Automated blood volume estimation in surgical drains for clinical decision support.

OBJECTIVE: Monitoring Jackson Pratt and Hemovac drains plays a crucial role in assessing a patient's recovery and identifying potential postoperative complications. Accurate and regular monitoring of the blood volume in the drain is essential for making decisions about patient care. However, transferring blood to a measuring cup and recording it is a challenging task for both patients and doctors, exposing them to bloodborne pathogens such as the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). To automate the recording process with a non-contact approach, we propose an innovative approach that utilizes deep learning techniques to detect a drain in a photograph, compute the blood level in the drain, estimate the blood volume, and display the results on both web and mobile interfaces. MATERIALS AND METHODS: Our system employs semantic segmentation on images taken with mobile phones to effectively isolate the blood-filled portion of the drain from the rest of the image and compute the blood volume. These results are then sent to mobile and web applications for convenient access. To validate the accuracy and effectiveness of our system, we collected the Drain Dataset, which consists of 1,004 images taken under various background and lighting conditions. RESULTS: With an average error rate of less than 5% in milliliters, our proposed approach achieves highly accurate blood level detection and estimation, as demonstrated by our trials on this dataset. The system also exhibits robustness to variations in lighting conditions and drain shapes, ensuring its applicability in different clinical scenarios. CONCLUSIONS: The proposed automated blood volume estimation system can significantly reduce the time and effort required for manual measurements, enabling healthcare professionals to focus on other critical tasks. The dataset and annotations are available at: https://www.kaggle.com/datasets/ayenahin/liquid-volume-detection-from-drain-images and the code for the web application is available at https://github.com/itsjustaplant/AwesomeProject.git.

COMT rs4680 and DRD2 rs6275 variants and their association with YMRS scores in children with early-onset bipolar disorder

Background and objectives: Bipolar disorder (BD) is a clinical status with at least one manic, hypomanic or mixed attacks. Genetic factors take part significantly in early-onset BD (EOBD). Dopamine receptors (DRD) act in neurological mechanisms like motivation, learning, memory, and, control of neuroendocrine signaling. DRD2 receptor has been reported to influence the stability of DRD2 transcript. Catechol-O-Methyl transferase (COMT) inactivates catecholamines and Val158Met variation on COMT has effects on COMT activity. This study aims to explore DRD2 and COMT variants in the clinical development of EOBD.MethodsIn this case-control study, 102 EOBD patients and 168 healthy control subjects were used. DRD2 rs6275 and COMT Val158Met variations were detected by real-time polymerase chain reaction (RT-PCR). Young Mania Rating Scale (YMRS) was utilized to determine the EOBD severity.ResultsFor DRD2 rs6275 and COMT Val158Met polymorphisms, no significant relationship was observed in the genotype and allele frequencies between patient and control groups. Nevertheless, TT genotype carriers of DRD2 rs6275 polymorphism demonstrated significantly increased YMRS scores when compared with CC and CT genotype carriers (p = 0.039). Nevertheless, no significant difference was observed between COMT Val158Met genotypes and YMRS scores.ConclusionsWe suggest that the DRD2 rs6275 TT variant can be associated with symptom severity in children with EOBD and can have a clinical significance in EOBD pathogenesis. However, these results need to be confirmed with larger samples of patient and control groups. (AU)

C-reactive protein/albumin ratio greater than 7.1 is a good candidate to be used as an inflammation biomarker to predict perforation in appendicitis.

OBJECTIVE: We aimed at identifying novel biomarkers to predict perforation in patients with acute appendicitis. PATIENTS AND METHODS: Medical records of patients who underwent appendectomy due to acute appendicitis were reviewed. Complete blood count and biochemistry panel results of these patients were analyzed. This study included 58 patients, 42 (72.4%) male and 16 (27.6%) female. The mean age of the patients was 33.8±14.1 years (range: 18-75). 49 (84.5%) patients had non-perforated acute appendicitis. Perforated acute appendicitis was observed in 9 (15.5%) patients. RESULTS: Patients with perforated appendicitis had higher appendiceal diameter, C-reactive protein (CRP) level, CRP/albumin and monocyte/lymphocyte (M/L) compared to patients with non-perforated appendicitis. Moreover, patients with perforated appendicitis had lower lymphocyte count than those with no perforation. Sensitivity rates of appendiceal diameter, CRP level, CRP/Albumin and M/L for perforated appendicitis were similar (89%). However, the most specific biomarker for perforation was CRP/albumin (87.8%), followed by CRP (85.7%), M/L (63.3%) and appendiceal diameter (57.1%). Patients with CRP/albumin>7.1, CRP>32.7 mg/L, M/L>0.44 and appendiceal diameter>9.8 mm were most likely to have appendiceal perforation. CONCLUSIONS: We suggest that CRP/albumin, CRP, M/L, appendiceal diameter and lymphocyte count can be used to predict perforation in patients with acute appendicitis. However, the most specific inflammation biomarker indicating perforated acute appendicitis is CRP/Albumin>7.1.

Can expected pCO2 be calculated by pCO2=HCO3+15 formula in central venous blood gas samples?

OBJECTIVE: In mixed acid-base disorders, it is essential to identify the dominant disorder, either metabolic or respiratory. The calculation of expected partial carbondioxide (pCO2) value obtained from arterial blood gas sample can give a clue to the physician about the main disorder. There are several formulas to calculate the expected pCO2 which are not practical to use and require an arterial blood gas sample. The aim of this study is to investigate whether expected pCO2 could be calculated with a simple formula by adding 15 to the bicarbonate (HCO3) value obtained from a central venous blood gas sample. PATIENTS AND METHODS: 50 (42.7%) female and 67 (57.3%) male patients aged 18 years and older, hospitalized in the Intensive Care Unit (ICU) between January 2022 and June 2022, whose arterial and central venous blood gas samples were drawn at the same time, were included in this study. Expected pCO2 values were calculated with both Winter's (pCO2 = 1.5 × HCO3 + 8) and simple (pCO2 = HCO3 + 15) formulas from the data obtained from arterial and jugular central venous blood gas samples. RESULTS: A statistically significant strong positive correlation was identified between arterial and venous expected pCO2 values, which were calculated by using both Winter's and simple formulas [Pearson's correlation coefficient (r) = 1, p<0.001]. CONCLUSIONS: In ICU patients, (pCO2 = HCO3 + 15) formula can be used to calculate expected pCO2 in central venous blood gas samples to identify the primary disorder as metabolic or respiratory in mixed acid-base disorders.

Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time.

The real time application of autoregressive (AR) spectral analysis to a 20-MHz pulsed Doppler blood flowmeter is presented. The system consists of a TMS 320C25 digital signal processor with a 80286 based PC/AT microcomputer and associated interfacing circuitry. The AR method was used for in vivo spectral analysis of the signals obtained from a 20-MHz pulsed Doppler flowmeter in real time. The data obtained from digital and coronary arteries were processed using both AR and FFT spectral analysis methods. Also the data obtained from a stenosis coronary artery under surgical operation were processed using both methods. When the results are compared, it is seen that autoregressive analysis has given better results. Therefore the technique can be used in the examining of small vessels such as renal, iliac, mesenteric, coronary and digital arteries.