Poor nutritional status is associated with incomplete functional recovery in elderly patients with mild traumatic brain injury

BackgroundThe geriatric nutritional risk index (GNRI) is a simple index for evaluating the nutrition status of elderly patients. Many investigations have demonstrated that this index is associated with the prognosis of several diseases. This study aims to identify the relationship between the GNRI and recovery in elderly mild traumatic brain injury (mTBI) patients.MethodsA total of 228 mTBI patients older than 65 years were included in this study. mTBI was defined as an injury to the brain with a loss of consciousness of 30 min or less, a duration of posttraumatic amnesia of <24 h, and an admission Glasgow Coma Scale (GCS) score of 13–15. The Glasgow Outcome Scale Extended (GOSE), an outcome scale assessing functional independence, work, social activities, and personal relationships, was applied to assess the recovery of the patients. The clinical outcome was divided into complete recovery (GOSE = 8) and incomplete recovery (GOSE ≤ 7) at 6 months after the injury. Multivariate logistic regression was applied to evaluate the association between the GNRI and recovery of elderly mTBI patients, with adjustment for age, sex, hypertension, diabetes, and other important factors.ResultsThe receiver operating curve (ROC) analysis demonstrated that the cutoff value of GNRI was 97.85, and the area under the curve (AUC) was 0.860. Compared to the patients with a high GNRI, the patients with a low GNRI were older, had a higher prevalence of anemia, acute subdural hematoma, and subarachnoid hemorrhage, had a higher age-adjusted Charlson Comorbidity Index value, and had lower levels of albumin, lymphocytes, and hemoglobin. Multivariable analysis showed that high GNRI was associated with a lower risk of 6-month incomplete recovery (OR, 0.770, 95% CI: 0.709–0.837, p < 0.001).ConclusionThe GNRI has utility as part of the objective risk assessment of incomplete 6-month functional recovery in elderly patients with mTBI

A Case of Parathyroid Carcinoma in Renal Hyperparathyroidism

Introduction: Parathyroid carcinoma is a rare malignant endocrine tumor that is usually associated with primary hyperparathyroidism. The coexistence of parathyroid carcinoma and renal hyperparathyroidism is a rare phenomenon. Hence, we present a case of parathyroid carcinoma in a patient with tertiary hyperparathyroidism. Case Presentation: Our patient is a 31-year-old woman with a past medical history of end-stage renal failure (ESRF), on hemodialysis for the past 18 years. She was referred by her nephrologist to the endocrine surgery department for consideration of parathyroidectomy in view of long-standing tertiary hyperparathyroidism complicated by hypercalcemia. Bedside ultrasonography scan (US) of the thyroid revealed three parathyroid glands and a hypoechoic right lower pole thyroid nodule with central calcification. Fine-needle aspiration cytology was performed for the suspected thyroid nodule on the same day, which eventually yielded a follicular lesion of undetermined significance. A right hemithyroidectomy and total parathyroidectomy with deltoid implantation was performed. Intraoperative exploration revealed that the thyroid nodule noted at initial US was found to be the right superior parathyroid gland invading into the right thyroid itself. The right superior parathyroid gland was excised en bloc with the right hemithyroidectomy. Post-operatively, the patient was hypocalcemic but was discharged well on post-operative day 5. Histopathological diagnosis of the right hemithyroidectomy specimen containing the right superior parathyroid gland was consistent with that of parathyroid carcinoma. Conclusion: Parathyroid carcinoma is a rare entity that is difficult to diagnose. In patients with ESRF, the presence of concurrent tertiary hyperparathyroidism makes this even more challenging

Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Groundwater recharge (GR) is a key component of regional and global water cycles and is a critical flux for water resource management. However, recharge estimates are difficult to obtain at regional scales due to the lack of an accurate measurement method. Here, we estimate GR using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data. The regional-scale GR rate is calculated based on the groundwater storage fluctuation, which is, in turn, calculated from the difference between GRACE and root zone soil water storage from GLDAS data. We estimated GR in the Ordos Basin of the Chinese Loess Plateau from 2002 to 2012. There was no obvious long-term trend in GR, but the annual recharge varies greatly from 30.8 to 66.5 mm year−1, 42% of which can be explained by the variability in the annual precipitation. The average GR rate over the 11-year period from GRACE data was 48.3 mm year−1, which did not differ significantly from the long-term average recharge estimate of 39.9 mm year−1 from the environmental tracer methods and one-dimensional models. Moreover, the standard deviation of the 11-year average GR is 16.0 mm year−1, with a coefficient of variation (CV) of 33.1%, which is, in most cases, comparable to or smaller than estimates from other GR methods. The improved method could provide critically needed, regional-scale GR estimates for groundwater management and may eventually lead to a sustainable use of groundwater resources

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology

Influence of Native Video Advertisement Duration and Key Elements on Advertising Effectiveness in Mobile Feeds

This research investigated the influence of advertisement (ad) duration and key elements (titles, logos, and texts) on advertising effectiveness in mobile feeds. We recruited 40 participants (27 men and 13 women) who are aged from 20 to 43 years (M = 29.33, SD = 6.67). The participants were assigned randomly to four groups to watch four different types of ads: 6-second ads with key elements, 15-second ads with key elements, 15-second ads without key elements, and 30-second ads without key elements. We measured advertising effectiveness from four aspects: users’ attention, emotion, memory, and attitudes. During the experiment, a researcher recorded participants’ electroencephalography and eye movements. After the experiment, participants were required to complete a questionnaire and were interviewed. Results showed that participants felt more positive when watching 6-second duration ads in mobile feeds than the 15-second and 30-second ads; however, their memory of the ads was worse. The participants paid more attention to the key elements rather than the content of the ads. This research elucidated the features of native video ads in mobile feeds and provided some useful suggestions for advertisers who design video ads