Cardiac Computed Tomography Angiography in Infants and Young Children Without Sedation.

Cardiac computed tomography (CT) imaging plays a pivotal role in the diagnosis and management of infants and young children with congenital heart disease (CHD). While the benefits of CT imaging are well-established, the challenge lies in adapting these procedures to the unique requirements of infants and young children. Traditionally, sedation has been a common practice to ensure cooperation and motion control during imaging. However, using sedation introduces its challenges including potential risks, limitations, and cost implications. In this study, authors explore the feasibility, safety, and diagnostic accuracy of unsedated cardiac CT examinations in infants and young children. This study proves cardiac CT can be performed in India without sedation using simple restraining techniques. This approach aligns with the cultural and familial dynamics prevalent in the country and holds the potential to address economic and infrastructure challenges.

Predictive value of serum ß2-microglobulin in cardiac valve calcification in maintenance hemodialysis patients.

Background: Cardiac valve calcification (CVC) is associated with adverse cardiovascular events. We studied the risk factors of CVC in maintenance hemodialysis (MHD) patients and the value of serum ß2-microglobulin (ß2-MG) levels in predicting the incidence of CVC. ß2-MG is a middle molecular weight toxin. In recent years, researchers found that elevated blood ß2-MG was associated with coronary, thoracic, and abdominal aortic calcifications with significant correlations. ß2-MG has been emerging as a strong biomarker for cardiovascular mortality in uremic patients but its role in CVC is not well studied. This study looked specifically at CVC occurrence in relation to ß2-MG for MHD patients. Methods: Patients who underwent MHD for more than 3 months in the First People's Hospital of Nantong City from November 2012 to November 2019 with complete available data were included in the study. The patients were divided into the CVC group and the non-CVC group. The general information and clinical laboratory indicators of the patients were collected in a retrospective manner. We analyzed the risk factors for developing CVC in MHD patients using binary logistic regression method. Receiver operating characteristic (ROC) curves were used to calculate the cut-off value of ß2-MG for predicting CVC. The decision tree (DT) method was used to classify and explore the probability of CVC in patients with MHD. Results: The ß2-MG in the CVC group was significantly higher than that in the non-CVC group (t=6.750, P<0.001). Multivariate binary logistic regression analysis showed that gender, age, serum ß2-MG, and hemodialysis (HD) adequacy (Kt/V urea) were independent risk factors for CVC in MHD patients. ROC analysis showed that a ß2-MG value of 25 µg/L was the best cut-off point for predicting CVC in MHD patients. According to binary logistic regression analysis, the ß2-MG ≥25 µg/L group was 3.39 times more likely to develop CVC than the ß2-MG <25 µg/L group [odds ratio (OR), 3.39; 95% confidence interval (CI), 1.63-7.06; P=0.001]. The DT model determined that serum ß2-MG ≥25 µg/L and age >69 years were important determinants for predicting CVC in MHD patients. Conclusions: Serum ß2-MG in MHD patients has a positive correlation with the severity and occurrence of CVC.

The Role of a Clinical Psychologist in Pediatric Nephrology.

Renal disease in pediatric patients tends to have a broad clinical spectrum from milder disease to severe progressive renal dysfunction requiring renal replacement therapy. Patients with chronic kidney disease (CKD) can have additional comorbidities including hypertension, diabetes, and obesity, which can add to the disease burden. The psychosocial or the mental component of the pediatric CKD patient is often overlooked by health care professionals due to a lack of resources and training in identifying psychological disorders. In addition, many components of kidney disease like fatigue from anemia and cognitive impairment make it difficult for the untrained physician to identify underlying psychological disorders. This review explores the complex psychosocial issues in patients with kidney disease and the need of more comprehensive multidisciplinary approach for treatment.

Automated versus manual B-lines counting, left ventricular outflow tract velocity time integral and inferior vena cava collapsibility index in COVID-19 patients.

Background and Aims: The incorporation of artificial intelligence (AI) in point-of-care ultrasound (POCUS) has become a very useful tool to quickly assess cardiorespiratory function in coronavirus disease (COVID)-19 patients. The objective of this study was to test the agreement between manual and automated B-lines counting, left ventricular outflow tract velocity time integral (LVOT-VTI) and inferior vena cava collapsibility index (IVC-CI) in suspected or confirmed COVID-19 patients using AI integrated POCUS. In addition, we investigated the inter-observer, intra-observer variability and reliability of assessment of echocardiographic parameters using AI by a novice. Methods: Two experienced sonographers in POCUS and one novice learner independently and consecutively performed ultrasound assessment of B-lines counting, LVOT-VTI and IVC-CI in 83 suspected and confirmed COVID-19 cases which included both manual and AI methods. Results: Agreement between automated and manual assessment of LVOT-VTI, and IVC-CI were excellent [intraclass correlation coefficient (ICC) 0.98, P < 0.001]. Intra-observer reliability and inter-observer reliability of these parameters were excellent [ICC 0.96-0.99, P < 0.001]. Moreover, agreement between novice and experts using AI for LVOT-VTI and IVC-CI assessment was also excellent [ICC 0.95-0.97, P < 0.001]. However, correlation and intra-observer reliability between automated and manual B-lines counting was moderate [(ICC) 0.52-0.53, P < 0.001] and [ICC 0.56-0.69, P < 0.001], respectively. Inter-observer reliability was good [ICC 0.79-0.87, P < 0.001]. Agreement of B-lines counting between novice and experts using AI was weak [ICC 0.18, P < 0.001]. Conclusion: AI-guided assessment of LVOT-VTI, IVC-CI and B-lines counting is reliable and consistent with manual assessment in COVID-19 patients. Novices can reliably estimate LVOT-VTI and IVC-CI using AI software in COVID-19 patients.

Bioacoustic signal analysis through complex network features.

The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features - the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

Acclimatization through thermal diffusivity tuning of coconut oil - A mode mismatched dual-beam thermal lens study.

BACKGROUND: Ayurvedic medicinal oils traditionally prepared by blending herbal extracts in different compositions are commonly used for treatment and improving health. The estimation of the thermal properties of medicinal oils is essential for practical applications. OBJECTIVE: The present work aims to expound the ability of medicinal oils for the acclimatization of body temperature by determining its thermal diffusivity and thereby providing a validation to the traditional knowledge. MATERIALS AND METHODS: The medicinal oils are prepared by incorporating black pepper (Piper nigrum), aloe vera (Aloe barbadensis), hibiscus bud (Hibiscus rosa-sinensis) and Ocimum sanctum in coconut oil base. The samples are subjected to thermal diffusivity study using the mode-mismatched dual-beam thermal lens technique. RESULTS: The study reveals that the incorporation of black pepper (Piper nigrum), having hot potency (Ushna veerya), to the base fluid lowers the thermal diffusivity value, suggesting its potential in heat-trapping. The addition of aloe vera (Aloe barbadensis), hibiscus bud (Hibiscus rosa-sinensis), and O. sanctum dissipates heat energy quickly, thus increases the thermal diffusivity of coconut oil revealing a cold potency (Sheeta veerya). The study provides a validation for traditional knowledge and delineates the possiblity of thermal diffusivity tuning of the base fluids. CONCLUSION: The thermal diffusivity tuning through incorporation of herbal extracts can effectively be used to acclimatize the human body temperature with the surroundings. A higher thermal diffusivity value induces a cooling effect and the lower value causes heating effect. This, opens up the possibility of using thermally tuned oils depending on climate and geographical location.

Tetralogy of Fallot with pulmonary atresia and aortopulmonary window may mimic common arterial trunk.

Tetralogy of Fallot with pulmonary atresia is a group of congenital cardiac malformations, which is defined by the absence of luminal continuity between both ventricles and the pulmonary artery, and an interventricular communication. Pulmonary arterial supply in patients with tetralogy of Fallot with pulmonary atresia can be via the arterial duct or from collateral arteries arising directly or indirectly from the aorta (systemic-to-pulmonary artery collaterals), or rarely both. The rarest sources of pulmonary blood flow are aortopulmonary window and fistulous communication with the coronary artery.Herein, we describe an outflow tract malformation, tetralogy of Fallot with pulmonary atresia and aortopulmonary window, which was misdiagnosed as common arterial trunk. We emphasise the morphological differences.

Uncommon Iatrogenic Devices Seen on Chest Radiographs.

Chest radiograph (CXR) is the most common imaging performed for both inpatients and outpatients. With advances in medicine and technology, newer devices/prosthesis are being used in the treatment of cardiothoracic conditions. Some of these are common while others are seen only in a handful of cases, especially in patients being treated or referred from tertiary care centers. It is important to know about these devices, their functionality, and radiographic appearances. Many of these devices also help us in understanding the clinical condition of the patient, as some are only used in unstable patients. Newer methods of life support are now available in intensive care units and these also can be seen on CXRs. In this review, we present various iatrogenic devices that we come across on a CXR and highlight important features to determine their correct placement and potential complications. The review looks at cardiac temporary and permanent pacing devices, cardiac interventional devices used to treat congenital heart disease, newer cardiac monitoring devices, and unusual surgical devices that one may come across on a CXR. We also suggest a stepwise algorithm to assess these devices on a CXR.

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.

T1 mapping and conditional survival in paediatric dilated cardiomyopathy with advanced heart failure.

Myocardial fibrosis is associated with adverse events in idiopathic dilated cardiomyopathy. Cardiac MRI with late gadolinium enhancement can detect myocardial fibrosis. We evaluated the conditional survival of children and adolescents based on native T1 mapping (combined proton signal from myocytes and interstitium prior to contrast administration by the measurement of myocardial and blood relaxation time) as a means to assess myocardial fibrosis. This retrospective case-cohort over a 3-year period included all consecutive patients (aged ≤ 21 years) with advanced heart failure from dilated cardiomyopathy (echocardiographic left ventricular ejection fraction ≤ 45% and NYHA class ≥ 2) who underwent cardiac MRI.Conditional survival (follow-up ≥ 6 months after cardiac MRI) was assessed to include NYHA functional class and time to event (death or heart transplantation). A total of 57 patients (mean age 11.7 ± 6.1 years; 58% male) had a median NYHA Class III (31/57) and median left ventricular ejection fraction 25% (20-38%). Survival data were available in 82% patients (46/57) and the crude mortality rate was 24% (11/46) and one patient (2%) underwent heart transplantation. The median native T1 was elevated at 1351 ms (95% CI 1332, 1394) and it showed no difference between the groups who survived to those who died. Performing a multilevel regression analysis on prognosis failed to predict 6-month conditional survival.

Isolated left ventricular apical hypoplasia in a young child.

A 16-month-old, healthy, asymptomatic male child presented with a diagnosis of dilated cardiomyopathy. Cardiovascular examination and chest radiograph were normal. ECG revealed sinus rhythm, and the augmented vector left lead showed raised ST segment, T wave inversion and q waves. Echocardiography showed a globular left ventricle with notched cardiac apex, abnormal echogenicity in the left ventricular apical myocardium, single papillary muscle and normal biventricular function. Cardiac MRI scan revealed a globular left ventricle with fibrofatty changes and retraction of the apex, the papillary muscles closely approximated, and the right ventricle wrapping around the apex of the left ventricle. This is described as isolated left ventricular apical hypoplasia. Diagnosis of this rare entity can be made by MRI, and it has been diagnosed largely in adults. The pathophysiology and long-term outcomes are unknown. We characterise the echocardiography findings of this rare anomaly in a child for the first time in the literature.

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)-quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

Phase portrait for high fidelity feature extraction and classification: A surrogate approach.

This paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense, low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD) data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.

Time series and fractal analyses of wheezing: a novel approach.

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Quantification of Liver Iron Overload: Correlation of MRI and Liver Tissue Biopsy in Pediatric Thalassemia Major Patients Undergoing Bone Marrow Transplant.

Determination of the magnitude of body iron stores helps to identify individuals at risk of iron-induced organ damage in Thalassemia patients. The most direct clinical method of measuring liver iron concentration (LIC) is through chemical analysis of needle biopsy specimens. Here we present a noninvasive method for the measurement of LIC in vivo using magnetic resonance imaging (MRI). Twenty-three pediatric Thalassemia major patients undergoing bone marrow transplantation at our centre were studied. All 23 patients had MRI T2* and R2* decay time for evaluation of LIC on a 1.5 Tesla MRI system followed by liver tissue biopsy for the assessment of iron concentration using an atomic absorption spectrometry. Simultaneously, serum ferritin levels were measured by enzymatic assay. We have correlated biopsy LIC with liver T2* and serum ferritin values with liver R2*. Of the 23 patients 11 were males, the mean age was 8.3 ± 3.7 years. The study results showed a significant correlation between biopsy LIC and liver T2* MRI (r = 0.768; p < 0.001). Also, there was a significant correlation between serum ferritin levels and liver R2* MRI (r = 0.5647; p < 0.01). Two patients had high variance in serum ferritin levels (2100 and 4100 mg/g) while their LIC was around 24 mg/g, whereas the difference was not seen in T2* MRI. Hence, the liver T2* MRI is a better modality for assessing LIC. Serum ferritin is less reliable than quantitative MRI. The liver T2* MRI is a safe, reliable, feasible and cost-effective method compared to liver tissue biopsy for LIC assessment.

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.