Nevin Manimala

PLoS One. 2022 Oct 21;17(10):e0265293. doi: 10.1371/journal.pone.0265293. eCollection 2022.

ABSTRACT

Though native to Scotland, the grey wolf (Canis lupus) was extirpated c.250 years ago as part of a global eradication drive. The global population has recently expanded, now occupying 67% of its former range. Evidence is growing that apex predators provide a range of ecological benefits, most stemming from the reduction of overgrazing by deer-something from which Scotland suffers. In this study, we build a rule-based habitat suitability model for wolves on the Scottish mainland. From existing literature, we identify the most important variables as land cover, prey density, road density and human density, and establish thresholds of suitability for each. Fuzzy membership functions are used to assign suitability values to each variable, followed by fuzzy overlay to combine all four: a novel approach to habitat suitability modelling for terrestrial mammals. Model sensitivity is tested for land cover and prey density, as these variables constitute a knowledge gap and an incomplete dataset, respectively. The Highlands and Grampian mountains emerge strongly and consistently as the most suitable areas, largely due to high negative covariance between prey density and road/human density. Sensitivity testing reveals the models are fairly robust to changes in prey density, but less robust to changes in the scoring of land cover, with the latter altering the distribution of land mainly through the 70-100% suitability range. However, in statistical significance tests, only the least and most generous versions of the model emerge as giving significantly different results. Depending on the version of the model, a contiguous area of between 10,139km2 and 18,857km2 is shown to be 80 to 100% suitable. This could be sufficient to support between 50 and 94 packs of four wolves, if the average pack range size is taken to be 200km2. We conclude that in terms of habitat availability, reintroduction should be feasible.

PMID:36269698 | DOI:10.1371/journal.pone.0265293

J Adolesc Young Adult Oncol. 2022 Oct 20. doi: 10.1089/jayao.2022.0044. Online ahead of print.

ABSTRACT

Significance: Elevated survival rates in young adult cancer survivors (YACS) are accompanied by high morbidity levels resulting in an array of unmet needs limiting full life potential. Physical activity (PA) improves physical, psychological, and social aspects of health after a cancer diagnosis. There are no standardized PA guidelines tailored to YACS. Therefore, there is a critical need to understand areas of clinical relevance/agreement on PA use and implementation in young adult (YA) survivorship care. Aim: To identify expert consensus areas on the assessment, prescription, and implementation of PA in YA survivorship care; identify areas of clinical relevance and endorsement of PA as a health optimization strategy in YA survivorship care. Methods: A four-round modified Delphi study of international multidisciplinary experts (Round I/II n = 18; Round III n = 57, Round IV n = 45) in exercise oncology, symptom management, survivorship care, youth cancer care was conducted. Qualitative content analysis, descriptive statistics (% agreement, standard deviation, mean), and inter-rater reliability (Kappa) were calculated. Results: Experts reached a consensus on clinical providers needed to assess, refer, and provide PA interventions, the need for guidelines, and essential care delivery system components to foster the integration of PA integration in YA survivorship care as a health optimization activity. Conclusions/Implications: Cancer care integration models should optimize the unique preferences, strengths, and developmental stage of YA affected by cancer. The study adds to the existing literature on multidisciplinary teams needed to provide clinical expertise and organizational support to foster PA integration into YA survivorship care.

PMID:36269579 | DOI:10.1089/jayao.2022.0044

Mol Pharm. 2022 Oct 21. doi: 10.1021/acs.molpharmaceut.2c00662. Online ahead of print.

ABSTRACT

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [³H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

PMID:36269563 | DOI:10.1021/acs.molpharmaceut.2c00662

J Endocrinol Invest. 2022 Oct 21. doi: 10.1007/s40618-022-01935-y. Online ahead of print.

ABSTRACT

PURPOSE: Adrenal cortical adenomas (ACAs) represent one of the most common endocrine neoplasms. Recently, a genetic syndrome, characterized by tumor-suppressor ARMC5-gene mutations and causing primary macronodular bilateral adrenal hyperplasia with concomitant meningiomas of the central nervous system, has been described. Apart from this rare disorder and despite the well-known influence of steroid hormones on meningiomas, no data are available about the association between ACAs and meningiomas.

METHODS: We investigated the prevalence of ACAs in a group of patients with cerebral meningioma undergoing unenhanced chest CT scans before attending surgical treatment. Patients with meningioma were age- and sex-matched in a 1:3 ratio with hospitalized patients for COVID-19.

RESULTS: Fifty-six patients with meningioma were included and matched with 168 control patients with COVID-19. One-hundred forty-four (66.1%) were female and the median age was 63 years. Twenty ACAs were detected in the overall population (8.9% of the subjects): 10 in patients with meningioma (18%) and the remaining 10 (6%) in the control group (p = 0.007). Multivariate analysis showed that age and presence of meningioma were statistically associated with the presence of ACAs (p = 0.01, p = 0.008).

CONCLUSION: We report, for the first time, a higher prevalence of ACAs in patients with meningioma as compared to age- and sex-matched controls. Larger studies are needed to confirm our data and to clarify the characteristics of the ACAs in patients with meningioma. Whether the detection of ACAs should prompt a neuroimaging evaluation to exclude the presence of meningiomas needs also to be considered.

PMID:36269557 | DOI:10.1007/s40618-022-01935-y

Ther Innov Regul Sci. 2022 Oct 21. doi: 10.1007/s43441-022-00470-5. Online ahead of print.

ABSTRACT

BACKGROUND: Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality.

MATERIAL AND METHODS: Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI’s observed value.

RESULTS: Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement.

CONCLUSION: The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

PMID:36269551 | DOI:10.1007/s43441-022-00470-5

J Gastrointest Cancer. 2022 Oct 21. doi: 10.1007/s12029-022-00866-4. Online ahead of print.

ABSTRACT

PURPOSE: Anal cancer is a rare form of gastrointestinal malignancy, and treatment is often confined to specialist centres. It has a high cure rate with non-surgical approach resulting in organ preservation. The current accepted schedule is chemo-radiotherapy (CRT) with 5-fluorouracil and mitomycin with radiotherapy doses between 50.4 and 53.2 Gray in 28 fractions.

METHODS: This study included patients who had histological confirmation of squamous cell carcinoma and had completed the full CRT course for anal cancer between 2008 and 2018 in our centre. Data was collected retrospectively assessing demographics, staging, surgery, relapse, latest follow-up, date of death, CRT regimen and TNM stage. Outcome data and stoma reversal rate were analysed.

RESULTS: Overall, 87 patients were included in the study. At diagnosis 94.3% of patients had T2-T4 disease, and 44.8% had involvement of positive loco-regional lymph nodes. Overall survival (OS) probability at 1, 3 and 5 years were 98.8%, 87.4% and 83.7%, respectively. Results also revealed a statistically significant effect of time from diagnosis to the start of radiotherapy on OS (p = 0.039). Sixty-nine (79.3%) patients achieved complete remission at last follow-up. Twenty-one patients (24%) underwent surgery for a de-functioning stoma, and only five of these patients subsequently received stoma reversal surgery.

CONCLUSIONS: Our data reflects the efficacy of CRT as the primary modality of treatment in the management of anal squamous cell carcinoma with effective organ preservation and disease control. Early stoma reversal may also enhance quality of life.

PMID:36269537 | DOI:10.1007/s12029-022-00866-4

Breast Cancer Res Treat. 2022 Oct 21. doi: 10.1007/s10549-022-06763-5. Online ahead of print.

ABSTRACT

PURPOSE: The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease.

METHODS: We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model’s ability to predict RS risk category (low: RS ≤ 25; high: RS > 25).

RESULTS: Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%.

CONCLUSION: Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases.

PMID:36269526 | DOI:10.1007/s10549-022-06763-5

Oral Radiol. 2022 Oct 21. doi: 10.1007/s11282-022-00660-9. Online ahead of print.

ABSTRACT

This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study’s protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.

PMID:36269515 | DOI:10.1007/s11282-022-00660-9

Matern Child Health J. 2022 Oct 21. doi: 10.1007/s10995-022-03506-2. Online ahead of print.

ABSTRACT

OBJECTIVES: Social and structural barriers drive disparities in prenatal care utilization among minoritized women in the United States. This study examined the impact of Arizona’s Health Start Program, a community health worker (CHW) home visiting intervention, on prenatal care utilization among an ethno-racially and geographically diverse cohort of women.

METHODS: We used Health Start administrative and state birth certificate data to identify women enrolled in the program during 2006-2016 (n = 7,117). Propensity score matching was used to generate a statistically-similar comparison group (n = 53,213) of women who did not participate in the program. Odds ratios were used to compare rates of prenatal care utilization. The process was repeated for select subgroups, with post-match regression adjustments applied where necessary.

RESULTS: Health Start participants were more likely to report any (OR 1.24, 95%CI 1.02-1.50) and adequate (OR 1.08, 95%CI 1.01-1.16) prenatal care, compared to controls. Additional specific subgroups were significantly more likely to receive any prenatal care: American Indian women (OR 2.22, 95%CI 1.07-4.60), primipara women (OR 1.64, 95%CI 1.13-2.38), teens (OR 1.58, 95%CI 1.02-2.45), women in rural border counties (OR 1.45, 95%CI 1.05-1.98); and adequate prenatal care: teens (OR 1.31, 95%CI 1.11-1.55), women in rural border counties (OR 1.18, 95%CI 1.05-1.33), primipara women (OR 1.18, 95%CI 1.05-1.32), women with less than high school education (OR 1.13, 95%CI 1.00-1.27).

CONCLUSIONS FOR PRACTICE: A CHW-led perinatal home visiting intervention operated through a state health department can improve prenatal care utilization among demographically and socioeconomically disadvantaged women and reduce maternal and child health inequity.

PMID:36269498 | DOI:10.1007/s10995-022-03506-2

Hist Philos Life Sci. 2022 Oct 21;44(4):49. doi: 10.1007/s40656-022-00528-5.

ABSTRACT

This paper contributes to the ongoing reassessment of the controversy between William Bateson and Karl Pearson by characterising what we call “Batesonian Mendelism” and “Pearsonian biometry” as coherent and competing scientific outlooks. Contrary to the thesis that such a controversy stemmed from diverging theoretical commitments on the nature of heredity and evolution, we argue that Pearson’s and Bateson’s alternative views on those processes ultimately relied on different appraisals of the methodological value of the statistical apparatus developed by Francis Galton. Accordingly, we contend that Bateson’s belief in the primacy of cross-breeding experiments over statistical analysis constituted a minimal methodological unifying condition ensuring the internal coherence of Batesonian Mendelism. Moreover, this same belief implied a view of the study of heredity and evolution as an experimental endeavour and a conception of heredity and evolution as fundamentally discontinuous processes. Similarly, we identify a minimal methodological unifying condition for Pearsonian biometry, which we characterise as the view that experimental methods had to be subordinate to statistical analysis, according to methodological standards set by biometrical research. This other methodological commitment entailed conceiving the study of heredity and evolution as subsumable under biometry and primed Pearson to regard discontinuous hereditary and evolutionary processes as exceptions to a statistical norm. Finally, we conclude that Batesonian Mendelism and Pearsonian biometry represented two potential versions of a single genetics-based evolutionary synthesis since the methodological principles and the phenomena that played a central role in the former were also acknowledged by the latter-albeit as fringe cases-and conversely.

PMID:36269490 | DOI:10.1007/s40656-022-00528-5