Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Both linear (alin) and rotational (arot) accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant alin and arot as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model consistency and dependency in results: the Dartmouth Head Injury Model (DHIM) and Simulated Injury Monitor (SIMon). For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated to the product of the magnitude and duration of arot, or effectively, the rotational velocity, but not to alin. Responses from arot-only were comparable to the full-DOFs counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., <1%) at typical difference percentage levels of 1–4% on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics, and demonstrate the feasibility of estimating strain-related responses from isolated arot for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table

Initial partial response and stable disease according to RECIST indicate similar survival for chemotherapeutical patients with advanced non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Stable disease (SD) has ambiguous clinical significance for patients according to the dominant Response Evaluation Criteria in Solid Tumours (RECIST). The primary aims of the study were: (1) to clarify the clinical significance of SD by comparing the progression-free survival (PFS) of response and SD patients with advanced non-small cell lung cancer (NSCLC) after the first two courses of the standard first-line platinum-based chemotherapy; (2) to explore the relationship between the percentage change in tumour size and PFS among initial SD patients, in order to provide some guidance for clinicians in deciding continuation/termination of the current treatment at a relative early time.</p> <p>Methods</p> <p>A total of 179 advanced NSCLC patients whose baseline CT image was available for review were included in the study. Another CT image was taken in the initial assessment after chemotherapy. A comparison of PFS between initial partial response (PR) and SD was used to determine whether significant differences exist. The relationship between the early percentage of change in tumour size of initial SD patients and their PFS was investigated. In addition, overall survival (OS), the secondary endpoint in this study, was investigated as well.</p> <p>Results</p> <p>Patients with initial PR are not significantly distinguished from those with initial SD when their PFS is concerned (median PFS 249 days [95% confidence interval, 187-310 days] versus 220 days [95% confidence interval, 191-248 days], p > 0.05). Their median OS was 364 days (95% confidence interval, 275-452 days) for the initial PR patients versus 350 days (95% confidence interval, 293-406 days) for the initial SD patients, which suggests no significant difference as well p > 0.05). In addition, all the initial SD patients enjoyed similar PFS and OS.</p> <p>Conclusions</p> <p>Initial PR and SD enjoy similar PFS and OS for patients with advanced NSCLC. Within the initial SD subgroup, different percentages of tumour shrinkage or increase undergo similar PFS and OS. RECIST remains a reliable norm in assessing the effectiveness of chemotherapy for patients with advanced NSCLC before functional assessment has been integrated into the criteria.</p

Water sorptivity of unsaturated fractured sandstone: fractal modeling and neutron radiography experiment

The spontaneous imbibition of water into the matrix and gas-filled fractures of unsaturated porous media is an important phenomenon in many geotechnical applications. Previous studies have focused on the imbibition behavior of water in the matrix, but few works have considered spontaneous imbibition along fractures. In this work, a new fractal model, considering the water losses from the fracture to the matrix, was established to predict the sorptivity of rough-walled fracture. A fractal model, considering the fractal dimension of tortuosity, was modified to estimate the sorptivity of the matrix. Both of the models have a time exponent α and can be simplified to the classical Lucas–Washburn (L–W) equation with α = 0.50. To verify the proposed models, quantitative data on the imbibition of water in both the matrix and the fracture of unsaturated sandstone were acquired by neutron radiography. The results show that the motion of the wetting front in both the matrix and the fracture does not obey the L–W equation. Both theory and experimental observations indicate that fracture can significantly increase spontaneous imbibition in unsaturated sandstone by capillary action. Compared with the classical L–W equation, the models proposed in this study offers a better description of the dynamic imbibition behaviour of water in unsaturated fractured sandstone and, thus, more reliable predictions of the sorptivity of the matrix and the fracture. Moreover, a new method to estimate the time exponent of rough-walled fracture in sandstone was also provided

Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors

PurposeTumor consistency is important for pituitary neuroendocrine tumors (PitNETs) resection to improve surgical outcomes. In this study, we evaluated the T2-WI of PitNETs and defined a specific T2-WI signaling manifestation, the “Mosaic sign,” to predict tumor consistency and resection of PitNETs.DesignA retrospective review of MRI and tumor histology of 137 consecutive patients who underwent endoscopic endonasal resection for PitNETs was performed.MethodsThe “Mosaic sign” was defined by the ratio of the tumor itself T2-WI signals, and characterized by multiple intratumor hyperintense dots. The degree of tumor resection was an assessment by postoperative MRI examination. The presence of the “Mosaic sign” was compared with patients' basic information, tumor consistency, tumor pathological staining, and surgical result. To determine whether the presence or absence of “Mosaic sign” could predict tumor consistency and guide surgical resection of tumors.ResultsStatistical analysis showed that the consistency of the tumor and the degree of resection were correlated with the “Mosaic sign”. In the 137 cases of T2-WI, 43 had “Mosaic sign”, 39 cases had soft tumor consistency, and 4 were classified as fibrous, of which 42 were completely resected and 1 was subtotal resected. Of the 94 patients without “Mosaic sign”, the consistency of tumor of 54 cases were classified as soft, the remaining 40 cases were fibrous, 80 cases were completely resected, and 14 cases were subtotal resected. Postoperative cerebrospinal fluid leakage occurred in 1 patient. The number of corticotroph adenomas in the group of “Mosaic sign” was higher, with the statistical difference between the two groups (P = 0.0343).ConclusionsThe presence of the “Mosaic sign” in T2-WI may provide preoperative information for pituitary adenomas consistency and effectively guide surgical approaches

A polymorphism in porcine miR-22 is associated with pork color

MicroRNAs (miRNAs) are posttranscriptional regulators that play key roles in meat color regulation. Changes in miRNA expression affect their target mRNAs, leading to multifunctional effects on biological processes and phenotypes. In this study, a G &gt; A mutation site located upstream of the precursor miR-22 sequence in Suhuai pigs was significantly correlated with the meat color parameter a*(redness) of the porcine longissimus dorsi (LD) muscle. AA genotype individuals had the highest average meat color a* value and the lowest miR-22 level. When G &gt; A mutation was performed in the miR-22 overexpression vector, miR-22 expression significantly decreased. Considering that Ca2+ homeostasis is closely related to pig meat color, our results further demonstrated that ELOVL6 is a direct target of miR-22 in pigs. The effects of miR-22 on skeletal muscle intracellular Ca2+ were partially caused by the suppression of ELOVL6 expression

Blank peak current-suppressed electrochemical aptameric sensing platform for highly sensitive signal-on detection of small molecule

In this contribution, an electrochemical aptameric sensing scheme for the sensitive detection of small molecules is proposed using adenosine as a target model. A ferrocene (Fc)-functionalized thiolated aptamer probe is adapted and immobilized onto an electrode surface. Introducing a recognition site for EcoRI into the aptamer sequence not only suppresses the peak current corresponding to blank sample but also provides a signal-on response mechanism. In the absence of adenosine, the aptamer can fold into a hairpin structure and form a cleavable double-stranded region. Fc is capable of being removed from electrode surface by treatment with endonuclease, and almost no peak current is observed. The adenosine/aptamer binding induces the conformational transition of designed aptamer, dissociating the cleavable double-stranded segment. Therefore, the integrated aptamer sequence is maintained when exposing to endonuclease, generating a peak current of Fc. Utilizing the present sensing scheme, adenosine even at a low concentration can give a detectable current signal. Thus, a detection limit of 10−10 M and a linear response range from 3.74 × 10−9 to 3.74 × 10−5 M are achieved. The proposed proof-of-principle of a novel electrochemical sensing is expected to extend to establish various aptameric platforms for the analysis of a broad range of target molecules of interest

Dark energy model with higher derivative of Hubble parameter

In this letter we consider a dark energy model in which the energy density is a function of the Hubble parameter $H$ and its derivative with respect to time $\rho_{de}=3\alpha \ddot{H}H^{-1}+3\beta\dot{H}+3\gamma H^2$. The behavior of the dark energy and the expansion history of the Universe depend heavily on the parameters of the model $\alpha$, $\beta$ and $\gamma$. It is very interesting that the age problem of the well-known three old objects can be alleviated in this models.Comment: 11 pages, 6 figures, the correct version accepted for publication in PL