Novel metabolomic profile of subjects with non-classic apparent mineralocorticoid excess

Renal Hypokalemia: An Endocrine Perspective

The majority of disorders that cause renal potassium wasting present with abnormalities in adrenal hormone secretion. While these findings frequently lead patients to seek endocrine evaluation, clinicians often struggle to accurately diagnose these conditions, delaying treatment and adversely impacting patient care. At the same time, growing insight into the genetic and molecular basis of these disorders continues to improve their diagnosis and management. In this review, we outline a practical integrated approach to the evaluation of renal hypokalemia syndromes that are seen in endocrine practice while highlighting recent advances in understanding of the genetics and pathophysiology behind them.

MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning

Introduction

Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs and the outcome of infections. Mass Spectrometry (MS), and more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories to identify bacterial species and detect AMR. The analysis of AMR with deep learning is still recent, and most models depend on filters and preprocessing techniques manually applied on spectra.

Methods

This study propose a deep neural network, MSDeepAMR, to learn from raw mass spectra to predict AMR. MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus under different antibiotic resistance profiles. Additionally, a transfer learning test was performed to study the benefits of adapting the previously trained models to external data.

Results

MSDeepAMR models showed a good classification performance to detect antibiotic resistance. The AUROC of the model was above 0.83 in most cases studied, improving the results of previous investigations by over 10%. The adapted models improved the AUROC by up to 20% when compared to a model trained only with external data.

Discussion

This study demonstrate the potential of the MSDeepAMR model to predict antibiotic resistance and their use on external MS data. This allow the extrapolation of the MSDeepAMR model to de used in different laboratories that need to study AMR and do not have the capacity for an extensive sample collection.

Renin, Aldosterone, and Cortisol in Pregnancy-Induced Hypertension

INTRODUCTION

We aimed to assess renin, aldosterone, and cortisol in the early stages of pregnancy-induced hypertension (PIH), i. e., at the time of diagnosis.

METHODS

During the postural test, we measured aldosterone, renin [Liason DiaSorin Inc. (Italy)], as well as cortisol, sodium, potassium, and 24-h urinary sodium and potassium excretion in 62 women with newly diagnosed PIH, 70 healthy women during the 3rd trimester of pregnancy, and in 22 healthy non-pregnant women.

RESULTS

In all groups, there was a significant increase in aldosterone and renin in upright versus supine posture (p<0.01). Both supine and upright aldosterone concentrations were higher in healthy pregnant women than in women with PIH and the lowest in healthy not-pregnant [supine (median±intequartile range): 25.04±18.4 ng/dL, 18.03±12.58 ng/dL, and 7.48±4.78 ng/dL, p<0.001, upright: 31.60±21.32 ng/dL, 25.11±13.15 ng/dL, and 12.4±12.4 ng/dL, p<0.001, for healthy pregnant, pregnant with PIH, and non-pregnant, respectively]. Supine renin concentrations were higher only in healthy pregnant (p<0.001), while in the upright position, there was a difference only between healthy pregnant and women with PIH (p=0.002). Both in supine and upright positions, there was no difference in the aldosterone-to-renin ratio between healthy pregnant women and women with PIH, though, in both groups, the ratio was higher than in non-pregnant women (p<0.001). Morning cortisol concentrations and 24-h urinary sodium excretion were lower in women with PIH than in healthy pregnant (p<0.001, p=0.002, respectively).

CONCLUSION

Hyperaldosteronism is not involved in the etiology of PIH. In PIH, there is also a tendency towards lower sodium excretion and lower morning cortisol concentrations.

A genome-wide association study identifies a novel association between SDC3 and apparent treatment-resistant hypertension

BACKGROUND

Compared with patients who require fewer antihypertensive agents, those with apparent treatment-resistant hypertension (aTRH) are at increased risk for cardiovascular and all-cause mortality, independent of blood pressure control. However, the etiopathogenesis of aTRH is still poorly elucidated.

METHODS

We performed a genome-wide association study (GWAS) in first cohort including 586 aTRHs and 871 healthy controls. Next, expression quantitative trait locus (eQTL) analysis was used to identify genes that are regulated by single nucleotide polymorphisms (SNPs) derived from the GWAS. Then, we verified the genes obtained from the eQTL analysis in the validation cohort including 65 aTRHs, 96 hypertensives, and 100 healthy controls through gene expression profiling analysis and real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

The GWAS in first cohort revealed four suggestive loci (1p35, 4q13.2-21.1, 5q22-23.2, and 15q11.1-q12) represented by 23 SNPs. The 23 significant SNPs were in or near LAPTM5, SDC3, UGT2A1, FTMT, and NIPA1. eQTL analysis uncovered 14 SNPs in 1p35 locus all had same regulation directions for SDC3 and LAPTM5. The disease susceptible alleles of SNPs in 1p35 locus were associated with lower gene expression for SDC3 and higher gene expression for LAPTM5. The disease susceptible alleles of SNPs in 4q13.2-21.1 were associated with higher gene expression for UGT2B4. GTEx database did not show any statistically significant eQTLs between the SNPs in 5q22-23.2 and 15q11.1-q12 loci and their influenced genes. Then, gene expression profiling analysis in the validation cohort confirmed lower expression of SDC3 in aTRH but no significant differences on LAPTM5 and UGT2B4, when compared with controls and hypertensives, respectively. RT-qPCR assay further verified the lower expression of SDC3 in aTRH.

CONCLUSIONS

Our study identified a novel association of SDC3 with aTRH, which contributes to the elucidation of its etiopathogenesis and provides a promising therapeutic target.

Apparent mineralocorticoid excess: comprehensive overview of molecular genetics

Apparent mineralocorticoid excess is an autosomal recessive form of monogenic disease characterized by juvenile resistant low-renin hypertension, marked hypokalemic alkalosis, low aldosterone levels, and high ratios of cortisol to cortisone metabolites. It is caused by defects in the HSD11B2 gene, encoding the enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2), which is primarily involved in the peripheral conversion of cortisol to cortisone. To date, over 50 deleterious HSD11B2 mutations have been identified worldwide. Multiple molecular mechanisms function in the lowering of 11β-HSD2 activity, including damaging protein stability, lowered affinity for the substrate and cofactor, and disrupting the dimer interface. Genetic polymorphism, environmental factors as well as epigenetic modifications may also offer an implicit explanation for the molecular pathogenesis of AME. A precise diagnosis depends on genetic testing, which allows for early and specific management to avoid the morbidity and mortality from target organ damage. In this review, we provide insights into the molecular genetics of classic and non-classic apparent mineralocorticoid excess and aim to offer a comprehensive overview of this monogenic disease.

Machine-Learning Applications in Oral Cancer: A Systematic Review

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.