Purpose: The prevalence of multi-morbidities with colorectal cancer (CRC) is known to be increasing. Particularly prognosis of CRC patients co-diagnosed with metabolic syndrome (MetSyn) was largely unknown. We aimed to examine the death risk of CRC patients according to the multiple MetSyn morbidities. Materials and Methods: We identified CRC patients with MetSyn from the electronic medical records (EMR) systems in five independent hospitals during 2006-2011. Information on deaths was jointly retrieved from EMR, cause of death registry and chronic disease surveillance as well as study-specific questionnaire. Cox proportional hazards regression was used to calculate the overall and CRC-specific hazards ratios (HR) comparing MetSyn CRC cohort with reference CRC cohort. Results: A total of 682 CRC patients in MetSyn CRC cohort were identified from 24 months before CRC diagnosis to 1 month after. During a median follow-up of 92 months, we totally observed 584 deaths from CRC, 245 being in MetSyn cohort and 339 in reference cohort. Overall, MetSyn CRC cohort had an elevated risk of CRC-specific mortality (HR, 1.49; 95% confidence interval [CI], 1.07 to 1.90) and overall mortality (HR, 1.43; 95% CI, 1.09 to 1.84) compared to reference cohort after multiple adjustment. Stratified analyses showed higher mortality risk among women (HR, 1.87; 95% CI, 1.04 to 2.27) and specific components of MetSyn. Notably, the number of MetSyn components was observed to be significantly related to CRC prognosis. Conclusion Our findings supported that multi-morbidities of MetSyn associated with elevated death risk after CRC. MetSyn should be considered as an integrated medical condition more than its components in CRC prognostic management.

Purpose: The prevalence of multi-morbidities with colorectal cancer (CRC) is known to be increasing. Particularly prognosis of CRC patients co-diagnosed with metabolic syndrome (MetSyn) was largely unknown. We aimed to examine the death risk of CRC patients according to the multiple MetSyn morbidities. Materials and Methods: We identified CRC patients with MetSyn from the electronic medical records (EMR) systems in five independent hospitals during 2006-2011. Information on deaths was jointly retrieved from EMR, cause of death registry and chronic disease surveillance as well as study-specific questionnaire. Cox proportional hazards regression was used to calculate the overall and CRC-specific hazards ratios (HR) comparing MetSyn CRC cohort with reference CRC cohort. Results: A total of 682 CRC patients in MetSyn CRC cohort were identified from 24 months before CRC diagnosis to 1 month after. During a median follow-up of 92 months, we totally observed 584 deaths from CRC, 245 being in MetSyn cohort and 339 in reference cohort. Overall, MetSyn CRC cohort had an elevated risk of CRC-specific mortality (HR, 1.49; 95% confidence interval [CI], 1.07 to 1.90) and overall mortality (HR, 1.43; 95% CI, 1.09 to 1.84) compared to reference cohort after multiple adjustment. Stratified analyses showed higher mortality risk among women (HR, 1.87; 95% CI, 1.04 to 2.27) and specific components of MetSyn. Notably, the number of MetSyn components was observed to be significantly related to CRC prognosis. Conclusion Our findings supported that multi-morbidities of MetSyn associated with elevated death risk after CRC. MetSyn should be considered as an integrated medical condition more than its components in CRC prognostic management.

Objective:To explore the normal ranges of perfusion parameters between cerebral hemisphere, cerebellar hemisphere and brain anatomical subregions (56 pairs) in different gender and age groups with multiple post labeling delay time (Multi-PLD) arterial spin labeling (ASL) imaging.Methods:From November 2020 to December 2020, 42 healthy adult volunteers (Male 25, Female 17) were recruited to perform 7 PLD ASL imaging, including 21 young adults (15 males and 6 females, aged 23—35 years) and 21 seniors (10 males and 11 females, aged 36—74 years). The data was processed offline by Cereflow software to obtain arterial arrival time (ATT) and corrected cerebral blood flow (CBF) and cerebral blood volume (CBV) perfusion parameters. SimpleITK standardization function was used to standardize the calculated perfusion image according to the anatomical automatic labeling (AAL) template. Therefore, CBF, ATT, CBV perfusion values of brain subregions were obtained. Paired samples t test, Wilcoxon rank sum test, independent samples t test and Mann-Whitney U test were used to compare the differences of perfusion parameters in the cerebral hemisphere, the cerebellar hemisphere, brain subregions depending on side, gender and age. Pearson correlation analysis was used to compare the correlations of perfusion parameters with age. Results:CBF in 62.5% (35/56) subregions and CBV in 44.6% (25/56) subregions were higher in right side than those in left side. ATT in most brain anatomical subregions (16/56) were higher in left side. The CBF [(35.30±8.31) vs. (34.34±7.53) ml·100g -1·min -1, P=0.021], CBV [(0.47±0.11) vs. (0.45±0.09) ml/100g, P<0.001], ATT [(1.30±0.10) vs. (1.24±0.11) s, P<0.001] in left cerebellar hemisphere were higher than that of right side. The CBF (28/56) of cerebral hemisphere, cerebellar hemisphere and brain subregions was higher in females than that in males, while ATT in 83.9% (47/56) subregions was lower than that in males (all P<0.05). CBV in female subjects was higher only in 5 brain regions (superior occipital gyrus, middle occipital gyrus, inferior occipital gyrus, superior parietal gyrus and cerebelum_7b) (all P<0.05). In young subjects, CBF in 44.6% (25/56) subregions and CBV in 33.9% (19/56) subregions were higher than those in the senior group (all P<0.05). The ATT in most subregions in young group were lower than those in senior group, but the difference was statistically significant only in rectus gyrus ( P=0.026) and paracentral lobule ( P=0.006). The CBF ( r=-0.430, P=0.005) and CBV ( r=-0.327, P=0.035) of cerebral hemisphere were negatively correlated with age. The CBF (24/25, r range:-0.497 —-0.343, all P<0.05) and CBV (16/19, r range:-0.474 —-0.322, all P<0.05) in most subregions were negatively correlated with age, while ATT was positively correlated (gyrus rectus: r=0.311, P=0.045; paracentral lobule: r=0.392, P=0.010). Conclusions:Multi-PLD ASL imaging could be applied for quantitative analysis of brain perfusion. The perfusion parameters of anatomical subregions are different depending on side, gender, and age.

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.
Algorithms ; Computational Biology ; methods ; Diagnostic Imaging ; Genomics ; methods ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Machine Learning ; Neural Networks (Computer) ; Protein Structure, Secondary ; Proteins ; metabolism