Identifying Sex-Specific Serum Patterns of Alzheimer's Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy.

Alzheimer's disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.

Direct visualization of nanoparticle morphology in thermally sintered nanoparticle ink traces and the relationship among nanoparticle morphology, incomplete polymer removal, and trace conductivity.

A key challenge encountered by printed electronics is that the conductivity of sintered metal nanoparticle (NP) traces is always several times smaller than the bulk metal conductivity. Identifying the relative roles of the voids and the residual polymers on NP surfaces in sintered NP traces, in determining such reduced conductivity, is essential. In this paper, we employ a combination of electron microscopy imaging and detailed simulations to quantify the relative roles of such voids and residual polymers in the conductivity of sintered traces of a commercial (Novacentrix) silver nanoparticle-based ink. High resolution transmission electron microscopy imaging revealed details of the morphology of the inks before and after being sintered at 150 °C. Prior to sintering, NPs were randomly close packed into aggregates with nanometer thick polymer layers in the interstices. The 2D porosity in the aggregates prior to sintering was near 20%. After heating at 150 °C, NPs sintered together into dense aggregates (nanoaggregates or NAgs) with sizes ranging from 100 to 500 nm and the 2D porosity decreased to near 10%. Within the NAgs, the NPs were mostly connected via sintered metal bridges, while the outer surfaces of the NAgs were coated with a nanometer thick layer of polymer. Motivated by these experimental results, we developed a computational model for calculating the effective conductivity of the ink deposit represented by a prototypical NAg consisting of NPs connected by metallic bonds and having a polymer layer on its outer surface placed in a surrounding medium. The calculations reveal that a NAg that is 35%-40% covered by a nanometer thick polymeric layer has a similar conductivity compared to prior experimental measurements. The findings also demonstrate that the conductivity is less influenced by the polymer layer thickness or the absolute value of the NAg dimensions. Most importantly, we are able to infer that the reduced value of the conductivity of the sintered traces is less dependent on the void fraction and is primarily attributed to the incomplete removal of the polymeric material even after sintering.

Applications, fluid mechanics, and colloidal science of carbon-nanotube-based 3D printable inks.

Additive manufacturing, also known as 3D printing (3DP), is a novel and developing technology, which has a wide range of industrial and scientific applications. This technology has continuously progressed over the past several decades, with improvement in productivity, resolution of the printed features, achievement of more and more complex shapes and topographies, scalability of the printed components and devices, and discovery of new printing materials with multi-functional capabilities. Among these newly developed printing materials, carbon-nanotubes (CNT) based inks, with their remarkable mechanical, electrical, and thermal properties, have emerged as an extremely attractive option. Various formulae of CNT-based ink have been developed, including CNT-nano-particle inks, CNT-polymer inks, and CNT-based non-nanocomposite inks (i.e., CNT ink that is not in a form where CNT particles are suspended in a polymer matrix). Various types of sensors as well as soft and smart electronic devices with a multitude of applications have been fabricated with CNT-based inks by employing different 3DP methods including syringe printing (SP), aerosol-jet printing (AJP), fused deposition modeling (FDM), and stereolithography (SLA). Despite such progress, there is inadequate literature on the various fluid mechanics and colloidal science aspects associated with the printability and property-tunability of nanoparticulate inks, specifically CNT-based inks. This review article, therefore, will focus on the formulation, dispersion, and the associated fluid mechanics and the colloidal science of 3D printable CNT-based inks. This article will first focus on the different examples where 3DP has been employed for printing CNT-based inks for a multitude of applications. Following that, we shall highlight the various key fluid mechanics and colloidal science issues that are central and vital to printing with such inks. Finally, the article will point out the open existing challenges and scope of future work on this topic.

Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.

Quality of Life, Sexual Health, and Associated Factors Among the Sexually Active Adults in a Metro City of India: An Inquiry During the COVID-19 Pandemic-Related Lockdown.

Background: Sexual dysfunction (SD) and its effect on our life is an important but less studied topic especially during post-COVID era. This study examines the extent of SD and other mental health predictors and their effect on quality of life. Methods: A cross-sectional survey of sexually active adults was conducted in an Indian metro-city. Along with sociodemographic data, sexual dysfunction, depression, anxiety, stress, and quality of life were assessed by Arizona Sexual Experience Scale (ASEX), Depression Anxiety and Stress Scale (DASS), and WHOQOL-BREF, respectively. Structural equations modeling was used to understand their relationship. Results: Out of the total 1,376 respondents, 80.52% were male, 65.98% were married, and 48.54% were graduates. The mean age of the participants was 34.42 (±9.34) years. Of the participants, 27.18% had sexual dysfunction. Majority of the respondents did not have depression (59.30%), anxiety (52.33%), or stress (44.48%). Mild and moderate levels were the commonest findings among those who had depression, anxiety, or stress. Among the respondents, 27.18% had sexual dysfunction as per the ASEX instrument. Increase in age and female gender were associated with sexual dysfunction overall and also all its components. Presence of depression adversely affected ease of achieving orgasm and satisfaction from orgasm and was associated with sexual dysfunction overall. The respondents had a mean score of 73.57 (±13.50) as per the WHO-QOL. Depression and stress emerged as statistically significant factors for poor quality of life, while sexual dysfunction was not associated statistically. Conclusion: More than one-fourth of the study population reported sexual dysfunction during the first wave of the pandemic in India. The study findings highlight the role of poor mental health issues in this regard. In fact, issues like depression and stress were associated with poor quality of life as well. The current findings unequivocally warrant specific interventions to improve mental health of the respondents.

Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis.

Importance: Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond or not respond to TNFis could provide opportunities to personalize treatment strategies. Objective: To develop models of the probability of short-term response to TNFi treatment in individual patients with active AS. Design, Setting, and Participants: This is a retrospective cohort study using data of the TNFi group (ie, treatment group) from 10 randomized clinical trials (RCTs) of TNFi treatment among patients with active AS, conducted from 2002 to 2016. Participants were adult patients with active AS who failed nonsteroidal anti-inflammatory drugs. Included RCTs were phase 3 and 4 studies that assessed the efficacy of an originator TNFi at week 12 and/or week 24, either compared with placebo or an antirheumatic drug. The cohort was divided into a training and a testing set. Data analysis was conducted from July 1, 2019, to November 30, 2020. Exposures: All included patients received an originator TNFi for at least 12 weeks. Main Outcomes and Measures: Outcomes included major response and no response based on the change of AS Disease Activity Score at 12 weeks. Machine learning algorithms were applied to estimate the probability of having major response and no response for individual patients. Results: The study included 1899 participants from 10 trials. The training set included 1207 individuals (mean [SD] age, 39 [12] years; 908 [75.2%] men), of whom 407 (33.7%) had major response and 414 (34.3%) had no response. In the reduced logistic regression models, accuracy was 0.74 for major response and 0.75 for no response. The probability of major response increased with higher C-reactive protein (CRP) level, patient global assessment (PGA), and Bath AS Disease Activity Index (BASDAI) question 2 score and decreased with higher body mass index (BMI) and Bath AS Functional Index (BASFI) score. The probability of no response increased with age and BASFI score, and decreased with higher CRP level, BASDAI question 2 score, and PGA. In the testing set (692 participants; mean [SD] age, 38 [11] years; 533 [77.0%] men), models demonstrated moderate to high accuracy. Conclusions and Relevance: In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making.

Grain-Scale Anisotropic Analysis of Steady-State Creep in Oligocrystalline SAC Solder Joints.

Heterogeneous integration is leading to unprecedented miniaturization of solder joints, often with thousands of joints within a single package. The thermomechanical behavior of such SAC solder joints is critically important to assembly performance and reliability, but can be difficult to predict due to the significant joint-to-joint variability caused by the stochastic variability of the arrangement of a few highly-anisotropic grains in each joint. This study relies on grain-scale testing to characterize the mechanical behavior of such oligocrystalline solder joints, while a grain-scale modeling approach has been developed to assess the effect of microstructure that lacks statistical homogeneity. The contribution of the grain boundaries is modeled with isotropic cohesive elements and identified by an inverse iterative method that extracts material properties by comparing simulation with experimental measurements. The properties are extracted from the results of one test and validated by verifying reasonable agreement with test results from a different specimen. Equivalent creep strain heterogeneity within the same specimen and between different specimens are compared to assess typical variability due to the variability of microstructure.

Principles of pediatric lupus nephritis in a prospective contemporary multi-center cohort.

Lupus nephritis (LN) is a life-threatening manifestation of systemic lupus erythematosus (SLE) and is more common in children than adults. The epidemiology and management of childhood-onset SLE (cSLE) have changed over time, prompting the need to reassess expected outcomes. The purpose of this study is to use the Childhood Arthritis and Rheumatology Research Alliance (CARRA) prospective registry to validate historical principles of LN in a contemporary, real-world cohort. After an extensive literature review, six principles of LN in cSLE were identified. The CARRA registry was queried to evaluate these principles in determining the rate of LN in cSLE, median time from cSLE diagnosis to LN, short-term renal outcomes, and frequency of rituximab as an induction therapy. Of the 677 cSLE patients in the CARRA registry, 32% had documented LN. Decline in kidney function was more common in Black cSLE patients than non-Black patients (p = 0.04). Black race was associated with worse short-term renal outcomes. In short-term follow up, most children with LN had unchanged or improved kidney function, and end stage kidney disease (ESKD) was rare. Ongoing follow-up of cSLE patients in the CARRA registry will be necessary to evaluate long-term outcomes to inform risk, management, and prognosis of LN in cSLE.

Predictive Mechanistic Model of Creep Response of Single-Layered Pressure-Sensitive Adhesive (PSA) Joints.

This paper explores the uniaxial tensile creep response of acrylic-based pressure-sensitive adhesive (PSA), which exhibits a unique multi-phase creep response that does not have the classical steady-state region due to multiple transitions caused by several competing mechanisms: (i) cavity nucleation and growth in the interior of the adhesive material of the PSA system, as well as at the interfaces between the PSA and the substrate; (ii) fibrillation of the bulk adhesive, and (iii) interfacial mechanical locking between the adhesive and the bonding substrate. This results in multiple regimes of strain hardening and strain softening, evidenced by multiple regions of steady-state creep, separated by strong transitions in the creep rates. This complex, multi-phase, nonlinear creep response cannot be described by conventional creep constitutive models commonly used for polymers in commercial finite element codes. Accordingly, based on the empirical uniaxial tensile creep response and the mechanisms behind it, a new mechanistic model was proposed. This is capable of quantitatively capturing the characteristic features of the multiphase creep response of single-layered PSA joints as a function of viscoelastic bulk properties and free energy of the PSA material, substrate surface roughness, and interfacial surface energy between the adhesive and substrate. This is the first paper to present the modeling approach for capturing unique multi-phase creep behavior of PSA joint under tensile loading.

Ultrathin and Ultrasensitive Printed Carbon Nanotube-Based Temperature Sensors Capable of Repeated Uses on Surfaces of Widely Varying Curvatures and Wettabilities.

In this paper, we demonstrate the ability to fabricate temperature sensors by using our newly developed carbon nanotube-graphene oxide (CNT-GO) ink to print temperature-sensitive traces on highly flexible, thin, and adhesive PET (polyethylene terephthalate) tapes, which in turn are integrated on surfaces of different curvatures and wettabilities. Therefore, the strategy provides a facile, low-cost, and environmentally friendly method to deploy printed temperature sensors on surfaces of widely varying curvatures and wettabilities. The temperature sensing occurs through a thermally induced change in the resistance of the printed traces and we quantify the corresponding negative temperature coefficient of resistance (α) for different conditions of curvatures and wettabilities. In addition, we identify that at low temperatures (below 15 °C), the printed traces show an α value that can be as large (in magnitude) as 60 × 10-3/°C, which is several times higher than the typical α values reported for temperature sensors fabricated with CNT or other materials. Furthermore, we achieve the printing of traces that are only 1-3 µm thick on a 50 µm-thick PET film: therefore, our design represents an ultrathin additively fabricated temperature sensor that can be easily integrated for wearable electronic applications. Finally, we show that despite being subjected to repeated temperature cycling, there is little degradation of the CNT-GO microarchitectures, making these printed traces capable of repeated uses as potential temperature sensors.

A control theoretic three timescale model for analyzing energy management in mammalian cancer cells.

Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1 α (HIF-1 α ) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).

The Pronounced Impact of Hip Fractures on Psychosocial Well-being.

BACKGROUND: There are little data on the psychosocial well-being of hip fracture patients. Previous studies lacked a control group to isolate the impact of hip fractures from general aging. We sought to overcome these limitations and quantify the impact of hip fractures on psychosocial well-being. METHODS: We identified a cohort 65 years and older patients who were driving and mobile from the National Health and Aging Trends Study. Participants with exactly one hip fracture between 2011 and 2017 were isolated, and their outcomes an after hip fracture were compared with control subjects via multivariate logistic regression. RESULTS: Hip fracture patients reported a notable decrease in driving frequency and mobility in the first year after fracture, along with an increase in depressive symptomatology and decreased participation in activities. Measures of mobility and activities among survivors returned to peer group levels within 2 years. A larger social network was associated with improved outcomes. DISCUSSION: Hip fracture survivors experience notable declines in function and well-being in the first year after a hip fracture compared with peers. Although mortality is high, surviving patients experience measurable gains in function and well-being in the 3 years after the fracture. These data aid surgeons in counseling families and patients after hip fracture. LEVEL OF EVIDENCE: Level I-prognostic study: inception cohort study. STUDY DESIGN: Prospective-patients enrolled at a uniform point in course of disease and followed over time.

Pre-operative withholding of infliximab and the risk of infections after major surgery in patients with rheumatoid arthritis.

OBJECTIVES: Withholding TNF inhibitors (TNFI) before surgery has been recommended due to concern for post-operative infection. We examined the risks of post-operative infections and mortality in patients with RA in relation to the pre-operative timing of infliximab infusion. METHODS: In this population-based retrospective cohort study, we used US Medicare claims data from 2007 to 2015 to identify patients with RA who underwent coronary artery bypass grafting (CABG), aortic or vascular surgery, or bowel resection, and who were treated with infliximab in the 90 days prior to surgery. We examined associations between the timing of infusion and infections and mortality in the 30 days after surgery. We adjusted for the predicted probability of post-operative infection or death, demographic characteristics, use of MTX, post-operative blood transfusion and hospital volume. RESULTS: We studied 712 patients with CABG, 244 patients with vascular surgery and 862 patients with bowel resections. Post-operative pneumonia occurred in 7.4-11.9%, urinary tract infection in 9.0-15.2%, surgical site infection in 3.2-18.9%, sepsis in 4.2-9.6% and death in 3.5-7.0% among surgery cohorts. There was no association between the time from last infliximab dose to surgery and the risk of post-operative infection or mortality in any surgical cohort. No subgroups were identified that had an increased risk of infection with more proximate use of infliximab. CONCLUSION: Among elderly patients with RA, risks of infection and mortality after major surgery were not related to the pre-operative timing of infliximab infusion.

Incidence of Hip Fracture Over 4 Decades in the Framingham Heart Study.

Importance: Age-adjusted hip fracture incidence is decreasing in the US. The decrease has been attributed to osteoporosis treatment, but the cause is unknown. Objective: To examine the decrease in hip fracture incidence over the past 40 years in the US. Design, Setting, and Participants: A population-based cohort study using participants in the Framingham Heart Study was conducted. A total of 4918 men and 5634 women were followed up prospectively for the first hip fracture between January 1, 1970, and December 31, 2010. Data were analyzed from May 1, 2019, to May 30, 2020. Main Outcomes and Measures: Incidence of hip fracture and contemporaneous prevalence of risk factors for hip fractures analyzed with age-period-cohort models. Results: The study contained more than 105 000 person-years in 10 552 individuals with a gradual shift toward the offspring participants in the 1980s and 1990s. Women represented more than 55% of the study sample over the years. Adjusted for age, the incidence of hip fracture decreased by 4.4% (95% CI, 6.8%-1.9%) per year from 1970 to 2010. Both period associations (P < .001) and birth cohort associations (P < .001) were statistically significant. For example, in persons aged 85 to 89 years, the incidence of hip fracture was 759 per 100 000 person-years in the offspring group compared with 2018 per 100 000 person-years in the original cohort. The decrease in hip fracture incidence was coincident with a decrease in smoking and heavy drinking. Smoking decreased from 38% in the 1970s to 15% in the late 2000s, while heavy drinking decreased from 7.0% to 4.5%. The prevalence of other risk factors for hip fracture, such as underweight (body mass index <18.5), obesity (body mass index >30), and early menopause (age <45 years) were stable over the study period. When persons who never smoked were evaluated, a change in the incidence of -3.2% (95% CI, -6.0% to -0.4%) per year was observed. The difference between the decrease of the entire population and nonsmokers of 1.5% per year was similar to the hazard ratio conferred by smoking (hazard ratio, 1.5; 95% CI, 1.14-1.96). Conclusions and Relevance: In this study, individuals born more recently appeared to have a low risk for hip fracture. Reductions in smoking and heavy drinking were the risk factor changes coincident with the observed decrease in hip fracture. Attributing the decrease in hip fracture incidence up to 2010 solely to better treatment is not supported by these data, emphasizing the need to treat patients with osteoporosis while continuing to encourage public health interventions for smoking cessation and heavy drinking.

Regional Variation in Rates of Total Knee Arthroplasty Among Medicare Beneficiaries.

Importance: Rates of total knee arthroplasty vary widely across the United States. Whether this variation is associated with differences in patient characteristics or physician practice is unknown. Objectives: To determine regional variations in rates of total knee arthroplasty after accounting for the prevalence of knee arthritis and other potentially associated patient risk factors and to assess the correlation of these variations with measures of access to care and surgical indications. Design, Setting, and Participants: This retrospective national cohort study used Medicare data on more than 24 million deidentified beneficiaries annually from 2011 to 2015. Individuals included had fee-for-service coverage, were 65 to 89 years of age, and resided in 1 of 306 health referral regions. Data were analyzed from September 13, 2018, to August 15, 2019. Main Outcomes and Measures: Rate of primary total knee arthroplasty indexed to the national rate using observed to expected ratios. The expected numbers of arthroplasty procedures were derived from estimates based on beneficiaries' demographic and clinical characteristics. Observed to expected ratios were confounded by race/ethnicity; thus race/ethnicity-stratified analyses were conducted. Results: In 2011, there were 218 282 total knee arthroplasty procedures among 24 583 706 white Medicare beneficiaries (mean [SD] age 74.2 [6.9] years; 54.6% women). The rate of arthroplasty during the study period (5 years) was 9.3 per 1000 person-years. Adjustment for clinical characteristics reduced the spread in observed to expected ratios among regions by 29% compared with adjustment for age and sex alone. However, substantial variation remained, with observed to expected ratios that ranged from 0.61 in Newark, New Jersey, to 1.82 in Idaho Falls, Idaho. High ratios were primarily present in the upper Midwest, Great Plains, and Mountain West regions. Higher ratios were associated with regions where beneficiaries had fewer outpatient visits (Spearman correlation [r], -0.64; 95% CI, -0.70 to -0.56) and with regions having more surgeons per capita who performed knee arthroplasty (r = 0.27; 95% CI, 0.16-0.37). Higher ratios were associated with higher rates of arthroplasty procedures among beneficiaries with dementia (r = 0.36; 95% CI, 0.25-0.46), peripheral vascular disease (r = 0.52; 95% CI, 0.42-0.61), and skin ulcers (r = 0.43; 95% CI, 0.32-0.53), which are relative contraindications to arthroplasty. Conclusions and Relevance: Substantial regional variation in rates of total knee arthroplasty remained after adjustment for patient characteristics. Coexistence of high observed to expected ratios and high rates among patients at greater surgical risk suggested overuse of knee arthroplasty in some regions.

Catestatin improves insulin sensitivity by attenuating endoplasmic reticulum stress: In vivo and in silico validation.

Obesity is characterized by a state of chronic, unresolved inflammation in insulin-targeted tissues. Obesity-induced inflammation causes accumulation of proinflammatory macrophages in adipose tissue and liver. Proinflammatory cytokines released from tissue macrophages inhibits insulin sensitivity. Obesity also leads to inflammation-induced endoplasmic reticulum (ER) stress and insulin resistance. In this scenario, based on the data (specifically patterns) generated by our in vivo experiments on both diet-induced obese (DIO) and normal chow diet (NCD) mice, we developed an in silico state space model to integrate ER stress and insulin signaling pathways. Computational results successfully followed the experimental results for both DIO and NCD conditions. Chromogranin A (CgA) peptide catestatin (CST: hCgA 352 - 372 ) improves obesity-induced hepatic insulin resistance by reducing inflammation and inhibiting proinflammatory macrophage infiltration. We reasoned that the anti-inflammatory effects of CST would alleviate ER stress. CST decreased obesity-induced ER dilation in hepatocytes and macrophages. On application of Proportional-Integral-Derivative (PID) controllers on the in silico model, we checked whether the reduction of phosphorylated PERK resulting in attenuation of ER stress, resembling CST effect, could enhance insulin sensitivity. The simulation results clearly pointed out that CST not only decreased ER stress but also enhanced insulin sensitivity in mammalian cells. In vivo experiment validated the simulation results by depicting that CST caused decrease in phosphorylation of UPR signaling molecules and increased phosphorylation of insulin signaling molecules. Besides simulation results predicted that enhancement of AKT phosphorylation helps in both overcoming ER stress and achieving insulin sensitivity. These effects of CST were verified in hepatocyte culture model.

Metabolic pathway engineering: Perspectives and applications.

BACKGROUND: Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering. CONCLUSION: In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Racial/Ethnic Differences in Dialysis Discontinuation and Survival after Hospitalization for Serious Conditions among Patients on Maintenance Dialysis.

BACKGROUND: Racial and ethnic minorities on dialysis survive longer than whites, and are less likely to discontinue dialysis. Both differences have been attributed by some clinicians to better health among minorities on dialysis. METHODS: To test if racial and ethnic differences in dialysis discontinuation reflected better health, we conducted a retrospective cohort study of survival and dialysis discontinuation among patients on maintenance dialysis in the US Renal Data System after hospitalization for either stroke (n=60,734), lung cancer (n=4100), dementia (n=40,084), or failure to thrive (n=42,950) between 2003 and 2014. We examined the frequency of discontinuation of dialysis and used simulations to estimate survival in minorities relative to whites if minorities had the same pattern of dialysis discontinuation as whites. RESULTS: Blacks, Hispanics, and Asians had substantially lower frequencies of dialysis discontinuation than whites in each hospitalization cohort. Observed risks of mortality were also lower for blacks, Hispanics, and Asians. In simulations that assigned discontinuation patterns similar to those found among whites across racial and ethnic groups, differences in survival were markedly attenuated and hazard ratios approached 1.0. Survival and dialysis discontinuation frequencies among American Indians and Alaska Natives were close to those of whites. CONCLUSIONS: Racial and ethnic differences in dialysis discontinuation were present among patients hospitalized with similar health events. Among these patients, survival differences between racial and ethnic minorities and whites were largely attributable to differences in the frequency of discontinuation of dialysis.

Shape-driven arrest of coffee stain effect drives the fabrication of carbon-nanotube-graphene-oxide inks for printing embedded structures and temperature sensors.

Carbon nanotube (CNT) based binder-free, syringe-printable inks, with graphene oxide (GO) being used as the dispersant, have been designed and developed. We discovered that the printability of the ink is directly attributed to the uniform deposition of the GO-CNT agglomerates, as opposed to the 'coffee-staining' despite these aggregates being micron-sized. The ellipsoidal nature of the micron-scale GO-CNT agglomerates/particles enables these particles to severely perturb the air-water interface, triggering a large long-range capillary interaction that causes the uniform deposition by overcoming the "coffee-stain"-forming forces from the evaporation-mediated flows. We evaluated the properties of this ink and identified a temperature-dependent resistance with a negative temperature coefficient of resistance (TCR) α ranging from ∼-10-3 to -10-2/°C depending on ink compositions. Finally, the printing is conducted on flat and curved surfaces, for developing polymer-ink embedded structures that might serve as precursors to syringe-printable CNT-based nanocomposites, and for fabricating sensor-like patterns that for certain ink compositions demonstrate α∼-10-3/°C with a large averaged resistance drop (per unit temperature) of -3.5 Ω°C-1.

Interhospital variation in mortality among patients with systemic lupus erythematosus and sepsis in the USA.

OBJECTIVE: To determine whether the risk of mortality in patients with SLE hospitalized with sepsis varies among hospitals in the USA. METHODS: We used the National Inpatient Sample (2002-2011) to obtain national population-based data on outcomes for adults with SLE admitted with sepsis, and compared it with that for patients without SLE admitted with sepsis at the same hospital. We computed expected mortality based on patient demographic characteristics, comorbidities and major organ dysfunction, and calculated observed/expected (O/E) mortality ratios separately for patients with SLE and without SLE for each hospital. We then computed the ratio of these O/E ratios within hospitals to assess relative SLE mortality. We considered hospitals with a risk ratio (RR) of ⩾2.0 as having high relative SLE mortality. RESULTS: Among 424 hospitals that treated a total of 4024 patients with SLE and sepsis, the risk of in-hospital mortality varied from 0% to 60% (median 11.1%). The RR ranged from 0 to 9.75, with a median of 0.84, indicating that O/E mortality was similar in patients with and without SLE at the average hospital. Sixty-one hospitals (14.4%) had a RR of ⩾2.0, indicating higher mortality among patients with SLE. Hospitals that on average treated ⩾3.9 patients with SLE and sepsis annually were less likely to have a RR of ⩾2.0 than hospitals that treated fewer patients (10% vs 17%; P = 0.004). CONCLUSION: Mortality among patients with SLE and sepsis varied widely between hospitals, and was lower at hospitals that treated more of these patients.