DNA methylation modulated genetic variant effect on gene transcriptional regulation.

BACKGROUND: Expression quantitative trait locus (eQTL) analysis has emerged as an important tool in elucidating the link between genetic variants and gene expression, thereby bridging the gap between risk SNPs and associated diseases. We recently identified and validated a specific case where the methylation of a CpG site influences the relationship between the genetic variant and gene expression. RESULTS: Here, to systematically evaluate this regulatory mechanism, we develop an extended eQTL mapping method, termed DNA methylation modulated eQTL (memo-eQTL). Applying this memo-eQTL mapping method to 128 normal prostate samples enables identification of 1063 memo-eQTLs, the majority of which are not recognized as conventional eQTLs in the same cohort. We observe that the methylation of the memo-eQTL CpG sites can either enhance or insulate the interaction between SNP and gene expression by altering CTCF-based chromatin 3D structure. CONCLUSIONS: This study demonstrates the prevalence of memo-eQTLs paving the way to identify novel causal genes for traits or diseases associated with genetic variations.

Artificial Intelligence based wrapper for high dimensional feature selection.

BACKGROUND: Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper algorithm primarily focuses on shortening the path to find an optimal feature set. However, it underutilizes the capability of feature subset models, which impacts feature selection and its predictive performance. METHOD AND RESULTS: This study proposes a novel Artificial Intelligence based Wrapper (AIWrap) algorithm that integrates Artificial Intelligence (AI) with the existing wrapper algorithm. The algorithm develops a Performance Prediction Model using AI which predicts the model performance of any feature set and allows the wrapper algorithm to evaluate the feature subset performance in a model without building the model. The algorithm can make the wrapper algorithm more relevant for high-dimensional data. We evaluate the performance of this algorithm using simulated studies and real research studies. AIWrap shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms and wrapper algorithms. CONCLUSION: AIWrap approach provides an alternative algorithm to the existing algorithms for feature selection. The current study focuses on AIWrap application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.

Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions.

OBJECTIVES: Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions. METHODS: This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation). RESULTS: At multivariable analysis, one of four selected radiomic features (wavelet-HHL_glcm_ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions. CONCLUSION: Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy. KEY POINTS: In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment.

Morphologic Pattern, Frequency, and Spatial Distribution of Lymphovascular Invasion Foci in Radical Prostatectomy Specimens.

Introduction. Lymphovascular invasion (LVI) is an adverse pathological finding in radical prostatectomy (RP) specimens associated with increased risk of metastatic disease. Its variable incidence may be attributed to underreporting. We characterized the location, quantity, and morphology of LVI foci in RP specimens and assessed the relationship between LVI and cribriform and intraductal carcinoma and metastatic risk. Methods. Two pathologists reviewed retrospectively 54 RP specimens reported as positive for LVI. Ambiguous cases were confirmed by immunostaining for ERG, CD31 and D2-40. Results. In 4/54 (7.4%), LVI could not be confirmed. Main mimickers of LVI were retraction artifact and dislodged tumor cells. Based on our review, the most important criteria to distinguish LVI from its mimickers were a corrugated lining of vascular spaces, endothelial nuclei bulging into the lumen, and presence of proteinaceous material. The LVI frequency per case ranged from 1 to 109 (median 7.5). In 47/50 (94%) cases with LVI, the associated carcinoma comprised cribriform pattern and/or intraductal carcinoma. The most common morphology of LVI foci was cribriform, occurring in 43/50 specimens, representing 469/843 (56%) of LVI foci. Most LVI foci were intraprostatic and located at the carcinoma-stroma interface. Particularly the risk of bone metastases during follow-up was independently associated with higher frequency of LVI foci (P = .009). Conclusions. The detailed description of prostatic LVI, and awareness of their predominant location and morphology may help its identification and improve the diagnostic accuracy of LVI in pathology reporting. The clinical impact of LVI quantification in prostate cancer needs validation by further studies.

Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma.

OBJECTIVES: Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score). METHODS: We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint. RESULTS: The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively. CONCLUSIONS: In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor). KEY POINTS: • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

Prevalence of adverse pathology features in grade group 2 prostatectomy specimens with syn- or metachronous metastatic disease.

BACKGROUND: To validate the importance of recently established adverse histopathology features (cribriform pattern and intraductal carcinoma) as contra-indication for deferred treatment of Gleason score 7 (3 + 4) (grade group [GG] 2) prostate cancer, we investigated their frequency in GG2 radical prostatectomies with syn- or metachronous metastatic disease. METHODS: GG2 prostatectomy specimens of patients with concomitant lymph node metastasis or distant metastasis at follow-up were identified in a clinical database of a tertiary care center and their pathology was reviewed for pathological stage, lymphovascular invasion, Gleason grade 4 subpatterns, presence of tertiary grade 5, and ductal adenocarcinoma histology. A control group of 99 GG2 prostatectomy specimens who had no metastatic disease (controls) was reviewed for the same adverse pathological features. RESULTS: Of 1860 GG2 prostatectomy specimens (operated between 2002 and 2020), 45 (2.4%) had concurrent regional lymph node metastases or distant metastases at follow-up. Pathological stage distribution of cases and controls was 24% and 79% pT2, 42% and 15% pT3a, 33% and 6.1% pT3b -T4, respectively (p < 0.001). Eleven of 45 cases (24%) had ≤10% Gleason grade 4 component. Cribriform pattern or intraductal carcinoma was present in 84% of cases versus 34% of controls (p < 0.001), tertiary grade 5 in 16% of cases versus 5% controls (p = 0.05) and ductal adenocarcinoma in 16% of cases versus 2% of controls (p = 0.004). Among the seven cases without cribriform or intraductal carcinoma, two displayed ductal adenocarcinoma features. CONCLUSIONS: Well-established unfavorable histopathologic features (intraductal and cribriform pattern carcinoma, ductal adenocarcinoma) are represented in about 90% of GG2 prostate cancers with local or distant metastatic disease and are much less common (38%) in those without metastatic disease. Strikingly, about 25% of GG2 prostatectomy cases with metastatic disease had an organ-confined disease and/or a small percentage of Gleason grade 4 pattern. This further emphasizes the relative importance of these adverse histopathological features (cribriform, intraductal, and ductal adenocarcinoma) rather than percentage Gleason grade 4 as contra-indicator of deferred treatment for patients with GG2 prostate cancer.

Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation.

OBJECTIVES: In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model. METHODS: Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses. RESULTS: The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40-5.87, p < 0.001)/5.28 (95% CI 2.35-11.86, p < 0.001), respectively, along with age 1.02 (1.01-1.04, p = 0.01)/1.02 (1.00-1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543-0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496-0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524-0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities. CONCLUSION: A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings. KEY POINTS: • At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging. • Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters. • Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.

Dynamic model updating (DMU) approach for statistical learning model building with missing data.

BACKGROUND: Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. METHOD AND RESULTS: This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. CONCLUSION: DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

HDSI: High dimensional selection with interactions algorithm on feature selection and testing.

Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.

Role of laboratory services in primary health center (PHC) outpatient department performance: an Indian case study.

BACKGROUND: In resource-constrained settings, primary health centers (PHCs) are critical for universal health coverage. Laboratory service is one of its important components. While PHC and its performance are focused, its laboratory service has been neglected in developing countries like India. AIM: To determine the role of different level of PHC laboratory services on the overall PHC performance. METHODS: Cross-sectional study based on 42 PHCs of Osmanabad District, Maharashtra, India was performed. The study used levels of laboratory services in PHC as independent parameter and PHC outpatient department (OPD) visits per day (≤ 80 versus > 80) as dependent parameter. The control parameters used in the study were number of medical doctors, availability of laboratory technicians (LTs) and population coverage by PHC. Field visit was done to collect data on levels of laboratory services, but secondary source was used for other parameters. The logistic regression analysis was performed in study. FINDINGS: The study found variation in PHC population coverage (10 788-74 702) and OPD visits per day (40-182) across PHC. Strong positive association was observed between levels of laboratory services and number of OPD visits per day in PHC. PHC offering both malaria and tuberculosis in-house testing had higher odds (4.81) of getting more OPDs (≥ 80 OPD visits per day) as compared to PHC not offering in-house testing facility for malaria and tuberculosis. This association was stronger in PHCs with lower population coverage (0-75 quartile) as compared to PHCs with higher population coverage (75-100 quartile). CONCLUSION: Focus on laboratory services is needed to enhance the existing PHCs performance. Skill-up gradation of existing LT could help in improving the contribution of the existing laboratories in PHC functioning.

Sugarcane Molasses - A Potential Dietary Supplement in the Management of Iron Deficiency Anemia.

Iron deficiency anemia (IDA) is a serious public health problem that debilitates ∼1.6 billion people globally every year, the majority being pregnant women and children from developing countries. In India, for example, in spite of several operational programs at the national level using iron-folic acid and other allopathic interventions, IDA is still prevalent. Traditional medicines, such as Ayurveda, prescribe herbal formulations containing sugarcane derivatives for the management of pandu, a condition similar to IDA. This article reviews molasses, a sugar industry by-product, as a potential raw material to develop nutraceutical products for IDA. Molasses contains iron and its absorption enhancers, such as sulfur, fructose, and copper, which make it a potential dietary supplement for IDA. More research, product development, and evidence of safety and efficacy of molasses in IDA management can provide a tasty and cost-effective dietary supplement, particularly for children. However, there are challenges, such as competition for raw material from refined sugar manufacturers, quality control, etc., that need to be overcome.

Critical analysis of India's National Mission on Medicinal Plants (NMMP) in providing access to quality botanical drugs to improve public health.

Drugs play an important role in improving health of the population. Medicinal plants help in addressing the health issues of a large section of the population - especially the low and middle-income people. However, there are some concerns about the supply, efficacy and safety in using them. This study reviews India's major initiative toward medicinal plants namely, the National Mission on Medicinal Plants to meet medicinal plants challenges. The study analyzed the mission's probable shortcomings due to its design and operational details. This study used "content analysis" approach for analysis of mission's publicly available documents, viz. "Operational guidelines" and its two amendments. The study identified prevalent 28 shortcomings in the original document related to clarity of the document; accountability, transparency and stakeholders' representation. These challenges were partially addressed in two amendments, which indicate persistence of shortcomings in design and operational details. The mission can help in improving and strengthening the Ayurveda, Yoga, Unani, Siddha and Homeopathy program by addressing those shortcomings.

Proposed correlation of modern processing principles for Ayurvedic herbal drug manufacturing: A systematic review.

Quality Ayurvedic herbal medicines are potential, low-cost solutions for addressing contemporary healthcare needs of both Indian and global community. Correlating Ayurvedic herbal preparations with modern processing principles (MPPs) can help develop new and use appropriate technology for scaling up production of the medicines, which is necessary to meet the growing demand. Understanding the fundamental Ayurvedic principles behind formulation and processing is also important for improving the dosage forms. Even though Ayurvedic industry has adopted technologies from food, chemical and pharmaceutical industries, there is no systematic study to correlate the traditional and modern processing methods. This study is an attempt to provide a possible correlation between the Ayurvedic processing methods and MPPs. A systematic literature review was performed to identify the Ayurvedic processing methods by collecting information from English editions of classical Ayurveda texts on medicine preparation methods. Correlation between traditional and MPPs was done based on the techniques used in Ayurvedic drug processing. It was observed that in Ayurvedic medicine preparations there were two major types of processes, namely extraction, and separation. Extraction uses membrane rupturing and solute diffusion principles, while separation uses volatility, adsorption, and size-exclusion principles. The study provides systematic documentation of methods used in Ayurveda for herbal drug preparation along with its interpretation in terms of MPPs. This is the first step which can enable improving or replacing traditional techniques. New technologies or use of existing technologies can be used to improve the dosage forms and scaling up while maintaining the Ayurvedic principles similar to traditional techniques.