Fallesenpitts6314

Child malnutrition remains a matter of concern in India as the current levels are high and the decline is slow. National Family Health Survey (NFHS-4, 2015-16) data, for the first time, provides credible, good quality data at district level on social, household and health characteristics.

Techniques of spatial analysis on data in respect of 640 districts were used to identify spatial characteristics of the nutrition levels for children in the 0-60-month age group. Further, the principal component analysis (PCA) was used to identify 7 important correlates of the malnutrition out of 21 relevant components provided in the NFHS-4. The paper further uses three techniques, ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM) to assess the strength of correlation between the malnutrition levels and the shortlisted correlates.

The use of SLM and SEM shows improvement in the strength of the association (high R-square) compared to OLS. Women's height and Iodized salt in stunting, child anaemia in wasting, women's height and child anaemia in underweight were found to be significant factors (P<0.01) along with spatial autoregressive constant.

Such analysis, in combination with PCA, has shown to be more effective in prioritizing the programme interventions for tackling child malnutrition.

Such analysis, in combination with PCA, has shown to be more effective in prioritizing the programme interventions for tackling child malnutrition.We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.This work investigates the impact of iodinated contrast medium (ICM) on radiation dose in computed tomography (CT) scans using linear models established through a phantom study. Thermoluminescence dosemeters (TLDs) were calibrated using semi-conductor X-ray dosemeters. An electron density phantom, with a vial containing TLDs and different concentrations of iodinated blood, were scanned at different tube voltages. Irradiated TLD outputs were measured and absorbed dose to iodinated blood calculated. CT numbers (tissue attenuation as measured by Hounsfield units) were plotted against absorbed doses to obtain linear models. Data from 49 real patient scans were used to validate the linear models. At each X-ray energy, CT numbers were linearly correlated with the absorbed doses, that is with the increase of blood iodine concentration, the CT number increased and the absorbed dose increased accordingly. ICM can cause an increase of organ dose; the average dose increases were 31.8 ± 8.9% for thyroid, 37.1 ± 9.2% for cardiac muscle, 77.7 ± 14.0% for cardiac chamber, 7.1 ± 2.3% for breast, 26.1 ± 7.3% for liver, 39.8 ± 11.8% for spleen, 96.3 ± 12.2% for renal cortex and 82.4 ± 11.6% for medulla nephrica. ICM used in enhanced CT scan resulted in increased organ doses. Our models for estimating organ dose based on CT number were established by experiment and verified in clinical use.

This study examined whether smokers' harm perceptions of nicotine replacement therapy (NRT) and nicotine vaping products (NVPs) relative to cigarettes predicted their subsequent use as smoking cessation aids during their last quit attempt (LQA).

We analyzed data from 1,315 current daily smokers (10+ cigarettes per day) who were recruited at Wave 1 (2016), and who reported making a quit attempt by Wave 2 (2018) of the International Tobacco Control Four Country Smoking and Vaping Surveys in Australia, Canada, England, and the United States. We used multinomial logistic regression models to examine prospective associations between harm perceptions of (a) NRT and (b) NVPs and their use at LQA, controlling for socio-demographic and other potential confounders.

Smokers who perceive that (a) NRT and (b) NVPs are much less harmful than cigarettes were more likely to subsequently use the respective product as an aid than using no aid or other aids during LQA (adjusted relative risk ratio [aRRR] = 3.79, 95%CI = 2 these products over nicotine replacement therapy to aid smoking cessation. Education targeting misperceptions of nicotine products' harmfulness relative to cigarettes may enable smokers to make informed choices about which are appropriate to aid smoking cessation.

Nicotine replacement therapy and nicotine vaping products are two commonly used smoking cessation aids. This study demonstrates that misperceptions of the harms of nicotine products relative to cigarettes influence their use for smoking cessation. Believing that nicotine vaping products are much less harmful than cigarette smoking may lead some smokers to prefer these products over nicotine replacement therapy to aid smoking cessation. Education targeting misperceptions of nicotine products' harmfulness relative to cigarettes may enable smokers to make informed choices about which are appropriate to aid smoking cessation.Systems-based metabolic engineering enables cells to enhance product formation by predicting gene knockout and overexpression targets using modeling tools. FOCuS, a novel metaheuristic tool, was used to predict flux improvement targets in terpenoid pathway using the genome-scale model of Saccharomyces cerevisiae, iMM904. Some of the key knockout target predicted includes LYS1, GAP1, AAT1, AAT2, TH17, KGD-m, MET14, PDC1 and ACO1. It was also observed that the knockout reactions belonged either to fatty acid biosynthesis, amino acid synthesis pathways or nucleotide biosynthesis pathways. Similarly, overexpression targets such as PFK1, FBA1, ZWF1, TDH1, PYC1, ALD6, TPI1, PDX1 and ENO1 were established using three different existing gene amplification algorithms. Most of the overexpression targets belonged to glycolytic and pentose phosphate pathways. Each of these targets had plausible role for improving flux toward sterol pathway and were seemingly not artifacts. Moreover, an in vitro study as validation was carried with overexpression of ALD6 and TPI1. It was found that there was an increase in squalene synthesis by 2.23- and 4.24- folds, respectively, when compared with control. In general, the rationale for predicting these in silico targets was attributed to either increasing the acetyl-CoA precursor pool or regeneration of NADPH, which increase the sterol pathway flux.Life and death are 2 fundamental concepts regarding existence of organisms. People often signify these concepts using symbols to facilitate communications, but how the brain learns and represents these symbols remains unclear. In the present study, we quantified behavioral and brain responses during learning associations between words ("life" or "death") with shapes as concrete referents. Behavioral responses to word-shape pairs showed an affirmative response bias to life-shape pairs but a denial response bias to death-shape pairs. Multimodal brain imaging results revealed that the right frontal and dorsal cingulate cortices monitored these response biases, respectively. Moreover, relative to unlearned shapes, life-related shapes induced increased alpha (9-14 Hz) oscillations in the right parietal cortex and precuneus, whereas death-related shapes enhanced beta (15-30 Hz) oscillations in the left parietal cortex, superior temporal sulcus, and precuneus. Our findings unraveled distinct neurocognitive mechanisms underlying learning and representations of concrete referents of life and death concepts.

During the COVID-19 pandemic, the burden of nosocomial infections caused by MDR pathogens has caused a shortage of polymyxins. Thus, we evaluated the in vitro synergism and antibiofilm activity of antimicrobial combinations and propose a test kit for synergism against carbapenem-resistant Acinetobacter baumannii (CRAB).

Fifty-six CRAB isolates were tested for synergy between meropenem, gentamicin and ampicillin/sulbactam. MICs were determined by broth microdilution. Synergism was tested using chequerboard analysis, followed by a time-kill curve. Additionally, minimum biofilm eradication concentration was determined and the antibiofilm activity of the combinations was evaluated by MTT assay and biomass reduction. A test kit was developed for routine laboratory testing to detect synergism.

All CRAB isolates were resistant to gentamicin and ampicillin/sulbactam. Chequerboard synergism occurred against 75% of the isolates. Meropenem + ampicillin/sulbactam was the most frequent combination with synergism (69%), followed by ampicillin/sulbactam + gentamicin (64%) and meropenem + gentamicin (51%). All combinations presented only bacteriostatic activity and no bactericidal or antibiofilm effects. The routine laboratory test showed 100% accuracy compared with other in vitro assays.

Our study demonstrates the potential role of antibiotic combinations against planktonic bacteria. In vitro synergism is possible and can be an alternative treatment for patients with CRAB infection during a polymyxin shortage.

Our study demonstrates the potential role of antibiotic combinations against planktonic bacteria. In vitro synergism is possible and can be an alternative treatment for patients with CRAB infection during a polymyxin shortage.Motivational congruency has been examined using tasks where participants perform approach or avoidance movements towards socially positive or negative faces. Language is tightly intertwined with interpersonal cognition. Thus, similar situations could be represented by means of language in interpersonal contexts adjectives furnish valence to people (e.g. someone is cordial or arrogant), and attitudinal verbs define direction to relationship-actions approach-avoidance (e.g. accept vs. reject). In an Electroencephalography (EEG) study, 40 participants were presented with sentences where a character was valenced (e.g. "Arthur is cordial/arrogant") before being the target of a relationship-actions ("Grisela welcomed/ignored Arthur at the party"). We analyzed both Event-related potential (ERP) amplitude and time-frequency power in response to the attitudinal verb. For ERP amplitudes, we found a significant cluster between 280 and 370 ms, covering part of the development of a N400-like ERP component. This cluster reflects an interaction driven by congruency between motivational direction and target valence. Likewise, time-frequency power analysis revealed an enhancement of theta rhythms under incongruent conditions, most likely indexing conflict processing. Results support that relationship-actions are represented as approach and avoidance and thus involve conflict processing and resolution of incongruent situations. Implications for the interweaving of affective language and social cognition within Embodiment Simulation Theory are discussed.Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11 000 adult and >4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https//tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival's ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.

Understanding the miss rate and characteristics of missed pharyngeal and laryngeal cancers during upper gastrointestinal endoscopy may aid in reducing the endoscopic miss rate of this cancer type. However, little is known regarding the miss rate and characteristics of such cancers. Therefore, the aim of this study was to investigate the upper gastrointestinal endoscopic miss rate of oro-hypopharyngeal and laryngeal cancers, the characteristics of the missed cancers, and risk factors associated with the missed cancers.

Patients who underwent upper gastrointestinal endoscopy and were pathologically diagnosed with oro-hypopharyngeal and laryngeal squamous cell carcinoma from January 2019 to November 2020 at our institution were retrospectively evaluated. Missed cancers were defined as those diagnosed within 15months after a negative upper gastrointestinal endoscopy.

A total of 240 lesions were finally included. Eighty-five lesions were classified as missed cancers, and 155 lesions as non-missed cancers. The upper gastrointestinal endoscopic miss rate for oro-hypopharyngeal and laryngeal cancers was 35.4%. Multivariate analysis revealed that a tumor size of <13 mm (odds ratio 1.96, P=0.026), tumors located on the anterior surface of the epiglottis/valleculae (odds ratio 2.98, P=0.045) and inside of the pyriform sinus (odds ratio 2.28, P=0.046) were associated with missed cancers.

This study revealed a high miss rate of oro-hypopharyngeal and laryngeal cancers during endoscopic observations. High-quality upper gastrointestinal endoscopic observation and awareness of missed cancer may help reduce this rate.

This study revealed a high miss rate of oro-hypopharyngeal and laryngeal cancers during endoscopic observations. High-quality upper gastrointestinal endoscopic observation and awareness of missed cancer may help reduce this rate.Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin (PRL). Leveraging this proliferative response to PRL may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the PRL-mediated JAK-STAT signaling pathway in pancreatic beta cells. Here, we applied the model to identify the importance of particular signaling proteins in shaping the response of a population of beta cells. We simulated a population of 10 000 heterogeneous cells with varying initial protein concentrations responding to PRL stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing PRL receptor strengthens negative feedback mediated by the inhibitor suppressor of cytokine signaling. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG's encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning. Availability The source code is available in https//github.com/gu-yaowen/CurrMG.Postsynaptic proteins play critical roles in synaptic development, function, and plasticity. Dysfunction of postsynaptic proteins is strongly linked to neurodevelopmental and psychiatric disorders. SAP90/PSD95-associated protein 4 (SAPAP4; also known as DLGAP4) is a key component of the PSD95-SAPAP-SHANK excitatory postsynaptic scaffolding complex, which plays important roles at synapses. However, the exact function of the SAPAP4 protein in the brain is poorly understood. Here, we report that Sapap4 knockout (KO) mice have reduced spine density in the prefrontal cortex and abnormal compositions of key postsynaptic proteins in the postsynaptic density (PSD) including reduced PSD95, GluR1, and GluR2 as well as increased SHANK3. These synaptic defects are accompanied by a cluster of abnormal behaviors including hyperactivity, impulsivity, reduced despair/depression-like behavior, hypersensitivity to low dose of amphetamine, memory deficits, and decreased prepulse inhibition, which are reminiscent of mania. Furthermore, the hyperactivity of Sapap4 KO mice could be partially rescued by valproate, a mood stabilizer used for mania treatment in humans. Together, our findings provide evidence that SAPAP4 plays an important role at synapses and reinforce the view that dysfunction of the postsynaptic scaffolding protein SAPAP4 may contribute to the pathogenesis of hyperkinetic neuropsychiatric disorder.Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https//github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.The COVID-19 pandemic has changed the paradigms for disease surveillance and rapid deployment of scientific-based evidence for understanding disease biology, susceptibility, and treatment. We have organized a large-scale genome-wide association study in SARS-CoV-2 infected individuals in Sao Paulo, Brazil, one of the most affected areas of the pandemic in the country, itself one of the most affected in the world. Here we present the results of the initial analysis in the first 5233 participants of the BRACOVID study. We have conducted a GWAS for Covid-19 hospitalization enrolling 3533 cases (hospitalized COVID-19 participants) and 1700 controls (non-hospitalized COVID-19 participants). Models were adjusted by age, sex and the 4 first principal components. A meta-analysis was also conducted merging BRACOVID hospitalization data with the Human Genetic Initiative (HGI) Consortia results. BRACOVID results validated most loci previously identified in the HGI meta-analysis. In addition, no significant heterogeneity according to ancestral group within the Brazilian population was observed for the two most important COVID-19 severity associated loci 3p21.31 and Chr21 near IFNAR2. Using only data provided by BRACOVID a new genome-wide significant locus was identified on Chr1 near the genes DSTYK and RBBP5. The associated haplotype has also been previously associated with a number of blood cell related traits and might play a role in modulating the immune response in COVID-19 cases.

Data on long-term safety of growth hormone (GH) replacement in adults with GH deficiency (GHD) are needed.

We aimed to evaluate the safety of GH in the full KIMS (Pfizer International Metabolic Database) cohort.

The worldwide, observational KIMS study included adults and adolescents with confirmed GHD. Patients were treated with GH (Genotropin [somatropin]; Pfizer, NY) and followed through routine clinical practice. Adverse events (AEs) and clinical characteristics (eg, lipid profile, glucose) were collected.

A cohort of 15 809 GH-treated patients were analyzed (mean follow-up of 5.3 years). AEs were reported in 51.2% of patients (treatment-related in 18.8%). Crude AE rate was higher in patients who were older, had GHD due to pituitary/hypothalamic tumors, or adult-onset GHD. AE rate analysis adjusted for age, gender, etiology, and follow-up time showed no correlation with GH dose. A total of 606 deaths (3.8%) were reported (146 by neoplasms, 71 by cardiac/vascular disorders, 48 by cerebrovascular disorders). Overall, de novo cancer incidence was comparable to that in the general population (standard incidence ratio 0.92; 95% CI, 0.83-1.01). De novo cancer risk was significantly lower in patients with idiopathic/congenital GHD (0.64; 0.43-0.91), but similar in those with pituitary/hypothalamic tumors or other etiologies versus the general population. Neither adult-onset nor childhood-onset GHD was associated with increased de novo cancer risks. Neutral effects were observed in lipids/fasting blood glucose levels.

These final KIMS cohort data support the safety of long-term GH replacement in adults with GHD as prescribed in routine clinical practice.

These final KIMS cohort data support the safety of long-term GH replacement in adults with GHD as prescribed in routine clinical practice.

There are inequalities experienced by minority ethnic groups in the UK in organ donation and transplant services, with significant variation in relation to demand for, access to and waiting times for these services.

A narrative review of research obtained via several databases, including PubMed and Medline, was conducted.

A vision of equity and inclusion, which meets the need of the heterogeneous UK population, can only be realized by adopting a culturally competent approach to systems-wide working in organ donation in four core areas-transplant services; workforce and staff training; diversity and inclusion research; and public engagement.

Most of the data on the background of organ donors and recipients use general categories such as Asian or Black. We need to progress to a position of more granular data by more specific ethnicity so that we can better understand the trends and target action accordingly.

By positively embracing the heterogeneity of the UK population, demand for transplantation can be reduced through a sustained commitment to public health interventions and culturally competent approaches in the management of long-term conditions.

Improved access to transplantation and reduced waiting times can be achieved to increase the number of organ donors from minority ethnic groups if there are concerted and adequately resourced culturally competent interventions with concomitant evaluation programmes.

Improved access to transplantation and reduced waiting times can be achieved to increase the number of organ donors from minority ethnic groups if there are concerted and adequately resourced culturally competent interventions with concomitant evaluation programmes.Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.The litter size of mouse strains is determined by the number of oocytes naturally ovulated. Many attempts have been made to increase litter sizes by conventional superovulation regimens (e.g., using equine or human gonadotropins, eCG/hCG but had limited success because of unexpected decreases in the numbers of embryos surviving to term. Here, we examined whether rat-derived anti-inhibin monoclonal antibodies (AIMAs) could be used for this purpose. When C57BL/6 female mice were treated with an AIMA and mated, the number of healthy offspring per mouse increased by 1.4-fold (11.9 vs. 8.6 in controls). By contrast, treatment with eCG/hCG or anti-inhibin serum resulted in fewer offspring than in nontreated controls. The overall efficiency of production based on all females treated (including nonpregnant ones) was improved 2.4 times with AIMA compared with nontreated controls. The AIMA treatment was also effective in ICR mice, increasing the litter size from 15.3 to 21.2 pups. We then applied this technique to an in vivo genome-editing method (improved genome-editing via oviductal nucleic acid delivery, i-GONAD) to produce C57BL/6 mice deficient for tyrosinase. The mean litter size following i-GONAD increased from 4.8 to 7.3 after the AIMA treatment and genetic modifications were confirmed in 80/88 (91%) of the offspring. Thus, AIMA treatment is a promising method for increasing the litter size of mice and may be applied for the easy proliferation of mouse colonies as well as in vivo genetic manipulation, especially when the mouse strains are sensitive to handling.

The relationship between cigarette smoking status and SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) severity is highly debated. We conducted a retrospective cohort study of >2.4 million adults in a large healthcare system to evaluate whether smoking is associated with SARS-CoV-2 infection and disease severity.

This retrospective cohort study of 2,427,293 adults in KPNC from 3/5/2020 (baseline) to 12/31/2020 (pre-vaccine) included smoking status (current, former, never), socio-demographics, and comorbidities from the electronic health record. SARS-CoV-2 infection (identified by a positive PCR test) and COVID-19 severity (hospitalization, ICU admission or death ≤30 days of COVID-19 diagnosis) were estimated in time-to-event analyses using Cox proportional hazard regression models adjusting for covariates. Secondary analyses examined COVID-19 severity among patients with COVID-19 using logistic regression.

During the study, 44,270 patients had SARS-CoV-2 infection. Current smoking was assigation. Results support prioritizing individuals with smoking-related comorbidities for vaccine outreach and treatments as they become available.Alzheimer's disease (AD) patients suffer progressive cerebral atrophy before dementia onset. However, the region-specific atrophic processes and the influences of age and apolipoprotein E (APOE) on atrophic trajectory are still unclear. By mapping the region-specific nonlinear atrophic trajectory of whole cerebrum from amnestic mild cognitive impairment (aMCI) to AD based on longitudinal structural magnetic resonance imaging data from Alzheimer's disease Neuroimaging Initiative (ADNI) database, we unraveled a quadratic accelerated atrophic trajectory of 68 cerebral regions from aMCI to AD, especially in the superior temporal pole, caudate, and hippocampus. Besides, interaction analyses demonstrated that APOE ε4 carriers had faster atrophic rates than noncarriers in 8 regions, including the caudate, hippocampus, insula, etc.; younger patients progressed faster than older patients in 32 regions, especially for the superior temporal pole, hippocampus, and superior temporal gyrus; and 15 regions demonstrated complex interaction among age, APOE, and disease progression, including the caudate, hippocampus, etc. (P  less then  0.05/68, Bonferroni correction). Finally, Cox proportional hazards regression model based on the identified region-specific biomarkers could effectively predict the time to AD conversion within 10 years. In summary, cerebral atrophic trajectory mapping could help a comprehensive understanding of AD development and offer potential biomarkers for predicting AD conversion.An accurate quantitative method for four prohibited ephedrine substances in human urine has been established, based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The quantitative bias caused by pretreatment operations and matrix effects was reduced by the dilute and shoot pretreatment method. The good separation of isomers was achieved with the advantages of the UPLC instrument and Agilent Poroshell 120 EC-C8 UPLC column. Stable quantitative ions were selected during the analysis with MS/MS. The result of the method validation experiment showed an excellent linearity between 50% and 200% threshold concentration with a correlation coefficient (r2) greater than 0.999. The coefficient of variation at the limit of quantification and threshold was less then 20% and 10%, respectively. The uncertainty was below the maximum uncertainty specified in the technical document of the World Anti-Doping Agency (WADA). The analytical result using this method has passed the WADA-external quality assessment scheme. The anti-doping laboratory has applied the method in routine tests and reported adverse analytical finding.

Little is known about the international impact of E-cigarette or Vaping-Associated Lung Injury ('EVALI') on youth perceptions of vaping harms.

Repeat cross-sectional online surveys of youth aged 16-19 years in England, Canada, and the United States before (2017, 2018), during (2019 August/September), and after (2020 February/March, 2020 August) the 'EVALI' outbreak (N = 63380). Logistic regressions assessed trends, country differences, and associations between exposure to negative news stories about vaping and vaping harm perceptions.

Exposure to negative news stories increased between 2017 and February-March 2020 in England (12.6% to 34.2%), Canada (16.7% to 56.9%), and the United States (18.0% to 64.6%), accelerating during (2019) and immediately after (February-March 2020) the outbreak (p < .001) before returning to 2019 levels by August 2020. Similarly, the accurate perception that vaping is less harmful than smoking declined between 2017 and February-March 2020 in England (77.3% to 62.2%), Canadwhile perceptions of harm were sustained. Exposure to negative news stories also predicted two of the three harm perception measures. Overall, findings suggest that 'EVALI' may have exacerbated youth's perceptions of vaping harms internationally.Ribonucleic acid (RNA) is a pivotal nucleic acid that plays a crucial role in regulating many biological activities. Recently, one study utilized a machine learning algorithm to automatically classify RNA structural events generated by a Mycobacterium smegmatis porin A nanopore trap. Although it can achieve desirable classification results, compared with deep learning (DL) methods, this classic machine learning requires domain knowledge to manually extract features, which is sophisticated, labor-intensive and time-consuming. Meanwhile, the generated original RNA structural events are not strictly equal in length, which is incompatible with the input requirements of DL models. To alleviate this issue, we propose a sequence-to-sequence (S2S) module that transforms the unequal length sequence (UELS) to the equal length sequence. Furthermore, to automatically extract features from the RNA structural events, we propose a sequence-to-sequence neural network based on DL. In addition, we add an attention mechanism to capture vital information for classification, such as dwell time and blockage amplitude. Through quantitative and qualitative analysis, the experimental results have achieved about a 2% performance increase (accuracy) compared to the previous method. The proposed method can also be applied to other nanopore platforms, such as the famous Oxford nanopore. It is worth noting that the proposed method is not only aimed at pursuing state-of-the-art performance but also provides an overall idea to process nanopore data with UELS.The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug-drug interactions (DDIs) accurately is promotive to prevent unanticipated interactions, which may cause significant harm to patients. Currently, numerous computational studies are focusing on potential DDIs prediction on account of traditional experiments in wet lab being time-consuming, labor-consuming, costly and inaccurate. These approaches performed well; however, many approaches did not consider multi-scale features and have the limitation that they cannot predict interactions among novel drugs. In this paper, we proposed a model of BioDKG-DDI, which integrates multi-feature with biochemical information to predict potential DDIs through an attention machine with superior performance. Molecular structure features, representation of drug global association using drug knowledge graph (DKG) and drug functional similarity features are fused by attention machine and predicted through deep neural network. A novel negative selecting method is proposed to certify the robustness and stability of our method. Then, three datasets with different sizes are used to test BioDKG-DDI. Furthermore, the comparison experiments and case studies can demonstrate the reliability of our method. Upon our finding, BioDKG-DDI is a robust, yet simple method and can be used as a benefic supplement to the experimental process.It has been shown that transcranial ultrasound stimulation (TUS) is capable of attenuating myelin loss and providing neuroprotection in animal models of brain disorders. In this study, we investigated the ability of TUS to promote remyelination in the lysolecithin (LPC)-induced local demyelination in the hippocampus. Demyelination was induced by the micro-injection of 1.5 μL LPC (1%) into the rat hippocampus and the treated group received daily TUS for 5 or 12 days. Magnetic resonance imaging techniques, including magnetization transfer ratio (MTR) and T2-weighted imaging, were used to longitudinally characterize the demyelination model. Furthermore, the therapeutic effects of TUS on LPC-induced demyelination were assessed by Luxol fast blue (LFB) staining. Our data revealed that reductions in MTR values observed during demyelination recover almost completely upon remyelination. The MTR values in demyelinated lesions were significantly higher in TUS-treated rats than in the LPC-only group after undergoing TUS. Form histological observation, TUS significantly reduced the size of demyelinated lesion 7 days after LPC administration. This study demonstrated that MTR was a sensitive and reproducible quantitative marker to assess remyelination process in vivo during TUS treatment. These findings might open new promising treatment strategies for demyelinating diseases such as multiple sclerosis.

Coronary plaques that are prone to rupture and cause adverse cardiac events are characterized by large plaque burden, large lipid content, and thin fibrous caps. Statins can halt the progression of coronary atherosclerosis; however, the effect of the proprotein convertase subtilisin kexin type 9 inhibitor alirocumab added to statin therapy on plaque burden and composition remains largely unknown.

To determine the effects of alirocumab on coronary atherosclerosis using serial multimodality intracoronary imaging in patients with acute myocardial infarction.

The PACMAN-AMI double-blind, placebo-controlled, randomized clinical trial (enrollment May 9, 2017, through October 7, 2020; final follow-up October 13, 2021) enrolled 300 patients undergoing percutaneous coronary intervention for acute myocardial infarction at 9 academic European hospitals.

Patients were randomized to receive biweekly subcutaneous alirocumab (150 mg; n = 148) or placebo (n = 152), initiated less than 24 hours after urgent percutaneo844.Testis, the only organ responsible for generating sperm, is by far the organ with the largest variety of proteins and tissue-specific proteins in humans. In testis, spermatogenesis is a multi-step complex process well-accepted that protein and mRNA are decoupled in certain stages of spermatogenesis. With the fast development of mass spectrometry-based proteomics, it is possible to systemically study protein abundances and modifications in testis and sperm to help us understand the molecular mechanisms of spermatogenesis. This review provides an overview of the recent progress of proteomics analysis on spermatogenesis, including protein expression and multiple post-translational modifications, such as phosphorylation, glycosylation, ubiquitylation, and acetylation.

Combinations of PBP3-active β-lactams with developmental diazabicyclooctanes (DBOs), e.g. zidebactam, remain active against many MBL producers via an enhancer effect. We explored how this activity is affected by inoculum.

MICs of zidebactam and its cefepime and ertapenem combinations (WCK 5222 and WCK 6777, respectively) were determined by BSAC agar dilution at inocula from 3-6 × 103 to 3-6 × 105 cfu/spot. Isolates, principally Klebsiella spp., were chosen as having previously tested resistant to zidebactam or its cefepime combination, and by β-lactamase type.

MICs of zidebactam, tested alone, were strongly inoculum dependent regardless of β-lactamase type; MICs of its cefepime and ertapenem combinations likewise were strongly inoculum dependent-rising ≥32-fold across the inoculum range tested-but only for MBL producers. Combination MICs for isolates with non-MBLs, including those with OXA-48 (where the enhancer effect remains critical for ertapenem/zidebactam) were much less inoculum dependent, particu lower end of BSAC's inoculum range.The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https//github.com/ucasdp/BiTSC2.

The impact of goal setting in pharmacy preceptor development was evaluated using the Habits of Preceptors Rubric (HOP-R), a criterion-referenced assessment developed to assess, quantify, and demonstrate growth across 11 preceptor habits.

This study retrospectively evaluated initial and follow-up survey responses from the 2019-2020 Clinician Educators Program cohort at Midwestern University College of Pharmacy, Glendale Campus. Enrollees in this teaching and learning curriculum (TLC) were invited to assess their precepting habits using the HOP-R after attending the first seminar and again toward the end of the longitudinal program. Using online surveys, participants rated their precepting capabilities as developing, proficient, accomplished, or master level for each habit. In the initial survey, each participant selected a habit of focus for deliberate development and established an individualized goal using the specific, measurable, achievable, relevant, and time-bound (SMART) framework. In the follow-up assing both their self-selected habit of focus and adjacent habits while enrolled in a TLC. SMART goals facilitated qualitative and quantitative assessment of development.Alzheimer's disease is linked to increased levels of amyloid beta (Aβ) in the brain, but the mechanisms underlying neuronal dysfunction and neurodegeneration remain enigmatic. Here, we investigate whether organizational characteristics of functional presynaptic vesicle pools, key determinants of information transmission in the central nervous system, are targets for elevated Aβ. Using an optical readout method in cultured hippocampal neurons, we show that acute Aβ42 treatment significantly enlarges the fraction of functional vesicles at individual terminals. We observe the same effect in a chronically elevated Aβ transgenic model (APPSw,Ind) using an ultrastructure-function approach that provides detailed information on nanoscale vesicle pool positioning. Strikingly, elevated Aβ is correlated with excessive accumulation of recycled vesicles near putative endocytic sites, which is consistent with deficits in vesicle retrieval pathways. Using the glutamate reporter, iGluSnFR, we show that there are parallel functional consequences, where ongoing information signaling capacity is constrained. Treatment with levetiracetam, an antiepileptic that dampens synaptic hyperactivity, partially rescues these transmission defects. Our findings implicate organizational and dynamic features of functional vesicle pools as targets in Aβ-driven synaptic impairment, suggesting that interventions to relieve the overloading of vesicle retrieval pathways might have promising therapeutic value.

Lipoprotein(a) (Lp[a]) is an important risk factor for atherothrombotic cardiovascular disease and aortic stenosis, for which there are no treatments approved by regulatory authorities.

To assess adverse events and tolerability of a short interfering RNA (siRNA) designed to reduce hepatic production of apolipoprotein(a) and to assess associated changes in plasma concentrations of Lp(a) at different doses.

A single ascending dose study of SLN360, an siRNA targeting apolipoprotein(a) synthesis conducted at 5 clinical research unit sites located in the US, United Kingdom, and Australia. The study enrolled adults with Lp(a) plasma concentrations of 150 nmol/L or greater at screening and no known clinically overt cardiovascular disease. Participants were enrolled between November 18, 2020, and July 21, 2021, with last follow-up on December 29, 2021.

Participants were randomized to receive placebo (n = 8) or single doses of SLN360 at 30 mg (n = 6), 100 mg (n = 6), 300 mg (n = 6), or 600 mg (n = 6), administ-270 to -174) nmol/L, with maximal median percentage changes of -10% (IQR, -16% to 1%), -46% (IQR, -64% to -40%), -86% (IQR, -92% to -82%), -96% (IQR, -98% to -89%), and -98% (IQR, -98% to -97%), for the placebo group and the 30-mg, 100-mg, 300-mg, and 600-mg SLN360 groups, respectively. The duration of Lp(a) lowering was dose dependent, persisting for at least 150 days after administration.

In this phase 1 study of 32 participants with elevated Lp(a) levels and no known cardiovascular disease, the siRNA SLN360 was well tolerated, and a dose-dependent lowering of plasma Lp(a) concentrations was observed. The findings support further study to determine the safety and efficacy of this siRNA.

ClinicalTrials.gov Identifier NCT04606602; EudraCT Identifier 2020-002471-35.

ClinicalTrials.gov Identifier NCT04606602; EudraCT Identifier 2020-002471-35.A significant proportion of patients suffering from acute myeloid leukemia (AML) cannot be cured by conventional chemotherapy, relapsed disease being a common problem. Molecular targeting of essential oncogenic mediators is an attractive approach to improving outcomes for this disease. The hematopoietic transcription factor c-MYB has been revealed as a central component of complexes maintaining aberrant gene expression programs in AML. We have previously screened the Connectivity Map database to identify mebendazole as an anti-AML therapeutic targeting c-MYB. In the present study we demonstrate that another hit from this screen, the steroidal lactone withaferin A (WFA), induces rapid ablation of c-MYB protein and consequent inhibition of c-MYB target gene expression, loss of leukemia cell viability, reduced colony formation and impaired disease progression. Although WFA has been reported to have pleiotropic anti-cancer effects, we demonstrate that its anti-AML activity depends on c-MYB modulation and can be partially reversed by a stabilized c-MYB mutant. c-MYB ablation results from disrupted HSP/HSC70 chaperone protein homeostasis in leukemia cells following induction of proteotoxicity and the unfolded protein response by WFA. The widespread use of WFA in traditional medicines throughout the world indicates that it represents a promising candidate for repurposing into AML therapy.The 9th web-based European Conference on Infections in Leukemia (ECIL-9), held September 16-17, 2021, reviewed the risk of infections and febrile neutropenia associated with more recently approved immunotherapeutic agents and molecular targeted drugs for the treatment of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Novel antibody based treatment approaches (inotuzumab ozogamicin, gemtuzumab ozogamicin, flotetuzumab), isocitrate dehydrogenases inhibitors (ivosidenib, enasidenib, olutasidenib), FLT3 kinase inhibitors (gilteritinib, midostaurin, quizartinib), a hedgehog inhibitor (glasdegib) as well as a BCL2 inhibitor (venetoclax) were reviewed with respect to their mode of action, their immunosuppressive potential, their current approval and the infectious complications and febrile neutropenia reported from clinical studies. Evidence-based recommendations for prevention and management of infectious complications and specific alerts regarding the potential for drug-drug interactions were developed and discussed in a plenary session with the panel of experts until consensus was reached.