Search

Background: Contrast-induced nephropathy can occur post percutaneous coronary intervention (PCIs). Objectives: To establish the multivariable relationships in all post-PCI patients. Methods: Renal function assessed by calculating estimated glomerular filtration rate using the modification of diet in renal disease formula and then indexed to respective BSA in consecutive 1020 post-PCI patients from June 2021 to September 2022. The complete raw data of various variables were segregated and analyzed in Microsoft Excel under various headings. Then studied for their clinical significance and interrelationships. Statistical significance was studied with number, percentages, maximum, minimum, mean, standard deviation etc., and specific statistics (includes: descriptive statistics provides number, %, interquartile range, etc., correlation statistics-provides Pearson ‘r’ correlation and regression statistics-provides the relationships). Finally, all the results were presented in the form of tables and figures from the data visualization technique. Results: The complete analysis of all 1020 PCI patient’s data suggests that age, left ventricular ejection fraction by two-dimensional echocardiography, body mass index, pre-procedural serum creatinine values and volumes of contrast used, and diastolic blood pressure (BP) has directly related to good cardiorenal outcome. Conclusions: Post-PCI acute kidney injury is a known entity. Renal functions were affected by multiple variables. Prerenal assessment and management, adequate (pre and post-PCI) hydration therapy, avoiding or minimizing nephrotoxic agents, BP control, staged PCI, precision PCI with additional procedures will not only reduce the contrast agent burden on kidney and will have good cardiorenal outcome.

With technical advancements, transcatheter closure of sinus venosus atrial septal defect (SVASD) proves safe and effective, even in developing countries. A 38-year-old male with recurrent palpitations underwent transesophageal echocardiography and cardiac computerized tomography, confirming a 14 mm SVASD with left-to-right shunt. Under GA and mechanical ventilation, we deployed a 19–25 mm ZYPHER-covered peripheral stent using 22 mm × 60 mm Z-Med II Balloon guidance. An angiogram confirmed adequate defect coverage. Postprocedure, occasional ventricular premature contractions and intermittent atrioventricular block were noted on 72-h Holter. Follow-up showed symptom resolution, with transthoracic echocardiography confirming stent placement and no residual shunt, endorsing device closure as a viable alternative to surgery.

IntroductionSoybean is a rich source of bioactive components with good nutritional support and is easily available. In the treatment of cancer, green synthesis of silver nanoparticles (AgNPs) from plant-based samples has gained attentions due to its potency and feasibility. In the present study, using soybean extracts (GM), silver nanoparticles are synthesized and analyzed for their anticancer potency.MethodsThe synthesized GM-AgNPs were characterized via UV–Vis spectroscopy, Fourier transform-infrared (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy-dispersive X-ray (EDX) techniques for further analysis. Antibacterial activity was evaluated using the disc method and anti-hemolysis activity using the in vitro method, followed by anticancer property evaluation by cytotoxicity, cell migration, apoptosis, and cell cycle.Results and discussionOur results showed that the synthesized GM-AgNPs were spiral-shaped with a size range of 5–50 nm. The antibacterial activity against Staphylococcus aureus and Klebsiella pneumoniae showed the maximum zone of inhibition at 250 μg/mL in comparison with gentamicin. On exploring the anti-hemolysis efficiency, at 200 μg/mL, GM-AgNPs showed no hemolysis in comparison to the extract which showed 40% hemolysis. On analysis of GM-AgNPs against the breast cancer cell line, the nanoparticles displayed the IC50 value of 74.04 μg/mL. Furthermore, at the IC50 concentration, cancer cell migration was reduced. The mechanism of action of GM-AgNPs confirmed the initiation of apoptosis and cell cycle arrest in the sub-G0/G1 (growth phase) phase by 48.19%. In gene expression and protein expression analyses, Bax and Bcl-2 were altered to those of normal physiology.

In deep learning, it is possible that training efficiency will suffer as a result of redundant data. A lower amount of training data, on the other hand, may result in a model that is unable to capture the necessary features hidden within the dataset. In this paper, we use machine-deep-statistical model to analyse the stability of thermal storage systems i.e., battery in terms of managing the energy storage. These three models offer a prominent success across various applications and in this study, it uses life prediction, state estimation, defect diagnosis, fault detection, behaviour and property analysis via proper modelling and optimization. The validation is conducted in terms of various metrics to estimate the proposed model against various methods. The results of simulation shows that the proposed method achieves higher degree of accuracy with reduced prediction errors than other methods.

Introduction: Soybean is a rich source of bioactive components with good nutritional support and is easily available. In the treatment of cancer, green synthesis of silver nanoparticles (AgNPs) from plant-based samples has gained attentions due to its potency and feasibility. In the present study, using soybean extracts (GM), silver nanoparticles are synthesized and analyzed for their anticancer potency. Methods: The synthesized GM-AgNPs were characterized via UV-Vis spectroscopy, Fourier transform-infrared (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energydispersive X-ray (EDX) techniques for further analysis. Antibacterial activity was evaluated using the disc method and anti-hemolysis activity using the in vitro method, followed by anticancer property evaluation by cytotoxicity, cell migration, apoptosis, and cell cycle. Results and discussion: Our results showed that the synthesized GM-AgNPs were spiral-shaped with a size range of 5-50 nm. The antibacterial activity against Staphylococcus aureus and Klebsiella pneumoniae showed the maximum zone of inhibition at 250 μg/mL in comparison with gentamicin. On exploring the antihemolysis efficiency, at 200 μg/mL, GM-AgNPs showed no hemolysis in comparison to the extract which showed 40% hemolysis. On analysis of GMAgNPs against the breast cancer cell line, the nanoparticles displayed the IC50 value of 74.04 μg/mL. Furthermore, at the IC50 concentration, cancer cell migration was reduced. The mechanism of action of GM-AgNPs confirmed the initiation of apoptosis and cell cycle arrest in the sub-G0/G1 (growth phase) phase by 48.19%. In gene expression and protein expression analyses, Bax and Bcl2 were altered to those of normal physiology. [ABSTRACT FROM AUTHOR]

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system’s renewable power projections.

In this investigation, Inconel 600 is used as the workpiece material, which is typically applied in aerospace products, chemical processing equipment, furnace parts etc., due to its good resistance to stress and wear resistance. In this work, the effect of machining parameters of Wire Electric Discharge Machining (WEDM) process is investigated. Molybdenum wire electrode of 0.1mm diameter and de-ionized water dielectric medium are used for machining. Voltage, Pulse on-time, Pulse Off-time, Peak Current and Bed speed are considered as the machining parameters. Recast layer thickness is considered as the response parameter. The type of design of experiments considered for this work is Taguchi's L18 orthogonal array. Analysis of variance is indicating the percentage contribution of each machining parameters on response parameters. The optimum combination of machining parameters yielded minimum Recast layer thickness 4.11µm. [ABSTRACT FROM AUTHOR]

Efficient use of available resources in agricultural production is important to minimize carbon footprint considering the state of climate change. In this context, the current research was conducted to identify carbon and energy-efficient fodder cropping systems for sustainable livestock production. Annual monocropping, perennial monocropping, annual cereal + legume intercropping and perennial cereal + legume intercropping systems were evaluated by employing a randomized complete block design with three replications under field conditions. The lucerne (Medicago sativa L.) monocropping system recorded significantly lower carbon input (274 kg-CE ha−1 year−1) and showed higher carbon indices viz., carbon sustainability index (165.8), the carbon efficiency ratio (166.8) and carbon efficiency (347.5 kg kg-CE−1) over other systems. However, higher green fodder biomass led to statistically higher carbon output (78,542 kg-CE ha−1 year−1) in the Bajra–Napier hybrid (Pennisetum glaucum × Pennisetum purpureum) + lucerne perennial system. Similar to carbon input, lower input energy requirement (16,106 MJ ha−1 year−1) and nutrient energy ratio (25.7) were estimated with the lucerne perennial system. However, significantly higher energy output (376,345 and 357,011 MJ ha−1 year−1) and energy indices viz., energy use efficiency (13.3 and 12.2), energy productivity (5.8 and 5.3 kg MJ−1), net energy (327,811 and 347,961 MJ ha−1 year−1) and energy use efficiency (12.3 and 11.2) were recorded with Bajra–Napier hybrid + legume [lucerne and cowpea (Vigna unguiculata (L.) Walp.)] cropping systems, respectively. However, these systems were on par with the lucerne monocropping system. Additionally, Bajra–Napier hybrid + legume [cowpea, sesbania (Sesbania grandiflora (L.) Pers.) and lucerne] cropping systems also showed higher human energy profitability. Concerning various inputs’ contribution to total carbon and energy input, chemical fertilizers were identified as the major contributors (73 and 47%), followed by farmyard manure (20 and 22%) used to cultivate crops, respectively, across the cropping systems. Extensive use of indirect (82%) and non-renewable energy sources (69%) was noticed compared to direct (18%) and renewable energy sources (31%). Overall, perennial monocropping and cereal + legume cropping systems performed well in terms of carbon and energy efficiency. However, in green biomass production and carbon and energy efficiency, Bajra–Napier hybrid + legume (lucerne and cowpea) cropping systems were identified as the best systems for climate-smart livestock feed production.

Epilepsy is a neurological disorder that is distinguished by the presence of seizures, which are caused by abnormal brain activity. On average, about 60% of epilepsy cases are due to focal seizures, which affect a specified part of the cerebrum. Epileptic seizures can be cured at the initial stage, but untreated seizure for a long time cause severe effect and sometimes leads to death. Therefore, accurate classification of the epileptic Electroencephalogram (EEG) signal is an essential step in the diagnosis of epilepsy, aiding physicians in making the diagnosis. This research presents an effective classification of epileptic EEG signals using the improved atomic search optimization (IASO) algorithm and the random search strategy (RSS). The IASO is used to select the appropriate features but has a high probability to enhance the convergence rate. However, due to a lack of population variety and exploratory capabilities and this convergence rate may decrease in some circumstances. As a result, the RSS is offered to enhance the solution and improvise the exploration capabilities. The raw data of EEG signals are obtained from TUH, BONN, and BERN datasets. The raw data is pre-processed using an adaptive filtering method. The IASO is utilized in selecting the relevant features which ease the process of classification using LSTM. The results show that IASO attained better classification accuracy of 98.40% for TUH dataset which is comparatively lower than the existing Deep network model with 97%. [ABSTRACT FROM AUTHOR]

A field experiment was carried out at College of Agriculture, V.C. Farm, Mandya during kharif 2021 to study the efficacy of pre and post emergent herbicides for weed management in maize (Zea mays L.). The experiment was laid out in Randomized Complete Block Design (RCBD) with fourteen treatments and replicated thrice. The treatments comprised of sole and combined application of pre-emergence herbicides (atrazine and pendimethalin) at 3 days after sowing and post emergence herbicides (2,4-D, topramezone and tembotrione) at 3-4 weed leaf stages were compared with two hand weedings (at 20 and 40 DAS), weed free check and unweeded check. Among chemical weed management practices, application of atrazine (50% WP) @ 1 kg a.i. ha-1 as pre-emergence at 3 DAS followed by topramezone (33.6% SC) @ 50 g a.i. ha-1 as post emergence application at 3-4 weed leaf stages recorded significantly lower total weed density (2.61, 2.34 and 4.26 no. 0.25 m-2 at 20, 40 DAS and at harvest, respectively) and lower total weed dry weight (0.90, 1.31 and 1.49 g. 0.25 m-2 at 20, 40 DAS and at harvest, respectively) with weed control efficiency of 70.4 per cent and weed index of 1.5 per cent at harvest. The same treatment recorded higher kernel yield (11469 kg ha-1), stover yield (13542 kg ha-1), net monetary returns (Rs.132141 ha-1) and B:C ratio (3.47). Hence, it was found to be suitable and economical for effective control of weeds in maize. [ABSTRACT FROM AUTHOR]

Dextrocardia is a rare non-fatal congenital anomaly. Most cases were diagnosed incidentally and throws lot of diagnostic and therapeutic challenges in general and management in particular. We come across 3 different cases with Dextrocardia. First case was asymptomatic young female for regular health check. Second case was middle aged male with Acute MI. Coronary Angiogram revealed lesion in RCA. Managed with Primary PTCA and Stent to RCA. Third case was elderly female with multiple comorbidties came with Positive TMT. Coronary Angiogram revealed TVD. Underwent CABG with 2 grafts. All three cases were treated successfully. We faced lot of Diagnostic to Therapeutic challenges. This includes - ECG & Chest Ray Interpretations, 2D Echo observations, Accessing and engaging coronary ostium, Contrast overload and Recognition of parts of cardiac surface anatomy especially during cardiac surgery. Dextrocardia is not an uncommon congenital cardiac anomaly. Its importance increases whenever it is associated with other cardiac illness in general and ACS in particular. Difficulty exists in both structural and functional assessment of the heart and coronary arteries. Various techniques are used to overcome these problems. Cautious and timely intervention is required. This will not only reduce the time taken for intervention (if required), but also minimizes the complications in a safe and cost-effective manner. [ABSTRACT FROM AUTHOR]