Category: Nevin Manimala

Lasers Med Sci. 2022 Mar 4. doi: 10.1007/s10103-021-03443-6. Online ahead of print.

ABSTRACT

Photobiomodulation therapy (PBM) is often used to treat musculoskeletal disorders such as chronic non-specific low back pain (NSCLBP) as it can have positive effects on biomarkers-creatine kinase (CK) and serum cortisol levels-related to stress caused by physical exercise, such as deep water running (DWR) or by pain. The aim of this study was to evaluate the effects of the combination of PBM and aquatic exercise (DWR) on the intensity of pain, disability, 6-min walk test adapted (6WTA), and on cortisol and creatine kinase (CK) levels in a population with NSCLBP. The participants were allocated into three groups: TG_PBM (Photobiomodulation and Training Group), TG_PLA (Placebo Photobiomodulation and Training Group), and the G_PBM (Photobiomodulation Group). Information regarding anthropometric data, blood pressure, and heart rate were collected, and the questionnaires were applied: IPAQ-Short Form, Oswestry Disability Index, and the Visual Analog Scale for Pain. The submaximal exercise test (6WTA) was performed. Blood was collected for analysis of cortisol and CK levels. The training sessions were performed twice a week, for 4 weeks. In the intragroup comparisons, there were statistically significant changes in the TG_PBM and G_PBM groups in the outcomes pain intensity, disability (reductions in both groups), and in cortisol (increased in the TG_PBM and reduced in the G_PBM); in the TG_PLA group, there was a statistically significant reduction only in the outcome of pain intensity. In the intergroup comparison, in the comparison between TG_PBM and TG_PLA, there was a statistically significant difference in the level of cortisol, as well as in the comparison between TG_PBM and G_PBM, in which there was a statistically significant difference for this same outcome (cortisol) and for the 6WTA outcome. The effects of the combination of PBM and aquatic exercise have positive effects on reducing pain intensity, disability, and cortisol levels, but its effects on other variables (6WTA and CK) are too small to be considered significant. Trial registration number: NCT03465228-April 3, 2019; retrospectively registered (ClinicalTrials.gov).

PMID:35246766 | DOI:10.1007/s10103-021-03443-6

Eur J Clin Pharmacol. 2022 Mar 5. doi: 10.1007/s00228-022-03303-4. Online ahead of print.

ABSTRACT

PURPOSE: Examine SSRIs’ efficacy in treating depression, anxiety, PTSD, and substance use in individuals with addiction.

METHODS: From their inception until August 6, 2021, we searched Google Scholar, PubMed, Scopus, OVID MEDLINE, and Academic Search Complete. We included randomized controlled trials (RCTs) and omitted open-label studies. Bayesian analysis was performed. Bayes factor (BF) established efficacy and tau (τ) statistical heterogeneity. The RoB2 method assessed potential biases. Subgroup analysis was carried out to determine SSRI performance. Treatment duration, SSRI dosage, and attrition rate were all examined in meta-regression.

RESULTS: We investigated 64 RCTs with 6128 participants. SSRIs reduced depressive symptoms in opioid, alcohol, cocaine, cannabis, and nicotine use disorders (d = 0.353, BF > 99); social anxiety symptoms in alcohol use disorder (d = 0.875, BF > 99); and generalized anxiety symptoms in opioid, alcohol, cocaine, marijuana, and nicotine use disorders (d = 0.346, BF = 4.236). Evidence for PTSD was inconclusive. SSRIs facilitated abstinence for opioid, alcohol, cocaine, cannabis, and nicotine use (d = 0.325, BF > 99); reduced craving for alcohol, cocaine, and nicotine use (d = 0.533, BF = 24.129); and reduced alcohol use (d = 0.452, BF > 99) and cocaine use (d = 0.255, BF = 3.87). Fluoxetine showed the highest antidepressant effect. There was no effect of attrition rate, SSRI dosage, or treatment length on SSRI’s efficacy.

CONCLUSIONS: Results support the use of SSRIs to treat substance use, depression, and anxiety in individuals with addiction.

PROTOCOL REGISTRATION: PROSPERO registration number: CRD42020164944.

PMID:35246699 | DOI:10.1007/s00228-022-03303-4

Mol Psychiatry. 2022 Mar 4. doi: 10.1038/s41380-022-01485-y. Online ahead of print.

ABSTRACT

A hallmark of the anterior cingulate cortex (ACC) is its functional heterogeneity. Functional and imaging studies revealed its importance in the encoding of anxiety-related and social stimuli, but it is unknown how microcircuits within the ACC encode these distinct stimuli. One type of inhibitory interneuron, which is positive for vasoactive intestinal peptide (VIP), is known to modulate the activity of pyramidal cells in local microcircuits, but it is unknown whether VIP cells in the ACC (VIP^ACC) are engaged by particular contexts or stimuli. Additionally, recent studies demonstrated that neuronal representations in other cortical areas can change over time at the level of the individual neuron. However, it is not known whether stimulus representations in the ACC remain stable over time. Using in vivo Ca²⁺ imaging and miniscopes in freely behaving mice to monitor neuronal activity with cellular resolution, we identified individual VIP^ACC that preferentially activated to distinct stimuli across diverse tasks. Importantly, although the population-level activity of the VIP^ACC remained stable across trials, the stimulus-selectivity of individual interneurons changed rapidly. These findings demonstrate marked functional heterogeneity and instability within interneuron populations in the ACC. This work contributes to our understanding of how the cortex encodes information across diverse contexts and provides insight into the complexity of neural processes involved in anxiety and social behavior.

PMID:35246635 | DOI:10.1038/s41380-022-01485-y

Brief Bioinform. 2022 Mar 4:bbac057. doi: 10.1093/bib/bbac057. Online ahead of print.

ABSTRACT

The Cellular Thermal Shift Assay (CETSA) plays an important role in drug-target identification, and statistical analysis is a crucial step significantly affecting conclusion. We put forward ProSAP (Protein Stability Analysis Pod), an open-source, cross-platform and user-friendly software tool, which provides multiple methods for thermal proteome profiling (TPP) analysis, nonparametric analysis (NPA), proteome integral solubility alteration and isothermal shift assay (iTSA). For testing the performance of ProSAP, we processed several datasets and compare the performance of different algorithms. Overall, TPP analysis is more accurate with fewer false positive targets, but NPA methods are flexible and free from parameters. For iTSA, edgeR and DESeq2 identify more true targets than t-test and Limma, but when it comes to ranking, the four methods show not much difference. ProSAP software is available at https://github.com/hcji/ProSAP and https://zenodo.org/record/5763315.

PMID:35246677 | DOI:10.1093/bib/bbac057

Mol Psychiatry. 2022 Mar 4. doi: 10.1038/s41380-022-01475-0. Online ahead of print.

ABSTRACT

Alzheimer’s disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency <0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values <10^-6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (p_meta = 4.74 × 10^-8). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (p_meta = 1 × 10^-6). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants.

PMID:35246634 | DOI:10.1038/s41380-022-01475-0

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