Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features.

Objectives: Natural language processing (NLP) algorithms can interpret unstructured text for commonly used terms and phrases. Pancreatic pathologies are diverse and include benign and malignant entities with associated histologic features. Creating a pancreas NLP algorithm can aid in electronic health record coding as well as large database creation and curation.

Towards AI-driven longevity research: An overview.

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g.

Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation.

In the last decade, an increasing number of users have started reporting adverse drug events (ADEs) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use natural language processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question.

Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.

Unlabelled: Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + .

Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets.

Background: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments.

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment.

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios.

Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations.

The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull's progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions.

Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports.

Purpose: Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common.

New Insights in Cheese Yield Capacity of the Milk of Italian Brown and Italian Friesian Cattle in the Production of High-Moisture Mozzarella.

The aim of the present study is to investigate the effect of κ-casein B content in milk on the yield of high-moisture mozzarella cheese. The study was carried out by monitoring the production of eight mozzarella cheese batches at four cheese making factories. At each factory, two cheese making trials were performed in parallel: one using bulk milk from Italian Brown cattle and the other using bulk milk from Italian Friesian cattle.

Estimation of maximum thermo-hygrometric index thresholds affecting milk production in Italian Brown Swiss cattle.

It is known that heat stress affects dairy cow performance in multiple ways: physiological, behavioral, reproductive, and productive. The aim of the present study was to determine if a threshold of temperature-humidity index (THI) exists for multiple milk production traits (milk yield, fat-corrected milk, protein and fat yield and percentage, energy-corrected milk, cheese production, and cheese yield) in Italian Brown Swiss dairy cows from the period 15 d before the day of the Italian Breeders Association test-day sampling. A 10-yr data set (2009-2018) containing 202,776 test-day records of 23,296 Brown Swiss cows was matched with the maximum THI.

Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy.

Context: Clinicians lack reliable methods to predict which patients with congestive heart failure (CHF) will benefit from cardiac resynchronization therapy (CRT). Symptom burden may help to predict response, but this information is buried in free-text clinical notes. Natural language processing (NLP) may identify symptoms recorded in the electronic health record and thereby enable this information to inform clinical decisions about the appropriateness of CRT.

Can machine learning improve patient selection for cardiac resynchronization therapy?

Rationale: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice guidelines.

Objective: To apply machine learning to create an algorithm that predicts CRT outcome using electronic health record (EHR) data avaible before the procedure.

Methods And Results: We applied machine learning and natural language processing to the EHR of 990 patients who received CRT at two academic hospitals between 2004-2015.

Do Neural Information Extraction Algorithms Generalize Across Institutions?

Purpose: Natural language processing (NLP) techniques have been adopted to reduce the curation costs of electronic health records. However, studies have questioned whether such techniques can be applied to data from previously unseen institutions. We investigated the performance of a common neural NLP algorithm on data from both known and heldout (ie, institutions whose data were withheld from the training set and only used for testing) hospitals.

Genome-wide association study for cheese yield and curd nutrient recovery in dairy cows.

Cheese production and consumption are increasing in many countries worldwide. As a result, interest has increased in strategies for genetic selection of individuals for technological traits of milk related to cheese yield (CY) in dairy cattle breeding. However, little is known about the genetic background of a cow's ability to produce cheese.

Genome-wide association of coagulation properties, curd firmness modeling, protein percentage, and acidity in milk from Brown Swiss cows.

Cheese production is increasing in many countries, and a desire toward genetic selection for milk coagulation properties in dairy cattle breeding exists. However, measurements of individual cheesemaking properties are hampered by high costs and labor, whereas traditional single-point milk coagulation properties (MCP) are sometimes criticized. Nevertheless, new modeling of the entire curd firmness and syneresis process (CFt equation) offers new insight into the cheesemaking process.

Fine mapping for Weaver syndrome in Brown Swiss cattle and the identification of 41 concordant mutations across NRCAM, PNPLA8 and CTTNBP2.

Bovine Progressive Degenerative Myeloencephalopathy (Weaver Syndrome) is a recessive neurological disease that has been observed in the Brown Swiss cattle breed since the 1970's in North America and Europe. Bilateral hind leg weakness and ataxia appear in afflicted animals at 6 to 18 months of age, and slowly progresses to total loss of hind limb control by 3 to 4 years of age. While Weaver has previously been mapped to Bos taurus autosome (BTA) 4∶46-56 Mb and a diagnostic test based on the 6 microsatellite (MS) markers is commercially available, neither the causative gene nor mutation has been identified; therefore misdiagnosis can occur due to recombination between the diagnostic MS markers and the causative mutation.

Short communication: characterization of a monoclonal antibody for kappa-casein B of cow's milk.

A monoclonal antibody (antik-B) against an oligopeptide of 23 AA corresponding to the region 131-153 of bovine kappa-casein (kappa-CN) B was generated using the Human Combinatorial Antibody Library (HuCAL) technology. Both AA substitutions distinguishing kappa-CN A and B are located in that region (positions 136 and 148). In this study, the reactivity of antik-B to milk samples collected from cows previously genotyped as CSN3*AA, CSN3*AB, and CSN3*BB was tested.

Estimation of genetic parameters for perinatal sucking behavior of Italian Brown Swiss calves.

Brown Swiss breeders sometimes experience difficulties in feeding calves because of the weak sucking ability of the calves in the early days of life. For the welfare of the calves, they should be suckled by their dams or should aggressively ingest colostrum immediately after birth. The composition of colostrum changes rapidly during the first few days of lactation, and the ability of calves to absorb the Ig decreases quickly as well.

Additional support for an association between OLR1 and milk fat traits in cattle.

The bovine oxidized LDL receptor 1 (OLR1) was chosen as a candidate gene for association tests with milk composition traits. Genotyping of 773 Italian Brown Swiss for a SNP at position 8232 in OLR1 (NW_215807:g.8232C>A) revealed a frequency of 0.

Genetic parameters of Italian brown Swiss for levels of herd yield.

Genetic parameters were estimated for yields and percentages of milk, fat, and protein for registered Italian Brown Swiss cows. Data were 72,690 mature equivalent 305-d first lactation yields split by herd average milk into four files. An expectation-maximization REML algorithm was used on a multiple-trait model with equal design matrices for fixed and random effects.