This new nursing diagnosis supports nurses to manage the risk for malnutrition and optimize older people’s nutrition.

Serum neurofilament light (sNfL) has been studied as a biomarker of disease activity in multiple sclerosis (MS). Several factors, including age, can influence its dynamics, and several studies have shown that sNfL increases with age in controls. Our objective was to explore the relationship of sNfL and age at different MS disease stages, including remission and after a gadolinium-enhancing (Gad+) lesion.

We included 94 patients with MS with annual sNfL measurements performed with a single-molecule array assay. We used multivariable linear mixed-effects models with random intercept to test the association between age and sNfL during remission and after a Gad+ lesion (ie, within 90 days after the Gad+ lesion). The model was adjusted for medication use and sex.

We report a positive association between sNfL level and age during remission (adjusted estimate = 1.18% yearly increase, 95% CI = .34-2.03%, P = .008). In contrast, a negative interaction between age and Gad+ lesion status was observed (adjusted estimate = -1.73%, 95% CI = -2.85 to -.58%, P = .004).

We propose that younger patients experience a greater elevation in sNfL than older patients in response to Gad+ lesions. Our study provides potential insights into the effects of aging on neuroinflammation in MS.

We propose that younger patients experience a greater elevation in sNfL than older patients in response to Gad+ lesions. Our study provides potential insights into the effects of aging on neuroinflammation in MS.

Diffusion-weighted whole-body imaging with background suppression (DWIBS) is used for the diagnosis and staging of cancers. The medical cost of an MR examination including DWIBS is $123, which is 80% less expensive than the cost ($798) of F18-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) examination.

This study examined the efficacy of DWIBS for relapses after lung cancer resection. A total of 55 patients who had pulmonary resection of lung cancer, postoperative computed tomography (CT) every six months, and DWIBS and FDG-PET/CT (every year) were enrolled in this study. If a metastatic lesion was detected on CT scan, DWIBS and FDG-PET/CT were also used.

A total of 55 patients who underwent pulmonary resections for lung cancer, and had CT, DWIBS and FDG-PET/CT examination during follow-up after pulmonary resection were enrolled in this study. Lung cancer in 32 patients relapsed. Postoperative radiographic examinations revealed pulmonary metastases in 17 patients, bone mstic accuracy and is less expensive in medical costs for the detection of a relapse. DWIBS could potentially replace FDG-PET/CT after lung cancer resection.

Dementia with Lewy bodies (DLB) is the second most prevalent cause of degenerative dementia next to Alzheimer’s disease (AD). Though current DLB diagnostic criteria employ several indicative biomarkers, relative preservation of the medial temporal lobe as revealed by structural MRI suffers from low sensitivity and specificity, making them unreliable as sole supporting biomarkers. In this study, we investigated how a deep learning approach would be able to differentiate DLB from AD with structural MRI data.

Two-hundred and eight patients (101 DLB, 69 AD, and 38 controls) participated in this retrospective study. Gray matter images were extracted using voxel-based morphometry (VBM). ROCK inhibitor In order to compare the conventional statistical analysis with deep-learning feature extraction, we built a classification model for DLB and AD with a residual neural network (ResNet) type of convolutional neural network architecture, which is one of the deep learning models. The anatomically standardized gray matter images extracted in the same way as for the VBM process were used as inputs, and the classification performance achieved by our model was evaluated.

Conventional statistical analysis detected no significant atrophy other than fine differences on the middle temporal pole and hippocampal regions. The feature extracted by the deep learning method differentiated DLB from AD with 79.15％ accuracy compared to the 68.41% of the conventional method.

Our results confirmed that the deep learning method with gray matter images can detect fine differences between DLB and AD that may be underestimated by the conventional method.

Our results confirmed that the deep learning method with gray matter images can detect fine differences between DLB and AD that may be underestimated by the conventional method.

Long non-coding RNAs (lncRNAs) have been found to be involved in the development of many cancers. In this study, we aimed to identify the molecular mechanisms of lncRNA BAALC antisense RNA 1 (BAALC-AS1) in regulating the malignancy of esophageal squamous cell carcinoma (ESCC).

The expression of BAALC-AS1 in cancer patients was analyzed using a tissue microarray. The protein and RNA levels of BAALC-AS1 were determined by Western blotting analysis and quantitative reverse transcription-PCR (RT-qPCR), respectively. The cell proliferation was determined by cell viability assays, bromodeoxyuridine incorporation, and flow cytometry. The relationships among BAALC-AS1, RasGAPSH3 domain-binding protein 2 (G3BP2), and c-Myc were determined using RNA immunoprecipitation, RNA pull-down assays, and luciferase assays.

The expression of BAALC-AS1 was highly up-regulated and associated with malignant phenotypes in ESCC tissues and cell lines. In vivo and in vitro assays showed that BAALC-AS1 promoted ESCC cell proliferation, migration, and invasion. BAALC-AS1 directly interacted with G3BP2, and thereby inhibited the degradation of c-Myc RNA 3′-UTR by G3BP2, thus leading to the accumulation of c-Myc expression. Additionally, c-Myc acted as a transcription factor that can induce the expression of BAALC-AS1 by directly binding to its promoter region.

BAALC-AS1/G3BP2/c-Myc feedback loop plays a critical role in the development of ESCC, which might provide a novel therapeutic target and facilitate the development of new therapeutic strategies for the treatment of ESCC.

BAALC-AS1/G3BP2/c-Myc feedback loop plays a critical role in the development of ESCC, which might provide a novel therapeutic target and facilitate the development of new therapeutic strategies for the treatment of ESCC.