An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Early development and adaptive functioning in children with Bardet-Biedl syndrome

This study had two aims. Aim one investigated achievement of 10 developmental milestones in children with Bardet-Biedl syndrome (BBS). Aim one data were derived from retrospective responses by caregivers of individuals with BBS who are enrolled in the Clinical Registry Investigating Bardet-Biedl syndrome (CRIBBS). CRIBBS is a natural history registry acquiring serial observations. Aim two investigated early adaptive skills using the Adaptive Behavior Assessment System (ABAS-II 0-5) completed by caregivers of children with BBS aged from 0 to 5. There were 652 individuals with milestone information (with some variability based on availability of information for specific milestones), and 101 individuals (including 95 among the 652) with ABAS-II information. Results revealed wide-ranging delays in adaptive skills, particularly in the domain of Self-Care. Expressive language appears to be the most frequently delayed developmental milestone. We found a difference by BBS genotype wherein individuals with BBS1 had higher adaptive/developmental scores than individuals with BBS10. Age also carried a significant association with adaptive skills diverging farther from a normative trajectory as children with BBS progress through early childhood.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

METHODS

The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.

RESULTS

HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.

CONCLUSIONS

Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Medical economic vulnerability: a next step in expanding the farm resilience scholarship

In recent years, the long-standing questions of why, how, and which farm families continue farming in the face of ongoing changes have increasingly been studied through the resilience lens. While this body of work is providing updated and novel insights, two limitations, a focus on macro-level challenges faced by the farm operation and a mismatch between the scale of challenges and resilience measures, likely limit our understanding of the factors at play. We use the example of medical economic vulnerability, a micro-level challenge traditionally confined to the household sphere of the agri-family system, as a way to call attention to these limitations. Focusing on United States (U.S.) farm households, we assess: (1) To what extent are they experiencing medical economic vulnerability when using objective and subjective outcome measures? (2) Which demographic and farm characteristics are associated with experiencing medical economic vulnerability? (3) What is the association between institutional arrangements and medical economic vulnerability? Our analysis of over 900 surveys coupled with a conceptual framework merging complementary insights from three bodies of literature revealed seemingly large differences in the prevalence of medical economic vulnerability across the objective and subjective measures with the subjective measure indicating a general sentiment of medical economic vulnerability in a majority of respondents. Conversely, limited variations were noted in who experiences medical vulnerability on the basis of demographic and farm characteristics, with stronger associations being connected to the households' health insurance arrangements. We conclude with three implications of our findings for the farm resilience literature.

Spread of Antigenically Drifted Influenza A(H3N2) Viruses and Vaccine Effectiveness in the United States During the 2018-2019 Season

BACKGROUND

Increased illness due to antigenically drifted A(H3N2) clade 3C.3a influenza viruses prompted concerns about vaccine effectiveness (VE) and vaccine strain selection. We used US virologic surveillance and US Influenza Vaccine Effectiveness (Flu VE) Network data to evaluate consequences of this clade.

METHODS

Distribution of influenza viruses was described using virologic surveillance data. The Flu VE Network enrolled ambulatory care patients aged ≥6 months with acute respiratory illness at 5 sites. Respiratory specimens were tested for influenza by means of reverse-transcriptase polymerase chain reaction and were sequenced. Using a test-negative design, we estimated VE, comparing the odds of influenza among vaccinated versus unvaccinated participants.

RESULTS

During the 2018-2019 influenza season, A(H3N2) clade 3C.3a viruses caused an increasing proportion of influenza cases. Among 2763 Flu VE Network case patients, 1325 (48%) were infected with A(H1N1)pdm09 and 1350 (49%) with A(H3N2); clade 3C.3a accounted for 977 (93%) of 1054 sequenced A(H3N2) viruses. VE was 44% (95% confidence interval, 37%-51%) against A(H1N1)pdm09 and 9% (-4% to 20%) against A(H3N2); VE was 5% (-10% to 19%) against A(H3N2) clade 3C.3a viruses.

CONCLUSIONS

The predominance of A(H3N2) clade 3C.3a viruses during the latter part of the 2018-2019 season was associated with decreased VE, supporting the A(H3N2) vaccine component update for 2019-2020 northern hemisphere influenza vaccines.

Effects of Influenza Vaccination in the United States During the 2017-2018 Influenza Season

BACKGROUND

The severity of the 2017-2018 influenza season in the United States was high, with influenza A(H3N2) viruses predominating. Here, we report influenza vaccine effectiveness (VE) and estimate the number of vaccine-prevented influenza-associated illnesses, medical visits, hospitalizations, and deaths for the 2017-2018 influenza season.

METHODS

We used national age-specific estimates of 2017-2018 influenza vaccine coverage and disease burden. We estimated VE against medically attended reverse-transcription polymerase chain reaction-confirmed influenza virus infection in the ambulatory setting using a test-negative design. We used a compartmental model to estimate numbers of influenza-associated outcomes prevented by vaccination.

RESULTS

The VE against outpatient, medically attended, laboratory-confirmed influenza was 38% (95% confidence interval [CI], 31%-43%), including 22% (95% CI, 12%-31%) against influenza A(H3N2), 62% (95% CI, 50%-71%) against influenza A(H1N1)pdm09, and 50% (95% CI, 41%-57%) against influenza B. We estimated that influenza vaccination prevented 7.1 million (95% CrI, 5.4 million-9.3 million) illnesses, 3.7 million (95% CrI, 2.8 million-4.9 million) medical visits, 109 000 (95% CrI, 39 000-231 000) hospitalizations, and 8000 (95% credible interval [CrI], 1100-21 000) deaths. Vaccination prevented 10% of expected hospitalizations overall and 41% among young children (6 months-4 years).

CONCLUSIONS

Despite 38% VE, influenza vaccination reduced a substantial burden of influenza-associated illness, medical visits, hospitalizations, and deaths in the United States during the 2017-2018 season. Our results demonstrate the benefit of current influenza vaccination and the need for improved vaccines.