's, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer's. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates' clinical-translation chance.

Hemmati Shiva, Rasekhi Kazerooni Haniyeh

2022-Dec-04

PDL-1 inhibitor, adjuvant, anticancer peptide, antigen-presenting cell, antimicrobial peptide, artificial intelligence, biofilm, immune checkpoint, immunotherapy, vaccination

Radiology Radiology

Developing Artificial Intelligence Models for Extracting Oncologic Outcomes from Japanese Electronic Health Records.

In Advances in therapy

INTRODUCTION : A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals.

METHODS : We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models and time to progression of disease (TTP) for each line of treatment based on the treatment response extracted by AI models.

RESULTS : For the UMH data set, the BERT model exhibited higher precision accuracy compared to the Naïve Bayes or the Longformer models, respectively (precision [0.42 vs. 0.47 or 0.22], recall [0.63 vs. 0.46 or 0.33] and F1 scores [0.50 vs. 0.46 or 0.27]). When this BERT model was applied to LDI data, prediction accuracy remained quite similar. The Kaplan-Meier plots of TTP (months) showed similar trends for the first (median 14.9 [95% confidence interval 11.5, 21.1] and 16.8 [12.6, 21.8]), the second (7.8 [6.7, 10.7] and 7.8 [6.7, 10.7]), and the later lines of treatment for the predicted data by the BERT model and the manually curated data.

CONCLUSION : We developed AI models to extract treatment responses in patients with lung cancer using a large EHR database; however, the model requires further improvement.

Araki Kenji, Matsumoto Nobuhiro, Togo Kanae, Yonemoto Naohiro, Ohki Emiko, Xu Linghua, Hasegawa Yoshiyuki, Satoh Daisuke, Takemoto Ryota, Miyazaki Taiga

2022-Dec-22

Artificial intelligence, BERT, Electronic health records database, Lung cancer, Real-world data, Retrospective study

Internal Medicine Internal Medicine

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.

In Journal of nephrology

With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.

Badrouchi Samarra, Bacha Mohamed Mongi, Hedri Hafedh, Ben Abdallah Taieb, Abderrahim Ezzedine

2022-Dec-22

Artificial intelligence, Kidney transplantation, Machine learning, Nephrology

General General

Building and benchmarking the motivated deception corpus: Improving the quality of deceptive text through gaming.

In Behavior research methods

When one studies fake news or false reviews, the first step to take is to find a corpus of text samples to work with. However, most deceptive corpora suffer from an intrinsic problem: there is little incentive for the providers of the deception to put their best effort, which risks lowering the quality and realism of the deception. The corpus described in this project, the Motivated Deception Corpus, aims to rectify this problem by gamifying the process of deceptive text collection. By having subjects play the game Two Truths and a Lie, and by rewarding those subjects that successfully fool their peers, we collect samples in such a way that the process itself improves the quality of the text. We have amassed a large corpus of deceptive text that is strongly incentivized to be convincing, and thus more reflective of real deceptive text. We provide results from several configurations of neural network prediction models to establish machine learning benchmarks on the data. This new corpus is demonstratively more challenging to classify with the current state of the art than previous corpora.

Barsever Dan, Steyvers Mark, Neftci Emre

2022-Dec-22

BERT, Corpus, Deception, Lie, Machine learning, Natural language processing, Neural networks, Text, Truth

General General

Military veterans and civilians' mental health diagnoses: an analysis of secondary mental health services.

In Social psychiatry and psychiatric epidemiology

PURPOSE : Healthcare provision in the United Kingdom (UK) falls primarily to the National Health Service (NHS) which is free at the point of access. In the UK, there is currently no national marker to identify military veterans in electronic health records, nor a requirement to record it. This study aimed to compare the sociodemographic characteristics and recorded mental health diagnoses of a sample of veterans and civilians accessing secondary mental health services.

METHODS : The Military Service Identification Tool, a machine learning computer tool, was employed to identify veterans and civilians from electronic health records. This study compared the sociodemographic characteristics and recorded mental health diagnoses of veterans and civilians accessing secondary mental health care from South London and Maudsley NHS Foundation Trust, UK. Data from 2,576 patients were analysed; 1288 civilians and 1288 veterans matched on age and gender.

RESULTS : Depressive disorder was the most prevalent across both groups in the sample (26.2% veterans, 15.5% civilians). The present sample of veterans accessing support for mental health conditions were significantly more likely to have diagnoses of anxiety, depressive, psychosis, personality, and stress disorders (AORs ranging 1.41-2.84) but less likely to have a drug disorder (AOR = 0.51) than age- and gender-matched civilians.

CONCLUSION : Veterans accessing secondary mental health services in South London had higher risks for many mental health problems than civilians accessing the same services. Findings suggest that military career history is a key consideration for probable prognosis and treatment, but this needs corroborating in other geographical areas including national population-based studies in the UK.

Williamson Charlotte, Palmer Laura, Leightley Daniel, Pernet David, Chandran David, Leal Ray, Murphy Dominic, Fear Nicola T, Stevelink Sharon A M

2022-Dec-22

Armed forces, Civilian, Mental health, Military, Secondary healthcare, Veteran