Nevin Manimala

Sci Rep. 2021 Oct 29;11(1):21293. doi: 10.1038/s41598-021-99727-6.

ABSTRACT

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app ( https://auto-q-pcr.com/ ) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

PMID:34716395 | DOI:10.1038/s41598-021-99727-6

Sci Rep. 2021 Oct 29;11(1):21333. doi: 10.1038/s41598-021-00780-y.

ABSTRACT

Inventories of seismically induced landslides provide essential information about the extent and severity of ground effects after an earthquake. Rigorous assessment of the completeness of a landslide inventory and the quality of a landslide susceptibility map derived from the inventory is of paramount importance for disaster management applications. Methods and materials applied while preparing inventories influence their quality, but the criteria for generating an inventory are not standardized. This study considered five landslide inventories prepared by different authors after the 2015 Gorkha earthquake, to assess their differences, understand the implications of their use in producing landslide susceptibility maps in conjunction with standard landslide predisposing factors and logistic regression. We adopted three assessment criteria: (1) an error index to identify the mutual mismatches between the inventories; (2) statistical analysis, to study the inconsistency in predisposing factors and performance of susceptibility maps; and (3) geospatial analysis, to assess differences between the inventories and the corresponding susceptibility maps. Results show that substantial discrepancies exist among the mapped landslides. Although there is no distinct variation in the significance of landslide causative factors and the performance of susceptibility maps, a hot spot analysis and cluster/outlier analysis of the maps revealed notable differences in spatial patterns. The percentages of landslide-prone hot spots and clustered areas are directly proportional to the size of the landslide inventory. The proposed geospatial approaches provide a new perspective to the investigators for the quantitative analysis of earthquake-triggered landslide inventories and susceptibility maps.

PMID:34716368 | DOI:10.1038/s41598-021-00780-y

Sci Rep. 2021 Oct 29;11(1):21326. doi: 10.1038/s41598-021-00872-9.

ABSTRACT

Periprocedural antithrombotic management with glycoprotein IIb/IIIa inhibitors (GPI) for intracranial artery stenting is still controversial. We sought to assess the safety and efficacy of prophylactic tirofiban infusion for acute intracranial intraprocedural stent thrombosis in routine clinical practice. From January 2013 to December 2019, consecutive patients treated with endovascular stenting for symptomatic intracranial atherosclerotic stenosis (ICAS) were identified and dichotomized by whether tirofiban was used. The efficacy and safety outcomes were compared by propensity score matching. A total of 160 consecutive patients in the tirofiban group and 177 patients in the non-tirofiban group were enrolled. Propensity score matching analysis selected 236 matched patients. One acute intraprocedural stent thrombosis (AIST) occurred in patients receiving prophylactic tirofiban, while 8 in the non-tirofiban group. The incidence of AIST in the tirofiban group was significantly lower than that in the non-tirofiban group (0.8% vs 6.8%, P = 0.039). The periprocedural ischemic events (8.5% vs 5.1%, P = 0.424), periprocedural intracranial hemorrhage (4.2% vs 0.8%, P = 0.219) and 30-day total mortality (3.4% vs 0%, P = 0.125) were not statistically different between the two groups. Compared with conventional stenting angioplasty without tirofiban, tirofiban prophylactic infusion can lower the incidence of AIST, without increasing the risk of periprocedural intracranial hemorrhage and 30-day total mortality. However, there is no superiority in reducing periprocedural ischemic events. The current study adds more important insights to the available clinical evidence on the use of tirofiban during stenting of ICAS.

PMID:34716365 | DOI:10.1038/s41598-021-00872-9

NPJ Parkinsons Dis. 2021 Oct 29;7(1):96. doi: 10.1038/s41531-021-00236-0.

ABSTRACT

Multiple system atrophy (MSA) is classified into two main types: parkinsonian and cerebellar ataxia with oligodendrogliopathy. We examined microstructural alterations in the white matter and the substantia nigra pars compacta (SNc) of patients with MSA of parkinsonian type (MSA-P) using multishell diffusion magnetic resonance imaging (dMRI) and myelin sensitive imaging techniques. Age- and sex-matched patients with MSA-P (n = 21, n = 10 first and second cohorts, respectively), Parkinson’s disease patients (n = 19, 17), and healthy controls (n = 20, 24) were enrolled. Magnetization transfer saturation imaging (MT-sat) and dMRI were obtained using 3-T MRI. Measurements obtained from diffusion tensor imaging (DTI), free-water elimination DTI, neurite orientation dispersion and density imaging (NODDI), and MT-sat were compared between groups. Tract-based spatial statistics analysis revealed differences in diffuse white matter alterations in the free-water fractional volume, myelin volume fraction, and intracellular volume fraction between the patients with MSA-P and healthy controls, whereas free-water and MT-sat differences were limited to the middle cerebellar peduncle in comparison with those with Parkinson’s disease. Region-of-interest analysis of white matter and SNc revealed significant differences in the middle and inferior cerebellar peduncle, pontine crossing tract, corticospinal tract, and SNc between the MSA-P and healthy controls and/or Parkinson’s disease patients. Our results shed light on alterations to brain microstructure in MSA.

PMID:34716335 | DOI:10.1038/s41531-021-00236-0

Nat Commun. 2021 Oct 29;12(1):6250. doi: 10.1038/s41467-021-26479-2.

ABSTRACT

Understanding the trajectory, duration, and determinants of antibody responses after SARS-CoV-2 infection can inform subsequent protection and risk of reinfection, however large-scale representative studies are limited. Here we estimated antibody response after SARS-CoV-2 infection in the general population using representative data from 7,256 United Kingdom COVID-19 infection survey participants who had positive swab SARS-CoV-2 PCR tests from 26-April-2020 to 14-June-2021. A latent class model classified 24% of participants as ‘non-responders’ not developing anti-spike antibodies, who were older, had higher SARS-CoV-2 cycle threshold values during infection (i.e. lower viral burden), and less frequently reported any symptoms. Among those who seroconverted, using Bayesian linear mixed models, the estimated anti-spike IgG peak level was 7.3-fold higher than the level previously associated with 50% protection against reinfection, with higher peak levels in older participants and those of non-white ethnicity. The estimated anti-spike IgG half-life was 184 days, being longer in females and those of white ethnicity. We estimated antibody levels associated with protection against reinfection likely last 1.5-2 years on average, with levels associated with protection from severe infection present for several years. These estimates could inform planning for vaccination booster strategies.

PMID:34716320 | DOI:10.1038/s41467-021-26479-2

Thorax. 2021 Oct 29:thoraxjnl-2021-217142. doi: 10.1136/thoraxjnl-2021-217142. Online ahead of print.

ABSTRACT

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.

METHOD: The Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLP_v2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCO_m2012) models. Lung cancer occurrence over 5-6 years was measured in ever-smokers aged 50-80 years and compared with 5-year (LLP_v2) and 6-year (PLCO_m2012) predicted risk.

RESULTS: Over 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLP_V2 produced a c-statistic of 0.700 (0.694-0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCO_m2012 showed similar performance (c-statistic: 0.679 (0.673-0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLP_v2) and 0.15% (PLCO_m2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.

CONCLUSION: Risk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.

PMID:34716280 | DOI:10.1136/thoraxjnl-2021-217142

Proc Natl Acad Sci U S A. 2021 Nov 2;118(44):e2100482118. doi: 10.1073/pnas.2100482118.

ABSTRACT

In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks. We propose two algorithms that perform clustering on several small subgraphs and finally patch the results into a single clustering. The main advantage of these algorithms is that they significantly bring down the computational cost of traditional algorithms, including spectral clustering, semidefinite programs, modularity-based methods, likelihood-based methods, etc., without losing accuracy, and even improving accuracy at times. These algorithms are also, by nature, parallelizable. Since most traditional algorithms are accurate, and the corresponding optimization problems are much simpler in small problems, our divide-and-conquer methods provide an omnibus recipe for scaling traditional algorithms up to large networks. We prove the consistency of these algorithms under various subgraph selection procedures and perform extensive simulations and real-data analysis to understand the advantages of the divide-and-conquer approach in various settings.

PMID:34716259 | DOI:10.1073/pnas.2100482118

J Neurosci. 2021 Oct 28:JN-RM-2687-20. doi: 10.1523/JNEUROSCI.2687-20.2021. Online ahead of print.

ABSTRACT

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable – self-motion – by non-linearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex) different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multi-dimensional high-variance subspace. Fully embracing this approach requires highly non-linear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENTMany BMI decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time BMI control.

PMID:34716229 | DOI:10.1523/JNEUROSCI.2687-20.2021

Diabetes Care. 2021 Oct 29:dc202806. doi: 10.2337/dc20-2806. Online ahead of print.

ABSTRACT

OBJECTIVE: Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD.

RESEARCH DESIGN AND METHODS: We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial.

RESULTS: Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29-3.29]). Similar phenotypes and outcomes were identified in EXSCEL.

CONCLUSIONS: In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.

PMID:34716214 | DOI:10.2337/dc20-2806

Cold Spring Harb Mol Case Stud. 2021 Oct 29:mcs.a006137. doi: 10.1101/mcs.a006137. Online ahead of print.

ABSTRACT

An SLC30A9 associated cerebro renal syndrome was first reported in consanguineous Bedouin kindred by Perez et al in 2017. While the function of the gene has not yet been fully elucidated, it may be implicated in Wnt signaling, nuclear regulation, as well as cell and mitochondrial zinc regulation. In this research report, we present a female proband with two distinct, inherited autosomal recessive loss of function SLC30A9 variants from unrelated parents. To our knowledge, this is the first reported case of a possible SLC30A9 associated cerebro renal syndrome in a nonconsanguineous family. Furthermore, a limited statistical analysis was conducted to identify possible allele frequency differences between populations. Our findings provide further support for an SLC30A9 associated cerebro renal syndrome and may help further clarify the function of this gene.

PMID:34716203 | DOI:10.1101/mcs.a006137