Strain rate-dependent failure mechanics of the intervertebral disc under tension/compression and constitutive analysis.

BACKGROUND: The intervertebral disc exhibits not only strain rate dependence (viscoelasticity), but also significant asymmetry under tensile and compressive loads, which is of great significance for understanding the mechanism of lumbar disc injury under physiological loads. OBJECTIVE: In this study, the strain rate sensitive and tension-compression asymmetry of the intervertebral disc were analyzed by experiments and constitutive equation. METHOD: The Sheep intervertebral disc samples were divided into three groups, in order to test the strain rate sensitive mechanical behavior, and the internal displacement as well as pressure distribution. RESULTS: The tensile stiffness is one order of magnitude smaller than the compression stiffness, and the logarithm of the elastic modulus is approximately linear with the logarithm of the strain rate, showing obvious tension-compression asymmetry and rate-related characteristics. In addition, the sensitivity to the strain rate is the same under these two loading conditions. The stress-strain curves of unloading and loading usually do not coincide, and form a Mullins effect hysteresis loop. The radial displacement distribution is opposite between the anterior and posterior region, which is consistent with the stress distribution. By introducing the damage factor into ZWT constitutive equation, the rate-dependent viscoelastic and weakening behavior of the intervertebral disc can be well described.

Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study.

BACKGROUND: Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM: To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS: A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS: Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION: Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

The role of the CXCR6/CXCL16 axis in the pathogenesis of fibrotic disease.

CXC chemokine receptor 6 (CXCR6), a seven-transmembrane domain G-protein-coupled receptor, plays a pivotal regulatory role in inflammation and tissue damage through its interaction with CXC chemokine ligand 16 (CXCL16). This axis is implicated in the pathogenesis of various fibrotic diseases and correlates with clinical parameters that indicate disease severity, activity, and prognosis in organ fibrosis, including afflictions of the liver, kidney, lung, cardiovascular system, skin, and intestines. Soluble CXCL16 (sCXCL16) serves as a chemokine, facilitating the migration and recruitment of CXCR6-expressing cells, while membrane-bound CXCL16 (mCXCL16) functions as a transmembrane protein with adhesion properties, facilitating intercellular interactions by binding to CXCR6. The CXCR6/CXCL16 axis is established to regulate the cycle of damage and repair during chronic inflammation, either through modulating immune cell-mediated intercellular communication or by independently influencing fibroblast homing, proliferation, and activation, with each pathway potentially culminating in the onset and progression of fibrotic diseases. However, clinically exploiting the targeting of the CXCR6/CXCL16 axis requires further elucidation of the intricate chemokine interactions within fibrosis pathogenesis. This review explores the biology of CXCR6/CXCL16, its multifaceted effects contributing to fibrosis in various organs, and the prospective clinical implications of these insights.

Failure mechanical properties of lumbar intervertebral disc under high loading rate.

BACKGROUND: Lumbar disc herniation (LDH) is the main clinical cause of low back pain. The pathogenesis of lumbar disc herniation is still uncertain, while it is often accompanied by disc rupture. In order to explore relationship between loading rate and failure mechanics that may lead to lumbar disc herniation, the failure mechanical properties of the intervertebral disc under high rates of loading were analyzed. METHOD: Bend the lumbar motion segment of a healthy sheep by 5° and compress it to the ultimate strength point at a strain rate of 0.008/s, making a damaged sample. Within the normal strain range, the sample is subjected to quasi-static loading and high loading rate at different strain rates. RESULTS: For healthy samples, the stress-strain curve appears collapsed only at high rates of compression; for damaged samples, the stress-strain curves collapse both at quasi-static and high-rate compression. For damaged samples, the strengthening stage becomes significantly shorter as the strain rate increases, indicating that its ability to prevent the destruction is significantly reduced. For damaged intervertebral disc, when subjected to quasi-static or high rates loading until failure, the phenomenon of nucleus pulposus (NP) prolapse occurs, indicating the occurrence of herniation. When subjected to quasi-static loading, the AF moves away from the NP, and inner AF has the greatest displacement; when subjected to high rates loading, the AF moves closer to the NP, and outer AF has the greatest displacement. The Zhu-Wang-Tang (ZWT) nonlinear viscoelastic constitutive model was used to describe the mechanical behavior of the intervertebral disc, and the fitting results were in good agreement with the experimental curve. CONCLUSION: Experimental results show that, both damage and strain rate have a significant effect on the mechanical behavior of the disc fracture. The research work in this article has important theoretical guiding significance for preventing LDH in daily life.

A Silver Lining of Neuropathic Pain: Predicting Favorable Functional Outcome in Spinal Cord Injury.

Background: Neuropathic pain (NP) is a common and severe problem following spinal cord injury (SCI). However, its relationship with functional outcome remains unclear. Methods: A retrospective explorative analysis was performed on SCI patients admitted to a tertiary academic medical center between January 2018 and June 2022. The candidate predictor variables, including demographics, clinical characteristics and complications, were analyzed with logistic and linear regression. Spinal Cord Independence Measure (SCIM) scores at discharge and mean relative functional gain (mRFG) of SCIM were as outcome parameters. Results: A total of 140 SCI patients included for the final analysis. Among them, 44 (31.43%) patients were tetraplegics, and 96 (68.57%) patients were paraplegics; 68 (48.57%) patients developed NP, and 72 (51.43%) patients did not. Logistic and linear regression analyses of SCIM at discharge both showed that NP [OR=3.10, 95% CI (1.29,7.45), P=0.01; unstandardized ß=11.47, 95% CI (4.95,17.99), P<0.01; respectively] was significantly independent predictors for a favorable outcome (SCIM at discharge ≥ 50, logistic regression results) and higher SCIM total score at discharge (linear regression results). Besides, NP [unstandardized ß=15.67, 95% CI (8.94,22.41), P<0.01] was also independently associated with higher mRFG of SCIM scores. Furthermore, the NP group had significantly higher mRFG, SCIM total scores and subscales (self-care, respiration and sphincter management, and mobility) at discharge compared to the non-NP group. However, there were no significant differences in mRFG, SCIM total score or subscales at discharge among the NP subgroups in terms of locations (at level pain, below level pain, and both) or timing of occurrence (within and after one month after SCI). This study also showed that incomplete injury, lumbar-sacral injury level and non-anemia were significantly independent predictors for a favorable outcome, and higher mRFG of SCIM scores (except for non-anemia). Conclusion: NP appears independently associated with better functional recovery in SCI patients, suggesting the bright side of this undesirable complication. These findings may help to alleviate the psychological burden of NP patients and ultimately restore their confidence in rehabilitation.

Nitric oxide synthase 2 genetic variation rs2297514 associates with a decreased susceptibility to extremity post-traumatic osteomyelitis in a Chinese Han population.

Background: Previous studies have indicated that nitric oxide synthase 2 (NOS2) genetic variations are involved in delayed fracture healing and fracture non-union. Whether these genetic variants associate with the development of osteomyelitis (OM) remains unclear. Here, we analyzed the potential relationships between NOS2 genetic variations and the risk of developing post-traumatic OM (PTOM) in a Chinese Han population. Methods: Altogether 704 participants, including 336 PTOM patients and 368 healthy controls, were genotyped of rs2297514 and rs2248814 of the NOS2 gene using the SNaPshot genotyping method. Results: Outcomes showed that the frequency of allele C of rs2297514 in the patient group was significantly lower than that in the control group (48.7% vs. 54.5%, P = 0.029, OR = 0.792, 95% CI 0.642 - 0.976). In addition, significant associations were found between rs2297514 and susceptibility to PTOM by the recessive model (P = 0.007, OR = 0.633, 95% CI 0.453 - 0.884), and the homozygous model (P = 0.039, OR = 0.648, 95% CI 0.429 - 0.979). Moreover, patients with the CC genotype of rs2297514 had lower inflammatory biomarkers levels than the TT genotype, especially for the C-reactive protein (CRP) level (median: 4.1 mg/L vs. 8.9 mg/L, P = 0.027). However, no significant relationship was noted between rs2248814 and the risk of developing PTOM. Conclusion: In this Chinese cohort, rs2297514 is correlated with a decreased risk of PTOM development, with genotype CC as a protective factor.

Inhibition of DRP1-dependent mitochondrial fission by Mdivi-1 alleviates atherosclerosis through the modulation of M1 polarization.

BACKGROUND: Inflammation and immune dysfunction with classically activated macrophages(M1) infiltration are important mechanisms in the progression of atherosclerosis (AS). Dynamin-related protein 1 (DRP1)-dependent mitochondrial fission is a novel target for alleviating inflammatory diseases. This study aimed to investigate the effects of DRP1 inhibitor Mdivi-1 on AS. METHODS: ApoE-/- mice were fed with a high-fat diet supplemented with or without Mdivi-1. RAW264.7 cells were stimulated by ox-LDL, pretreated with or without MCC950, Mito-TEMPO, or Mdivi-1. The burden of plaques and foam cell formation were determined using ORO staining. The blood lipid profles and inflammatory cytokines in serum were detected by commercial kits and ELISA, respectively. The mRNA expression of macrophage polarization markers, activation of NLRP3 and the phosphorylation state of DRP1 were detected. Mitochondrial reactive oxygen species (mito-ROS), mitochondrial staining, ATP level and mitochondrial membrane potential were detected by mito-SOX, MitoTracker, ATP determination kit and JC-1 staining, respectively. RESULTS: In vivo, Mdivi-1 reduced the plaque areas, M1 polarization, NLRP3 activation and DRP1 phosphorylation at Ser616. In vitro, oxidized low-density lipoprotein (ox-LDL) triggered M1 polarization, NLRP3 activation and abnormal accumulation of mito-ROS. MCC950 and Mito-TEMPO suppressed M1 polarization mediated foam cell formation. Mito-TEMPO significantly inhibited NLRP3 activation. In addition, Mdivi-1 reduced foam cells by inhibiting M1 polarization. The possible mechanisms responsible for the anti-atherosclerotic effects of Mdivi-1 on reducing M1 polarization were associated with suppressing mito-ROS/NLRP3 pathway by inhibiting DRP1 mediated mitochondrial fission. In vitro, similar results were observed by DRP1 knockdown. CONCLUSION: Inhibition of DRP1-dependent mitochondrial fission by Mdivi-1 alleviated atherogenesis via suppressing mito-ROS/NLRP3-mediated M1 polarization, indicating DRP1-dependent mitochondrial fission as a potential therapeutic target for AS.

Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas.

BACKGROUND: Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. PURPOSE: To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. MATERIAL AND METHODS: This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. RESULTS: Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). CONCLUSION: Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Primary motor hand area corticospinal excitability indicates overall functional recovery after spinal cord injury.

Background: After spinal cord injury (SCI), the excitability of the primary motor cortex (M1) lower extremity area decreases or disappears. A recent study reported that the M1 hand area of the SCI patient encodes the activity information of both the upper and lower extremities. However, the characteristics of the M1 hand area corticospinal excitability (CSE) changes after SCI and its correlation with extremities motor function are still unknown. Methods: A retrospective study was conducted on the data of 347 SCI patients and 80 healthy controls on motor evoked potentials (MEP, reflection of CSE), extremity motor function, and activities of daily living (ADL) ability. Correlation analysis and multiple linear regression analysis were conducted to analyze the relationship between the degree of MEP hemispheric conversion and extremity motor function/ADL ability. Results: The CSE of the dominant hemisphere M1 hand area decreased in SCI patients. In 0-6 m, AIS A grade, or non-cervical injury SCI patients, the degree of M1 hand area MEP hemispheric conversion was positively correlated with total motor score, lower extremity motor score (LEMS), and ADL ability. Multiple linear regression analysis further confirmed the contribution of MEP hemispheric conversion degree in ADL changes as an independent factor. Conclusion: The closer the degree of M1 hand area MEP hemispheric conversion is to that of healthy controls, the better the extremity motor function/ADL ability patients achieve. Based on the law of this phenomenon, targeted intervention to regulate the excitability of bilateral M1 hand areas might be a novel strategy for SCI overall functional recovery.

Diffusion-relaxation correlation spectrum imaging for predicting tumor consistency and gross total resection in patients with pituitary adenomas: a preliminary study.

OBJECTIVE: To evaluate the ability of diffusion-relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). METHODS: Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. RESULTS: Tumor consistency was classified as soft (n = 28) or hard (n = 16). Hard PAs presented higher [Formula: see text] (p = 0.001) and lower [Formula: see text] (p = 0.013) than soft PAs, while no significant difference was found in other parameters. [Formula: see text] significantly correlated with the level of collagen content (r = 0.448, p = 0.002). Knosp grade (odds ratio [OR], 0.299; 95% confidence interval [CI], 0.124-0.716; p = 0.007) and [Formula: see text] (OR, 0.834, per 1% increase; 95% CI, 0.731-0.951; p = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p < 0.05). CONCLUSION: DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. CLINICAL RELEVANCE STATEMENT: DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. KEY POINTS: • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). • [Formula: see text] correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and [Formula: see text] achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).

Irrigating degradation properties of silk fibroin-collagen type II composite cartilage scaffold in vitro and in vivo.

Silk fibroin-collagen type II scaffolds are promising in cartilage tissue engineering due to their suitable biological functionality to promote proliferation of chondrocytes in vitro. However, their degradation properties, which are of crucial importance as scaffold degradation should consistent with the new tissue formation process, are still unknown. In this study, degradability of silk fibroin-collagen type II cartilage scaffolds was probed both in vitro and in vivo. In vitro degradation experiments show that the scaffolds decreased 32.25 % ± 0.62 %, 34.27 % ± 0.96 %, 36.27 % ± 2.39 % in weight after 8 weeks of degradation at the irrigation velocity of 0 mL/min, 7.89 mL/min and 15.79 mL/min. The degradation ratio, which increases with time and increasing irrigation velocity, is described by combining the built mathematic model and finite element modeling method. The scaffolds after 8 weeks of degradation in vitro keep their mechanical structural integrity to support new tissues. In vivo degradation experiments conducted in rabbits further show that the scaffolds degrade gradually, be absorbed with time and finally collapse in structure. The degradation process is accompanied by the growth of fibrous tissues and the scaffold is filled by fibrous tissues after 12 weeks of implantation. Immunohistology analysis shows that the inflammation caused by scaffolds is controllable and gradually alleviates with time. To sum up, silk fibroin-collagen type II cartilage scaffolds, which show suitable mechanical properties and biocompatibility during degradation in vitro and in vivo, have great potential in cartilage repair. The novelty of the study is that it not only introduces a mathematical model to predict the irrigation degradation ratio, but also provides experimental degradation data support for clinical application of silk fibroin-collagen type II cartilage scaffolds.

Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury.

BACKGROUND: Although current guidelines didn't support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate between furosemide-responsive (FR) and furosemide-unresponsive (FU) oliguric AKI. METHODS: From Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD), oliguric AKI patients with urine output (UO) < 0.5 ml/kg/h for the first 6 h after ICU admission and furosemide infusion ≥ 40 mg in the following 6 h were retrospectively selected. The MIMIC-IV cohort was used in training a XGBoost model to predict UO > 0.65 ml/kg/h during 6-24 h succeeding the initial 6 h for assessing oliguria, and it was validated in the eICU-CRD cohort. We compared the predictive performance of the XGBoost model with the traditional logistic regression and other ML models. RESULTS: 6897 patients were included in the MIMIC-IV training cohort, with 2235 patients in the eICU-CRD validation cohort. The XGBoost model showed an AUC of 0.97 (95% CI: 0.96-0.98) for differentiating FR and FU oliguric AKI. It outperformed the logistic regression and other ML models in correctly predicting furosemide diuretic response, achieved 92.43% sensitivity (95% CI: 90.88-93.73%) and 95.12% specificity (95% CI: 93.51-96.3%). CONCLUSION: A boosted ensemble algorithm can be used to accurately differentiate between patients who would and would not respond to furosemide in oliguric AKI. By making the model explainable, clinicians would be able to better understand the reasoning behind the prediction outcome and make individualized treatment.

In Vitro Hypoglycemic Diterpenoids from the Roots of Croton yunnanensis.

Fifteen compounds including nine new diterpenes were isolated from the roots of Croton yunnanensis. By HRESIMS, NMR, ECD data, and X-ray diffraction analysis, the new compounds were characterized as eight neo-clerodane diterpenes (compounds 1-8) and one 15,16-dinor-ent-pimarane diterpene (9). All diterpenes were assayed for their hypoglycemic activities. Compounds 1-4, 6, 7, and 10 promoted glucose uptake activity in insulin-resistant 3T3-L1 adipocytes. Compounds 1 and 6 showed insulin sensitizing activity, potentiating conspicuously their glucose uptake activity at a concentration of 20 µM when treated synergistically with low-concentration insulin at 1 nM.

A Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota.

Most microbiome studies regarding the ruminant digestive tract have focused on the rumen microbiota, whereas only a few studies were performed on investigating the gut microbiota of ruminants, which limits our understanding of this important component. Herein, the gut microbiota of 30 Caprinae animals (sheep and goats) from six provinces in China was characterized using ultradeep (>100 Gbp per sample) metagenome shotgun sequencing. An inventory of Caprinae gut microbial species containing 5,046 metagenomic assembly genomes (MAGs) was constructed. Particularly, 2,530 of the genomes belonged to uncultured candidate species. These genomes largely expanded the genomic repository of the current microbes in the Caprinae gut. Several enzymes and biosynthetic gene clusters encoded by these Caprinae gut species were identified. In summary, our study extends the gut microbiota characteristics of Caprinae and provides a basis for future studies on animal production and animal health. IMPORTANCE We constructed a microbiota catalog containing 5,046 MAGs from Caprinae gut from six regions of China. Most of the MAGs do not overlap known databases and appear to be potentially new species. We also characterized the functional spectrum of these MAGs and analyzed the differences between different regions. Our study enriches the understanding of taxonomic, functional, and metabolic diversity of Caprinae gut microbiota. We are confident that the manuscript will be of utmost interest to a wide range of readers and be widely applied in future research.

Temporal changes in plaque characteristics after treatment and their relationship with stroke recurrence: a quantitative study using magnetic resonance imaging.

Background: Intracranial atherosclerotic disease (ICAD) is the major cause of ischemic stroke. Despite aggressive medical therapy, around 15% of patients with ICAD experience recurrence. The aim of the present study was to evaluate the temporal changes in intracranial arteriosclerotic plaques after medical treatment based on vessel wall magnetic resonance imaging (VWMRI) and to explore their relationship with stroke recurrence. Methods: A total of 67 symptomatic patients with ICAD who underwent initial and follow-up VWMRI were recruited into this retrospective cohort study. Stroke recurrence was defined as an ipsilateral stroke symptom after the initial attack. The clinical characteristics and plaque features, including stenosis ratio (measured based on luminal diameter or area), plaque thickness, plaque burden (PB), enhancement ratio (ER), and enhancement grade, were evaluated and compared between the initial and follow-up examinations. Changes in plaque characteristics were compared between patients with or without recurrence by univariable analyses. Multivariable regression was performed to investigate imaging markers for recurrent stroke. Results: The median interval between baseline and follow-up VWMRI was 334 days. A total of 13 cases (19.4%) experienced a stroke recurrence. After treatment, significant decreases in the stenosis ratio (area), PB, and ER were observed in cases without recurrence (all P<0.05), while no significant difference in plaque features was found for cases with recurrence. Univariable analyses showed that changes in stenosis ratio (area), plaque thickness, PB, and ER were significantly different between patients with and without recurrence (all P<0.05). Multivariable regression indicated that PB change was the only significant marker associated with stroke recurrence [odds ratio (OR) =1.112 per 1% increase, 95% confidence interval (CI): 1.010 to 1.224, P=0.031]. Conclusions: Patients with arteriosclerotic plaques who benefit from medical treatment show obvious decreases in stenosis (area), PB, and ER. The progression of PB may serve as an independent marker for predicting stroke recurrence.

High-resolution magnetic resonance imaging for predicting successful recanalization in patients with chronic internal carotid artery occlusion.

Objective: The main aim of the study was to investigate the predictive factors of high-resolution magnetic resonance imaging (HR-MRI) for successful recanalization in patients with chronic internal carotid artery occlusion (CICAO). Methods: We included 41 consecutive patients who had CICAO and underwent recanalization attempts. The demographics, clinical data, and HR-MRI features in relation to the technique success were collected and analyzed using univariate and multivariate analyses. A score-based prediction model was constructed using a regression coefficient-based scoring method. Results: Technical success was achieved in 26 (63.4%) patients, with a complication rate of 12.2% (5/41). Based on multivariate analysis, occlusions involving ophthalmic artery segment (C6) or above (OR: 0.036; 95% confidence interval [CI]: 0.004-0.336) and nontapered stump (OR: 0.064; 95% CI: 0.007-0.591) were identified as independent negative predictors of successful recanalization in patients with CICAO. Point scores were assigned according to the model coefficients, and the patients who scored 0, 1, or 2 points had success rates of 93.33% (14/15), 66.67% (12/18), or 0% (0/8), respectively. Conclusion: HR-MRI characteristics may be valuable in identifying candidates for endovascular recanalization in patients with CICAO. Occlusions involving the C6 segment or higher, as well as nontapered stumps, were independent negative predictors of technical success.

Tigliane Diterpenoids with Larvicidal, Antifungal, and α-Glucosidase Inhibitory Activities from Croton damayeshu.

Thirty-five tigliane diterpenoids and two ent-kaurane diterpenoids were isolated from the leaves of Croton damayeshu, and, among them, compounds 1-10 were characterized as new tigliane diterpenoids. The structures of compounds 1-10 were determined by analysis of their HRESIMS, NMR, and ECD data and by chemical methods. The isolates were assayed for their larvicidal, antifungal, and α-glucosidase inhibitory activities, and compounds 8-10 were found to possess larvicidal activities against Plutella xylostella with LC50 values of 0.19, 0.16, and 0.26 µM, respectively, comparable to the LC50 of 0.14 µM for the positive control, flubendiamide.

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks.

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them. RESULTS: Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction. AVAILABILITY AND IMPLEMENTATION: CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.