Impact of Short-Course Palliative Radiation Therapy on Pancreatic Cancer-Related Pain: Prospective Phase 2 Nonrandomized PAINPANC Trial.

PURPOSE: Clinical evidence is limited regarding palliative radiation therapy for relieving pancreatic cancer-related pain. We prospectively investigated pain response after short-course palliative radiation therapy in patients with moderate-to-severe pancreatic cancer-related pain. METHODS AND MATERIALS: In this prospective phase 2 single center nonrandomized trial, 30 patients with moderate-to-severe pain (5-10, on a 0-10 scale) of pancreatic cancer refractory to pain medication, were treated with a short-course palliative radiation therapy; 24 Gy in 3 weekly fractions (2015-2018). Primary endpoint was defined as a clinically relevant average decrease of ≥2 points in pain severity, compared with baseline, within 7 weeks after the start of treatment. Secondary endpoint was global quality of life (QoL), with a clinically relevant increase of 5 to 10 points (0-100 scale). Pain severity reduction and QoL were assessed 9 times using the Brief Pain Inventory and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C15-PAL, respectively. Both outcomes were analyzed using joint modeling. In addition, acute toxicity based on clinician reporting and overall survival (OS) were assessed. RESULTS: Overall, 29 of 30 patients (96.7%) received palliative radiation therapy. At baseline, the median oral morphine equivalent daily dose was 129.5 mg (range, 20.0-540.0 mg), which decreased to 75.0 mg (range, 15.0-360.0 mg) after radiation (P = .021). Pain decreased on average 3.15 points from baseline to 7 weeks (one-sided P = .045). Patients reported a clinically relevant mean pain severity reduction from 5.9 to 3.8 points (P = .011) during the first 3 weeks, which further decreased to 3.2 until week 11, ending at 3.4 (P = .006) in week 21 after the first radiation therapy fraction. Global QoL significantly improved from 50.5 to 60.8 during the follow-up period (P = .001). Grade 3 acute toxicity occurred in 3 patients and no grade 4 to 5 toxicity was observed. Median OS was 11.8 weeks, with a 13.3% 1-year actuarial OS rate. CONCLUSIONS: Short-course palliative radiation therapy for pancreatic cancer-related pain was associated with rapid, clinically relevant reduction in pain severity, and clinically relevant improvement in global QoL, with mostly mild toxicity.

Effects of mobile phone electromagnetic fields on brain waves in healthy volunteers.

The interaction between biological tissue and electromagnetic fields (EMF) is a topic of increasing interest due to the rising prevalence of background EMF in the past decades. Previous studies have attempted to measure the effects of EMF on brainwaves using EEG recordings, but are typically hampered by experimental and environmental factors. In this study, we present a framework for measuring the impact of EMF on EEG while controlling for these factors. A Bayesian statistical approach is employed to provide robust statistical evidence of the observed EMF effects. This study included 32 healthy participants in a double-blinded crossover counterbalanced design. EEG recordings were taken from 63 electrodes across 6 brain regions. Participants underwent a measurement protocol comprising two 18-min sessions with alternating blocks of eyes open (EO) and eyes closed (EC) conditions. Group 1 (n = 16) had EMF during the first session and sham during the second session; group 2 (n = 16) had the opposite. Power spectral density plots were generated for all sessions and brain regions. The Bayesian analysis provided statistical evidence for the presence of an EMF effect in the alpha band power density in the EO condition. This measurement protocol holds potential for future research on the impact of novel transmission protocols.

Design of the formalized and integrated Alzheimer's Disease Ontology and its application in retrieving textual data via text mining.

As one of the leading causes for dementia in the population, it is imperative that we discern exactly why Alzheimer's disease (AD) has a strong molecular association with beta-amyloid and tau. Although a clear understanding about etiology and pathogenesis of AD remains unsolved, scientists worldwide have dedicated significant efforts to discovering the molecular interactions linked to the pathological characteristics and potential treatments. Knowledge representations, such as domain ontologies encompassing our current understanding about AD, could greatly assist and contribute to disease research. This paper describes the construction and application of the integrated Alzheimer's Disease Ontology (ADO), combining selected concepts from the former version of the ADO and the Alzheimer's Disease Mapping Ontology (ADMO). In addition to the existing entities available from these knowledge models, essential knowledge about AD from public sources, such as newly discovered risk factor genes and novel treatments, was also integrated. The ADO can also be leveraged in text mining scenarios given that it is conceptually enriched with domain-specific knowledge as well as their relations. The integrated ADO consists of 39 855 total axioms. The ontology covers many aspects of the AD domain, including risk factor genes, clinical features, treatments and experimental models. The ontology complies with the Open Biological and Biomedical Ontology principles and was accepted by the foundry. In this paper, we illustrate the role of the presented ontology in extracting textual information from the SCAIView database and key measures in an ADO-based corpus. Database URL:  https://academic.oup.com/database.

Automated pose estimation reveals walking characteristics associated with lameness in broilers.

Walking ability of broilers can be improved by selective breeding, but large-scale phenotypic records are required. Currently, gait of individual broilers is scored by trained experts, however, precision phenotyping tools could offer a more objective and high-throughput alternative. We studied whether specific walking characteristics determined through pose estimation are linked to gait in broilers. We filmed male broilers from behind, walking through a 3 m × 0.4 m (length × width) corridor one by one, at 3 time points during their lifetime (at 14, 21, and 33 d of age). We used a deep learning model, developed in DeepLabCut, to detect and track 8 keypoints (head, neck, left and right knees, hocks, and feet) of broilers in the recorded videos. Using the keypoints of the legs, 6 pose features were quantified during the double support phase of walking, and 1 pose feature was quantified during steps, at maximum leg lift. Gait was scored on a scale from 0 to 5 by 4 experts, using the videos recorded on d 33, and the broilers were further classified as having either good gait (mean gait score ≤2) or suboptimal gait (mean gait score >2). The relationship of pose features on d 33 with gait was analyzed using the data of 84 broilers (good gait: 57.1%, suboptimal gait: 42.9%). Birds with suboptimal gait had sharper hock joint lateral angles and lower hock-feet distance ratios during double support on d 33, on average. During steps, relative step height was lower in birds with suboptimal gait. Step height and hock-feet distance ratio showed the largest mean deviations in broilers with suboptimal gait compared to those with good gait. We demonstrate that pose estimation can be used to assess walking characteristics during a large part of the productive life of broilers, and to phenotype and monitor broiler gait. These insights can be used to understand differences in the walking patterns of lame broilers, and to build more sophisticated gait prediction models.

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Motivation: Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. Results: The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28 151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. Availability and implementation: The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Critical assessment of transformer-based AI models for German clinical notes.

Objective: Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods: We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results: General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion: The domain-adaptation strategy's potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion: General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

Common data model for COVID-19 datasets.

MOTIVATION: A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling. RESULTS: To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability. AVAILABILITY AND IMPLEMENTATION: The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism.

The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Modeling in the animal sciences has received a boost by large-scale adoption of sensor technology, increased computing power, and the further development of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) models. Together with open-source programming languages, extensive modeling libraries, and heavy marketing, modeling reached a larger audience via AI. However, like most technological innovations, AI overpromised. By adopting an almost singular model-centric view to solving business needs, models failed to integrate with existing business processes. Models, especially AI, need data and both need humans. Together, they need room to learn and fail and by offering them as the end-solution to a problem, they are unable to act as sparring partners for all relevant stakeholders. In this article, we highlight fundamental model limitations exemplified via AI, and we offer solutions toward a more sustainable adoption of AI as a catalyst for modeling. This means sharing data and code and placing a more realistic view on models. Universities and industry both play a fundamental role in offering technological prowess and business experience to the future modeler. People, not technology, are the key to a more successful adoption of models.

Benchmarking table recognition performance on biomedical literature on neurological disorders.

MOTIVATION: Table recognition systems are widely used to extract and structure quantitative information from the vast amount of documents that are increasingly available from different open sources. While many systems already perform well on tables with a simple layout, tables in the biomedical domain are often much more complex. Benchmark and training data for such tables are however very limited. RESULTS: To address this issue, we present a novel, highly curated benchmark dataset based on a hand-curated literature corpus on neurological disorders, which can be used to tune and evaluate table extraction applications for this challenging domain. We evaluate several state-of-the-art table extraction systems based on our proposed benchmark and discuss challenges that emerged during the benchmark creation as well as factors that can impact the performance of recognition methods. For the evaluation procedure, we propose a new metric as well as several improvements that result in a better performance evaluation. AVAILABILITY AND IMPLEMENTATION: The resulting benchmark dataset (https://zenodo.org/record/5549977) as well as the source code to our novel evaluation approach can be openly accessed. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Discovery of a Novel Series of Potent and Selective Alkynylthiazole-Derived PI3Kγ Inhibitors.

Phosphoinositide 3-kinases (PI3Ks) are a family of enzymes that control a wide variety of cellular functions such as cell growth, proliferation, differentiation, motility, survival, and intracellular trafficking. PI3Kγ plays a critical role in mediating leukocyte chemotaxis as well as mast cell degranulation, making it a potentially interesting target for autoimmune and inflammatory diseases. We previously disclosed a novel series of PI3Kγ inhibitors derived from a benzothiazole core. The truncation of the benzothiazole core led to the discovery of a structurally diverse alkynyl thiazole series which displayed high PI3Kγ potency and subtype selectivity. Further medicinal chemistry optimization of the alkynyl thiazole series led to identification of compounds such as 14 and 32, highly potent, subtype selective, and CNS penetrant PI3Kγ inhibitors. Compound 14 showed robust inhibition of PI3Kγ mediated neutrophil migration in vivo.

The COVID-19 Ontology.

MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The effect of hydroxychloride trace minerals on the growth performance and carcass quality of grower/finisher pigs: a meta-analysis.

The current study compared the effect of hydroxychloride trace minerals (HTM) with the effect of inorganic trace minerals (ITM) on growth performance and carcass quality in grower-finisher pigs. The results of 6 studies conducted throughout Europe were combined into one meta-analysis. All included studies were performed using pigs from about 19 kg of body weight until slaughter. In all studies, 2 different mineral sources were compared, HTM and sulfates as ITM. Zn from either HTM or ITM was added at a level of 80 ppm to the diet, and Cu was added at a level of 15 ppm from the same source as Zn. In most studies, an additional treatment was included in which 20 ppm Zn was used from either source in combination with 15 ppm Cu from the same source. Diets were fed in 3 phases according to local commercial standards. The body weight, average daily gain, average daily feed intake, and gain:feed ratio were measured at the end of each phase. At the end of each study, the carcass yield, back fat thickness, and lean meat percentage were measured at commercial slaughterhouses. The meta-analysis was conducted using a MIXED model in SAS taking into account the within-study and between-study variation. The comparison was done only between HTM and ITM added at the same Zn level. No statistical differences were observed for growth performance or carcass characteristics between the mineral sources in pigs fed 20 ppm Zn. When 80 ppm Zn was used, a significant improvement in lean meat percentage was observed in pigs fed HTM compared with pigs fed ITM. In the overall study period, there was a tendency towards an increased gain:feed ratio in pigs fed 80 ppm Zn from HTM. In the last feeding phase, before slaughter gain:feed ratio and average daily gain were both significantly improved by 3.9%. In conclusion, HTM addition improved growth performance and lean meat percentage in grower-finisher pigs.

Technical note: concentrations of soluble, insoluble, and total dietary fiber in feed ingredients determined using Method AOAC 991.43 are not different from values determined using Method AOAC 2011.43 with the AnkomTDF Dietary Fiber Analyzer.

The primary objective of this experiment was to test the hypothesis that concentrations of soluble (SDF), insoluble (IDF), and total dietary fiber (TDF) in feed ingredients used in diets for pigs and poultry analyzed using Method AOAC 2011.25 are greater than values determined using Method AOAC 991.43. A second objective was to determine the variation that may exist among 3 laboratories using the 2 methods with the AnkomTDF Dietary Fiber Analyzer (Ankom Technology, Macedon, NY). The 3 laboratories were the Ministry of Agriculture Feed Industry Center (MAFIC) at China Agricultural University, Trouw Nutrition, and Hans H. Stein Monogastric Nutrition Laboratory at University of Illinois at Urbana-Champaign (UIUC). All laboratories analyzed SDF and IDF in feed ingredients in duplicate or triplicate using both methods AOAC 991.43 and 2011.25 with the AnkomTDF Dietary Fiber Analyzer. The 9 test ingredients were wheat, soybean meal, rapeseed meal, sugar beet pulp, peas, horse beans, native pea starch, and 2 samples of corn; 1 from Europe and 1 from China. All ingredient samples, with the exception of Chinese corn, were procured by Trouw Nutrition, ground to pass through a 0.5 mm screen, subsampled, and sent to MAFIC and UIUC. Data were analyzed using SDF, IDF, and TDF as response variables, replication as random effect, and method and location as fixed effects over all ingredients and within each ingredient. When averaged among 9 different ingredients, results indicated that SDF, IDF, and TDF values were not different with either method or at any laboratory. However, the concentration of IDF in corn, wheat, peas, and sugar beet pulp determined using Method AOAC 991.43 was greater (P < 0.05) compared with 2011.25. Soluble dietary fiber determined using Method AOAC 2011.25 was greater (P < 0.05) in corn, rapeseed meal, soybean meal, and sugar beet pulp compared with 991.43. There was no difference in TDF determined with either method, except for wheat having greater (P < 0.05) TDF when determined using Method AOAC 991.43. Interlaboratory variation for SDF, IDF, and TDF was 0.38, 0.87, 1.20, respectively, with Method AOAC 991.43 and 0.40, 0.93, and 1.27, respectively, with 2011.25. Therefore, values determined with the AnkomTDF Analyzer are repeatable among laboratories and can be used in feed formulation worldwide. In conclusion, it is recommended that Method AOAC 991.43 be used to determine SDF, IDF, and TDF in feed ingredients and diets for pigs and poultry.

Intravenous calcium infusion in a calving protocol disrupts calcium homeostasis compared with an oral calcium supplement.

Hypocalcemia is a common postpartum condition in dairy cows, which negatively affects health and production. Intravenous Ca infusions are commonly included in calving protocols to prevent or mitigate the effect of hypocalcemia in multiparous cows. Thus, we sought to contrast the effect of intravenous Ca infusion against voluntary oral Ca intake on Ca metabolism. Serum total Ca (tCa) and whole-blood ionized Ca (iCa) were monitored in 24 multiparous Holstein cows after parturition. Precalving diets were formulated with a positive dietary cation-anion difference of 172 mEq/kg of DM and contained 4.1 g of Ca/kg of DM. At parturition, cows were blocked by calving sequence and calcemic status as either normocalcemic (cutoff threshold of iCa ≥1.10 mmol/L) or hypocalcemic (cutoff threshold of iCa <1.10 mmol/L). Cows in each block were randomly assigned to 1 of 2 treatments: either an oral source of Ca (Ca-Oral; n = 12) or an intravenous source of Ca (Ca-IV; n = 12). Cows in the Ca-Oral group were offered a 20-L commercial Ca suspension (48 g of Ca) for voluntary consumption. The supplement contained Ca carbonate, Ca formate, Ca propionate, and other minerals and vitamins (Farm-O-San Reviva, Trouw Nutrition, Amersfoort, the Netherlands). Cows in the Ca-IV group received a 450-mL intravenous Ca solution (13 g of Ca) that contained 298 mg/mL of Ca gluconate, 33 mg/mL of magnesium chloride, and 82 mg/mL of boric acid (AmosCAL, Kommer-Biopharm BV, Heiloo, the Netherlands). Both treatments were initiated within 25 ± 10 min after calving. The oral Ca suspension was offered to cows in a 25-L bucket and was available for 10 min. All cows in the Ca-Oral group voluntarily consumed the entire 20 L of the Ca suspension within 5 min. Blood samples for Ca analyses were collected at 0 (before treatment initiation), 1, 3, 10, and 18 h relative to treatment, and at 0700 and 1900 h for the next 2 consecutive days, to represent the 24-, 36-, 48-, and 60-h sampling time points. In Ca-IV cows, both iCa and tCa concentrations peaked at 1 h (1.54 mmol/L for iCa and 2.85 mmol/L for tCa) and declined to a nadir at 24 h following treatment initiation (0.94 mmol/L for iCa and 1.74 mmol/L for tCa). Although whole-blood iCa and serum tCa were higher at 1 and 3 h in Ca-IV cows, concentrations of iCa were greater for Ca-Oral cows at 18, 24, and 36 h and for tCa at 24 and 36 h. Our data indicate that intravenous Ca infusion immediately induced a state of hypercalcemia followed by lower whole-blood iCa and serum tCa concentrations 24 h later compared with oral Ca.

Core Set of Patient-reported Outcomes in Pancreatic Cancer (COPRAC): An International Delphi Study Among Patients and Health Care Providers.

OBJECTIVE: To establish an international core set of patient-reported outcomes (PROs) selected by both patients and healthcare providers (HCPs) from the United States (US), Europe, and Asia. SUMMARY BACKGROUND DATA: PROs are increasingly recognized in pancreatic cancer studies. There is no consensus on which of the many available PROs are most important. METHODS: A multicenter Delphi study among patients with pancreatic cancer (curative- and palliative-setting) and HCPs in 6 pancreatic centers in the US (Baltimore, Boston), Europe (Amsterdam, Verona), and Asia (Mumbai, Seoul) was performed. In round 1, participants rated the importance of 56 PROs on a 1 to 9 Likert scale. PROs rated as very important (scores 7-9) by the majority (≥80%) of curative- and/or palliative-patients as well as HCPs were included in the core set. PROs not fulfilling these criteria were presented again in round 2, together with feedback on individual and group ratings. Remaining PROs were ranked based on the importance ratings. RESULTS: In total 731 patients and HCPs were invited, 501 completed round 1, and 420 completed both rounds. This included 204 patients in curative-setting, 74 patients in palliative-setting, and 142 HCPs. After 2 rounds, 8 PROs were included in the core set: general quality of life, general health, physical ability, ability to work/do usual activities, fear of recurrence, satisfaction with services/care organization, abdominal complaints, and relationship with partner/family. CONCLUSIONS: This international Delphi study among patients and HCPs established a core set of PROs in pancreatic cancer, which should facilitate the design of future pancreatic cancer trials and outcomes research.

ROCK inhibitors 3: Design, synthesis and structure-activity relationships of 7-azaindole-based Rho kinase (ROCK) inhibitors.

Rho kinase (ROCK) inhibitors are potential therapeutic agents for the treatment of a variety of disorders including hypertension, glaucoma and erectile dysfunction. Here we disclose a series of potent and selective ROCK inhibitors based on a substituted 7-azaindole scaffold. Substitution of the 3-position of 7-azaindole led to compounds such as 37, which possess excellent ROCK inhibitory potency and high selectivity against the closely related kinase PKA.

Soft document clustering using a novel graph covering approach.

BACKGROUND: In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. RESULTS: In this paper we present and discuss a novel graph-theoretical approach for document clustering and its application on a real-world data set. We will show that the well-known graph partition to stable sets or cliques can be generalized to pseudostable sets or pseudocliques. This allows to perform a soft clustering as well as a hard clustering. The software is freely available on GitHub. CONCLUSIONS: The presented integer linear programming as well as the greedy approach for this NP -complete problem lead to valuable results on random instances and some real-world data for different similarity measures. We could show that PS-Document Clustering is a remarkable approach to document clustering and opens the complete toolbox of graph theory to this field.