Ultrahigh-strength silicone aerogels reinforced by an armor-like epoxy framework via a temperature-controlled sequential reaction strategy

Aerogels with low density and high porosity are extremely attractive for high-performance insulation, but their brittleness, complicated fabrication, and poor mechanical properties greatly limit their practical applications. Herein, we report an ultrahigh-strength silicone aerogel with an armor-like epoxy framework via a temperature-controlled sequential reaction strategy. The key to this synthesis is forming a Si-O-Si framework via the polycondensation of silanes at 100 °C, followed by in-situ armoring an epoxy framework via an intermolecular cyclization at an elevated temperature of 150 °C. Owing to the enhanced framework, the resulting aerogel could withstand capillary tension in the drying process, enabling it to be dried at ambient pressure without shrinkage. The obtained aerogel possesses a tunable density of 0.17-0.45 g/cm3 and ultrahigh-strength with compressive modulus up to 37.8-244.3 MPa, which surpasses other polymer-reinforced silicone aerogels by a factor of five in mechanical properties. It also demonstrates outstanding thermal insulation, with an extremely low thermal conductivity from 0.025 to 0.051 W m-1 K-1 at room temperature, and maintains thermal characteristics across a temperature range of -20 to 300 °C. Furthermore, the aerogel composites prepared by the reinforcement of low-density fiber mats have tunable densities of 0.36-0.87 g/cm3, much enhanced tensile strengths of 15.9-72.3 MPa, and low thermal conductivities at room temperature of 0.042-0.078 W m-1 K-1. This study presents a cost-effective method for enhancing the production of silicone aerogel materials, offering considerable opportunities for their application in insulation, energy transport, and the aerospace sector.

[Long term follow-up evaluation of combined surgery for congenital tibial pseudarthrosis in children]

Objective: To explore the long-term effect of combined surgery for the treatment of congenital tibial pseudarthrosis in children. Methods: The clinical data of 44 children with congenital tibial pseudarthrosis who underwent combined surgery (tibial pseudarthrosis tissue resection, intramedullary rod fixation, Ilizarov external fixator fixation, wrapped autologous iliac bone graft) from August 2007 to October 2011 at the Department of Pediatric Orthopedics, Hunan Children's Hospital were collected retrospectively. There were 33 males and 11 females. The age at the time of surgery was (3.7±2.2)years (range:0.6 to 12.4 years), including 25 cases under 3 years old and 19 cases above 3 years old.Among them, 37 cases were complicated with neurofibromatosis type 1.The operation status, postoperative complications and follow-up results were recorded. Results: The follow-up time after surgery was (10.9±0.7)years (range:10 to 11 years).Thirty-nine out of 44 patients (88.6%) achieved initial healing of tibial pseudarthrosis, with an average healing time of (4.3±1.1)months (range:3 to 10months).In the last follow-up, 36 cases (81.8%) had unequal tibial length, 20 cases (45.4%) had refractures, 18 cases (40.9%) had ankle valgus, 9 cases (20.4%) had proximal tibial valgus, and 11 cases (25.0%) had high arched feet.Nine cases (20.4%) developed distal tibial epiphyseal plate bridging.17 cases (38.6%) had abnormal tibial mechanical axis.Seven cases (15.9%) developed needle infection, and one case (2.3%) developed tibial osteomyelitis. 21 patients (47.7%) had excessive growth of the affected femur.Five patients (11.3%) had ankle stiffness, and 34 patients (77.2%) had intramedullary rod displacement that was not in the center of the tibial medullary cavity.Among them, 8 cases (18.1%) protruded the tibial bone cortex and underwent intramedullary rod removal.18 children have reached skeletal maturity, while 26 children have not been followed up until skeletal maturity. Conclusion: Combined surgery for the treatment of congenital pseudarthrosis of the tibia in children has a high initial healing rate, but complications such as unequal tibia length, refracture, and ankle valgus occur during long-term follow-up, requiring multiple surgical treatments.

A5 A MOUSE MODEL TO UNRAVEL THE PATHOPHYSIOLOGICAL LINK BETWEEN CROHN’S DISEASE AND TYPE-2 DIABETES-ASSOCIATED METABOLIC DISORDERS

Background

Crohn’s disease (CD), an idiopathic inflammatory bowel disease (IBD), has been recently shown to increase the risk of developing type 2 diabetes (T2D). Moreover, treatment with anti-diabetic drugs has a protective role in preventing the severity and course of CD progression. However, the pathophysiological basis of T2D development in CD remains unclear. Findings have highlighted the contribution of adipose tissue (AT) to the development of chronic inflammatory diseases and have identified parallels between T2D and CD that may provide hints to common mechanisms of disease pathogenesis. Typically, microbial dysbiosis, hyperpermeable intestinal barrier, and intra-abdominal AT accumulation are the common features of both diseases, yet how the interplay of these factors contribute to pathogenesis is not known. Therefore, common pathogenic paradigms underlying both T2D and CD have led us to hypothesize that chronic intestinal inflammation serves as an initiator of AT dysfunction in CD, predisposing individuals to T2D. Further, the lack of appropriate animal models of CD with chronic intestinal inflammation that manifests accumulation of intra-abdominal AT, and extra-intestinal metabolic disorder as observed in CD and T2D patients has been a limitation.

Purpose

To develop a genetic mouse model to investigate if gut inflammation-mediated microbial dysbiosis and metabolic dysregulation of AT are at the nexus that cause T2D in CD.

Method

We developed a CD-mouse model, where we challenged Nod2-deficient mice (NOD2 being the strongest genetic risk factor contributing to CD) with a chronic inflammatory insult regime, using dextran sulfate sodium (cDSS) for 3 cycles. Subsequently, intraperitoneal insulin and oral glucose tolerance tests, metabolic caging, and MRI imaging of mice were performed. Changes in AT metabolism and microbial infiltration into AT were analyzed by quantitative real-time PCR (qRT-PCR) and/or immunohistochemistry (IHC).

Result(s)

Our new CD-mouse model revealed increased gut inflammation (TNF and type-I IFN) in Nod2-deficient mice compared to wild-type control mice post-cDSS. Surprisingly, Nod2-deficient mice gained body weight, which was at least in part accounted for by an increased intra-abdominal AT accumulation along with decreased AT fatty-acid metabolism (Cpt1a, Fabp4 expression) and AT browning (Ucp1, Cidea expression, and UCP-1 staining), reduced intestinal goblet cell numbers, increased gut bacterial infiltration within the fat, more insulin resistance and energy expenditure.

Conclusion(s)

This experimental mouse model mimicking CD-associated T2D will provide insights into how the microbiome-AT axis fuel chronic inflammation-mediated extra-intestinal metabolic disorder and immune dysregulation. Understanding these connections will be transformative, as it will help us devise novel therapeutic strategies to prevent T2D development in progressive CD patients.

Disclosure of Interest

None Declared

Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods

Thromboxane A2 synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (dSTD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, dSTD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.

Racial/ethnic differences in cardiovascular outcomes in a universal healthcare system: insights from the CARTaGENE cohort

Background

While prior studies have shown racial/ethnic differences in cardiovascular (CV) outcomes within private or mixed health care systems, it remains uncertain whether inequalities in cardiovascular outcomes exist between different races and ethnicities in universal health care contexts. We aimed to determine whether there are racial/ethnicity disparities in long-term CV outcomes within a single-payer universal health care system.

Methods

The CARTaGENE study is a population-based prospective cohort study with enrollment of 19,996 individuals between 40–69 years in 2009, in the province of Quebec, Canada. Participants residing in four large metropolitan areas were randomly chosen from the provincial health insurance registry by strata of age, sex, and postal codes. Follow-up was available up to 2016. For this analysis, we retained only participants without prior known CV disease. The primary composite endpoint was time to the first CV event or intervention (CV death, acute coronary syndrome, heart failure, coronary revascularization, ischemic stroke, or peripheral vascular event or revascularization). We used unadjusted and adjusted Cox proportional hazard models to evaluate the association of self-defined race/ethnicity with the primary endpoint.

Results

There were 17,802 eligible participants with a mean age of 51 years (52.5% females) with 111,312 person-years of follow-up (median follow-up of 6.6 years). South Asian (SA) participants had the highest prevalence of diabetes mellitus (29%) and hypertension (32%). After adjustment for age and sex, SA ethnicity was associated with a 95% relative increase in risk for CV events, while East/Southeast Asian (ESA) ethnicity was associated with a 42% relative decrease in risk for CV events compared to White participants. After further adjustment for socioeconomic status and CV risk factors, ESA ethnicity remained associated with a similar decreased CV risk. In contrast, the association of SA ethnicity with increased CV risk was attenuated after full adjustment for baseline characteristics (Table 1).

Conclusions

Racial/ethnic disparities in long-term CV outcomes are present in a single-payer universal healthcare setting. ESA ethnicity was associated with a lower risk of long-term CV outcomes. Future studies are needed to corroborate the reduced risk of long-term major CV events associated with ESA ethnicity. Understanding the reasons related to potential CV protection with ESA ethnicity could facilitate endeavors to reduce long-term CV outcomes in other races/ethnicities.

Funding Acknowledgement

Type of funding sources: Public hospital(s). Main funding source(s): McGill Health University Center

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by d S T D - P R O method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors' structural features. In addition, 10 quantitative structure-activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (r ²) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.

The Potential of Utilizing Mid-Energy X-Rays for In-Line Phase Sensitive Breast Cancer Imaging

OBJECTIVE

The objective of this study is to demonstrate the potential of utilizing mid-energy x-rays for in-line phase-sensitive breast cancer imaging by phantom studies.

METHODS

The midenergy (50-80kV) in-line phase sensitive imaging prototype was used to acquire images of the contrast-detail mammography (CDMAM) phantom, an ACR accreditation phantom, and an acrylic edge phantom. The low-dose mid-energy phase-sensitive images were acquired at 60 kV with a radiation dose of 0.9 mGy, while the high-energy phase-sensitive images were acquired at 90 kV with a radiation dose of 1.2 mGy. The Phase-Attenuation Duality (PAD) principle for soft tissue was used for the phase retrieval. A blind observer study was conducted and paired-sample T-test were performed to compare the mean differences in the two imaging systems.

RESULTS

The correct detection ratio for the CDMAM phantom for phase-contrast images acquired by the low-dose mid-energy system was 56.91%, whereas images acquired by the high-energy system correctly revealed only 40.97% of discs. The correct detection ratios were 57.88% and 43.41% for phase-retrieved images acquired by the low-dose mid-energy and high-energy imaging systems, respectively. The reading scores for all three groups of objects in the ACR phantom were higher for the mid energy imaging system as compared to the high-energy system for both phase-contrast and phase- retrieved images. The calculated edge enhancement index (EEI) from the acrylic edge phantom image for the mid-energy system was higher than that calculated for the high-energy imaging system. The quantitative analyses showed a higher Contrast to Noise Ratio (CNR) as well as a higher Figure of Merit (FOM) in images acquired by the low-dose mid-energy imaging system.

CONCLUSION

The PAD based retrieval method can be applied in mid-energy system without remarkably affecting the image quality, and in fact, it improves the lesion detectability with a patient dose saving of 25%.

QSAR studies on hepatitis C virus NS5A protein tetracyclic inhibitors in wild type and mutants by CoMFA and CoMSIA

Several 3D-QSAR models were built based on 196 hepatitis C virus (HCV) NS5A protein inhibitors. The bioactivity values EC₉₀ for three types of inhibitors, the wild type (GT1a) and two mutants (GT1a Y93H and GT1a L31V), were collected to build three datasets. The programs OMEGA and ROCS were used for generating conformations and aligning molecules of the dataset, respectively. Each dataset was randomly divided into a training set and a test set three times to reduce the contingency of only one random selection. QSAR models were computed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the datasets GT1a, GT1a Y93H, and GT1a L31V, the best models CoMFA-INDX, CoMSIA-SEHA, and CoMSIA-SEHA showed an r² value of 0.682 ± 0.033, 0.779 ± 0.036, and 0.782 ± 0.022 on the test sets, respectively. From the contour maps of the three best models, we summarized the favourable and unfavourable substituents on the tetracyclic core, the Z group, the proline group, and the valine group of inhibitors. We guessed the mutants could change the electrostatic surfaces of the wild type active pocket. In addition, we used ECFP analyses to find important substructures and could intuitively understand the results from QSAR models.

Relationship between corneal confocal microscopy and markers of peripheral nerve structure and function in Type 2 diabetes

AIMS

To investigate changes in corneal nerve morphology in Type 2 diabetes and to establish relationships between in vivo corneal confocal microscopy and markers of peripheral nerve structure and function.

PARTICIPANTS AND METHODS

We recruited 57 participants with Type 2 diabetes and 26 healthy controls of similar age and sex distribution. We also recruited a disease control group of 54 participants with Type 1 diabetes. All participants were assessed for distal symmetrical polyneuropathy using the Total Neuropathy Score. In vivo corneal confocal microscopy was used to assess corneal nerve fibre length, corneal nerve fibre density, corneal nerve branch density and inferior whorl length. Peripheral nerve structure was assessed using median nerve ultrasonography. Large fibre function was assessed according to median nerve axonal excitability. Small fibre function was assessed using Sudoscan^TM and the Survey of Autonomic Symptoms.

RESULTS

Corneal nerve fibre length, fibre density and branch density and inferior whorl length were significantly lower in individuals with Type 2 diabetes compared to controls (P<0.001 for all). In the Type 2 diabetes cohort, correlations were observed between neuropathy severity and corneal nerve fibre density (P=0.004), corneal nerve branch density (P=0.003), corneal nerve fibre length (P=0.002) and inferior whorl length (P=0.01). Significant correlations were observed between corneal confocal outcomes and axonal excitability measurements. No association was found between corneal confocal microscopy and median nerve cross-sectional area, Sudoscan measurements or the Survey of Autonomic Symptoms.

CONCLUSIONS

This study demonstrated significant changes in corneal nerves in individuals with Type 2 diabetes. Reductions in corneal nerve measures correlated with increasing neuropathy severity. Associations were found between corneal confocal microscopy and markers of voltage-gated potassium channel function.

P1500Effects of empagliflozin on cardiac function in patients with type 2 diabetes mellitus: echocardiographic substudy of the EMPA-HEART cardiolink-6 trial

Background

The EMPA-HEART trial showed a reduction in left ventricular (LV) mass index by cardiac MRI at 6 months in patients treated with Empagliflozin vs placebo. A secondary analysis of key echocardiographic parameters was performed to provide further insight into the mechanism of LV remodeling.

Methods

All patients enrolled prospectively underwent transthoracic echocardiography (TTE) at baseline and at 6 months. Measurements were performed according to the American Society of Echocardiography guidelines. Key outcomes of interest included changes in diastolic function and right ventricle parameters at 6 months in patients treated with Empagliflozin vs placebo.

Results

A total of 97 patients were enrolled (49 treated with Empagliflozin and 48 in the placebo group). There was no significant difference in the change in average E/E' at 6-months in the Empagliflozin group vs placebo (−0.4 vs +0.2, adjusted difference −0.2, 95% CI [−1.3 to 0.82], p=0.7) Similarly, there was no difference between the groups in secondary TTE parameters (Table 1). Subgroup analyses showed no benefit among patients with baseline LVEF >50% vs. ≤50%, and baseline LV mass index ≥60 g/m2 vs <60 g/m2.

Echocardiographic Parameter Placebo (n=48) Empagliflozin (n=49) Adjusted Difference Between Groups 95% CI P-Value Baseline 6 months Change Baseline 6 months Change LVEF (%)* 55.5 (8.7) 54.3 (8.9) −1.0 (6.5) 58.0 (7.5) 59.1 (8.57) 0.72 (5.1) 2.2 (−0.2, 4.7) 0.1 Diastolic Parameters:   Average E/e' 10.1 (3.1) 10.3 (2.5) 0.2 (3.0) 10.6 (3.0) 10.5 (3.6) −0.4 (2.5) −0.2 (−1.3, 0.8) 0.7   Medial E/e' 12.3 (3.9) 12.5 (3.6) 0.1 (3.7) 12.6 (4.2) 12.6 (5.2) −0.3 (3.3) −0.3 (−1.7, 1.1) 0.7   Lateral E/e' 8.0 (2.8) 8.2 (2.2) 0.2 (2.7) 8.7 (2.6) 8.4 (2.5) −0.4 (2.7) −0.1 (−1.0, 0.8) 0.8   E velocity (cm/sec) 68.6 (15.2) 70.6 (14.7) 1.8 (15.4) 74.4 (18.2) 71.2 (16.8) −3.2 (15.1) −2.3 (−7.9, 3.3) 0.4   A velocity (cm/sec) 74.7 (17.9) 77.9 (18.8) 2.9 (15.9) 76.2 (16.5) 75.8 (14.5) −1.4 (11.7) −3.5 (−8.9, 1.6) 0.2   LA volume index (mL/m2) 32.7 (7.9) 30.8 (8.1) −2.0 (6.7) 30.2 (6.7) 28.7 (5.5) −1.6 (6.5) −0.9 (−3.4, 1.6) 0.5 RV Parameters:   TAPSE (cm) 1.8 (0.5) 1.8 (0.4) 0.1 (0.4) 2.0 (1.2) 1.8 (0.4) −0.3 (1.4) −0.1 (−0.3, 0.1) 0.3   RV S' TDI (cm/sec) 10.9 (2.9) 10.6 (2.5) −0.1 (2.2) 10.4 (2.7) 10.2 (2.6) −0.4 (2.0) −0.3 (−1.2, 0.5) 0.4 *Measured by cardiac MRI. LA, left atrium; LVEF, left ventricular ejection fraction; RV, right ventricle; TAPSE, tricuspid annular plane systolic excursion; TDI, tissue Doppler imaging. Data expressed as mean (standard deviation).

Conclusion

This study showed no significant change in key echocardiographic parameters in patients treated with Empagliflozin, suggesting that changes in loading conditions induced by empagliflozin (i.e. preload) do not mediate the reduction in LV mass.

Effects of magnanimous therapy on coping, adjustment, and living function in advanced lung cancer

Objectives

We examined the effects of magnanimous therapy on psychological coping, adjustment, living function, and survival rate in patients with advanced lung cancer.

Methods

Patients with advanced lung cancer (n = 145) matched by demographics and medical variables were randomly assigned to an individual computer magnanimous therapy group (ic-mt), a group computer magnanimous therapy group (gc-mt), or a control group (ctrl). Over 2 weeks, the ic-mt and gc-mt groups received eight 40-minute sessions of ic-mt or gc-mt respectively, plus usual care; the ctrl group received only usual care. The Cancer Coping Modes Questionnaire (ccmq), the Psychological Adjustment Scale for Cancer Patients (pascp), and the Functional Living Index-Cancer (flic) were assessed at baseline and 2 weeks later. The relationships of changes in those indicators were analyzed, and survival rates were compared.

Results

The psychological coping style, adjustment, and living function of the ic-mt and gc-mt groups improved significantly after the intervention (p < 0.01). After 2 weeks, significant (p < 0.01) differences between the treatment groups and the ctrl group in coping style, adjustment, and living function suggested successful therapy. The changes in living function were correlated with changes in psychological coping and adjustment. No difference in efficacy between ic-mt and gc-mt was observed. The survival rate was 31.84% in the ic-mt group and 9.375% in the ctrl group at 2 years after the intervention.

Conclusions

In patients with advanced lung cancer, ic-mt and gc-mt were associated with positive short-term effects on psychological coping style, adjustment, and living function, although the magnitude of the effect did not differ significantly between the intervention approaches. The effects on living function are partly mediated by improvements in psychological coping and adjustment.

Critical care usage after major gastrointestinal and liver surgery: a prospective, multicentre observational study

BACKGROUND

Patient selection for critical care admission must balance patient safety with optimal resource allocation. This study aimed to determine the relationship between critical care admission, and postoperative mortality after abdominal surgery.

METHODS

This prespecified secondary analysis of a multicentre, prospective, observational study included consecutive patients enrolled in the DISCOVER study from UK and Republic of Ireland undergoing major gastrointestinal and liver surgery between October and December 2014. The primary outcome was 30-day mortality. Multivariate logistic regression was used to explore associations between critical care admission (planned and unplanned) and mortality, and inter-centre variation in critical care admission after emergency laparotomy.

RESULTS

Of 4529 patients included, 37.8% (n=1713) underwent planned critical care admissions from theatre. Some 3.1% (n=86/2816) admitted to ward-level care subsequently underwent unplanned critical care admission. Overall 30-day mortality was 2.9% (n=133/4519), and the risk-adjusted association between 30-day mortality and critical care admission was higher in unplanned [odds ratio (OR): 8.65, 95% confidence interval (CI): 3.51-19.97) than planned admissions (OR: 2.32, 95% CI: 1.43-3.85). Some 26.7% of patients (n=1210/4529) underwent emergency laparotomies. After adjustment, 49.3% (95% CI: 46.8-51.9%, P<0.001) were predicted to have planned critical care admissions, with 7% (n=10/145) of centres outside the 95% CI.

CONCLUSIONS

After risk adjustment, no 30-day survival benefit was identified for either planned or unplanned postoperative admissions to critical care within this cohort. This likely represents appropriate admission of the highest-risk patients. Planned admissions in selected, intermediate-risk patients may present a strategy to mitigate the risk of unplanned admission. Substantial inter-centre variation exists in planned critical care admissions after emergency laparotomies.

SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors

Cyclooxygenase-1 (COX-1) is one isoform of COX, and it is a main target of nonsteroidal anti-inflammatory drugs (NSAIDs). It is important to develop efficient and selective COX-1 inhibitors. In this work, 12 classification models for 1530 cyclooxygenase-1 (COX-1) inhibitors were built by support vector machine (SVM), decision tree (DT) and random forest (RF) methods. The best classification model (model 1A) was built by SVM with MACCS fingerprints. The classification accuracies for the training and test sets were 99.67% and 97.39%, respectively. The Matthews correlation coefficient (MCC) of the test set was 0.94. We also divided the 1530 COX-1 inhibitors into nine subsets according to their different scaffolds using Kohonen's self-organizing map (SOM). In addition, six quantitative structure-activity relationship (QSAR) models for 181 COX-1 inhibitors whose IC₅₀ were measured by enzyme immunoassay were built by multiple linear regression (MLR) and SVM. The best QSAR model (model 5A) was built by SVM with CORINA Symphony descriptors. The correlation coefficients of the training and test sets are 0.93 and 0.84, respectively. The models built in this study can be obtained from the authors.

Multiregion whole-exome sequencing of matched primary and metastatic tumors revealed genomic heterogeneity and suggested polyclonal seeding in colorectal cancer metastasis

Background

Distant metastasis accounts for 90% of deaths from colorectal cancer (CRC). Genomic heterogeneity has been reported in various solid malignancies, but remains largely under-explored in metastatic CRC tumors, especially in primary to metastatic tumor evolution.

Patients and methods

We conducted high-depth whole-exome sequencing in multiple regions of matched primary and metastatic CRC tumors. Using a total of 28 tumor, normal, and lymph node tissues, we analyzed inter- and intra-individual heterogeneity, inferred the tumor subclonal architectures, and depicted the subclonal evolutionary routes from primary to metastatic tumors.

Results

CRC has significant inter-individual but relatively limited intra-individual heterogeneity. Genomic landscapes were more similar within primary, metastatic, or lymph node tumors than across these types. Metastatic tumors exhibited less intratumor heterogeneity than primary tumors, indicating that single-region sequencing may be adequate to identify important metastasis mutations to guide treatment. Remarkably, all metastatic tumors inherited multiple genetically distinct subclones from primary tumors, supporting a possible polyclonal seeding mechanism for metastasis. Analysis of one patient with the trio samples of primary, metastatic, and lymph node tumors supported a mechanism of synchronous parallel dissemination from the primary to metastatic tumors that was not mediated through lymph nodes.

Conclusions

In CRC, metastatic tumors have different but less heterogeneous genomic landscapes than primary tumors. It is possible that CRC metastasis is, at least partly, mediated through a polyclonal seeding mechanism. These findings demonstrated the rationale and feasibility for identifying and targeting primary tumor-derived metastasis-potent subclones for the prediction, prevention, and treatment of CRC metastasis.

SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors

In this paper, structure-activity relationship (SAR, classification) and quantitative structure-activity relationship (QSAR) models have been established to predict the bioactivity of human epidermal growth factor receptor-2 (HER2) inhibitors. For the SAR study, we established six SAR (or classification) models to distinguish highly and weakly active HER2 inhibitors. The dataset contained 868 HER2 inhibitors, which was split into a training set including 580 inhibitors and a test set including 288 inhibitors by a Kohonen's self-organizing map (SOM), or a random method. The SAR models were performed using support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) methods. Among the six models, SVM models obtained superior results compared with other models. The prediction accuracy of the best model (model 1A) was 90.27% and the Matthews correlation coefficient (MCC) was 0.80 on the test set. For the QSAR study, we chose 286 HER2 inhibitors to establish six quantitative prediction models using MLR, SVM and MLP methods. The correlation coefficient (r) of the best model (model 4B) was 0.92 on the test set. The descriptors analysis showed that HAccN, lone pair electronegativity and π electronegativity were closely related to the bioactivity of HER2 inhibitors.

[Correlative study between X-ray type after healing of congenital pseudarthrosis of the tibia in children and postoperative refracture]

Objective: To investigate the relationship between postoperative X-ray type in 2 years after healing of congenital pseudarthrosis of the tibia(CPT) and refracture of CPT in children. Methods: A retrospective study was performed on 67 children patients with Crawford type Ⅳ CPT who were treated with combined surgeries from December 2007 to August 2012.There were 46 male and 21 female patients with 37 cases with left CPT and 30 cases with right CPT. There were 12 cases with proximal tibia dysplasia, 56 cases with neurofibromatosis type 1. The median age when operation was 2.8 years(from 0.6 to 11.2 years). The patients were divided into three groups, CPT with hypertrophic group, CPT with mediate group and CPT with atrophic group, on the basis of ratio of healing cross-sectional area and transition zone in pseudarthrosis.The incidence of refracture in the three groups were investigated. Results: The refracture rates of three groups were 13%(5/38), 14%(3/21), 5/8, respectively.The refracture rate difference between CPT with hypertrophic group and CPT with mediate group was not statistically significant(P=0.590). The refracture rate of CPT with atrophic group was statistically significant lower than that of CPT with hypertrophic group and CPT with mediate group(P=0.007, 0.019). In addition, the refracture-free cumulative survival rate of CPT with hypertrophic group or CPT with mediate group was higher than that of CPT with atrophic group with the statistically significant difference(both P<0.05). And the refracture-free cumulative survival rate in CPT with hypertrophic group was lower than that in CPT with mediate group, the difference was not significant(P>0.05). Conclusion: After the union of CPT, patients with hypertrophic, mediate type X ray characteristic showed lower incidence of refracture than those with atrophic type.

QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Plasmodium falciparum, the most fatal parasite that causes malaria, is responsible for over one million deaths per year. P. falciparum dihydroorotate dehydrogenase (PfDHODH) has been validated as a promising drug development target for antimalarial therapy since it catalyzes the rate-limiting step for DNA and RNA biosynthesis. In this study, we investigated the quantitative structure-activity relationships (QSAR) of the antimalarial activity of PfDHODH inhibitors by generating four computational models using a multilinear regression (MLR) and a support vector machine (SVM) based on a dataset of 255 PfDHODH inhibitors. All the models display good prediction quality with a leave-one-out q(2) >0.66, a correlation coefficient (r) >0.85 on both training sets and test sets, and a mean square error (MSE) <0.32 on training sets and <0.37 on test sets, respectively. The study indicated that the hydrogen bonding ability, atom polarizabilities and ring complexity are predominant factors for inhibitors' antimalarial activity. The models are capable of predicting inhibitors' antimalarial activity and the molecular descriptors for building the models could be helpful in the development of new antimalarial drugs.

Effect of stoichiometry on the magnetocrystalline anisotropy of Fe-Pt and Co-Pt from first-principles calculation

The effect of stoichiometry on magnetocrystalline anisotropy energy (MAE) of Fe1+xPt1-x and Co1+xPt1-x (-0.5 < x < 0.5) is studied by use of first-principles method. The calculated MAEs show maxima at x = 0 for both fully L10-ordered systems. Compared with that, the MAEs of partially L10-ordered systems reduce but their composition dependences do not change, without shift of the maximum MAE to Fe/Co-rich alloy as found in experiment at room temperature. In the off-stoichiometric alloys, the misoccupied Fe/Co and Pt show large MAEs, which is explained by the enhanced in-plane hybridization between Fe/Co and Pt. The composition dependence of the atom-resolved MAE is governed by the varying number of heterogeneous ligands around the atom. The MAE(T)/MAE(0) is discussed based on spontaneous magnetization and Curie temperature, which suggests that the temperature effect may contribute to the discrepancy between calculation and experiment in the composition dependence of MAE.

Computational models on quantitative prediction of bioactivity of HIV-1 integrase 3' processing inhibitors

In this study, four computational quantitative structure-activity relationship (QSAR) models were built to predict the bioactivity of 3' processing (3'P) inhibitors of HIV-1 integrase. Some 453 inhibitors whose bioactivity values were detected by the radiolabelling method were collected. The molecular structures were represented with MOE descriptors. In total, 21 descriptors were selected for modelling. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the training/test set divided by SOM, the correlation coefficients (r) were over 0.84, and for the training/test set split randomly, the r values were over 0.86. Some molecular properties such as hydrogen bond donor capacity, atomic partial charge properties, molecular refractivity, the number of aromatic bonds and molecular surface area, volume and shape properties played important roles for inhibiting 3' processing step of HIV-1 integrase.

Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Several QSAR (quantitative structure-activity relationship) models for predicting the inhibitory activity of 333 hepatitis C virus (HCV) NS5B polymerase inhibitors were developed. All the inhibitors are HCV polymerase non-nucleoside analogue inhibitors (NNIs) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors and 2D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Pearson correlation analysis was used to select the significant descriptors for building models. The whole dataset was split into a training set and a test set randomly or using a Kohonen's self-organizing map (SOM). Then, the inhibitory activity of 333 HCV NS5B polymerase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) method, respectively. For the test set of the best model (Model 2B), correlation coefficient of 0.91 was achieved. Some molecular descriptors, such as molecular complexity (Complexity), the number of hydrogen bonding donors (HDon) and the solubility of the molecule in water (log S) were found to be very important factors which determined the bioactivity of the HCV NS5B inhibitors. Some other molecular properties such as electrostatic and charge properties also played important roles in the interaction between the ligand and the protein. The selected molecular descriptors were further confirmed by analysing the interaction between two representative inhibitors and the polymerase in their crystal structures.

A robust general phase retrieval method for medical applications

From medical imaging perspective the robustness of a phase retrieval method is of critical importance. In this presentation we compare the robustness of two general phase retrieval methods, namely the transport of intensity equation inversion (TIE-inversion) method and the attenuation partition based (AP-based) method. We showed that the TIE-inversion method, regardless if being assisted with the Tikhonov regularization, failed to retrieve the phase maps in two experimental studies. The failure exposes this method's weakness as being unstable against the noise. In contrast, the sample phase maps are retrieved successfully by using the AP-based method. The stark performance differences of the two methods are rooted in their different techniques dealing with the singularity problem. This comparison shows that the robust AP-based phase retrieval method will be superior to the TIE-inversion method for medical imaging applications where radiation doses are stringently limited.

In-silico prediction of blood-brain barrier permeability

The ability of penetration of the blood-brain barrier is one of the significant properties of a drug or drug-like compound for the central nervous system (CNS), which is commonly expressed by log BB (log BB = log (C (brain)/C (blood))). In this work, a dataset of 320 compounds with log BB values was split into a training set including 198 compounds and a test set including 122 compounds according to their structure properties by a Kohonen's self-organizing map (SOM). Each molecule was represented by global and shape descriptors, 2D autocorrelation descriptors and RDF descriptors calculated by ADRIANA.Code. Several quantitative models for prediction of log BB were built by a multilinear regression (MLR), a support vector machine (SVM) and an artificial neural network (ANN) analysis. The models show good prediction performance on the test set compounds.