The Effects of Flexibility and Relative Muscle Strength on Injury in Running

When exercising, physical injury is almost inevitable. Although there is a multitude of practices to avoid injury, a large portion of luck is required to minimize injury proneness. In this paper, with the aid of a public dataset gait kinetics and kinematics, flexibility and strength are tested against the Boolean value of injury to conduct a linear binary regression model.

Development of the predictive model for I stage breast cancer

Objective: development of a predictive model based on binary regression to determine the likelihood of progression of I stage breast cancer.Materials and methods. A retrospective analysis of data of 385 patients with T1N0M0 stage breast cancer was performed. The minimum follow-up period was 120 months and the maximum made 256 months, with an average follow-up of 191 ± 36 months (16 ± 3 years). Using a forward stepwise selection (binary regression), the most important prognostic factors were selected, on the basis of which the predictive model “Risk Assessment Algorithm for Recurrence of Breast Carcinoma” was constructed.Results. During the study period, recurrence of stage I breast cancer was reported in 67 patients, representing 17.4 % of the total cohort. Five prognostic factors were selected by binary regression: grade, histological type, estrogen receptor expression, HER2 / neu overexpression and Ki-67 amplification. Kaplan–Meier analysis and Cox proportional hazards method demonstrated the influence of each of the selected factors on disease-free survival. Comparative analysis with other existing models showed that our prognostic model is inferior to Adjuvant! Online in terms of sensitivity (85 % ver- sus 95 %). However, it is superior in specificity (58 % versus 38 %), PPV (69 % versus 63 %) and AUC (84 % versus 70 %).Conclusions. In I stage breast cancer, factors such as grade, histological type, estrogen receptor expression, HER2 / neu overexpression and Ki-67 amplification are the most significant predictive factors influencing recurrence rates. The algorithm for assessing the risk of recurrence of stage I breast cancer can predict the risk of tumour progression with a sensitivity of 84 % and a specificity of 58 % (p <0.05).

An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

The increasing use of bicycles rises the interest in investigating the safety aspects of daily commuting. In this investigation, more than 14,000 cyclists’ injuries were analyzed to determine the relationship between severity, road infrastructure characteristics, and surface conditions using binary regression. Minor and major severity categories were distinguished. A binary equation consists of 28 factors is extracted. It has been found that each factor related to roadway characteristics has its negative and positive impacts on cyclist severity such as traffic control, location type, topography, and roadway divisions. Regarding the road surface components, good, paved, and marked roads are associated with a higher probability of major injuries due to the expected greater frequencies of cyclists on roads with good conditions. In conclusion, probabilities of major injuries are higher in urban areas, higher speed limits, signalized intersections, inclined topographies, one-way roads, and during the daytime which require more attention and better considerations.

The Rebound of Early Post‐operative Plasma dd-cfDNA in Kidney Transplant Recipients: A Single‐center Study

Background: It has been reported that donor derived cell-free DNA(dd-cfDNA) accounts for less than 1.2% of total cell free DNA in stable kidney allograft recipients, and dd-cfDNA may be a non-invasive biomarker of acute rejection. However, the kinetics of plasma dd-cfDNA level is still unclear, which hinders the further application of dd-cfDNA in kidney transplantation (KTx). The purpose of this study was to explore the correlation between plasma dd-cfDNA and delayed graft function(DGF） and pulmonary infection after KTx, and to explore the diagnostic value of dd-cfDNA in DGF. In addition, we tried to find out the factors related to the rebound of dd-cfDNA level.Methods: A total of 183 kidney transplant recipients were enrolled in this study. Peripheral blood samples (10ml) were collected on the 1st, 7th, 14th and 21st day after KTx, and 546 plasma samples were collected. Droplet digital PCR (DDPCR) was used to detect the level of dd-cfDNA(%) and Mann Whitney U test was used to analyze the relationship between dd-cfDNA level and DGF and pulmonary infection. Logistic binary regression analysis was used to analyze the clinical factors related to the increase of dd-cfDNA.Results: There was no significant difference between DGF group and non-DGF group of dd-cfDNA level (P > 0.05). The mean value of dd-cfDNA on day 1 (6.97%) was significantly higher than that on day 7 (1.17%), day 14 (1.09%) and day 21 (1.18%). Logistic binary regression analysis was performed for dd-cfDNA level rebound group and non-rebound group. Pulmonary infection (OR = 2.11, P = 0.028) and DGF (OR = 1.37, P = 0.42) were significantly correlated with rebound of dd-cfDNA. At the same time, on the 1st, 7th and 14th day after KTx, the levels of dd-cfDNA in pulmonary infection group was significantly higher than non-infection group (P < 0.05).Conclusion: Our results indicate that dd-cfDNA (%) can’t be used as a biomarker for predicting DGF. The rebound of plasma dd-cfDNA (%) level was significantly correlated with the presence of pulmonary infection. However, further confirmatory studies are necessary.

Differential Gait Decline in Parkinson’s Disease Enhances Discrimination of Gait Freezers from Non-Freezers

Background: Freezing of gait (FOG) is a debilitating feature of Parkinson’s disease (PD) for which treatments are limited. To develop neuroprotective strategies, determining whether disease progression is different in phenotypic variants of PD is essential. Objective: To determine if freezers have a faster decline in spatiotemporal gait parameters. Methods: Subjects were enrolled in a longitudinal study and assessed every 3– 6 months. Continuous gait in the levodopa ON-state was collected using a gait mat (Protokinetics). The slope of change/year in spatiotemporal gait parameters was calculated. Results: 26 freezers, 31 non-freezers, and 25 controls completed an average of 6 visits over 28 months. Freezers had a faster decline in mean stride-length, stride-velocity, swing-%, single-support-%, and variability in single-support-% compared to non-freezers (p < 0.05). Gait decline was not correlated with initial levodopa dose, duration of levodopa therapy, change in levodopa dose or change in Montreal Cognitive Assessment scores (p > 0.25). Gait progression parameters were required to obtain 95% accuracy in categorizing freezers and non-freezers groups in a forward step-wise binary regression model. Change in mean stride-length, mean stride-width, and swing-% variability along with initial foot-length variability, mean swing-% and apathy scores were significant variables in the model. Conclusion: Freezers had a faster temporal decline in objectively quantified gait, and inclusion of longitudinal gait changes in a binary regression model greatly increased categorization accuracy. Levodopa dosing, cognitive decline and disease severity were not significant in our model. Early detection of this differential decline may help define freezing prone groups for testing putative treatments.