Metabolism Score for Visceral Fat (METS-VF): A New Predictive Surrogate for CKD Risk

PURPOSE

Metabolic disorders are closely related to the occurrence and development of chronic kidney disease (CKD). We explored the prospective association between the Metabolic Score for Visceral Fat (METS-VF) and CKD in a 5-year follow-up study.

PATIENTS AND METHODS

In this cohort study, 631 adults not suffering from CKD from Wanzhai Town, in China in 2012 were included at baseline and followed up in 2017 and 2018. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between METS-VF and CKD risk. Area under the receiver operating characteristic curve (AUC) analyses were used to evaluate the ability of METS-VF, waist-to-height ratio (WhtR), visceral adiposity index (VAI), homeostatic model assessment of insulin resistance (HOMA-IR), body mass index (BMI) to predict CKD risk.

RESULTS

We identified 103 CKD cases during follow-up. After adjustment for confounding factors, comparing the lowest quartile of METS-VF, the OR (95% CI) of CKD risk in the highest quartile was 3.04 (1.39-6.64). The per Standard deviation (SD) increase in METS-VF was positively correlated with CKD risk. The AUC of METS-VF for predicting CKD risk was, in general, higher than that for WhtR, VAI, HOMA-IR, and BMI.

CONCLUSION

METS-VF may be an indicator for predicting CKD risk.

Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer's Disease

BACKGROUND

Alzheimer's disease is a progressive, irreversible, and fatal disease for which accumulation of amyloid beta is thought to play a key role in pathogenesis. Aducanumab is a human monoclonal antibody directed against aggregated soluble and insoluble forms of amyloid beta.

OBJECTIVES

We evaluated the efficacy and safety of aducanumab in early Alzheimer's disease.

DESIGN

EMERGE and ENGAGE were two randomized, double-blind, placebo-controlled, global, phase 3 studies of aducanumab in patients with early Alzheimer's disease.

SETTING

These studies involved 348 sites in 20 countries.

PARTICIPANTS

Participants included 1638 (EMERGE) and 1647 (ENGAGE) patients (aged 50-85 years, confirmed amyloid pathology) who met clinical criteria for mild cognitive impairment due to Alzheimer's disease or mild Alzheimer's disease dementia, of which 1812 (55.2%) completed the study.

INTERVENTION

Participants were randomly assigned 1:1:1 to receive aducanumab low dose (3 or 6 mg/kg target dose), high dose (10 mg/kg target dose), or placebo via IV infusion once every 4 weeks over 76 weeks.

MEASUREMENTS

The primary outcome measure was change from baseline to week 78 on the Clinical Dementia Rating Sum of Boxes (CDR-SB), an integrated scale that assesses both function and cognition. Other measures included safety assessments; secondary and tertiary clinical outcomes that assessed cognition, function, and behavior; and biomarker endpoints.

RESULTS

EMERGE and ENGAGE were halted based on futility analysis of data pooled from the first approximately 50% of enrolled patients; subsequent efficacy analyses included data from a larger data set collected up to futility declaration and followed prespecified statistical analyses. The primary endpoint was met in EMERGE (difference of -0.39 for high-dose aducanumab vs placebo [95% CI, -0.69 to -0.09; P=.012; 22% decrease]) but not in ENGAGE (difference of 0.03, [95% CI, -0.26 to 0.33; P=.833; 2% increase]). Results of biomarker substudies confirmed target engagement and dose-dependent reduction in markers of Alzheimer's disease pathophysiology. The most common adverse event was amyloid-related imaging abnormalities-edema.

CONCLUSIONS

Data from EMERGE demonstrated a statistically significant change across all four primary and secondary clinical endpoints. ENGAGE did not meet its primary or secondary endpoints. A dose- and time-dependent reduction in pathophysiological markers of Alzheimer's disease was observed in both trials.

Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy

The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicated process. In recent years, automatic contouring of TV and OARs has rapidly developed due to the advances in deep learning (DL). This technology has the potential to save time and to reduce intra- or inter-observer variability. In this paper, the authors provide an overview of RT, introduce the concept of DL, summarize the data characteristics of the included literature, summarize the possible challenges for DL in the future, and discuss the possible research directions.

J-shaped relationship between admission diastolic blood pressure and 2-year cardiovascular mortality in elderly patients with acute coronary syndrome

Objective

To investigate the relationship between admission diastolic blood pressure (DBP) and subsequent cardiovascular and all-cause mortality in elderly patients with acute coronary syndrome (ACS).

Methods

This is a retrospective observational study. Consecutive patients ≥65 years of age admitted for ACS at a 2,300-bed tertiary hospital from December 2012 to July 2019 were included. The association between admission DBP and cardiovascular and all-cause mortality during hospitalization and over the follow-up period among this population were analyzed using multivariate COX regression model. Results were presented according to DBP quartiles: Q1, less than 67 mm Hg; Q2, from 67 to 72 mm Hg; Q3, from 73 to 80 mm Hg; and Q4, above 80 mm Hg.

Results

A total of 6 785 patients were included in this cohort study. Mean (SD) patient age was 74.0 (6.5) years, and 47.6% were women. Mean (SD) follow-up time was 2.54 (1.82) years. A non-linear relation was observed between DBP at admission and cardiovascular and all-cause mortality during hospitalization and over the follow-up period using restricted cubic splines. After adjustment for potential confounders, patients in Q3 or Q2 had lower risk for 2-year cardiovascular death by Cox proportional hazard model compared with patients in Q4 (hazard ratio [HR] 0.66; 95% confidence interval [CI], 0.48–0.90, P=0.010, for Q3 vs Q4; and HR 0.72; 95% CI, 0.53–0.99, P=0.041, for Q2vs Q4), while patients in Q1 had similar risk for cardiovascular death with that of patients in Q4. Meanwhile, when compared with patients in Q1, patients in Q3 had lower risk for 2-year cardiovascular death (HR, 0.72; 95% CI, 0.53–0.97, P=0.033). However, lower or higher admission DBP was not an independent predictor of 2-year all-cause mortality in this population.

Conclusion

Among patients aged ≥65 years admitted for ACS, there is a J-curve relationship between supine admission DBP and risk for 2-year cardiovascular death, with a nadir at 73–80 mm Hg.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support Study population and selectionAdjusted multivariate COX regression

A generalized l 2,p-norm regression based feature selection algorithm

Feature selection is an important data dimension reduction method, and it has been used widely in applications involving high-dimensional data such as genetic data analysis and image processing. In order to achieve robust feature selection, the latest works apply the l 2 , 1 or l 2 , p -norm of matrix to the loss function and regularization terms in regression, and have achieved encouraging results. However, these existing works rigidly set the matrix norms used in the loss function and the regularization terms to the same l 2 , 1 or l 2 , p -norm, which limit their applications. In addition, the algorithms for solutions they present either have high computational complexity and are not suitable for large data sets, or cannot provide satisfying performance due to the approximate calculation. To address these problems, we present a generalizedl 2 , p -norm regression based feature selection ( l 2 , p -RFS) method based on a new optimization criterion. The criterion extends the optimization criterion of ( l 2 , p -RFS) when the loss function and the regularization terms in regression use different matrix norms. We cast the new optimization criterion in a regression framework without regularization. In this framework, the new optimization criterion can be solved using an iterative re-weighted least squares (IRLS) procedure in which the least squares problem can be solved efficiently by using the least square QR decomposition (LSQR) algorithm. We have conducted extensive experiments to evaluate the proposed algorithm on various well-known data sets of both gene expression and image data sets, and compare it with other related feature selection methods.

[The correlation between post-allo-HSCT CMV infection and the difference affinity of donor HLA-type recognition of CMV antigen peptide in children]

Objective: To explore the correlation between the affinity while donor's HLA type recognizing different cytomegalovirus (CMV) antigen peptide and the occurrence of CMV infection after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in children. Methods: To investigate the relationship between CMV reactivation, CMV infection or CMV related tissue/organ diseases and the different HLA-type recognition antigen peptide of the donors, we retrospectively analyzed the clinical data of 146 children with CMV infection for 6 months since from the time they underwent transplantation in Wuhan Children's Hospital. Results: Among 146 patients, the HLA type of 82 (56.16%) cases had high affinity with PP65 alone, and 34 cases of CMV infection occurred after transplantation (41.46%) . None of 5 cases that had a high affinity with IE-1 alone got CMV infection. None of 2 cases with no clear high-affinity peptide had CMV infection. Three of 5 cases that had a high affinity with PP65 and PP50 had CMV infection. Thirteen of 52 cases that had a high affinity with PP65 and IE-1 had CMV infection (25.00%) . HLA with exclusive PP50 high affinity was not encountered. Donors with a high-affinity HLA locus associated with IE-1 showed a lower incidence of CMV infection after HSCT compared to those carrying only the PP65 high-affinity allele (22.81% vs 41.46%, P=0.029) . Conclusion: HLA type with PP65 and IE-1 high-affinity covers approximately 99.8% of the donors. Stem cells generated from HLA donors with high affinity with the CMV antigen peptide IE-1 can reduce the risk of post-transplantation CMV-activated infection in children.

Three Novel Structural Variations at MHC and IL12B Predisposing to Psoriasis

BACKGROUND

Structural variations (SVs, defined as DNA variants ≥50 bp) have been associated with various complex human diseases. However, research to screen the whole genome for SVs predisposing to psoriasis is still lacking.

OBJECTIVES

This study aimed to investigate the association of SVs and psoriasis.

METHODS

We performed a genome-wide screen on SVs using an imputation method on 5 independent cohorts with 45,386 subjects from the Chinese Han population. Fine mapping analysis, genetic interaction analysis and RNA expression analysis were conducted to explore the mechanism of SVs.

RESULTS

We obtained 4,535 SVs in total and identified 2 novel deletions (esv3608550, OR=2.73, P<2.00×10^-308 ; esv3608542, OR=0.47, P=7.40×10^-28 ) at 6q21.33 (MHC), 1 novel Alu element insertion (esv3607339, OR=1.22, P=1.18×10^-35 ) at 5q33.3 (IL12B), and confirmed 1 previously reported deletion (esv3587563, OR=1.30, P=9.52×10^-60 ) at 1q21.2 (LCE) for psoriasis. Fine mapping analysis including SNPs and small Insertions/Deletions (InDels) revealed that esv3608550 and esv3608542 were independently associated with psoriasis, and a novel independent SNP (rs9378188, OR=1.65, P=3.46×10^-38 ) was identified at 6q21.33. By genetic interaction analysis and RNA expression analysis, we speculate that the association of 2 deletions at 6q21.33 with psoriasis might relate to their influence on the expression of HLA-C.

CONCLUSIONS

Our study constructed the most comprehensive SV map for psoriasis thus far and enriched the genetic architecture and pathogenesis of psoriasis as well as highlighted the nonnegligible impact of SVs on complex diseases.

Accuracy of Bolton's Analysis among Different Malocclusion Patients Plaster Models and Digital Models Obtained by Ex Vivo Scanning with iTero Scanner in Chinese Han Population

Aim

This investigation aimed to compare the accuracy of Bolton's analysis on plaster models of various malocclusion groups by utilizing digital calipers and iTero scanner.

Materials and Methods

The data consisted maxillary and mandibular plaster study casts of 61 patients (Class I-20, Class II-20, Class III-21) there were 31 males and 30 females. iTero®element scanner was utilized to scan the models and Bolton's analysis was performed on digital models. Also, the Digital caliper was utilized to perform the Manual measurements. Mesiodistal tooth widths, Anterior and Overall Bolton ratio was measured utilizing OrthoCAD™ software on digital models and plaster models with digital calipers. Statistical analysis was performed utilizing One-way ANOVA and independent T-test.

Results

Results revealed anterior and overall Bolton ratios showed significant differences (P < 0.05) for the measurements performed utilizing digital models. Anterior ratio for (Group 1) iTero measurements depicted the statistical significant value (P < 0.03) and overall ratio for (Group 2) digital caliper measurements depicted the statistical significant value (P < 0.02).

Conclusion

With the introduction of intra-oral laser scanners it has become more convenient for the practitioner to perform the intra-oral digital scanning and carry out the model analysis digitally and iTero scanner can also be utilized extra-orally to perform the scanning and model analysis. Our study concludes that intra-oral laser scanner like iTero is more convenient for an orthodontist, and can be utilized for extra-oral scanning of orthodontic dental models as the measurements obtained on digital models was as accurate as the conventional method.

Peta-electron volt gamma-ray emission from the Crab Nebula

The Crab Nebula is a bright source of gamma rays powered by the Crab Pulsar's rotational energy through the formation and termination of a relativistic electron-positron wind. We report the detection of gamma rays from this source with energies from 5 × 10^-4 to 1.1 peta-electron volts with a spectrum showing gradual steepening over three energy decades. The ultrahigh-energy photons imply the presence of a peta-electron volt electron accelerator (a pevatron) in the nebula, with an acceleration rate exceeding 15% of the theoretical limit. We constrain the pevatron's size between 0.025 and 0.1 parsecs and the magnetic field to ≈110 microgauss. The production rate of peta-electron volt electrons, 2.5 × 10³⁶ ergs per second, constitutes 0.5% of the pulsar spin-down luminosity, although we cannot exclude a contribution of peta-electron volt protons to the production of the highest-energy gamma rays.

Saliva and Serum Protein Adsorption on Chemically Modified Silica Surfaces

Biomaterials, once inserted in the oral cavity, become immediately covered by a layer of adsorbed proteins that consists mostly of salivary proteins but also of plasma proteins if the biomaterial is placed close to the gingival margin or if it becomes implanted into tissue and bone. It is often this protein layer, rather than the pristine biomaterial surface, that is subsequently encountered by colonizing bacteria or attaching tissue cells. Thus, to study this important initial protein adsorption from human saliva and serum and how it might be influenced through chemical modification of the biomaterial surface, we have measured the amount of protein adsorbed and analyzed the composition of the adsorbed protein layer using gel electrophoresis and western blotting. Here, we have developed an in vitro model system based on silica surfaces, chemically modified with 7 silane-based self-assembled monolayers that span a broad range of physicochemical properties, from hydrophilic to hydrophobic surfaces (water contact angles from 15° to 115°), low to high surface free energy (12 to 57 mN/m), and negative to positive surface charge (zeta potentials from –120 to +40 mV at physiologic pH). We found that the chemical surface functionalities exerted a substantial effect on the total amounts of proteins adsorbed; however, no linear correlation of the adsorbed amounts with the physicochemical surface parameters was observed. Only the adsorption behavior of a few singular protein components, from which physicochemical data are available, seems to follow physicochemical expectations. Examples are albumin in serum and lysozyme in saliva; in both, adsorption was favored on countercharged surfaces. We conclude from these findings that in complex biofluids such as saliva and serum, adsorption behavior is dominated by the overall protein-binding capacity of the surface rather than by specific physicochemical interactions of single protein entities with the surface.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.