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Angiotensin converting enzyme 2 (ACE2) serves as the primary receptor for the SARS-CoV-2 virus and has implications for the functioning of the cardiovascular system. Based on our previously published bioinformatic analysis, in this study we aimed to analyze the diagnostic and predictive utility of miRNAs (miR-10b-5p, miR-124-3p, miR-200b-3p, miR-26b-5p, miR-302c-5p) identified as top regulators of ACE2 network with potential to affect cardiomyocytes and cardiovascular system in patients with COVID-19. The expression of miRNAs was determined through qRT-PCR in a cohort of 79 hospitalized COVID-19 patients as well as 32 healthy volunteers. Blood samples and clinical data of COVID-19 patients were collected at admission, 7-days and 21-days after admission. We also performed SHAP analysis of clinical data and miRNAs target predictions and advanced enrichment analyses. Low expression of miR-200b-3p at the seventh day of admission is indicative of predictive value in determining the length of hospital stay and/or the likelihood of mortality, as shown in ROC curve analysis with an AUC of 0.730 and a p-value of 0.002. MiR-26b-5p expression levels in COVID-19 patients were lower at the baseline, 7 and 21-days of admission compared to the healthy controls (P < 0.0001). Similarly, miR-10b-5p expression levels were lower at the baseline and 21-days post admission (P = 0.001). The opposite situation was observed in miR-124-3p and miR-302c-5p. Enrichment analysis showed influence of analyzed miRNAs on IL-2 signaling pathway and multiple cardiovascular diseases through COVID-19-related targets. Moreover, the COVID-19-related genes regulated by miR-200b-3p were linked to T cell protein tyrosine phosphatase and the HIF-1 transcriptional activity in hypoxia. Analysis focused on COVID-19 associated genes showed that all analyzed miRNAs are strongly affecting disease pathways related to CVDs which could be explained by their strong interaction with the ACE2 network., (© 2024. The Author(s).)

Background: Anatomic virtual hepatectomy with precise liver segmentation for hemilivers, sectors, or Couinaud's segments using conventional three-dimensional simulation is not automated and artificial intelligence (AI)-based algorithms have not yet been applied., Methods: Computed tomography data of 174 living-donor candidates for liver transplantation (training data) were used for developing a new two-step AI algorithm to automate liver segmentation that was validated in another 51 donors (validation data). The Pure-AI (no human intervention) and ground truth (GT, full human intervention) data groups were compared., Results: In the Pure-AI group, the median Dice coefficients of the right and left hemilivers were highly similar, 0.95 and 0.92, respectively; sectors, posterior to lateral: 0.86-0.92, and Couinaud's segments 1-8: 0.71-0.89. Labeling of the first-order branch as hemiliver, right or left portal vein perfectly matched; 92.8% of the second-order (sectors); 91.6% of third-order (segments) matched between the Pure-AI and GT data., Conclusions: The two-step AI algorithm for liver segmentation automates anatomic virtual hepatectomy. The AI-based algorithm correctly divided all hemilivers, and more than 90% of the sectors and segments., (© 2023 Japanese Society of Hepato-Biliary-Pancreatic Surgery.)

Background: In patients with type 2 diabetes mellitus (T2DM) an association between severe hypoglycaemic episodes and the risk of cardiovascular (CV) morbidity and mortality has been previously established., Methods: We aimed to investigate the influence of hypoglycaemia on several diabetes-related and platelet-related miRNAs selected based on bioinformatic analysis and literature search, including hsa-miR-16, hsa-miR-34a, hsa-miR-129-2, hsa-miR-15a, hsa-miR-15b, hsa-miR-106a, miR-223, miR-126. Selected miRNAs were validated by qRT-PCR in 14 patients with T2DM on metformin monotherapy, without established CV disease and antiplatelet therapy during a stepwise hypoglycaemic clamp experiment and a follow-up 7 days after the clamp event. In order to identify which pathways and phenotypes are associated with validated miRNAs we performed target prediction on genes expressed with high confidence in platelets., Results: Circulating levels of miR-106a-5p, miR-15b, miR-15a, miR-16-5p, miR-223 and miR-126 were increased after euglycaemic clamp followed by hypoglycaemic clamp, each with its distinctive time trend. On the contrary, miR-129-2-3p, miR-92a-3p and miR-34a-3p remained unchanged. MiR-16-5p was negatively correlated with interleukin (IL)-6, intercellular adhesion molecule (ICAM) and vascular cell adhesion molecule (VCAM) (p = 0.002, p < 0.001, p = 0.016, respectively), whereas miR-126 was positively correlated with VCAM (p < 0.001). There were negative correlations between miR-16-5p, miR-126 and coagulation factors, including factor VIII and von Willebrand factor (vWF). Among all studied miRNAs, miR-126, miR-129-2-3p and miR-15b showed correlation with platelet function. Bioinformatic analysis of platelet-related targets of analyzed miRNAs showed strong enrichment of IL-2 signaling. We also observed significant enrichment of pathways and diseases related to cancer, CV diseases, hyperglycemia, and neurological diseases., Conclusions: Hypoglycaemia can significantly influence the expression of platelet-enriched miRNAs, with a time trend paralleling the time course of platelet activation. This suggests miRNAs could be exploited as biomarkers for platelet activation in response to hypoglycaemia, as they are probably released by platelets upon activation by hypoglycaemic episodes. Should they hold their promise in clinical endpoint studies, platelet-derived miRNAs might become helpful markers of CV risk in subjects with diabetes. Trial registration The study was registered at clinical trials.gov; Impact of Hypoglycaemia in Patients With DIAbetes Mellitus Type 2 on PLATElet Activation (Diaplate), trial number: NCT03460899., (© 2022. The Author(s).)

Background/purpose: Current conventional algorithms used for 3-dimensional simulation in virtual hepatectomy still have difficulties distinguishing the portal vein (PV) and hepatic vein (HV). The accuracy of these algorithms was compared with a new deep-learning based algorithm (DLA) using artificial intelligence., Methods: A total of 110 living liver donor candidates until 2017, and 46 donor candidates until 2019 were allocated to the training group and validation groups for the DLA, respectively. All PV or HV branches were labeled based on Couinaud's segment classification and the Brisbane 2000 Terminology by hepato-biliary surgeons. Misclassified and missing branches were compared between a conventional tracking-based algorithm (TA) and DLA in the validation group., Results: The sensitivity, specificity, and Dice coefficient for the PV were 0.58, 0.98, and 0.69 using the TA; and 0.84, 0.97, and 0.90 using the DLA (P < .001, excluding specificity); and for the HV, 0.81, 087, and 0.83 using the TA; and 0.93, 0.94 and 0.94 using the DLA (P < .001 to P = .001). The DLA exhibited greater accuracy than the TA., Conclusion: Compared with the TA, artificial intelligence enhanced the accuracy of extraction of the PV and HVs in computed tomography., (© 2021 Japanese Society of Hepato-Biliary-Pancreatic Surgery.)

Pretreatment of corn stover with dilute sulfuric acid at moderate temperature was investigated, and glucan digestibility by Cellic CTec2 and Celluclast on the pretreated biomass was compared. Pretreatments were carried out from 60 to 180 min at the temperature from 105 to 135 °C, with acid concentrations ranging from 0.5 to 2% (w/v). Significant portion of xylan was removed during pretreatment, and the glucan digestibility by CTec2 was significantly better than that by Celluclast in all cases. Analysis showed that glucan digestibility by both two enzymes correlated directly with the extent of xylan removal in pretreatment. Confidence interval was built to give a more precise range of glucan conversion and to test the significant difference among pretreatment conditions. Response surface model was built to obtain the optimal pretreatment condition to achieve high glucan conversion after enzymatic hydrolysis. Considering the cost and energy savings, the optimal pretreatment condition of 1.75% acid for 160 min at 135 °C was determined, and glucan conversion can achieve the range from 72.86 to 76.69% at 95% confidence level after enzymatic hydrolysis, making total glucan recovery up to the range from 89.42 to 93.25%.

Production of bioethanol from agricultural residues and hays (wheat, barley, and triticale straws, and barley, triticale, pearl millet, and sweet sorghum hays) through a series of chemical pretreatment, enzymatic hydrolysis, and fermentation processes was investigated in this study. Composition analysis suggested that the agricultural straws and hays studied contained approximately 28.62-38.58% glucan, 11.19-20.78% xylan, and 22.01-27.57% lignin, making them good candidates for bioethanol production. Chemical pretreatment with sulfuric acid or sodium hydroxide at concentrations of 0.5, 1.0, and 2.0% indicated that concentration and treatment agent play a significant role during pretreatment. After 2.0% sulfuric acid pretreatment at 121 degrees C/15 psi for 60 min, 78.10-81.27% of the xylan in untreated feedstocks was solubilized, while 75.09-84.52% of the lignin was reduced after 2.0% sodium hydroxide pretreatment under similar conditions. Enzymatic hydrolysis of chemically pretreated (2.0% NaOH or H2SO4) solids with Celluclast 1.5 L-Novozym 188 (cellobiase) enzyme combination resulted in equal or higher glucan and xylan conversion than with Spezyme(R) CP- xylanase combination. The glucan and xylan conversions during hydrolysis with Celluclast 1.5 L-cellobiase at 40 FPU/g glucan were 78.09 to 100.36% and 74.03 to 84.89%, respectively. Increasing the enzyme loading from 40 to 60 FPU/g glucan did not significantly increase sugar yield. The ethanol yield after fermentation of the hydrolyzate from different feedstocks with Saccharomyces cerevisiae ranged from 0.27 to 0.34 g/g glucose or 52.00-65.82% of the theoretical maximum ethanol yield.