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ObjectiveTo review and frequently update the available evidence on injury risk factors and epidemiology of injury in trail running.DesignLiving systematic review. Updated searches will be done every 6 months for a minimum period of 5 years.Data sourcesEight electronic databases were searched from inception to 18 March 2021.Eligibility criteriaStudies that investigated injury risk factors and/or reported the epidemiology of injury in trail running.ResultsNineteen eligible studies were included, of which 10 studies investigated injury risk factors among 2 785 participants. Significant intrinsic factors associated with injury are: more running experience, level A runner and higher total propensity to sports accident questionnaire (PAD-22) score. Previous history of cramping and postrace biomarkers of muscle damage is associated with cramping. Younger age and low skin phototypes are associated with sunburn. Significant extrinsic factors associated with injury are neglecting warm-up, no specialised running plan, training on asphalt, double training sessions per day and physical labour occupations. A slower race finishing time is associated with cramping, while more than 3 hours of training per day, shade as the primary mode of sun protection and being single are associated with sunburn. An injury incidence range 0.7–61.2 injuries/1000 hours of running and prevalence range 1.3% to 90% were reported. The lower limb was the most reported region of injury, specifically involving blisters of the foot/toe.ConclusionLimited studies investigated injury risk factors in trail running. Our review found eight intrinsic and nine extrinsic injury risk factors. This review highlighted areas for future research that may aid in designing injury risk management strategies for safer trail running participation.PROSPERO registration numberCRD42021240832.

Background and objectives: Starting the multicomponent training sessions with aerobic-based exercises or resistance-based exercises may have different effects on functional fitness and body composition. Thus, the aim of this study was to assess the effects of the order of exercises in elderly women’s physical fitness and body composition by multicomponent training. Materials and Methods: A sample of 91 elderly females, aged between 60 and 81, were randomly divided into three groups (A, B, C). Each group performed the following order of exercises: Group A consisted of warm-up followed by aerobic training, strength training, stretching and cool down; Group B consisted of warm-up followed by strength training, aerobic training, stretching and cool down; while the control group (C) did not perform any exercise. Functional fitness and body composition were assessed at 3 moments of the 32 weeks (baseline and after each 16-week) intervention. One-way ANOVA for comparison between groups, ANOVA for repeated measures and multiple linear regression were used for statistical analysis. Results: The results showed that the functional fitness and body composition varied over the 32 weeks of multicomponent training. However, group A seems to show higher improvements in more variables. Conclusion: In the current study, group A obtained better results in most of the evaluated parameters. Thus, to improve functional fitness, warm-up, followed by aerobic training, strength training and relaxation may be the most suitable training for elderly women This work is supported the Higher Institute of Educational Sciences of the Douro and by national funds (FCT—Portuguese Foundation for Science and Technology) under the project UIBD/DTP/04045/2020. info:eu-repo/semantics/publishedVersion

Our aim is to describe a family with a nonsyndromic form of hereditary gingival fibromatosis (HGF) and discuss genetic characteristics of this rare disease by reviewing reported cases. A mother and three descendants were diagnosed with HGF. There was marked variable expressivity: from severe generalized gingival overgrowth in a 16-year-old boy (the proband) to minimal manifestations in the mother. The proband was submitted to gingivectomy and gingivoplasty. In younger siblings, the disease remained stable for 5 years, suggesting that clinical surveillance is a good option. The diagnosis was supported by histopathological examination. Analysis of this family and literature-reported cases supports that HGF most frequently shows an autosomal dominant inheritance with high penetrance and variable expressivity. Neomutations and gonadal mosaicism do not seem to be a rare event. Although five loci have been mapped by linkage analysis, only two genes, SOS1 and REST, were identified in four families.

Background Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes’ organization, relation, and fundamental characteristics. Results This work provides a comprehensive landscape of the viral genome’s complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. Conclusions This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes’ organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.

Background and Objectives: Peri-implantitis treatment is still undefined. Regenerative treatment is expensive and technically demanding due to the need to handle biomaterials, membranes and different methodologies of decontamination. Resective treatment and implantoplasty might be a viable solution. This case series presents a 24 month retrospective observational study of 10 peri-implantitis patients treated with implantoplasty. Materials and Methods: In the present case series, 10 peri-implantitis patients (20 implants) were treated with a resective approach and implantoplasty. Previous to implantoplasty, all patients underwent non-surgical treatment. This surgery consisted in a full-thickness flap and implant surface exposure. The exposed non-osseointegrated implant body was submitted to implantoplasty. The flap was apically repositioned and sutured. Patients were accompanied for 24 months. Results: The mean initial probing depth (PD) (PD = 5.37 ± 0.86 mm), bleeding on probing (BoP = 0.12 ± 0.06%) and suppuration (Sup = 0.01 ± 0.01%) decreased significantly at the 12 month evaluation (PD = 2.90 ± 0.39 mm; BoP = 0.01 ± 0.01% and Sup = 0.00 ± 0.00%). Between the 12 and 24 month evaluations, there were no significant clinical changes (PD = 2.85 ± 0.45 mm; BoP = 0.01 ± 0.01% and Sup = 0.00 ± 0.00%). Mucosal recession (MR) had a significant increase between the baseline and the first 12 months (0.69 ± 0.99 mm vs. 1.96 ± 1.33 mm), but there were no significant changes between the 12th and 24th month (1.94 ± 1.48 mm). The success rate was 100% without implant fracture or loss. Conclusions: Resective surgery and implantoplasty might be a valid option in some specific peri-implantitis cases. Properly designed clinical trials are needed to confirm this possibility.

Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices (e.g. earlier disease diagnosis).We propose a solution to model patient trajectories that combines different types of information (e.g. clinical text, standard codes) and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism.The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available Medical Information Mart for Intensive Care (MIMIC)-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions.Herein, we explored DL-based techniques to model patient trajectories and propose two flexible architectures that explore patient admissions on an individual and sequence basis. The combination of clinical text with other types of information led to positive results, which can be further improved by including a fine-tuned version of clinicalBERT in the architectures. The proposed solution can be publicly accessed at https://github.com/bioinformatics-ua/PatientTM.

The purpose of the study was to test the within-week variations of the internal and external training intensity outcomes organized by days of the week. An 8-month observational period was conducted during the 2020–2021 season. The training sessions and matches of an elite volleyball team were monitored daily. The data comes from 14 players (two setters, five middle blockers, five outside hitters, and two opposites) of an elite team from the Portuguese 1st League (age: 21.7 ± 4.19 years of age; experience: 6.2 ± 3.8 years; body mass: 85.7 ± 8.69 kg; height: 192.4 ± 6.25 cm; BMI: 23.1 ± 1.40 kg/m2). The CR10 Borg scale was applied daily to measure the training intensity. The rate of perceived exertion (RPE) and the session-RPE were extracted as the internal outcomes. The external intensity was measured using an inertial measurement unit (IMU). The number of jumps, height average of jumps (JHA), minimum jump (MJ), maximal jump (MXJ), range jump (RJ), number of jumps (NJ), and training session density (D) were extracted as external intensity outcomes. The results showed that there was a difference between RPE and S-RPE (F (1.98) = 6.31, p = 0.01, η2 = 0.36, and F (1.73) = 28.30, p = 0.001, η2 = 0.72), as well as JHA and NJ (F (2.14) = 4.76, p = 0.02, η2 = 0.30, and F (1.77) = 4.77, p = 0.02, η2 = 0.30) within the microcycle. When analyzing the correlations between internal and external intensity, it was observed that there was a negative correlation between the Maximum Jump (4, 3, and 1 days before the Match day) (r2 = 0.34, r2 = 0.40, r2 = 0.41, respectively) and the Range Jump (3 and 1 days before the Match day (r2 = 0.33, r2 = 0.38, respectively) with the RPE (4 days before the Match day) and Maximum Jump (5, 4, 3, and 1 days before the Match day (r2 = 0.35, r2 = 0.39, r2 = 0.44, r2 = 0.34, respectively) and Range Jump (5, 4, 3, and 1 days before the Match day) (r2 = 0.34, r2 = 0.35, r2 = 0.40 and r2 = 0.36, respectively) with S_RPE (4 days before the Match day). Such findings show that higher internal intensities are correlated with lower external intensities in sessions further away from the game day. Such results could be an important tool for coaches to reflect, plan, monitor, and execute the training unit according to the temporal distance to the competition.

Background The purpose of this study was to characterize trail running injuries within a cohort of Portuguese male and female recreational trail running athletes. Methods The study was retrospective (12 months), with data collected through an online survey. A total of 719 athletes participated (529 male and 190 female, average age: 38.01±7.78 years). A valid questionnaire was used to collect: 1) demographic information; 2) general information; 3) training typology; 4) physical information; 5) injuries (body location, number, type, reason, treatment, time without practice); and 6) general information concerning the effects of injuries on respondents' daily lives. Results The results revealed that 87.8% of the sample contracted an injury resulting from this practice, with the toenails (24.8%), knees (17.5%), and ankles (14.5%) being the most-often reported locations of injuries and blisters (20%), irritation (chafing) (14%), superficial wounds (12%), sprains (11%), and iliotibial band syndrome (7%) being the most common injuries. The result of 10.0 injuries per 1000 h was found, with a negative and medium correlation (r=-0.344; P=0.000) between total exposure time and injuries per 1000 h. It was also found that those who do not perform warm-up have a significantly increased injury rate (P Conclusions The gathered evidence demonstrated a large number of dermatological and musculoskeletal injuries, and these tended to have higher incidences in athletes with less exposure time and who devalue warm-up exercises.

Electronic health records contain valuable information on patients' clinical history in the form of free text. Manually analyzing millions of these documents is unfeasible and automatic natural language processing methods are essential for efficiently exploiting these data. Within this, normalization of clinical entities, where the aim is to link entity mentions to reference vocabularies, is of utmost importance to successfully extract knowledge from clinical narratives. In this paper we present sieve-based models combined with heuristics and word embeddings and present results of our participation in the 2019 n2c2 (National NLP Clinical Challenges) shared-task on clinical concept normalization.

Endurance sports like trail running constitute an extensive individual modality causing numerous physiological changes to occur in the athlete. In this sense, an adequate monitoring of training load appears to be essential to improve competition performance. The aim of this study was two-fold: (i) to analyze trail runners&rsquo, weekly load variations in the four weeks leading up to a trail running competition, and (ii) to determine the relationship between the runners&rsquo, pacing in competitions and their physical fitness and workload parameters. Twenty-five amateur male trail runners (age: 36.23 &plusmn, 8.30 years old, minimum International Trail Running Association performance index: 600) were monitored daily for the duration of a season (52 weeks). External load (distance covered, pace) and internal load (rate of perceived exertion) were measured daily. Additionally, weekly workload measures of acute:chronic workload ratio (ACWR), training monotony, and training strain were calculated. The runners were also assessed for maximal aerobic speed (MAS) every four months. No significant differences in workload measures (p &gt, 0.05) were observed in the four weeks leading up to each short trail competition, however, leading up to the long trail, ultra-trail medium, and ultra-trail long/extra-long competitions, the differences in the runners&rsquo, workload measures were significant (p &lt, 0.05). In the short trail, pace was found to be moderately correlated with the ACWR of total distance (r = &minus, 0.334) and with training monotony of rate of perceived exertion (RPE) (r = &minus, 0.303). In the ultra-trail, a large correlation was observed between pace and elevation accumulated (r = 0.677). We concluded that significant workload differences from one week to the next only occurred in preparation for longer-distance competitions, with sudden acute load decreases and very low ACWR values reported mainly in weeks 1 and 2 of the taper. Meaningful relationships were found between performance (pace) and MAS for longer trails and between pace and MAS for ultra-trail competitions.

SETTING Although cough in tuberculosis (TB) is presumed to be important for transmission, there is little objective supporting evidence. OBJECTIVE To describe 24-h cough frequency in a group with TB, and investigate associations with household rates of infection. DESIGN Patients with a new diagnosis of pulmonary TB underwent 24-h cough frequency measurement at or just before initiation of anti-tuberculosis treatment. A group with latent Mycobacterium tuberculosis infection (LTBI) acted as controls. Rates of infection among household contacts of sputum smear-positive TB were measured using the interferon-gamma release assay and the tuberculin skin test, and compared with variables relating to the contacts themselves, and to the index case, including cough frequency. RESULTS Daily cough frequency in TB patients (n = 44) was variable (geometric mean [GM] 174, interquartile range [IQR] 68-475 coughs/24 h), higher than in LTBI (n = 17; GM 19 coughs/24 h, IQR 8-53; P < 0.001), and higher during the day than overnight (GM 8.9 coughs/h, IQR 4.1-19.0 vs. GM 2.9 coughs/h, IQR 0.7-13.4; P < 0.0001). Also, 24-h cough frequency in TB was associated with sputum smear status (P = 0.040), but not smoking (P = 0.475). Multivariable logistic regression confirmed that infection in contacts was independently associated with index case sputum smear grade (P = 0.014) and cough frequency (P = 0.022). CONCLUSION Measurement of 24-h cough frequency in pulmonary TB helps predict infectiousness and transmission patterns.

Introduction Cough in bronchiectasis is associated with significant impairment in health status. This study aimed to quantify cough frequency objectively with a cough monitor and investigate its relationship with health status. A secondary aim was to identify clinical predictors of cough frequency. Methods Fifty-four patients with bronchiectasis were compared with thirty-five healthy controls. Objective 24-h cough, health status (cough-specific: Leicester Cough Questionnaire LCQ and bronchiectasis specific: Bronchiectasis Health Questionnaire BHQ), cough severity and lung function were measured. The clinical predictors of cough frequency in bronchiectasis were determined in a multivariate analysis. Results Objective cough frequency was significantly raised in patients with bronchiectasis compared to healthy controls [geometric mean (standard deviation)] 184.5 (4.0) vs. 20.6 (3.2) coughs/24-h; mean fold-difference (95% confidence interval) 8.9 (5.2, 15.2); p

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct textmining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the textmining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-4/

Background and objective Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection. This article presents a multidisciplinary collaborative platform that was created to meet the needs of physicians and researchers, aiming at the creation of machine learning algorithms to facilitate the screening process. Methods Our proposal is a collaborative platform for textual and visual annotation of image datasets. The architecture and layout were optimized for annotating DR images by gathering feedback from several physicians during the design and conceptualization of the platform. It allows the aggregation and indexing of imagiology studies from diverse sources, and supports the creation and annotation of phenotype-specific datasets to feed artificial intelligence algorithms. The platform makes use of an anonymization pipeline and role-based access control for securing personal data. Results The SCREEN-DR platform has been deployed in the production environment of the SCREEN-DR project at http://demo.dicoogle.com/screen-dr , and the source code of the project is publicly available. We provide a description of the platform's interface and use cases it supports. At the time of publication, four physicians have created a total of 1826 annotations for 701 distinct images, and the annotated data has been used for training classification models.

Diabetic Retinopathy (DR) is a common complication of diabetes that may lead to blindness if not treated. However, since DR evolves without any symptoms in the initial stages, early detection and treatment can only be achieved through routine checks. This article presents the collaborative platform of the SCREEN-DR project that promotes partnership between physicians and researchers in the scope of a regional DR screening program. The role of researchers is to create classification algorithms to evaluate image quality, discard non-pathological cases, locate possible lesions and grade DR severity. Physicians are responsible for annotating datasets, including the visual delineation of lesions. The collaborative platform collects the studies, indexes the images metadata, and manages the creation of datasets and the respective annotation process. An advanced searching mechanism supports multimodal queries over annotated datasets and exporting of results for feeding artificial intelligence algorithms.

Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon. This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated. The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75 % of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism. The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.

The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/. Database URL: https://demo.bmd-software.com/egas/

BACKGROUND: The recent development of automated cough monitors has enabled objective assessment of cough frequency. A study was undertaken to determine whether short-duration recordings( < 6 h) accurately reflect 24-h cough frequency and to investigate their responsiveness. METHODS: One hundred adults with chronic cough underwent 24-h cough frequency monitoring with the Leicester Cough Monitor and completed cough visual analog scales (VASs) and the Leicester Cough Questionnaire (LCQ). Cough recordings were analyzed using customized software to derive cough frequencies from 1 to 6 h and 24-h recordings. Responsiveness was assessed with repeat assessments following therapeutic trials. RESULTS: The median (interquartile range) 24-h cough frequency was 11.5 (5.8-26.6) coughs/h. Four hours was considered the shortest recording duration that represented 24-h cough frequency( ρ= 0.9, P ≤ .001). Median 4-h cough frequency was 16.6 (7.3-36.8) coughs/h. Both 4-h and 24-h cough frequency correlated moderately with cough VAS ( ρ= 0.49, P ≤ .01 and ρ= 0.44, P ≤ .01)and LCQ ( ρ = - 0.48, P ≤ .01; ρ = - 0.50, P ≤ .01). Four-hour cough frequency was responsive to improvements in cough severity following trials of therapy. CONCLUSIONS: Four-hour cough frequency correlates highly with 24-h cough frequency recordings and relates equally well with subjective measures in chronic cough. Short-duration cough monitoring could be a practical tool to validate the presence of cough and assess response to trials of therapy in the clinic setting.

Background: This prospective, randomized, controlled clinical trial study compared the clinical outcomes of the biomaterial anorganic bovine-derived hydroxyapatite matrix/cell-binding peptide (ABM/P-15) as a biocompatible hydrogel carrier consisting of carboxymethylcellulose and glycerol or in particulate form when used as a bone replacement graft in the treatment of human periodontal infrabony defects. Methods: Nineteen patients with advanced chronic periodontitis were recruited. All patients had at least two non-adjacent intrabony osseous defects ‡3 mm after completion of cause-related periodontal therapy. The surgical procedures included access flaps for root instrumentation and filling the defect with ABM/P-15 in hydrogel or particulate form. Reentry access flap surgery was performed at 6 months. Changes in soft and hard tissue outcome measurements between baseline and 6 months were evaluated in all defects. Results: At 6 months, no significant differences between ABM/P15 hydrogel and ABM/P-15 particulate were demonstrated for the amount of defect fill (3.10 – 0.85 mm [75.0%] versus 3.09 – 1.11 mm [73.7%], respectively) or defect resolution (85.8% versus 81.9%). Changes in soft tissue clinical outcomes did not show significant differences between the treatments. Conclusion: This trial failed to demonstrate superiority of the novel ABM/P-15 hydrogel therapeutic modality over the standard ABM/ P-15 particulate graft in the treatment of intrabony periodontal defects. J Periodontol 2007;78:1855-1863.

The objective monitoring of cough for extended periods of time has long been recognized as an important step towards a better understanding of this symptom, and a better management of chronic cough patients. In this paper, we present a system for the automatic analysis of 24-h, continuous, ambulatory recordings of cough. The system uses audio recordings from a miniature microphone and the detection algorithm is based on statistical models of the time-spectral characteristics of cough sounds. We validated the system against manual counts obtained by a trained observer on 40 ambulatory recordings and our results show a median sensitivity value of 85.7%, median positive predictive value of 94.7% and median false positive rate of 0.8 events/h. An analysis application was developed, with a graphical user interface, allowing the use of the system in clinical settings by technical or medical staff. The result of the analysis of a recording session is presented as a concise, graphical-based report. The modular nature of the system interface facilitates its enhancement with the integration of further modules.

To evaluate the effectiveness of a bone substitute covered with a resorbable membrane versus open flap debridement for the treatment of periodontal infrabony defects.Ninety-seven patients with one infrabony defect, which was 3 mm or deeper and at least 2 mm wide were randomly allocated either to grafting with a bone substitute covered with a resorbable barrier (BG group) or open flap debridement (OFD group) according to a parallel group design in five European centres. Blinded outcome measures assessed tooth loss, complications, patient's satisfaction with treatment and aesthetics, changes in probing attachment levels (PAL), probing pocket depths (PPD), gingival recessions (REC), radiographic bone levels (RAD) on standardised periapical radiographs, plaque index (PI) and marginal bleeding index (MBI).49 patients were randomly allocated to the BG group and 48 to the OFD group. At baseline there were more mobile teeth in the BG group (29 versus 15). One year after treatment two patients dropped out from the BG group and no teeth were lost. Three complications (minor postoperative wound dehiscence) occurred in the BG group versus none in the OFD group, where the difference was not statistically significant. The BG group obtained significantly greater statistical PAL gain (mean difference = -0.8 mm, 95% CI [-1.51; -0.03], P = 0.0428), PPD reduction (mean difference = -1.1 mm, 95% CI [-1.84; -0.19], P = 0.0165) and RAD gain (mean difference = -1.2 mm, 95% CI [-2.0; -0.4], P = 0.0058) compared to the OFD group. No statistically significant differences between the groups were observed for gingival recession, or the patient's satisfaction with the treatment and aesthetics. There were some statistically significant differences between the centres for PAL and PPD with the Italian centres reporting better outcomes.The use of a bone substitute covered with a resorbable membrane yielded significantly better statistical clinical outcomes than open flap debridement in the treatment of periodontal infrabony defects deeper than 3 mm, with regard to PAL gain, PPD reduction and RAD gain.

To the Editor: Chronic cough is a difficult clinical problem, partly because there is an absence of well-validated means to assess cough [1, 2]. We have previously reported that the semi-automated computerised Leicester Cough Monitor detects cough accurately over 6 h and that cough frequency is increased in patients with chronic cough compared to controls [3, 4]. There remains uncertainty on the performance of the system over 24 h and across the range of expected cough frequency in larger populations. We set out to address these questions in healthy adult volunteers and adult volunteers with respiratory disease. 44 healthy volunteers were recruited from those responding to a poster advertisement. All reported no current respiratory symptoms, were nonsmokers with a

Objectives An ex vivo model was designed to profilometrically and histologically assess root changes resulting from scaling with a new ultrasonic device, designed for bone piezoelectric surgery, in comparison with curettes. Methods Three groups of 10 periodontal hopeless teeth were each subjected to different root instrumentation: Gracey curettes (CUR); ultrasonic piezoelectric device, Perio 100% setting, level 8 (P100); and ultrasonic piezoelectric device Surg 50% setting, level 1 (S50). After extraction, all teeth were photographed to visually assess the presence of dental calculus. The treated root surfaces were profilometrically evaluated (Ra, Rz, Rmax). Undecalcified histological sections were prepared to assess qualitative changes in cementum thickness. Statistical analysis was carried out using one-way anova test with a significance level of 95%. Results Both instruments proved to be effective in the complete removal of calculus. The CUR group presented the lowest Ra [2.28 μm (±0.58)] and S50 the highest [3.01 μm (±0.61)]. No statistically significant differences were detected among the three groups, for Ra, Rz and Rmax. Histologically, there was a cementum thickness reduction in all groups, being higher and more irregular in S50 group. Conclusions Within the limits of this study, there were no statistically significant differences in roughness parameters analyzed between curettes and the ultrasonic piezoelectric unit. This new instrument removes a smaller amount of cementum, mainly at the Perio 100% power setting, which appears to be the least damaging. The ultrasonic device is effective in calculus removal, proving to be as effective as curettes.

BackgroundThe oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.ResultsWe collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10−7), leading to a set of 46,579 PPIs to be further explored.ConclusionsWe believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available athttp://bioinformatics.ua.pt/software/oralint.

With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.

RATIONALE: Cough can be assessed with visual analog scales (VAS), health status measures, and 24-hour cough frequency monitors (CF(24)). Evidence for their measurement properties in acute cough caused by upper respiratory tract infection (URTI) and longitudinal data is limited. OBJECTIVES: To assess cough longitudinally in URTI with subjective and objective outcome measures and determine sample size for future studies. METHODS: Thirty-three previously healthy subjects with URTI completed cough VAS, Leicester Cough Questionnaire (LCQ-acute), and CF(24) monitoring (Leicester Cough Monitor) on three occasions, 4 days apart. Changes in subjects' condition were assessed with a global rating of change questionnaire. The potential for baseline first-hour cough frequency (CF(1)), VAS, and LCQ to identify low CF(24) was assessed. MEASUREMENTS AND MAIN RESULTS: Mean ± SD duration of cough at visit 1 was 4.1 ± 2.5 days. Geometric mean ± log SD baseline CF(24) and median (interquartile range) cough bouts were high (14.9 ± 0.4 coughs/h and 85 [39-195] bouts/24 h). Health status was severely impaired. There was a significant reduction in CF(24) and VAS, and improvement in LCQ, from visits 1-3. At visit 3, CF(24) remained above normal limits in 52% of subjects. The smallest changes in CF(24), LCQ, and VAS that subjects perceived important were 54%, 2- and 17-mm change from baseline, respectively. The sample sizes required for parallel group studies to detect these changes are 27, 51, and 25 subjects per group, respectively. CF(1) (

Motivation: The recognition of named entities (NER) is an elementary task in biomedical text mining. A number of NER solutions have been proposed in recent years, taking advantage of available annotated corpora, terminological resources and machine-learning techniques. Currently, the best performing solutions combine the outputs from selected annotation solutions measured against a single corpus. However, little effort has been spent on a systematic analysis of methods harmonizing the annotation results and measuring against a combination of Gold Standard Corpora (GSCs). Results: We present Totum, a machine learning solution that harmonizes gene/protein annotations provided by heterogeneous NER solutions. It has been optimized and measured against a combination of manually curated GSCs. The performed experiments show that our approach improves the F-measure of state-of-the-art solutions by up to 10% (achieving ≈70%) in exact alignment and 22% (achieving ≈82%) in nested alignment. We demonstrate that our solution delivers reliable annotation results across the GSCs and it is an important contribution towards a homogeneous annotation of MEDLINE abstracts. Availability and implementation: Totum is implemented in Java and its resources are available at http://bioinformatics.ua.pt/totum Contact: david.campos@ua.pt; rebholz@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). Results: We received a total of 37 runs from 14 different teams for the task. When evaluated using the goldstandard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. Conclusions: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

Background Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. Results A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%. Conclusions The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.

AIMS: Unexplained chronic cough is a common condition with no satisfactory treatments. Previous work has suggested that cough may be linked to neutrophilic airway inflammation. This study tested the hypothesis that long-term low-dose erythromycin reduces the induced sputum neutrophil count and 24 h cough frequency in patients with unexplained chronic cough. METHODS: 30 patients with an unexplained chronic cough lasting more than 8 weeks were randomly assigned to take 250 mg erythromycin once daily (n=15) or placebo (n=15) for 12 weeks in a double-blind parallel group study. Cough frequency, cough reflex sensitivity and cough severity were assessed at baseline, 6, 12 and 24 weeks. The primary outcome measure was change in 24 h cough frequency at 12 weeks. RESULTS: There was no difference in the change in cough frequency between the erythromycin and placebo groups at 12 weeks (mean difference in fold change 1.1; 95% CI 0.7 to 1.5; p=0.585) or at other times. There was a statistically significant between-treatment difference in the change in sputum neutrophils at 12 weeks (−10.2% vs +6.6% with erythromycin and placebo; mean difference 16.8%; 95% CI 1.6 to 32.1; p=0.03) but not at other times. There was no difference in the change in other measures of cough between treatments. CONCLUSIONS: Treatment with low-dose erythromycin for 12 weeks reduces the induced sputum neutrophil count but not cough frequency or severity in patients with unexplained chronic cough.

Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.

Summary Background Little is known about the frequency of cough in health and in patients with chronic cough. Methods We measured cough frequency and its relationship with other markers of cough severity in 20 patients with chronic cough and 9 healthy subjects using the Leicester Cough Monitor (LCM), which is an automated ambulatory digital cough monitor that records sound only. All subjects had a 6-h recording and recordings were manually counted. A subgroup of 6 normals and 6 patients with a stable chronic cough had repeat measurements up to 6 months apart. Results Mean ( sem ) cough counts/hour were 43(8) in patients with chronic cough and 2(1) in normals (mean difference 41; 95% confidence interval 24–59; P 0.001 ). The cough counts were repeatable (within subject standard deviation: 23 coughs/hour; intraclass correlation coefficient 0.8). Cough counts correlated significantly with physical ( r = - 0.6 , P = 0.03 ), social ( r = - 0.7 , P = 0.01 ) and total Leicester Cough Questionnaire (LCQ) health status scores ( r = - 0.6 , P = 0.03 ) and cough sensitivity (concentration of capsaicin causing 5 coughs: r = 0.9 , P = 0.008 ). Conclusion We have shown that there are marked differences in cough frequency between patients with chronic cough and healthy subjects, that these measurements are repeatable, and that they correlate with cough-specific health status.

Background Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Results Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p < 0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Conclusions Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.

Background:The content of the clinical notes that have been continuously collected along patients’health history has the potential to provide relevant information about treatments and diseases, and to increase the value of structured data available in Electronic Health Records (EHR) databases. EHR databases are currently being used in observational studies which lead to important findings in medical and biomedical sciences. However, the information present in clinical notes is not being used in those studies, since the computational analysis of this unstructured data is much complex in comparison to structured data. Methods:We propose a two-stage workflow for solving an existing gap in Extraction, Transformation and Loading (ETL) procedures regarding observational databases. The first stage of the workflow extracts prescriptions pre- sent in patient’s clinical notes, while the second stage harmonises the extracted information into their standard definition and stores the resulting information in a common database schema used in observational studies.Results:We validated this methodology using two distinct data sets, in which the goal was to extract and store drug related information in a new Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database. We analysed the performance of the used annotator as well as its limitations. Finally, we described some practical examples of how users can explore these datasets once migrated to OMOP CDM databases. Conclusion:With this methodology, we were able to show a strategy for using the information extracted from the clinical notes in business intelligence tools, or for other applications such as data exploration through the use of SQL queries. Besides, the extracted information complements the data present in OMOP CDM databases which was not directly available in the EHR database.

Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .