Notes

Quit ventricular noncompaction-a exceptional reason for triad: heart malfunction, ventricular arrhythmias, as well as endemic embolic situations: in a situation report.
The online version contains supplementary material available at 10.1007/s12571-020-01117-9.
The online version contains supplementary material available at 10.1007/s12571-020-01117-9.
Heart failure patients presenting with iron deficiency can benefit from systemic iron supplementation; however, there is the potential for iron overload to occur, which can seriously damage the heart. Therefore, myocardial iron (M-Iron) content should be precisely balanced, especially in already failing hearts. Unfortunately, the assessment of M-Iron via repeated heart biopsies or magnetic resonance imaging is unrealistic, and alternative serum markers must be found. This study is aimed at assessing M-Iron in patients with advanced heart failure (HF) and its association with a range of serum markers of iron metabolism.

Left ventricle (LV) myocardial biopsies and serum samples were collected from 33 consecutive HF patients (25 males) with LV dysfunction (LV ejection fraction 22 (11) %; NT-proBNP 5464 (3308) pg/ml) during heart transplantation. Myocardial ferritin (M-FR) and soluble transferrin receptor (M-sTfR1) were assessed by ELISA, and M-Iron was determined by Instrumental Neutron Activation Analysis in LV biopsies. Nonfailing hearts (
= 11) were used as control/reference tissue. Concentrations of serum iron-related proteins (FR and sTfR1) were assessed.

LV M-Iron load was reduced in all HF patients and negatively associated with M-FR (
= -0.37,
= 0.05). Of the serum markers, sTfR1/logFR correlated with (
= -0.42;
= 0.04) and predicted (in a step-wise analysis,

= 0.18;
= 0.04) LV M-Iron. LV M-Iron load (
g/g) can be calculated using the following formula 210.24-22.869 × sTfR1/logFR.

The sTfR1/logFR ratio can be used to predict LV M-Iron levels. Therefore, serum FR and sTfR1 levels could be used to indirectly assess LV M-Iron, thereby increasing the safety of iron repletion therapy in HF patients.
The sTfR1/logFR ratio can be used to predict LV M-Iron levels. Therefore, serum FR and sTfR1 levels could be used to indirectly assess LV M-Iron, thereby increasing the safety of iron repletion therapy in HF patients.
MicroRNAs (miRNAs) have been applied to cancer diagnosis taking into account their role in tumorigenesis. The main purpose of our study was to confirm the possibility of using miRNAs as noninvasive biomarkers for stomach adenocarcinoma (STAD) diagnosis.

A total of 246 participants (130 STAD patients and 116 healthy controls (HCs)) were enrolled in this 3-phase study. Five STAD pools and 3 HC pools (with 4 participants in each pool) were used for the screening of the 28 miRNAs using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The training phase (30 STAD patients vs. 24 HCs) and validation phase (80 STAD patients vs. selleck 80 HCs) were used to further verify the identity of these miRNAs. Kaplan-Meier survival analysis and bioinformatics analysis were also used.

The expression levels of miR-125b-5p and miR-196a-5p were upregulated in STAD serum, compared with the HCs, while miR-1-3p and miR-149-5p showed the opposite result. A four-serum miRNA panel was constructed, and the area under the receiver operating characteristic curve (AUC) was found to be 0.892 (95% CI 0.834 to 0.936, sensitivity = 86.25%, specificity = 78.75%). Only miR-125b-5p expression showed a significant difference between STAD patients and NCs in the survival analysis. The neurotrophin signaling pathway was associated with 4 miRNAs identified in STAD patients.

The four-serum miRNA panel has great potential to be used as a noninvasive biomarker for STAD diagnosis.
The four-serum miRNA panel has great potential to be used as a noninvasive biomarker for STAD diagnosis.Testicular germ cell tumors (TGCTs) are ever more affecting the young male population. Germ cell neoplasia in situ (GCNIS) is the origin of TGCTs, namely, seminomas (SE) and a heterogeneous group of nonseminomas (NS) comprising embryonal carcinoma, teratoma, yolk sac tumor, and choriocarcinoma. Response to the treatment and prognosis, especially of NS, depend on precise diagnosis with a necessity for discovery of new biomarkers. We aimed to perform comprehensive in silico analysis at the DNA, RNA, and protein levels of six prospective (HOXA9, MGMT, CFC1, PRSS21, RASSF1A, and MAGEC2) and six known TGCT biomarkers (OCT4, SOX17, SOX2, SALL4, NANOG, and KIT) and assess its congruence with histopathological analysis in all forms of TGCTs. Cancer Hallmarks Analytics Tool, the Search Tool for the Retrieval of Interacting Genes/Proteins database, and UALCAN, an interactive web resource for analyzing cancer OMICS data, were used. In 108 TGCT and 48 tumor-free testicular samples, the immunoreactivity score (IRS) was calculated. SE showed higher frequency in DNA alteration, while DNA methylation was significantly higher for all prospective biomarkers in NS. In GCNIS, we assessed the clinical positivity of RASSF1 and PRSS21 in 52% and 62% of samples, respectively, in contrast to low or nil positivity in healthy seminiferous tubules, TGTCs as a group, SE, NS, or all NS components. Although present in approximately 80% of healthy seminiferous tubules (HT) and GCNIS, HOXA9 was diagnostically positive in 64% of TGCTs, while it was positive in 82% of NS versus 29% of SE. Results at the DNA, mRNA, and protein levels on putative and already known biomarkers were included in the suggested panels that may prove to be important for better diagnostics of various forms of TGCTs.
Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC).

We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed.

The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group.
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