Guntur Medical College

Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

Transition metal based layered double hydroxides are important energy storage materials. The overall performances of the electrodes are dependent on conductivity, crystallinity, morphology, and surface area.

Glioblastomas (GBM) are highly aggressive primary brain tumors. Complex and dynamic tumor microenvironment (TME) plays a crucial role in the sustained growth, proliferation, and invasion of GBM. Several means of intercellular communication have been documented between glioma cells and the TME, including growth factors, cytokines, chemokines as well as extracellular vesicles (EVs). EVs carry functional genomic and proteomic cargo from their parental cells and deliver that information to surrounding and distant recipient cells to modulate their behavior. EVs are emerging as crucial mediators of establishment and maintenance of the tumor by modulating the TME into a tumor promoting system. Herein we review recent literature in the context of GBM TME and the means by which EVs modulate tumor proliferation, reprogram metabolic activity, induce angiogenesis, escape immune surveillance, acquire drug resistance and undergo invasion. Understanding the multifaceted roles of EVs in the niche of GBM TME will provide invaluable insights into understanding the biology of GBM and provide functional insights into the dynamic EV-mediated intercellular communication during gliomagenesis, creating new opportunities for GBM diagnostics and therapeutics.

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN.

BACKGROUND: The emergence of the COVID-19 pandemic has affected the lives of many people, including medical students. The present study explored internet addiction and changes in sleep patterns among medical students during the pandemic and assessed the relationship between them. METHODS: A cross-sectional study was carried out in seven countries, the Dominican Republic, Egypt, Guyana, India, Mexico, Pakistan, and Sudan, using a convenience sampling technique, an online survey comprising demographic details, information regarding COVID-19, the Pittsburgh Sleep Quality Index (PSQI), and the Internet Addiction Test (IAT). RESULTS: In total, 2749 participants completed the questionnaire. Of the total, 67.6% scored above 30 in the IAT, suggesting the presence of an Internet addiction, and 73.5% scored equal and above 5 in the PSQI, suggesting poor sleep quality. Internet addiction was found to be significant predictors of poor sleep quality, causing 13.2% of the variance in poor sleep quality. Participants who reported COVID-19 related symptoms had disturbed sleep and higher internet addiction levels when compared with those who did not. Participants who reported a diagnosis of COVID-19 reported poor sleep quality. Those living with a COVID-19 diagnosed patient reported higher internet addiction and worse sleep quality compared with those who did not have any COVID-19 patients in their surroundings. CONCLUSION: The results of this study suggest that internet addiction and poor sleep quality are two issues that require addressing amongst medical students. Medical training institutions should do their best to minimize their negative impact, particularly during the current COVID-19 pandemic.

This article investigates the combined analysis of load frequency control (LFC) and Automatic voltage regulation (AVR) for two-area hybrid system. Classical PID controller is habituated as secondary controller. A novel differential evolution artificial electric field algorithm (DE-AEFA) is proposed in this paper to tune controller parameters. Initially, DE-AEFA algorithm is applied to test system 1 comprising two-area non-reheat thermal turbines. Later on, the study is extended to combined model named test system 2 to investigate combined LFC and AVR problem. Further, the system is extended to the incorporation of HVDC link with the existing AC tie-line in parallel. System dynamic performance is predominantly enhanced by AC/DC rather than only AC tie-line. The proposed scheme robustness is demonstrated by conducting sensitivity analysis.

This final project develops an algorithm for automatic number plate recognition (ANPR). ANPR has gained much interest during the last decade along with the improvement of digital cameras and the gain in computational capacity. \n\t\t\t\t The text is divided in four chapters. The first, introduces the origins of digital \n\t\t\t\t image processing, also a little resume about the following algorithms that are \n\t\t\t\t needed for develop the system ANPR. The second chapter presents the objectives \n\t\t\t\t to be achieved in this project, as well as, the program used for his development. \n\t\t\t\t The following chapter explains the different algorithms that compound the system, \n\t\t\t\t which is built in five sections; the first is the initial detection of a possible number \n\t\t\t\t plate using edge and intensity detection to extract information from the image. The \n\t\t\t\t second and the third step, thresholding and normalization, are necessary to use the \n\t\t\t\t images in the following stages; the text of the plate is found and normalized. With \n\t\t\t\t the segmentation, each character of the plate is isolated for subsequent recognition. The last step reads the characters by correlation template matching, which is a simple but robust way of recognizing structured text with a small set of characters. It is evaluated the system’s speed and his error rate. Finally, the conclusions and future works are shown in the chapter four. The databases used consist of images under normal conditions and only Bulgarian’s numbers plate.

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

Lipases are serine hydrolases that degrade triglycerides, an attribute that treasures wide applications in biodiesel production, detergent, chemical industries, etc. The most sought after the application is in the high quality and economical production of biodiesel under mild reaction conditions and simplified product separation. For the said application, fungal lipases are ideal catalysts that could effectively catalyze esterification and transesterification reactions with their specific ability to release fatty acids from 1, 3 positions of acylglycerols. In the present work, to facilitate bulk synthesis, lipase production using Aspergillus niger MTCC 872 was studied by solid-state fermentation (SSF). The chosen fungal strain was evaluated for lipase production using a mixture of agro-industrial substrates viz. rice husk, cottonseed cake, and red gram husk in various combinations at flask level. Tri-substrate mixture (rice husk, cottonseed cake, and red gram husk) combined in the ratio of 2:1:1 has shown the maximum lipase activity 28.19 U/gds at optimum cultivation conditions of temperature 40 °C, moisture content 75% (v/w), pH 6.0 and initial spore concentration of 5.4 million spores per mL. Further studies were performed for scale-up of lipase from flask level to lab scale using tray fermenter. Lipase activity was found to be 24.38 U/gds and 21.62 U/gds for 100 g and 1000 g substrate respectively. This is the first report on the production of lipase from Aspergillus niger MTCC 872 using tri-substrate mixture of rice husk (RH), cottonseed cake (CSC), and red gram husk (RGH). Moreover, comparison between individual, binary, and tri-substrate mixture was carried out for which the highest lipase activity was observed for tri-substrate mixture. In addition, comparable results were found when scale-up was performed using tray fermenter. Thus, the current work signifies usage of agro-industrial residues as substrates for enzyme production by solid-state fermentation process as an effective alternative to submerged fermentation for industrial applications.

Internet of Things (IoT) is a revolutionary communication paradigm which plays an essential role in the field of remote monitoring and control operations. This paper presents the overview of IoT based remote monitoring and control systems which potentially solve societal issues in the field of healthcare, environment, home automation, transportation, military, agriculture, solid waste management, smart metering, surveillance, consumer asset tracking, smart grid, vehicular communication system and pilgrims monitoring.

A novel “<italic>turn-on</italic>” fluorescent chemosensor RDP-1 based on rhodamine tri methoxy benzaldehyde conjugate was synthesized, which showed high selectivity and sensitivity towards recognition of Pb²⁺in aqueous media.

Abstract Although most COVID-19 cases have occurred in low-resource countries, there is scarce information on the epidemiology of the disease in such settings. Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92.1% of cases and 59.7% of deaths occurring among individuals &lt;65 years old. The per-contact risk of infection is 9.0% (95% confidence interval: 7.5-10.5%) in the household and 2.6% (1.6-3.9%) in the community. Superspreading plays a prominent role in transmission, with 5.4% of cases accounting for 80% of infected contacts. The case-fatality ratio is 1.3% (1.0-1.6%), and median time-to-death is 5 days from testing. Primary data are urgently needed from low- and middle-income countries to guide locally-appropriate control measures.

Abstract In this paper, the impact of communication time delays (CTDs) on combined load frequency control (LFC) and automatic voltage regulation (AVR) of a multi-area system with hybrid generation units is addressed. Investigation reveals that CTDs have significant effect on system performance. A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution - artificial electric field algorithm (DE-AEFA). The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature. The investigation is further extended to integration of redox flow batteries (RFBs) and interline power flow controller (IPFC) with tie-lines. Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.

Alopecia areata (AA) is a common form of non-scarring hair loss of scalp and/or body. Genetic predisposition, autoimmunity, and environmental factors play a major role in the etiopathogenesis of AA. Patchy AA is the most common form. Atopy and autoimmune thyroiditis are most common associated conditions. Peribulbar and intrabulbar lymphocytic inflammatory infiltrate resembling "swarm of bees" is characteristic on histopathology. Treatment is mainly focused to contain the disease activity. Corticosteroids are the preferred treatments in form of topical, intralesional, or systemic therapy. Camouflage in the form of wigs may be an alternative option in refractory cases.

In the present work, a novel cardanol based benzoxazine was synthesised by reacting three different amines (aniline (CrAb),<italic>N</italic>,<italic>N</italic>-dimethylaminopropylamine (CrDb) and caprolactam modified<italic>N</italic>,<italic>N</italic>-dimethylaminopropylamine (CrCb)) with cardanol in the presence of formaldehyde under appropriate experimental conditions.

BACKGROUND: Bioprosthetic (BP) valves have been increasingly used for aortic valve replacement over the last decade. Due to their limited durability, patients presenting with failed BP valves are rising. Valve in Valve - Transcatheter Aortic Valve Implantation (ViV-TAVI) emerged as an alternative to the gold standard redo-Surgical Aortic Valve Replacement (redo-SAVR). However, the utility of ViV-TAVI is poorly understood. METHODS: A systematic electronic search of the scientific literature was done in PubMed, EMBASE, SCOPUS, Google Scholar, and ClinicalTrials.gov. Only studies which compared the safety and efficacy of ViV-TAVI and redo-SAVR head to head in failed BP valves were included. RESULTS: Six observational studies were eligible and included 594 patients, of whom 255 underwent ViV- TAVI and 339 underwent redo-SAVR. There was no significant difference between ViV-TAVI and redo- SAVR for procedural, 30 day and 1 year mortality rates. ViV-TAVI was associated with lower risk of permanent pacemaker implantation (PPI) (OR: 0.43, CI: 0.21-0.89; P = 0.02) and a trend toward increased risk of paravalvular leak (PVL) (OR: 5.45, CI: 0.94-31.58; P = 0.06). There was no significant difference for stroke, major bleeding, vascular complications and postprocedural aortic valvular gradients more than 20 mm-hg. CONCLUSION: Our results reiterate the safety and feasibility of ViV-TAVI for failed aortic BP valves in patients deemed to be at high risk for surgery. VIV-TAVI was associated with lower risk of permanent pacemaker implantation with a trend toward increased risk of paravalvular leak.

The novel corona virus disease 2019 (SARS-CoV 2) pandemic outbreak was alarming. The binding of SARS-CoV (CoV) spike protein (S-Protein) Receptor Binding Domain (RBD) to Angiotensin converting enzyme 2 (ACE2) receptor initiates the entry of corona virus into the host cells leading to the infection. However, considering the mutations reported in the SARS-CoV 2 (nCoV), the structural changes and the binding interactions of the S-protein RBD of nCoV were not clear. The present study was designed to elucidate the structural changes, hot spot binding residues and their interactions between the nCoV S-protein RBD and ACE2 receptor through computational approaches. Based on the sequence alignment, a total of 58 residues were found mutated in nCoV S-protein RBD. These mutations led to the structural changes in the nCoV S-protein RBD 3d structure with 4 helices, 10 sheets and intermittent loops. The nCoV RBD was found binding to ACE2 receptor with 11 hydrogen bonds and 1 salt bridge. The major hot spot amino acids involved in the binding identified by interaction analysis after simulations includes Glu 35, Tyr 83, Asp 38, Lys 31, Glu 37, His 34 amino acid residues of ACE2 receptor and Gln 493, Gln 498, Asn 487, Tyr 505 and Lys 417 residues in nCoV S-protein RBD. Based on the hydrogen bonding, RMSD and RMSF, total and potential energies, the nCoV was found binding to ACE2 receptor with higher stability and rigidity. Concluding, the hotspots information will be useful in designing blockers for the nCoV spike protein RBD. [Image: see text] Communicated by Ramaswamy H. Sarma

A mathematical analysis for a steady, incompressible, laminar Carreau nanofluid flow over a porous cone and wedge is considered. The novelty of this research work is to scrutinize an exponential space-dependent Cattaneo–Christov heat flux and quadratic Rosseland approximation effects. Two-phase nanofluid model, i.e., Brownian motion, and thermophoretic effects are included. This study also employs non-linear thermal radiation and convective surface boundary conditions to investigate heat transport phenomena. The Carreau nanofluid boundary layer equations are derived using the standard boundary layer approximations. By applying the similarity transformations, the managing set of partial differential equations (PDEs) becomes a system of connected non-linear ordinary differential equations (ODEs). The resultant non-linear ODEs are solved numerically utilizing the numerical method, particularly the bvp4c function in MATLAB. The significances of the fluid motion are visualized by sketching several Carreau nanofluid flow’s relevant parameters using graphic outputs. The effects of a wide range of thermophysical variables on liquid properties including velocity, temperature, concentration, skin-friction, Nusselt number, and Sherwood number are investigated and discussed. This study’s findings are compared to previously announced results within a limited range, where strong validation was seen. The results demonstrate that the opposite mechanism is observed with repercussion of Weissenberg number (We) on velocity profile and concentration, the enhancement of We increases the momentum boundary layer while it reduces the concentration boundary layer. The outcomes could be applied to the cooling of equipment, electronics, and various industrial units.

Preventing and controlling grape diseases is essential for a good grape harvest. With the help of “single shot multi-box detectors”, “faster region based convolutional neural networks”, & “You only look once-X,” the study improved grape leaf disease detection accuracy with effective attention mechanisms, which includes convolutional block attention module, squeeze & excitation networks, & efficient channel attention. The various attention techniques helped to emphasize important features while reducing the impact of irrelevant ones, which ultimately improved the precision of the models and allowed for real-time performance. As a result of examining the optimal models from the three types, it was found that the Faster (R-CNN) model had a lower precision value, while You only look once-X and SSD with various attention techniques required the fewest parameters with the highest precision, with the best real-time performance. In addition to providing insights into grape diseases & symptoms in automated agricultural production, this study provided valuable insights into grape leaf disease detection.

Importance: Chagas cardiomyopathy is associated with substantial morbidity and mortality. Precise estimates of the risk of developing cardiomyopathy among patients with the acute or indeterminate chronic forms of Chagas disease are lacking. Objective: To estimate the risk of developing chronic cardiomyopathy in patients with acute and indeterminate chronic forms of Chagas disease. Data Sources: A systematic search in the Cochrane Library, Embase, Latin American and Caribbean Health Sciences Literature (LILACS), Medline, and Web of Science Core Collection databases was conducted from October 8 to October 24, 2018. Studies published between January 1, 1946, and October 24, 2018, that were written in the English, Spanish, and Portuguese languages were included. Search terms included Chagas disease; development of cardiomyopathy; latency duration; and determinants of the Chagas latency period. Study Selection: Longitudinal observational studies of participants diagnosed with the acute phase of Chagas infection or the indeterminate chronic form of Chagas disease who were followed up until the development of cardiomyopathy were included. Studies were excluded if they did not provide sufficient outcome data. Of 10 761 records initially screened, 32 studies met the criteria for analysis. Data Extraction and Synthesis: Critical appraisals of studies were performed using checklists from the Joanna Briggs Institute Reviewer's Manual, and data were collected from published studies. A random-effects meta-analysis was used to obtain pooled estimated annual rates. Data were analyzed from September 11 to December 4, 2019. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline for the registration of the protocol, data collection and integrity, assessment of bias, and sensitivity analyses. Main Outcomes and Measures: Main outcomes were defined as the composite of the development of any new arrhythmias or changes in electrocardiogram results, dilated cardiomyopathy and segmental wall motion abnormalities in echocardiogram results, and mortality associated with Chagas disease. Results: A total of 5005 records were screened for eligibility. Of those, 298 full-text articles were reviewed, and 178 of those articles were considered for inclusion in the quantitative synthesis. After exclusions, 32 studies that included longitudinal observational outcomes were selected for the analysis; 23 of those studies comprised patients with the indeterminate chronic form of Chagas disease, and 9 of those studies comprised patients in the acute phase of Chagas infection. The analysis indicated that the pooled estimated annual rate of cardiomyopathy development was 1.9% (95% CI, 1.3%-3.0%; I2 = 98.0%; τ2 [ln scale] = 0.9992) in patients with indeterminate chronic Chagas disease and 4.6% (95% CI, 2.7%-7.9%; I2 = 86.6%; τ2 [ln scale] = 0.4946) in patients with acute Chagas infection. Conclusions and Relevance: Patients with the indeterminate chronic form of Chagas disease had a significant annual risk of developing cardiomyopathy. The annual risk was more than double among patients in the acute phase of Chagas infection.