Nevin Manimala

Horm Mol Biol Clin Investig. 2021 Nov 26. doi: 10.1515/hmbci-2021-0039. Online ahead of print.

ABSTRACT

OBJECTIVES: Studies have established a relationship between proinflammatory factors and implantation failure in IVF/ICSI cycles. Likewise, low-grade chronic inflammation is generally blamed for predisposing infertility. In the present study, we aimed to find a relationship between serum IL-6 and hs-CRP levels and IVF/ICSI cycle outcomes.

METHODS: A total of 129 patients who consented to participate and attended the IVF unit of our department for the treatment of infertility have been enrolled in this prospective cohort study. Serum levels of high sensitive C-reactive protein and interleukin 6 have been detected at the beginning of the IVF/ICSI ovulation induction cycle. Cycle outcomes have been compared between patients with and without clinical pregnancy achievement following ART treatments. IVF/ICSI cycle outcomes of these two groups were also comparable except the number of >14 mm follicles, retrieved oocytes, metaphase II oocytes, and fertilized oocytes (2 pronuclei) which were in favor of the clinical pregnancy group.

RESULTS: Mean serum hs-CRP levels were 3.08 mg/L (0.12-35.04) and 2.28 mg/L (0.09-22.52) patients with and without clinical pregnancy respectively. Mean serum IL-6 levels were 2 pg/mL (1-10.2) and 2 pg/mL (1-76.9) patients with and without clinical pregnancy respectively. Both tests were found to be statistically insignificant in predicting the success of the ART cycle in terms of implantation, clinical pregnancy, miscarriage, and live birth.

CONCLUSIONS: In the present study, we have not found any significant effect of hs-CRP and IL-6 levels in the IVF cycle. However, in the light of this and previous studies, large-scale research may prove the exact influence of these markers on IVF success.

PMID:34837488 | DOI:10.1515/hmbci-2021-0039

Liver Transpl. 2021 Nov 27. doi: 10.1002/lt.26379. Online ahead of print.

ABSTRACT

BACKGROUND: Biliary strictures affect 4-12% of pediatric liver transplants (P-LT). Biliary strictures can contribute to graft loss if left untreated, however there remains no consensus on the best course of treatment. Study objectives included analyses of outcomes associated with biliary stricture management strategies via PTC, ERCP or surgery.

METHODS: We identified P-LT recipients (2011-2016) with biliary strictures from the Society of Pediatric Liver Transplantation (SPLIT) registry and retrieved imaging, procedural and operative reports from individual centers. Sub-analyses were performed to specifically evaluate PTC and ERCP for “Optimal Biliary Outcome” (OBO), defined as survival with stricture resolution without recurrence or surgery.

RESULTS: 113 children with median 3.9 years of follow-up had strictures diagnosed 100 days (IQR 30, 290) post LT; 81% were isolated anastomotic strictures. Stricture resolution was achieved in 92% within 101 days, more frequently with isolated anastomotic strictures (96%). 20% of strictures recurred, more commonly in association with hepatic artery thrombosis (32%). Patient and graft survival at 1- and 3-years were 99%, 98% and 94%, 92% respectively. In a subgroup analysis of 79 patients with extrahepatic strictures managed by PTC/ERCP: 59% achieved OBO following a median 4 PTC, and 75% following a median 3 ERCP (P=0.0003). Among patients with OBO, those with ERCP had longer time intervals between successive procedures (41, 47, 54, 62, 71 days) than for PTC (27, 31, 36, 41, 48 days; P=0.0006).

CONCLUSIONS: Allograft salvage was successful across all interventions. Stricture resolution was achieved in 92%, with 20% risk of recurrence. Resolution without recurrence was highest in patients with isolated anastomotic strictures and without HAT.

PMID:34837468 | DOI:10.1002/lt.26379

Mol Ecol Resour. 2021 Nov 27. doi: 10.1111/1755-0998.13557. Online ahead of print.

ABSTRACT

By capturing various patterns of the structuring of genetic variation across populations, f -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariance of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F4-ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analyzed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

PMID:34837462 | DOI:10.1111/1755-0998.13557

Br J Cancer. 2021 Nov 26. doi: 10.1038/s41416-021-01641-1. Online ahead of print.

ABSTRACT

BACKGROUND: Aberrant activation of androgen receptor signalling following castration therapy is a common clinical observation in prostate cancer (PCa). Earlier, we demonstrated the role of MYB overexpression in androgen-depletion resistance and PCa aggressiveness. Here, we investigated MYB-androgen receptor (AR) crosstalk and its functional significance.

METHODS: Interaction and co-localization of MYB and AR were examined by co-immunoprecipitation and immunofluorescence analyses, respectively. Protein levels were measured by immunoblot analysis and enzyme-linked immunosorbent assay. The role of MYB in ligand-independent AR transcriptional activity and combinatorial gene regulation was studied by promoter-reporter and chromatin immunoprecipitation assays. The functional significance of MYB in castration resistance was determined using an orthotopic mouse model.

RESULTS: MYB and AR interact and co-localize in the PCa cells. MYB-overexpressing PCa cells retain AR in the nucleus even when cultured under androgen-deprived conditions. AR transcriptional activity is also sustained in MYB-overexpressing cells in the absence of androgens. MYB binds and promotes AR occupancy to the KLK3 promoter. MYB-overexpressing PCa cells exhibit greater tumorigenicity when implanted orthotopically and quickly regain growth following castration leading to shorter mice survival, compared to those carrying low-MYB-expressing prostate tumours.

CONCLUSIONS: Our findings reveal a novel MYB-AR crosstalk in PCa and establish its role in castration resistance.

PMID:34837075 | DOI:10.1038/s41416-021-01641-1

Nat Rev Genet. 2021 Nov 26. doi: 10.1038/s41576-021-00434-9. Online ahead of print.

ABSTRACT

The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential.

PMID:34837041 | DOI:10.1038/s41576-021-00434-9

Eur J Hum Genet. 2021 Nov 26. doi: 10.1038/s41431-021-00984-w. Online ahead of print.

ABSTRACT

Although variant alleles of hundreds of genes are associated with sensorineural deafness in children, the genes and alleles involved remain largely unknown in the Sub-Saharan regions of Africa. We ascertained 56 small families mainly of Yoruba ethno-lingual ancestry in or near Ibadan, Nigeria, that had at least one individual with nonsyndromic, severe-to-profound, prelingual-onset, bilateral hearing loss not attributed to nongenetic factors. We performed a combination of exome and Sanger sequencing analyses to evaluate both nuclear and mitochondrial genomes. No biallelic pathogenic variants were identified in GJB2, a common cause of deafness in many populations. Potential causative variants were identified in genes associated with nonsyndromic hearing loss (CIB2, COL11A1, ILDR1, MYO15A, TMPRSS3, and WFS1), nonsyndromic hearing loss or Usher syndrome (CDH23, MYO7A, PCDH15, and USH2A), and other syndromic forms of hearing loss (CHD7, OPA1, and SPTLC1). Several rare mitochondrial variants, including m.1555A>G, were detected in the gene MT-RNR1 but not in control Yoruba samples. Overall, 20 (33%) of 60 independent cases of hearing loss in this cohort of families were associated with likely causal variants in genes reported to underlie deafness in other populations. None of these likely causal variants were present in more than one family, most were detected as compound heterozygotes, and 77% had not been previously associated with hearing loss. These results indicate an unusually high level of genetic heterogeneity of hearing loss in Ibadan, Nigeria and point to challenges for molecular genetic screening, counseling, and early intervention in this population.

PMID:34837038 | DOI:10.1038/s41431-021-00984-w

Sci Rep. 2021 Nov 26;11(1):23023. doi: 10.1038/s41598-021-02548-w.

ABSTRACT

SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic-coherence-and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.

PMID:34837008 | DOI:10.1038/s41598-021-02548-w

Sci Rep. 2021 Nov 26;11(1):22997. doi: 10.1038/s41598-021-02476-9.

ABSTRACT

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37-73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors ‘hidden’ within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.

PMID:34837000 | DOI:10.1038/s41598-021-02476-9

Sci Rep. 2021 Nov 26;11(1):23002. doi: 10.1038/s41598-021-02330-y.

ABSTRACT

Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (D_v) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in D_v between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification.

PMID:34836989 | DOI:10.1038/s41598-021-02330-y

Sci Data. 2021 Nov 26;8(1):305. doi: 10.1038/s41597-021-01089-1.

ABSTRACT

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [¹⁸F]-fluorodeoxyglucose ([¹⁸F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [¹⁸F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis.

PMID:34836985 | DOI:10.1038/s41597-021-01089-1