Acute Changes in the Resting Brain Networks in Concussion Patients: Small-World Topology Perspective.

BACKGROUND: The acute changes that occur in the small-world topology of the brain in concussion patients remain unclear. Here, we investigated acute changes in the small-world organization of brain networks in concussion patients and their influence on persistent post-concussion symptoms. METHODS: Eighteen concussion patients and eighteen age-matched controls were enrolled in this study. All participants underwent computed tomography, magnetic resonance imaging (MRI), susceptibility weighted imaging, and blood oxygen level-dependent functional MRI. A complex network analysis method based on graph theory was used to calculate the parameters of small-world networks under different degrees of network sparsity. All subjects were evaluated using the Glasgow Coma Scale and Rivermead Postconcussion Symptom Questionnaire. RESULTS: Compared with the controls, the normalized cluster coefficient (γ) of whole brain networks in patients and the "small-world" index (σ) was slightly enhanced, whereas the standardized minimum path (λ) was slightly shorter. Whole brain effect (Eglobal) and local effect (Elocal) changes were not pronounced. Under the condition of minimum network sparsity (Dmin = 0.13), the numbers of nodes in the "right intraorbital superior frontal gyrus" (Anatomical Automatic Labeling, AAL26), right globus pallidus (AAL76), and bilateral temporal transverse gyrus (AAL79,80) in brain concussion patients were significantly lower. The numbers of nodes in the left subcapital lobe (AAL61) and left occipital gyrus (AAL51) were significantly higher, and the normalized cluster coefficients of the right intraorbital supraphalus (AAL26) and left posterior cingulate gyrus (AAL35) were significantly increased. The normalized clustering coefficients of the right triangular subfrontal gyrus (AAL55) (based on the normalized clustering coefficients of nodes in AAL14) and left sub-parietal lobes (AAL61) were significantly reduced. The mean local effects of nodes in the right intraorbital upper frontal gyrus (AAL26), left posterior cingulate gyrus (AAL35), and bilateral auxiliary motor cortex (AAL19, 20) were enhanced, whereas the mean local effects of the bilateral triangular inferior frontal gyrus (AAL13,14) and left insular cap (AAL11) were reduced (p < 0.05). CONCLUSIONS: The overall trend of network topology abnormalities in patients was random, and generalized and local functional abnormalities were seen. Changes in the function and affective circuitry of the resting default network were particularly pronounced in these patients, which we speculate may be one of the main drivers of the cognitive dysfunction and mood changes seen in concussion patients.

Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score.

Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model's efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.

Effects of mixed substrates of different agricultural and forestry residues on the cutting seedlings of Thuja sutchuenensis.

To screen environment-friendly seedling cultivation substrates which could replace peat and with less cost, we compared the effects of different agricultural and forestry residue mixed substrates on cutting propagation of Thuja sutchuenensis, in an experiment following randomized block design. There were five types of mixed substrates, including peat + vermiculite + perlite (T1), edible mushroom residue (EMR) + vermiculite + perlite (T2), carbo-nized rice husk (CRH) + vermiculite + perlite (T3), EMR + slag + sawdust (T4) and CRH + EMR + slag (T5). The results showed that the bulk density of T3 was the lowest, followed by T2, which significantly differed from other mixed substrates. The non-capillary porosity of T2 was significantly greater than that of T1, while the capillary porosity and the total porosity of T2 was lower than T1 and T3, respectively. T2 had the highest contents of total nitrogen, total phosphorus, total potassium, alkali-hydrolyzed nitrogen, available phosphorus, substrate moisture and the highest pH, which differed significantly from other mixed substrates in most chemical indicators. The membership function values of rooting rate and growth indicators of cuttings with different mixed substrates were in order of T2 > T3 > T1> T5 > T4. Most indicators with larger grey relation values were physical indicators. The top five indicators were capillary water capacity, total potassium, field water capacity, maximum water capacity, and total porosity, with both capillary water capacity and total potassium content ranking first. In general, the physicochemical properties, rooting rate, and growth characteristics of cuttings under T2 were better than those of other mixed substrates. The capillary water capacity and total potassium were the main factors affecting rooting and growth of cuttings. At the early stage of cutting, the physical properties of mixed substrate had greater effect on rooting rate and growth of cuttings than the chemical properties. Overall, our results suggested that T2 should be preferred in the cutting propagation of T. sutchuenensis.

Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms.

BACKGROUND: The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. MATERIALS AND METHODS: We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. RESULTS: With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736-0.913 [accuracy = 0.735-0.912], 0.602-0.828 [accuracy = 0.647-0.818], and 0.638-0.845 [accuracy = 0.618-0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. CONCLUSIONS: The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.