Tag: pubmed

Sci Rep. 2022 Mar 4;12(1):3577. doi: 10.1038/s41598-022-07419-6.

ABSTRACT

Pleiotrophin (PTN) is a cytokine involved in nerve tissue repair processes, neuroinflammation and neuronal survival. PTN expression levels are upregulated in the nigrostriatal pathway of Parkinson’s Disease (PD) patients. We aimed to characterize the dopaminergic injury and glial responses in the nigrostriatal pathway of mice with transgenic Ptn overexpression in the brain (Ptn-Tg) after intrastriatal injection of the catecholaminergic toxic 6-hydroxydopamine (6-OHDA) at a low dose (5 µg). Ten days after surgery, the injection of 6-OHDA induced a significant decrease of the number of tyrosine hydroxylase (TH)-positive neurons in the substantia nigra and of the striatal TH contents in Wild type (Wt) mice. In contrast, these effects of 6-OHDA were absent in Ptn-Tg mice. When the striatal Iba1 and GFAP immunoreactivity was studied, no statistical differences were found between vehicle-injected Wt and Ptn-Tg mice. Furthermore, 6-OHDA did not cause robust glial responses neither on Wt or Ptn-Tg mice 10 days after injections. In metabolomics studies, we detected interesting metabolites that significantly discriminate the more injured 6-OHDA-injected Wt striatum and the more protected 6-OHDA-injected Ptn-Tg striatum. Particularly, we detected groups of metabolites, mostly corresponding to phospholipids, whose trends were opposite in both groups. In summary, the data confirm lower 6-OHDA-induced decreases of TH contents in the nigrostriatal pathway of Ptn-Tg mice, suggesting a neuroprotective effect of brain PTN overexpression in this mouse model of PD. New lipid-related PD drug candidates emerge from this study and the data presented here support the increasingly recognized “lipid cascade” in PD.

PMID:35246557 | DOI:10.1038/s41598-022-07419-6

NPJ Genom Med. 2022 Mar 4;7(1):17. doi: 10.1038/s41525-022-00286-0.

ABSTRACT

To enhance the use of Whole Genome Sequencing (WGS) in clinical practice, it is still necessary to standardize data analysis pipelines. Herein, we aimed to define a WGS-based algorithm for the accurate interpretation of variants in inherited retinal dystrophies (IRD). This study comprised 429 phenotyped individuals divided into three cohorts. A comparison of 14 pathogenicity predictors, and the re-definition of its cutoffs, were performed using panel-sequencing curated data from 209 genetically diagnosed individuals with IRD (training cohort). The optimal tool combinations, previously validated in 50 additional IRD individuals, were also tested in patients with hereditary cancer (n = 109), and with neurological diseases (n = 47) to evaluate the translational value of this approach (validation cohort). Then, our workflow was applied for the WGS-data analysis of 14 individuals from genetically undiagnosed IRD families (discovery cohort). The statistical analysis showed that the optimal filtering combination included CADDv1.6, MAPP, Grantham, and SIFT tools. Our pipeline allowed the identification of one homozygous variant in the candidate gene CFAP20 (c.337 C > T; p.Arg113Trp), a conserved ciliary gene, which was abundantly expressed in human retina and was located in the photoreceptors layer. Although further studies are needed, we propose CFAP20 as a candidate gene for autosomal recessive retinitis pigmentosa. Moreover, we offer a translational strategy for accurate WGS-data prioritization, which is essential for the advancement of personalized medicine.

PMID:35246562 | DOI:10.1038/s41525-022-00286-0

Electrophoresis. 2022 Mar 4. doi: 10.1002/elps.202100348. Online ahead of print.

ABSTRACT

An international team spanning 19 sites across 18 biopharmaceutical and in vitro diagnostics (IVD) companies in the US, Europe, and China, along with 1 regulatory agency, was formed to compare the precision and robustness of ICIEF for the charge heterogeneity analysis of the NIST mAb and a rhPD-L1-Fc fusion protein on the iCE3 and the Maurice instruments. This information has been requested to help companies better understand how these instruments compare and how to transition ICIEF methods from iCE3 to the Maurice instrument. The different laboratories performed ICIEF on the NIST mAb and rhPD-L1-Fc with both the iCE3 and Maurice using analytical methods specifically developed for each of the molecules. After processing the electropherograms, statistical evaluation of the data was performed to determine consistencies within and between laboratory and outlying information. The apparent isoelectric point (pI) data generated, based on 2-point calibration, for the main isoform of the NIST mAb showed high precision between laboratories, with RSD values of less than 0.3% on both instruments. The SDs for the NIST mAb and the rhPD-L1-Fc charged variants percent peak area values for both instruments are less than 1.02% across different laboratories. These results validate the appropriate use of both the iCE3 and Maurice for ICIEF in the biopharmaceutical industry in support of process development and regulatory submissions of biotherapeutic molecules. Further, the data comparability between the iCE3 and Maurice illustrates that the Maurice platform is a next-generation replacement for the iCE3 that provides comparable data. This article is protected by copyright. All rights reserved.

PMID:35245390 | DOI:10.1002/elps.202100348

Stat Appl Genet Mol Biol. 2022 Mar 7;21(1). doi: 10.1515/sagmb-2021-0035.

ABSTRACT

Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

PMID:35245419 | DOI:10.1515/sagmb-2021-0035

PLoS One. 2022 Mar 4;17(3):e0265021. doi: 10.1371/journal.pone.0265021. eCollection 2022.

ABSTRACT

BACKGROUND: The prevalence of anxiety and depression in pregnant women has significantly increased after the spread of COVID-19 throughout the world. We carried out this meta-analysis to reveal the information about risk factors for depression and anxiety in pregnant women during the COVID-19 pandemic.

METHODS: We searched the PubMed, Embase and CNKI (China National Knowledge Infrastructure) databases for all articles. The odds ratio (OR) corresponding to the 95% confidence interval (95% CI) was used to assess the risk factors for mental health. The statistical heterogeneity among studies was assessed with the Q-test and I2 statistics.

RESULTS: We collected 17 studies including 15,050 pregnant women during the COVID-19 pandemic. Our results found that factors including decrease in the perception of general support and difficulties in household finances have damage effects on anxiety, and factors including undereducated, unemployed during pregnancy, with a chronic physical illness before pregnancy, decrease in the perception of general support, difficulties in household finances, disobey the isolation rules, and smoking during pregnancy have increased risk of depression.

CONCLUSION: Our meta-analysis revealed some risk factors for mental health in pregnant women during COVID-19 pandemic. Mental health interventions in pregnant women may involve targeted methods individually.

PMID:35245344 | DOI:10.1371/journal.pone.0265021

J Wildl Dis. 2022 Mar 4. doi: 10.7589/JWD-D-21-00037. Online ahead of print.

ABSTRACT

Coxiella burnetii is an intracellular bacterial pathogen that can be associated with significant reproductive disease or acute mortality in livestock and wildlife. A novel marine mammal-associated strain of C. burnetii has been identified in pinnipeds of the Northwestern Pacific Ocean. Little is known about C. burnetii infection in regard to reproductive success or population status. Our objective was to characterize the severity and extent of histologic lesions in 117 opportunistically collected placentas from presumed-normal northern fur seals (Callorhinus ursinus) in July 2011 on St. Paul Island, Alaska, where a high placental prevalence of C. burnetii had been reported. Sections were examined by histology and immunohistochemistry and impression smears with modified acid-fast stain. The nature and frequency of histologic changes were compared with target COM1 PCR-confirmed C. burnetii positive and negative placentas. Overall, histologic changes were similar to placental lesions described in aborting ruminants; however, changes were variable within and between placentas. Vasculitis and occasional intracellular bacteria were seen only in C. burnetii PCR-positive placentas. Dystrophic mineralization, edema, and inflammation were seen in PCR-positive and negative placentas, although they were statistically more common in PCR-positive placentas. Results suggest that C. burnetti and associated pathologic changes are multifocal and variable in placentas from these presumably live-born pups. Therefore, multiple sections of tissue from different placental areas should be examined microscopically and screened by PCR to ensure accurate diagnosis, as the genomes per gram of placenta may not necessarily represent the severity of placental disease. These limitations should inform field biologists, diagnosticians, and pathologists how best to screen and sample for pathogens and histopathology in marine mammal placental samples.

PMID:35245373 | DOI:10.7589/JWD-D-21-00037

PLoS One. 2022 Mar 4;17(3):e0264553. doi: 10.1371/journal.pone.0264553. eCollection 2022.

ABSTRACT

Aquaporins (AQPs) are a kind of transmembrane proteins that exist in various organs of the human body. AQPs play an important role in regulating water transport, lipid metabolism and glycolysis of cells. Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the kidney, and the prognosis is worse than other types of renal cell cancer (RCC). The impact of AQPs on the prognosis of ccRCC and the potential relationship between AQPs and the occurrence and development of ccRCC are demanded to be investigated. In this study, we first explored the expression pattern of AQPs by using Oncomine, UALCAN, and HPA databases. Secondly, we constructed protein-protein interaction (PPI) network and performed function enrichment analysis through STRING, GeneMANIA, and Metascape. Then a comprehensive analysis of the genetic mutant frequency of AQPs in ccRCC was carried out using the cBioPortal database. In addition, we also analyzed the main enriched biological functions of AQPs and the correlation with seven main immune cells. Finally, we confirmed the prognostic value of AQPs throughGEPIA and Cox regression analysis. We found that the mRNA expression levels of AQP0/8/9/10 were up-regulated in patients with ccRCC, while those of AQP1/2/3/4/5/6/7/11 showed the opposite. Among them, the expression differences of AQP1/2/3/4/5/6/7/8/9/11 were statistically significant. The differences in protein expression levels of AQP1/2/3/4/5/6 in ccRCC and normal renal tissues were consistent with the change trends of mRNA. The biological functions of AQPs were mainly concentrated in water transport, homeostasis maintenance, glycerol transport, and intracellular movement of sugar transporters. The high mRNA expression levels of AQP0/8/9 were significantly correlated with worse overall survival (OS), while those of AQP1/4/7 were correlated with better OS. AQP0/1/4/9 were prognostic-related factors, and AQP1/9 were independent prognostic factors. In general, this research has investigated the values of AQPs in ccRCC, which could become new survival markers for ccRCC targeted therapy.

PMID:35245343 | DOI:10.1371/journal.pone.0264553

PLoS One. 2022 Mar 4;17(3):e0264742. doi: 10.1371/journal.pone.0264742. eCollection 2022.

ABSTRACT

Death registration in Uganda remains extremely low, yet mortality statistics are vital in health policy, planning, resource allocation and decision-making. According to NIRA, only 1% of deaths are registered annually, while Uganda Bureau of Statistics estimates death registration at 24% for the period 2011-2016. The wide variation between the administrative and survey statistics can be attributed to the restriction to only certified death registration by NIRA while survey statistics relate to all forms of death notification and registration at the different sub-national levels. Registration of deaths is of critical importance to individuals and a country’s government. Legally, it grants administrative rights in management of a deceased’s estate, and access to social (insurance and pension) benefits of a deceased person. It is also essential for official statistics and planning purposes. There is an urgent need for continuous and real-time collection of mortality data or statistics in Uganda. These statistics are of significance in public health for identifying the magnitude and distribution of major disease problems, and are essential for the design, implementation, monitoring, and assessment of health programmes and policies. Lack of such continuous and timely data has negative consequences for the achievement of both national and Sustainable Development Goals 3, 11, 16, and 17. This study assessed the determinants of death registration and certification, using a survey of 2018-2019 deaths in 2,100 households across four administrative regions of Uganda and Kampala district. Multivariate-binary logistic regression was used to model factors associated with the likelihood of a death being registered or certified. We find that around one-third of deaths were registered while death certificates were obtained for less than 5% of the total deaths. Death registration and certification varied notably within Uganda. Uptake of death registration and certification was associated with knowledge on death registration, region, access to mass media, age of the deceased, place of death, occupation of the deceased, relationship to household head and request for death certificate. There is need for decentralization of death registration services; massive sensitization of communities and creating demand for death registration.

PMID:35245336 | DOI:10.1371/journal.pone.0264742

PLoS One. 2022 Mar 4;17(3):e0264892. doi: 10.1371/journal.pone.0264892. eCollection 2022.

ABSTRACT

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agent-based models can describe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 and assess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Autónoma de Buenos Aires, Argentina.

PMID:35245337 | DOI:10.1371/journal.pone.0264892

PLoS One. 2022 Mar 4;17(3):e0264578. doi: 10.1371/journal.pone.0264578. eCollection 2022.

ABSTRACT

In the 12th Five-Year Plan period, Sichuan province has selected strategic emerging industries such as new-generation information technology, new energy, high-end equipment manufacturing, new materials, biology, energy conservation and environmental protection. However, its strategic emerging industries are still in the stage of cultivation and development, and there are some problems such as low industrial agglomeration and obvious industrial restructuring. In essence, this is because the focus of the development of strategic emerging industries is not clear enough, and the location advantages are not fully utilized to select the most suitable strategic emerging industries for the development of the region. This paper constructs a regional strategic emerging industries selection decision model, applies ARCGIS to study the spatial distribution of strategic emerging industries in Sichuan province, and uses fuzzy comprehensive evaluation method (FCEM) and analytic hierarchy process (AHP) to solve the priority of developing strategic emerging industries. Conclusions: Firstly, Sichuan province should give priority to the development of new generation information technology industry and new energy vehicle industry, then high-end equipment manufacturing industry, energy-saving and environmental protection industry and new energy industry, and finally biological industry and new material industry. The larger the coefficient of influence is, the higher the comprehensive score is, while the larger the coefficient of sensitivity is, the lower the comprehensive score is. Secondly, the number of enterprises in the new-generation information technology industry and the new energy vehicle industry is still not dominant in Sichuan province. Finally, the study found that the current development of strategic emerging industries in Sichuan province is extremely unbalanced in various regions, and the phenomenon of competition and reconstruction is obvious.

PMID:35245329 | DOI:10.1371/journal.pone.0264578