Analisis Penerapan Sistem Akuntansi Penjualan Kredit Dan Penerimaan Kas Dalam Mendukung Pengendalian Intern Perusahaan (Studi Kasus PT. Smart Tbk Refinery Surabaya)

System of selling credit accounting and system of cash receiving from account receivable is the source of life to achieving company goals. This research on the system of credit sales and cash receipts to support the company internal control. This research was conducted at PT. SMART Tbk Refinery Surabaya. PT. SMART Tbk Refinery Surabaya only selling cooking oil in the form of branded product and trading product on credit. PT. SMART Tbk Refinery Surabaya still has any weakness on system of selling credit accounting and system of cash receiving from account receivable, some of the sales transaction activity that occurred less supportive of the company\u27s internal control. This study aims to provide information to companies about the advantages and weakness of credit sales accounting system and cash receipts that have been applied by the company

Targeted Metabolic Profiling of Post-Mortem Brain from Infants Who Died from Sudden Infant Death Syndrome

Currently little is known about the underlying pathophysiology associated with SIDS, and no objective biomarkers exist for the accurate identification of those at greatest risk of dying from SIDS. Using targeted metabolomics, we aim to profile the medulla oblongata of infants who have died from SIDS (<i>n</i> = 16) and directly compare their biochemical profile with age matched controls. Combining data acquired using ¹H NMR and targeted DI-LC-MS/MS, we have identified fatty acid oxidation as a pivotal biochemical pathway perturbed in the brains of those infants who have from SIDS (<i>p</i> = 0.0016). Further we have identified a potential central biomarker with an AUC (95% CI) = 0.933 (0.845–1.000) having high sensitivity (0.933) and specificity (0.875) values for discriminating between control and SIDS brains. This is the first reported study to use targeted metabolomics for the study of PM brain from infants who have died from SIDS. We have identified pathways associated with the disease and central biomarkers for early screening/diagnosis

Heat map of metabolomic differences between HFpEF and controls.

<p>Heat maps were generated with the concentrations of potential candidate metabolites with univariate analysis. Similar metabolites were arranged together for use in pathway analysis through intuitive pattern discovery. The heat map displays an increase in each metabolite in relative concentration as a red color and a decrease in a metabolite as a blue color. The metabolites are listed at the left side of each row, and the subjects are shown at the bottom of each column.</p

Unadjusted Comparisons.

<p>The NRI and IDI values are of the blended “natriuretic peptide & metabolites” model versus each individual model.</p><p>AUC = Area Under the Curve, NRI = Net Reclassification Improvement, IDI = Integrated Discrimination Improvement, CI = Confidence Interval</p><p>Unadjusted Comparisons.</p

Heat map of metabolomic differences between HFrEF and controls.

<p>Heat maps were generated with the concentrations of potential candidate metabolites with univariate analysis. Similar metabolites were arranged together for use in pathway analysis through intuitive pattern discovery. The heat map displays an increase in each metabolite in relative concentration as a red color and a decrease in a metabolite as a blue color. The metabolites are listed at the left side of each row, and the subjects are shown at the bottom of each column.</p

Demographic Details of Participants.

<p>* p-value < 0.05 compared to control</p><p>^† p-value < 0.05 compared to HFpEF</p><p>HFpEF = Heart Failure with Preserved Ejection Fraction, HFrEF = Heart Failure with reduced Ejection Fraction, NYHA = New York Heart Association, CAD = Coronary Artery Disease, LVEF = Left Ventricular Ejection Fraction, BMI = Body Mass Index, BNP = B-type Natriuretic Peptide, NT-proBNP = N terminal pro-BNP, ACEI = Angiotensin Converting Enzyme Inhibitor, ARB = Angiotensin Receptor Blocker, CCB = Calcium Channel Blocker.</p><p>Demographic Details of Participants.</p

Heat map of metabolomic differences between HFpEF and HFrEF.

<p>Heat maps were generated with the concentrations of potential candidate metabolites with univariate analysis. Similar metabolites were arranged together for use in pathway analysis through intuitive pattern discovery. The heat map displays an increase in each metabolite in relative concentration as a red color and a decrease in a metabolite as a blue color. The metabolites are listed at the left side of each row, and the subjects are shown at the bottom of each column.</p

Cardiac peptides and left ventricular ejection fraction (LVEF) in control and HF patients.

<p>Ambulatory patients with clinical diagnosis of HFpEF (n = 24), HFrEF (n = 20), and age-matched controls (n = 38) were selected for metabolomics analysis as part of the Alberta HEART (<u>H</u>eart Failure <u>E</u>tiology and <u>A</u>nalysis <u>R</u>esearch <u>T</u>eam) project. Plasma BNP and NT-proBNP levels were measured using a Biosite Triage reagent pack and Elecsys 2010 proBNP assay, respectively. LVEF was assessed by echocardiography and interpreted by cardiologists blinded to the metabolomics analysis. Data are presented as the median ± IQR. * p < 0.05 compared to the control group, # p < 0.05 compared to the HFpEF group.</p

First-trimester metabolomic prediction of stillbirth

<p><b>Background:</b> Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority.</p> <p><b>Objective:</b> To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach.</p> <p><b>Methods:</b> Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (<28 weeks and <32 weeks), those related to growth restriction/placental disorder, and unexplained stillbirths were evaluated.</p> <p><b>Results:</b> Targeted metabolites including glycine, acetic acid, L-carnitine, creatine, lysoPCaC18:1, PCaeC34:3, and PCaeC44:4 predicted stillbirth overall with an area under the curve [AUC, 95% confidence interval (CI)] = 0.707 (0.628–0.785). When combined with clinical predictors the AUC value increased to 0.740 (0.667–0.812). First-trimester targeted metabolites also significantly predicted early, unexplained, and placental-related stillbirths. Untargeted LC-MS features combined with other clinical predictors achieved an AUC (95%CI) = 0.860 (0.793–0.927) for the prediction of stillbirths overall. We found novel preliminary evidence that, verruculotoxin, a toxin produced by common household molds, might be linked to stillbirth.</p> <p><b>Conclusions:</b> We have identified novel biomarkers for stillbirth using metabolomics and demonstrated the feasibility of first-trimester prediction.</p

Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS.

<p>Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS.</p