The purpose of this study was to compare the effects of Small-Sided Games (SSG) vs. Interval Training (IT) in soccer training on aerobic fitness and physical enjoyment in youth elite soccer players ...during the last 8 weeks of the season. Seventeen U-16 male soccer players (age = 15.5 ± 0.6 years, and 8.5 years of experience) of a Spanish First Division club academy were randomized to 2 different groups for 6 weeks: SSG group (n = 9) and IT group (n = 8). In addition to the usual technical and tactical sessions and competitive games, the SSG group performed 11 sessions with different SSGs, whereas the IT group performed the same number of sessions of IT. Players were tested before and after the 6-week training intervention with a continuous maximal multistage running field test and the counter movement jump test (CMJ). At the end of the study, players answered the physical activity enjoyment scale (PACES). During the study, heart rate (HR) and session perceived effort (sRPE) were assessed. SSGs were as effective as IT in maintaining the aerobic fitness in elite young soccer players during the last weeks of the season. Players in the SSG group declared a greater physical enjoyment than IT (P = 0.006; ES = 1.86 ± 1.07). Coaches could use SSG training during the last weeks of the season as an option without fear of losing aerobic fitness while promoting high physical enjoyment.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the ...refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography–based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.
•Quantitative imaging analysis and deep learning are raising expectations to deliver correlations between imaging and biology of the disease.•Researchers are seeking applications for quantitative ...image analysis for COVID-19 for earlier detection, monitoring of progression, and prediction of outcomes.•High-quality quantitative imaging algorithms and processing tools are presented to establish imaging standards for meaningful future research•To obtain quick and reliable research results, generosity in creating open science research databases is a must.
The prevalence of bronchiectasis in the general population and in individuals undergoing lung cancer screening with low dose computed tomography (LDCT) is unknown. The aim of this study is to ...estimate the prevalence and impact of bronchiectasis in a screening lung cancer program.
3028 individuals participating in an international multicenter lung cancer screening consortium (I-ELCAP) were selected from 2000 to 2012. Patients with bronchiectasis on baseline CT were identified and compared to selected controls. Detection of nodules, need for additional studies and incidence of cancer were analyzed over the follow-up period.
The prevalence of bronchiectasis was 11.6%(354/3028). On the baseline LDCT, the number of subjects with nodules identified was 189(53.4%) in patients with bronchiectasis compared to 63(17.8%) in controls (p<0.001). The occurrence of false positives was higher in subjects with bronchiectasis (26%vs17%;p = 0.003). During follow-up, new nodules were more common among subjects with bronchiectasis (17%vs.12%; p = 0.008). The total number of false positives during follow-up was 29(17.06%) for patients with bronchiectasis vs. 88(12.17%) for controls (p = 0.008).The incidence rate of lung cancer during follow-up was 6.8/1000 and 5.1/1000 person-years for each group respectively (p = 0.62).
Bronchiectasis are common among current and former smokers undergoing lung cancer screening with LDCT. The presence of bronchiectasis is associated with greater incidence of new nodules and false positives on baseline and follow-up screening rounds. This leads to an increase need of diagnostic tests, although the lung cancer occurrence is not different.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background The COPD-Lung Cancer Screening Score (COPD-LUCSS) is a tool designed to help identify patients with COPD with the highest risk of developing lung cancer (LC). The COPD-LUCSS includes the ...determination of radiological emphysema, a potential limitation for its implementation in clinical practice. The diffusing capacity for carbon monoxide (DLCO) is a surrogate marker of emphysema and correlates well with CT-determined emphysema. Objective To explore the use of the COPD-LUCSS using the DLCO instead of radiological emphysema, as a tool to identify patients with COPD at higher risk of LC death. Methods The Body Mass Index, Airflow Obstruction, Dyspnea, Exercise Performance international cohort database was analyzed. By logistic regression analysis, we confirmed that the other parameters included in the COPD-LUCSS (age > 60, pack-years > 60, BMI < 25) were independently associated with LC death. We selected the best cutoff value for DLCO that independently predicted LC death. We then integrated the new COPD-LUCSS-DLCO assigning points to each parameter according to its hazard ratio value in the Cox regression model. The score ranges from 0 to 8 points. Results By regression analysis, age > 60, BMI <25 kg/m2 , pack-year history > 60, and DLCO < 60% were independently associated with LC diagnosis. Two COPD-LUCSS-DLCO risk categories were identified: low risk (scores 0-3) and high risk (scores 3.5-8). In comparison to patients at low risk, risk of death from LC increased 2.4-fold (95% CI, 2.0-2.7) in the high-risk category. Conclusions The COPD-LUCSS using DLCO instead of CT-determined emphysema is a useful tool to identify patients with COPD at risk of LC death and may help in its implementation in clinical practice.
COPD comorbidities network Divo, Miguel J; Casanova, Ciro; Marin, Jose M ...
European respiratory journal/The European respiratory journal,
09/2015, Letnik:
46, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Multimorbidity frequently affects the ageing population and their co-existence may not occur at random. Understanding their interactions and that with clinical variables could be important for ...disease screening and management.In a cohort of 1969 chronic obstructive pulmonary disease (COPD) patients and 316 non-COPD controls, we applied a network-based analysis to explore the associations between multiple comorbidities. Clinical characteristics (age, degree of obstruction, walking, dyspnoea, body mass index) and 79 comorbidities were identified and their interrelationships quantified. Using network visualisation software, we represented each clinical variable and comorbidity as a node with linkages representing statistically significant associations.The resulting COPD comorbidity network had 428, 357 or 265 linkages depending on the statistical threshold used (p≤0.01, p≤0.001 or p≤0.0001). There were more nodes and links in COPD compared with controls after adjusting for age, sex and number of subjects. In COPD, a subset of nodes had a larger number of linkages representing hubs. Four sub-networks or modules were identified using an inter-linkage affinity algorithm and their display provided meaningful interactions not discernible by univariate analysis.COPD patients are affected by larger number of multiple interlinked morbidities which clustering pattern may suggest common pathobiological processes or be utilised for screening and/or therapeutic interventions.
Aging is an important risk factor for most chronic diseases. Patients with COPD develop more comorbidities than non-COPD subjects. We hypothesized that the development of comorbidities ...characteristically affecting the elderly occur at an earlier age in subjects with the diagnosis of COPD.
We included all subjects carrying the diagnosis of COPD (n = 27,617), and a similar number of age and sex matched individuals without the diagnosis, extracted from the 727,241 records of individuals 40 years and older included in the EpiChron Cohort (Aragon, Spain). We compared the cumulative number of comorbidities, their prevalence and the mortality risk between both groups. Using network analysis, we explored the connectivity between comorbidities and the most influential comorbidities in both groups. We divided the groups into 5 incremental age categories and compared their comorbidity networks. We then selected those comorbidities known to affect primarily the elderly and compared their prevalence across the 5 age groups. In addition, we replicated the analysis in the smokers' subgroup to correct for the confounding effect of cigarette smoking. Subjects with COPD had more comorbidities and died at a younger age compared to controls. Comparison of both cohorts across 5 incremental age groups showed that the number of comorbidities, the prevalence of diseases characteristic of aging and network's density for the COPD group aged 56-65 were similar to those of non-COPD 15 to 20 years older. The findings persisted after adjusting for smoking.
Multimorbidity increases with age but in patients carrying the diagnosis of COPD, these comorbidities are seen at an earlier age.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Emphysema phenotypes and lung cancer risk González, Jessica; Henschke, Claudia I; Yankelevitz, David F ...
PloS one,
07/2019, Letnik:
14, Številka:
7
Journal Article
Recenzirano
Odprti dostop
To assess the relationship between lung cancer and emphysema subtypes.
Airflow obstruction and emphysema predispose to lung cancer. Little is known, however, about the lung cancer risk associated ...with different emphysema phenotypes. We assessed the risk of lung cancer based on the presence, type and severity of emphysema, using visual assessment.
Seventy-two consecutive lung cancer cases were selected from a prospective cohort of 3,477 participants enrolled in the Clínica Universidad de Navarra's lung cancer screening program. Each case was matched to three control subjects using age, sex, smoking history and body mass index as key variables. Visual assessment of emphysema and spirometry were performed. Logistic regression and interaction model analysis were used in order to investigate associations between lung cancer and emphysema subtypes.
Airflow obstruction and visual emphysema were significantly associated with lung cancer (OR = 2.8, 95%CI: 1.6 to 5.2; OR = 5.9, 95%CI: 2.9 to 12.2; respectively). Emphysema severity and centrilobular subtype were associated with greater risk when adjusted for confounders (OR = 12.6, 95%CI: 1.6 to 99.9; OR = 34.3, 95%CI: 25.5 to 99.3, respectively). The risk of lung cancer decreases with the added presence of paraseptal emphysema (OR = 4.0, 95%CI: 3.6 to 34.9), losing this increased risk of lung cancer when it occurs alone (OR = 0.7, 95%CI: 0.5 to 2.6).
Visual scoring of emphysema predicts lung cancer risk. The centrilobular phenotype is associated with the greatest risk.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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