Adjusting for regression effect in uncontrolled studies

When clinical studies require enrolled patients to have abnormal assays, the natural tendency of repeat measurements to regress toward the mean can lead to a false assessment of effectiveness of therapy. We propose a method to more accurately estimate the true effect of therapy by adjusting for a component of improvement that can be attributed to regression effect. The model we use allows for a combination of additive and/or multiplicative effects of the therapy.     

Biomechanical mechanism of lateral trunk lean gait for knee osteoarthritis patients

The biomechanical mechanism of lateral trunk lean gait employed to reduce external knee adduction moment (KAM) for knee osteoarthritis (OA) patients is not well known. This mechanism may relate to the center of mass (COM) motion. Moreover, lateral trunk lean gait may affect motor control of the COM displacement. Uncontrolled manifold (UCM) analysis is an evaluation index used to understand motor control and variability of the motor task. Here we aimed to clarify the biomechanical mechanism to reduce KAM during lateral trunk lean gait and how motor variability controls the COM displacement. Twenty knee OA patients walked under two conditions: normal and lateral trunk lean gait conditions. UCM analysis was performed with respect to the COM displacement in the frontal plane. We also determined how the variability is structured with regards to the COM displacement as a performance variable. The peak KAM under lateral trunk lean gait was lower than that under normal gait. The reduced peak KAM observed was accompanied by medially shifted knee joint center, shortened distance of the center of pressure to knee joint center, and shortened distance of the knee–ground reaction force lever arm during the stance phase. Knee OA patients with lateral trunk lean gait could maintain kinematic synergy by utilizing greater segmental configuration variance to the performance variable. However, the COM displacement variability of lateral trunk lean gait was larger than that of normal gait. Our findings may provide clinical insights to effectively evaluate and prescribe gait modification training for knee OA patients.     

The relation between limb segment coordination during walking and fall history in community-dwelling older adults

Control of the swing foot during walking is important to prevent falls. The trajectories of the swing foot are adjusted by coordination of the lower limbs, which is evaluated with uncontrolled manifold (UCM) analysis. A previous study that applied this analysis to walking revealed that older adults with fall history had compensatorily great segment coordination to stabilize the swing foot during normal walking. However, it is unknown whether the increase in segment coordination helps for preventing incident falls in the future. At baseline measurement, 30 older adults walked for 20 times at a comfortable speed. UCM analysis was performed to evaluate how the segment configuration in the lower limbs contributes to the swing foot stability. One year after the baseline visit, we asked the subjects if there were incident falls through a questionnaire. The univariate and multivariable logistic regression analyses were performed to assess the association between the index of segment coordination and incident falls with and without adjustment for gait velocity. Twenty-eight older adults who responded to the questionnaire were classified into older adults (n = 12) who had the incident fall and those (n = 16) who did not have falls. It was revealed that older adults who increased the segment coordination associated with swing foot stability tended to experience at least one fall within one year of measurement. The index of the UCM analysis can be a sensitive predictor of incident falls.     

Controlled and uncontrolled asthma display distinct alveolar tissue matrix compositions

Objective

Whether distal inflammation in asthmatics also leads to structural changes in the alveolar parenchyma remains poorly examined, especially in patients with uncontrolled asthma. We hypothesized that patients who do not respond to conventional inhaled corticosteroid therapy have a distinct tissue composition, not only in central, but also in distal lung.

Methods

Bronchial and transbronchial biopsies from healthy controls, patients with controlled atopic and patients with uncontrolled atopic asthma were processed for immunohistochemical analysis of fibroblasts and extracellular matrix molecules: collagen, versican, biglycan, decorin, fibronectin, EDA-fibronectin, matrix metalloproteinase (MMP)-9 and tissue-inhibitor of matrix metalloproteinase (TIMP)-3.

Results

In central airways we found increased percentage areas of versican and decorin in patients with uncontrolled asthma compared to both healthy controls and patients with controlled asthma. Percentage area of biglycan was significantly higher in both central airways and alveolar parenchyma of patients with uncontrolled compared to controlled asthma. Ratios of MMP-9/TIMP-3 were decreased in both uncontrolled and controlled asthma compared to healthy controls. In the alveolar parenchyma, patients with uncontrolled asthma had increased percentage areas of collagen, versican and decorin compared to patients with controlled asthma. Patients with uncontrolled asthma had significantly higher numbers of myofibroblasts in both central airways and alveolar parenchyma compared to patients with controlled asthma.

Conclusions

Tissue composition differs, in both central and distal airways, between patients with uncontrolled and controlled asthma on equivalent doses of ICS. This altered structure and possible change in tissue elasticity may lead to abnormal mechanical properties, which could be a factor in the persistent symptoms for patients with uncontrolled asthma.     

Ramifications of a redox switch within a normal cell: its absence in a cancer cell

A previously described model for cellular proliferation, based on the relationship of the cell cycle to redox parameters, is explored here to account for the origin of the cancerous cell and some of its key abnormal characteristics, such as the Warburg effect, apoptosis, aneuploidy, and uncontrolled proliferation. We describe how the redox switch that characterizes normal cells and its absence in cancer cells is responsible for the origin and characteristics of cancer cells. Metabolic and chromosomal changes resulting from the lack of such a redox switch in cancer cells are described. The effects of a well-known carcinogen, cigarette smoking, are also applied to the model. This report emphasizes the role of the threshold intracellular redox potential in regulating cells.     

The effects of fall history on kinematic synergy during walking

To prevent falls, control of the swing foot during walking is crucial. Recently, some studies demonstrated that the coordinated movement of lower limbs by kinematic synergy is important for stable walking. However, no study has been carried out to reveal the relation between falls and kinematic synergy, and it is unclear whether fall history alters the kinematic synergy. Thus, the purpose of this study was to test the effects of fall history on kinematic synergy using uncontrolled manifold (UCM) analysis. Older adults were divided into two groups: older adults without fall history (non-fallers, n = 14) and older adults with fall history of at least one fall in the 12 months prior to the measurements (fallers, n = 10). Subjects walked at their own comfortable speed on a pathway and kinematic data were collected. UCM analysis was performed to assess how variability of segmental configurations in the frontal plane, the mediolateral and vertical directions, affects the frontal trajectory of the swing foot. Fallers had a greater variability of segmental configurations than non-fallers in all phases. In the mediolateral direction, the kinematic synergy in fallers was significantly greater than that in non-fallers during the early and late swing phases. On the other hands, fallers continuously had greater kinematic synergy compared to non-fallers in the vertical direction. The results revealed that fall history increased the kinematic synergy, although fallers needed a greater variability of segmental configurations as a compensatory strategy to ensure kinematic synergy.     

Deep learning-based segmentation for disease identification

Plant diseases have recently increased and exacerbated due to several factors such as climate change, chemicals’ misuse and pollution. They represent a severe threat for both economy and global food security. Recently, several researches have been proposed for plant disease identification through modern image-based recognition systems based on deep learning. However, several challenges still require further investigation. One is related to the high variety of leaf diseases/ species along with constraints related to the collection and annotation of real-world datasets. Other challenges are related to the study of leaf disease in uncontrolled environment. Compared to major existing researches, we propose in this article a new perspective to handle the problem with two main differences: First, while most approach aims to identify simultaneously a pair of species-disease, we propose to identify diseases independently of leaf species. This helps to recognize new species holding diseases that were previously learnt. Moreover, instead of using the global leaf image, we directly predict disease on the basis of the local disease symptom features. We believe that this may decrease the bias related to common context and/or background and enables to build a more generalised model for disease classification. In particular, we propose an hybrid system that combines strengths of deep learning-based semantic segmentation with classification capabilities to respectively extract infected regions and determine their identity. For that, an extensive experimentation including a comparison of different semantic segmentation and classification CNNs has been conducted on PlantVillage dataset (leaves within homogeneous background) in order to study the extent of use of local disease symptoms features to identify diseases. Specifically, a particular enhancement of disease identification accuracy has been demonstrated in IPM and BING datasets (leaves within uncontrolled background).     

Sensitivity analysis for causal inference using inverse probability weighting

Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non‐parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non‐parametric method. We illustrate our method with two medical data sets.