Production of bacterial cellulose-based peptidopolysaccharide BC-L with anti-listerial properties using a co-cultivation strategy.

Bacterial cellulose (BC) has been found extensive applications in diverse domains for its exceptional attributes. However, the lack of antibacterial properties hampers its utilization in food and biomedical sectors. Leucocin, a bacteriocin belonging to class IIa, is synthesized by Leuconostoc that demonstrates potent efficacy against the foodborne pathogen, Listeria monocytogenes. In the current study, co-culturing strategy involving Kosakonia oryzendophytica FY-07 and Leuconostoc carnosum 4010 was used to confer anti-listerial activity to BC, which resulted in the generation of leucocin-containing BC (BC-L). The physical characteristics of BC-L, as determined by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA), were similar to the physical characteristics of BC. Notably, the experimental results of disc diffusion and growth curve indicated that the BC-L film exhibited a potent inhibitory effect against L. monocytogenes. Scanning electron microscopy (SEM) showed that BC-L exerts its bactericidal activity by forming pores on the bacterial cell wall. Despite the BC-L antibacterial mechanism, which involves pore formation, the mammalian cell viability remained unaffected by the BC-L film. The measurement results of zeta potential indicated that the properties of BC changed after being loaded with leucocin. Based on these findings, the anti-listerial BC-L generated through this co-culture system holds promise as a novel effective antimicrobial agent for applications in meat product preservation and packaging.

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. METHODS: Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. RESULTS: Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). CONCLUSIONS: The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Assessment of ultrashort echo time (UTE) T2* mapping at 3T for the whole knee: repeatability, the effects of fat suppression, and knee position.

Background: Knee tissues such as tendon, ligament and meniscus have short T2* relaxation times and tend to show little to no signal in conventional magnetic resonance acquisitions. An ultrashort echo time (UTE) technique offers a unique tool to probe fast-decaying signals in these tissues. Clinically relevant factors should be evaluated to quantify the sensitivity needed to distinguish diseased from control tissues. Therefore, the objectives of this study were to (I) quantify the repeatability of UTE-T2* relaxation time values, and (II) evaluate the effects of fat suppression and (III) knee positioning on UTE-T2* relaxation time quantification. Methods: A dual-echo, three-dimensional center-out radially sampling UTE and conventional gradient echo sequences were utilized to image gadolinium phantoms, one ex-vivo specimen, and five in-vivo subjects on a clinical 3T scanner. Scan-rescan images from the phantom and in-vivo experiments were used to evaluate the repeatability of T2* relaxation time values. Fat suppressed and non-suppressed images were acquired for phantoms and the ex-vivo specimen to evaluate the effect of fat suppression on T2* relaxation time quantifications. The effect of knee positioning was evaluated by imaging in-vivo subjects in extended and flexed positions within the knee coil and comparing T2* relaxation times quantified from tissues in each position. Results: Phantom and in-vivo measurements demonstrated repeatable T2* mapping, where the percent difference between T2* relaxation time quantified from scan-rescan images was less than 8% for the phantom and knee tissues. The coefficient of variation across fat suppressed and non-suppressed images was less than 5% for the phantoms and ex-vivo knee tissues, showing that fat suppression had a minimal effect on T2* relaxation time quantification. Knee position introduced variability to T2* quantification of the anterior cruciate ligament, posterior cruciate ligament, and patellar tendon, with percent differences exceeding 20%, but the meniscus showed a percent difference less than 10%. Conclusions: The 3D radial UTE sequence presented in this study could potentially be used to detect clinically relevant changes in mean T2* relaxation time, however, reproducibility of these values is impacted by knee position consistency between scans.

Identification and Characterization of a Predominant Hydrophobin in the Edible Mushroom Grifola frondosa.

Hydrophobins (HFBs) are a group of small, secreted amphipathic proteins of fungi with multiple physiological functions and potential commercial applications. In this study, HFB genes of the edible mushroom, Grifola frondosa, were systematically identified and characterized, and their transcriptional profiles during fungal development were determined. In total, 19 typical class I HFB genes were discovered and bioinformatically analyzed. Gene expression profile examination showed that Gf.hyd9954 was particularly highly upregulated during primordia formation, suggesting its major role as the predominant HFB in the lifecycle of G. frondosa. The wettability alteration profile and the surface modification ability of recombinant rGf.hyd9954 were greater than for the Grifola HFB HGFII-his. rGf.hyd9954 was also demonstrated to form the typical class I HFB characteristic-rodlet bundles. In addition, rGf.hyd9954 was shown to possess nanoparticle characteristics and emulsification activities. This research sheds light on the regulation of fungal development and its association with the expression of HFB genes.