Genotyping-by-sequencing targets genic regions and improves resolution of genome-wide association studies in autotetraploid potato.

KEY MESSAGE: De novo genotyping in potato using methylation-sensitive GBS discovers SNPs largely confined to genic or gene-associated regions and displays enhanced effectiveness in estimating LD decay rates, population structure and detecting GWAS associations over 'fixed' SNP genotyping platform. Study also reports the genetic architectures including robust sequence-tagged marker-trait associations for sixteen important potato traits potentially carrying higher transferability across a wider range of germplasm. This study deploys recent advancements in polyploid analytical approaches to perform complex trait analyses in cultivated tetraploid potato. The study employs a 'fixed' SNP Infinium array platform and a 'flexible and open' genome complexity reduction-based sequencing method (GBS, genotyping-by-sequencing) to perform genome-wide association studies (GWAS) for several key potato traits including the assessment of population structure and linkage disequilibrium (LD) in the studied population. GBS SNPs discovered here were largely confined (~ 90%) to genic or gene-associated regions of the genome demonstrating the utility of using a methylation-sensitive restriction enzyme (PstI) for library construction. As compared to Infinium array SNPs, GBS SNPs displayed enhanced effectiveness in estimating LD decay rates and discriminating population subgroups. GWAS using a combined set of 30,363 SNPs identified 189 unique QTL marker-trait associations (QTL-MTAs) covering all studied traits. The majority of the QTL-MTAs were from GBS SNPs potentially illustrating the effectiveness of marker-dense de novo genotyping platforms in overcoming ascertainment bias and providing a more accurate correction for different levels of relatedness in GWAS models. GWAS also detected QTL 'hotspots' for several traits at previously known as well as newly identified genomic locations. Due to the current study exploiting genome-wide genotyping and de novo SNP discovery simultaneously on a large tetraploid panel representing a greater diversity of the cultivated potato gene pool, the reported sequence-tagged MTAs are likely to have higher transferability across a wider range of potato germplasm and increased utility for expediting genomics-assisted breeding for the several complex traits studied.

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data.

KEY MESSAGE: An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

A core-shell magnet Mn70Bi30grown at seeds in magnetic fields and its impacts on its spin-dynamics, Curie point and other tailored properties.

The MnBi alloys is a model series of rare-Earth free magnets for surge of technologies of small parts of automobiles, power generators, medical tools, memory systems, and many others. The magnetics stem primarily at unpaired Mn-3d5spins (a 4.23µBmoment) align parallel via an orbital moment 0.27µBof Bi-5d106s2p3in a crystal lattice. Thus, using a surplus Mn (over Bi) in a Mn70Bi30type alloy designs a spin-rich system of duly tailored properties useful for magnetics and other devices. In this view, we report here a strategy of a refined alloy powder Mn70Bi30can grow into small crystals of hexagonal (h) plates at seeds as annealed in magnetic fields (in H2gas). So, small h-plates (30 to 50 nm widths) are grown up at (002) facets, wherein the edges are turned down in a spiral (≤2.1 nm thicknesses) in a core-shell structure. The results are described with x-ray diffraction, lattice images and magnetic properties of a powder Mn70Bi30(milled in glycine) is annealed at 573 K for different time periods, so to the Mn/Bi order at the permeable facets (seeds). Duly annealed samples exhibit an enhanced magnetization,Ms→ 70.8 emu g-1, with duly promoted coercivityHc→ 10.810 kOe (15.910 kOe at 350 K), energy-product 14.8 MGOe, and the crystal-field-anisotropy,K1→ 7.6 × 107erg cm-3, reported at room temperature. Otherwise,Msshould decline at any surplus 3d5-Mn spins order antiparallel at the antisites. Enhanced Curie point 658.1 K (628 K at Mn50Bi50alloy) anticipates that a surplus Mn does favor the Mn-Bi exchange interactions. Proposed spin models well describe the spin-dynamics and lattice relaxations (on anneals) over the lattice volume (with twins) and spin clusters.

Application of blockchain technology in shaping the future of food industry based on transparency and consumer trust.

Food Industries, at this moment, are moving towards a new phase, and this phase will be governed by consumers and not by the industry leaders. The report shows that claims on sustainability, health, wellness, and transparency would govern the future trends in the food industry. Currently, there are several cases of misleading and false claims which hamper consumer trust. So, to uphold consumer trust, authentication of claims through transparency in the food supply chain is required, and blockchain technology can bring transparency at relatively low transaction costs. Once in a blockchain network, data is very difficult to manipulate, with no single point of authority to mess and collapse the system. Though we see mostly the financial systems using blockchain's decentralized functionality, there is a growing trend of innovative applications being built in the supply chain area for contracts and operations. With effort in the right direction and over time, blockchain will recast how operations and processes are done across the industry, including public sectors. The paper reviews the opportunity for the blockchain in enabling food industries for future-readiness, empowering the consumers in verifying the product claims and thus prevent themselves from food fraud. In doing so, the paper considers the future trends in the food industry, identifies current food fraud cases, and outlines the various applications in the agri-food chain and challenges associated with it.

Role of psychrotrophic bacteria and cold-active enzymes in composting methods adopted in cold regions.

Temperature-dependent composting is a challenging task but is worthy if it is done in the right manner. Cold composting has been known to be practiced since ancient times but there were not enough advancements to overcome the long mesophilic phase and bring the compost maturation to a short period. The composting processes that have been well practiced are discussed and the role of psychrotrophic bacteria that produce cold tolerant hydrolytic enzymes has been highlighted. In this chapter, the mechanism of substrate degradation has been elaborated to better understand the need of specific bacteria for a specific kind of substrate allowing fast and efficient decomposition. This chapter attempts to pave an appropriate way and suggest the best-suited method of composting for efficient production of compost by the conservation of heat in cold regions.

Structural ordering at magnetic seeds with twins at boundaries of a core-shell alloy Mn60Bi40and tailored magnetic properties.

A spin Mn3d5-rich Mn60Bi40alloy reveals a model system in order to tailor profound magnetic properties at unpaired 3d5spins in such alloys of a core-shell structure. As annealed (at a critical temperature 573 K in H2gas), a refined powder (in glycine) grows onα-MnBi seeds (crystallites) present in it at Mn/Bi atoms order over topological layers, preferentially along (110) planes, at a self-confined structure at seeds of an anisotropic shape of hexagonal (h) plates (25-85 nm widths). In terms of the HRTEM images, the atoms turn down at edges (at the plates grow up) in a spiral layer, ≤ 2.1 nm thickness, of small core-shells. A spin model is proposed to delineate a way at the spins can pin down at the edges, form single magnetic domains, and raise coercivity (Hc), with no much loss of net magnetic moment. The X-ray diffraction and HRTEM images corroborate the results of topological pacing of atoms at the h-plates at anneals. A novelty is that a core-shell leads to tailor a superbHc, as much as 11.110 kOe (16.370 kOe at 350 K), with a fairly large magnetization, 76.5 emu g-1, at near 300 K. An enhanced Curie point 650.1 K (628 K at Mn50Bi50alloy) confers a surplus 3d5-Mn spin sensitively tunesα-MnBi stoichiometry and so its final magnetic structure. A refined alloy powder so made is useful to make powerful magnets and devices in the forms of films and bonded magnets in different shapes for uses as small tools, tweezers, and other devices.

The generation of diverse traveling pulses and its solution scheme in an excitable slow-fast dynamics.

In this article, we report on the generation and propagation of traveling pulses in a homogeneous network of diffusively coupled, excitable, slow-fast dynamical neurons. The spatially extended system is modeled using the nearest neighbor coupling theory, in which the diffusion part measures the spatial distribution of coupling topology. We derive analytically the conditions for traveling wave profiles that allow the construction of the shape of traveling nerve impulses. The analytical and numerical results are used to explore the nature of propagating pulses. The symmetric or asymmetric nature of traveling pulses is characterized, and the wave velocity is derived as a function of system parameters. Moreover, we present our results for an extended excitable medium by considering a slow-fast biophysical model with a homogeneous, diffusive coupling that can exhibit various traveling pulses. The appearance of series of pulses is an interesting phenomenon from biophysical and dynamical perspective. Varying the perturbation and coupling parameters, we observe the propagation of activities with various amplitude modulations and transition phases of different wave profiles that affect the speed of pulses in certain parameter regimes. We observe different types of traveling pulses, such as envelope solitons and multi-bump solutions, and show how system parameters and coupling play a major role in the formation of different traveling pulses. Finally, we obtain the conditions for stable and unstable plane waves.

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method.

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier's performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

Effect of age, comorbidity and remission status on outcome of COVID-19 in patients with hematological malignancies.

BACKGROUND: There is scarcity of data on outcome of COVID-19 in patients with hematological malignancies. Primary objective of study was to analyse the 14-day and 28-day mortality. Secondary objectives were to correlate age, comorbidities and remission status with outcome. METHODS: Retrospective multicentre observational study conducted in 11 centres across India. Total 130 patients with hematological malignancies and COVID-19 were enrolled. RESULTS: Fever and cough were commonest presentation. Eleven percent patients were incidentally detected. Median age of our cohort was 49.5 years. Most of our patients had a lymphoid malignancy (n = 91). One-half patients (52%) had mild infection, while moderate and severe infections contributed to one-fourth each. Sixty seven patients (52%) needed oxygen For treatment of COVID-19 infection, half(n = 66) received antivirals. Median time to RT-PCR COVID-19 negativity was 17 days (7-49 days). Nearly three-fourth (n = 95) of our patients were on anticancer treatment at time of infection, of which nearly two-third (n = 59;64%) had a delay in chemotherapy. Overall, 20% (n = 26) patients succumbed. 14-day survival and 28-day survival for whole cohort was 85.4% and 80%, respectively. One patient succumbed outside the study period on day 39. Importantly, death rate at 1 month was 50% and 60% in relapse/refractory and severe disease cohorts, respectively. Elderly patients(age ≥ 60) (p = 0.009), and severe COVID-19 infection (p = 0.000) had a poor 14-day survival. The 28-day survival was significantly better for patients in remission (p = 0.04), non-severe infection (p = 0.00), and age < 60 years (p = 0.05). CONCLUSIONS: Elderly patients with hematological malignancy and severe covid-19 have worst outcomes specially when disease is not in remission.

Combining conventional QTL analysis and whole-exome capture-based bulk-segregant analysis provides new genetic insights into tuber sprout elongation and dormancy release in a diploid potato population.

Tuber dormancy and sprouting are commercially important potato traits as long-term tuber storage is necessary to ensure year-round availability. Premature dormancy release and sprout growth in tubers during storage can result in a significant deterioration in product quality. In addition, the main chemical sprout suppressant chlorpropham has been withdrawn in Europe, necessitating alternative approaches for controlling sprouting. Breeding potato cultivars with longer dormancy and slower sprout growth is a desirable goal, although this must be tempered by the needs of the seed potato industry, where dormancy break and sprout vigour are required for rapid emergence. We have performed a detailed genetic analysis of tuber sprout growth using a diploid potato population derived from two highly heterozygous parents. A dual approach employing conventional QTL analysis allied to a combined bulk-segregant analysis (BSA) using a novel potato whole-exome capture (WEC) platform was evaluated. Tubers were assessed for sprout growth in storage at six time-points over two consecutive growing seasons. Genetic analysis revealed the presence of main QTL on five chromosomes, several of which were consistent across two growing seasons. In addition, phenotypic bulks displaying extreme sprout growth phenotypes were subjected to WEC sequencing for performing BSA. The combined BSA and WEC approach corroborated QTL locations and served to narrow the associated genomic regions, while also identifying new QTL for further investigation. Overall, our findings reveal a very complex genetic architecture for tuber sprouting and sprout growth, which has implications both for potato and other root, bulb and tuber crops where long-term storage is essential.

Small core-shell Mn0.5Bi0.5-Bi (⩽3 at%) magnets, the anisotropic growth of crystallite nanoplates, interface-bridging, and tailored magnetic properties.

The binary alloy Mn0.5+xBi0.5-x, x ⩽ 0.05, is a promising rare-earth-free magnetic material, with high-energy-density (a critical characteristic for electric motors and power electronics), low cost, and significant magnetic properties for multiple uses at room temperature. In this article, we report how a free Bi, when precipitated over Mn0.5+xBi0.5-x (x ⩽ 0.05) of small crystallites, diffuses back into a stable Mn0.5+xBi0.5-x, x → 0, via a peritectic reaction, which facilitates preferential growth of small core-shell crystallites with multiple facets, having the potential for tailored magnetic properties. This growth travels slowly in the anisotropic channels of vacancies on annealing the reactive nanopowder at a critical 573 K temperature in Ar gas. Thus, an initial crystallite size of D ∼ 27 nm grows to only 38 nm in a reaction extended over a period of 96 h. A transient phase, x > 0, which has Bi vacancies, primarily grows in the (101) and (110) facets, filling the vacancies over a 6.41% larger crystal density. If any excess Mn is present, it segregates over a saturated phase, combines with free Bi, and ultimately forms a stable alloy phase. The small crystallites contain an inbuilt surface Bi-layer (shell), with a 1-2 nm thickness, in a core-shell of nanoplates (20-60 nm width), as shown in the high resolution transmission electron microscope images. In the proposed microscopic model, with hybridized Mn-d5 and Bi-p3 electrons (also spins), the magnetic properties are readily controlled. Thus, at 300 K, a maximum coercivity Hc = 9.850 kOe (14.435 kOe at 350 K) develops (Hc = 5.010 kOe in the initial) in critical single domains (D ∼ 33 nm). A net 72.5 emu g-1 magnetization occurs, with an enhanced TC = 641.5 K (600.5 K at x ∼ 0.05) on an order of enhanced anisotropy constant K1, demonstrating the significant effects of this core-shell structure of small crystallites.

Dynamics of a modified excitable neuron model: Diffusive instabilities and traveling wave solutions.

We examine the dynamics of a spatially extended excitable neuron model between phase state and stable/unstable equilibrium point depending on the parameter regimes. The solitary wave profiles in the excitable medium are characterized by an improved Hindmarsh-Rose (H-R) spiking-bursting neuron model with an injected decaying current function. Linear stability and the nature of deterministic system dynamics are analyzed. Further investigation for the existence of wave using the reaction-diffusion H-R system and the criteria for diffusion-driven instabilities are performed. An approximation method is introduced to analyze traveling wave profiles for the oscillatory neuron model that allows the explicit analytical treatment of both the speed equations and shape of the traveling wave solution. The solitary wave profiles exhibited by the system are explored. The analytical expression for the solution scheme is validated with good accuracy in a wide range of the biophysical parameters of the system. The traveling wave fronts and speed equations control the variations of the information transmission, and the speed of signal transmission may be affected by the injection of certain drugs.

Prophylactic low dose continuous calcium infusion during peripheral blood stem cell (PBSC) collections to reduce citrate related toxicity.

BACKGROUND: Citrate toxicity is one of the most frequent complications of apheresis procedures. It is caused by the infusion of the acid citrate dextrose (ACD), which chelates the calcium ions. AIMS: The aim of this study is to assess the effectiveness of prophylactic continuous infusion of calcium gluconate over intermittent bolus infusion to reduce citrate toxicity during large volume peripheral blood stem cell collection. MATERIALS AND METHODS: We retrospectively analysed the records of PBSC collection procedures performed from March 2010 to December 2013. Donors were selected as per the set guidelines. Machine used to perform the procedures was Cobe spectra. The study population was divided into 2 groups. One composed of intermittent intravenous bolus infusion at the onset of hypocalcaemic symptoms, the other composed of calcium gluconate administration as continuous infusion throughout the procedure. RESULT: The most common reported hypocalcaemic symptoms were mild perioral paresthesia followed by digital numbness. Of the 50 individuals who were injected with bolus calcium 40 (80%) individuals suffered from symptoms of hypocalcaemia, whereas 23 of 66 individuals (34.8%) suffered from hypocalcaemia in the continuous infusion group. This difference was significant (P < 0.001). Both groups were compared with respect to age, gender ratio, weight of the individuals, total blood volume processed, ACD used, calcium gluconate dose used, time taken for the procedure, the product volume. Significant difference was noticed only with respect to the product volume. This implies that the groups were comparable with respect to parameters such as age, gender ratio, weight of the individuals, total blood volume processed, ACD used, calcium gluconate dose used, and the time taken for the procedure. Also that significantly more products (244 v/s 204 ml) was collected in the continuous infusion group. CONCLUSIONS: Our results show that prophylactic continuous IV administration of low dose calcium-gluconate throughout the PBSC harvesting procedure reduced the incidence as well as the severity of citrate related toxicity. This increases his/her tolerance to withstand longer durations of the procedure and collect more volume of the product, hence may reduce the number of sittings of the procedure.

Molecular evolution, characterization and expression profiling of uterine aldoketoreductase 1B5 gene in endometrium of goat (Capra hircus).

Aldoketoreductase 1B5 (AKR1B5), a member of the Aldoketoreductase family, is involved in the production of Prostaglandin F2α (PGF2α) as one of vital prostaglandin F synthase (PGFS). PGs (Prostaglandins) play a crucial role in female reproductive system. In the present study, we cloned and characterized the full-length open reading frame of AKR1B5 gene in Black Bengal (BB) goat. The complete coding sequence of AKR1B5 comprises an entire open reading frame of 951 bp, encoding 316 amino acid (AA) residues. BB AKR1B5 showed >82.9% identity with that of cattle, rabbit, human, and rat at nucleotide and amino acid levels, respectively. Further, a systematic study of AKR1B5 sequence evolution was also conducted using Phylogenetic Analysis by Maximum Likelihood (PAML), entropy plot, and Blossum 62 in a phylogenetic context. Analysis of nonsynonymous to synonymous nucleotide substitution rate ratios (Ka/Ks) revealed that negative selection may have been operating on this gene during evolution in goat, cattle, rabbit, human, and rat, which showed its conservation across species. Further, expression of AKR1B5 was determined by quantitative real-time PCR in goat endometrial tissues at different stages of the estrous cycle and early pregnancy. Our results indicated its high expression at luteolytic phase (stage III; day 16-21) during the estrous cycle. However, during early (day ∼30-40) pregnancy the expression was highest as compared to estrous cycle.

Construction of a dense SNP map of a highly heterozygous diploid potato population and QTL analysis of tuber shape and eye depth.

KEY MESSAGE: Generation of a dense SNP-based linkage map of a diploid potato population and identification of major QTLs for tuber shape and eye depth on chromosomes 2 and 10. This paper reports the construction of a genetic map of a highly heterozygous full-sib diploid potato population (06H1) based on the use of a set of 8,303 single nucleotide polymorphism (SNP) markers. The map contains 1,355 distinct loci and 2,157 SNPs, 802 of which co-segregate with other markers. We find high levels of collinearity between the 12 chromosomal maps with a recently improved version of the potato genome assembly, with the expected genetic clustering in centromeric regions. The linkage maps are used in combination with highly detailed phenotypic assessments conducted over two growing seasons to perform quantitative trait loci analysis of two important potato traits, tuber shape and eye depth. The major loci segregating for tuber shape in 06H1 map to loci on chromosomes 2 and 10, with smaller effects mapping to three other chromosomes. A major locus for tuber eye depth co-locates with the tuber shape locus on chromosome 10. To assess when tuber shape is established in the developing tuber, we have performed staged observations of tuber formation. Our observations suggest that tuber shape is determined very early in tuber development.

Genome-wide QTL and bulked transcriptomic analysis reveals new candidate genes for the control of tuber carotenoid content in potato (Solanum tuberosum L.).

KEY MESSAGE: Genome-wide QTL analysis of potato tuber carotenoid content was investigated in populations of Solanum tuberosum Group Phureja that segregate for flesh colour, revealing a novel major QTL on chromosome 9. The carotenoid content of edible plant storage organs is a key nutritional and quality trait. Although the structural genes that encode the biosynthetic enzymes are well characterised, much less is known about the factors that determine overall storage organ content. In this study, genome-wide QTL mapping, in concert with an efficient 'genetical genomics' analysis using bulked samples, has been employed to investigate the genetic architecture of potato tuber carotenoid content. Two diploid populations of Solanum tuberosum Group Phureja were genotyped (AFLP, SSR and DArT markers) and analysed for their tuber carotenoid content over two growing seasons. Common to both populations were QTL that explained relatively small proportions of the variation in constituent carotenoids and a major QTL on chromosome 3 explaining up to 71 % of the variation in carotenoid content. In one of the populations (01H15), a second major carotenoid QTL was identified on chromosome 9, explaining up to 20 % of the phenotypic variation. Whereas the major chromosome 3 QTL was likely to be due to an allele of a gene encoding ß-carotene hydroxylase, no known carotenoid biosynthetic genes are located in the vicinity of the chromosome 9 QTL. A unique expression profiling strategy using phenotypically distinct bulks comprised individuals with similar carotenoid content provided further support for the QTL mapping to chromosome 9. This study shows the potential of using the potato genome sequence to link genetic maps to data arising from eQTL approaches to enhance the discovery of candidate genes underlying QTLs.

Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images.

Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.

Influence of COVID-19 pandemic in India on coronary artery disease clinical presentation, angiography, interventions and in-hospital outcomes: a single centre prospective registry-based observational study.

OBJECTIVE: The study examined the influence of the COVID-19 pandemic in India on variation in clinical features, management and in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI). DESIGN: Prospective registry-based observational study. SETTING: A tertiary care hospital in India participant in the American College of Cardiology CathPCI Registry. PARTICIPANTS: 7089 successive patients who underwent PCI from April 2018 to March 2023 were enrolled (men 5627, women 1462). Details of risk factors, clinical presentation, coronary angiography, coronary interventions, clinical management and in-hospital outcomes were recorded. Annual data were classified into specific COVID-19 periods according to Government of India guidelines as pre-COVID-19 (April 2018 to March 2019, n=1563; April 2019 to March 2020, n=1594), COVID-19 (April 2020 to March 2020, n=1206; April 2021 to March 2022, n=1223) and post-COVID-19 (April 2022 to March 2023, n=1503). RESULTS: Compared with the patients in pre-COVID-19 and post-COVID-19 periods, during the first COVID-19 year, patients had more hypertension, non-ST elevation myocardial infarction (NSTEMI), lower left ventricular ejection fraction (LVEF) and multivessel coronary artery disease (CAD). In the second COVID-19 year, patients had more STEMI, lower LVEF, multivessel CAD, primary PCI, multiple stents and more vasopressor and mechanical support. There were 99 (1.4%) in-hospital deaths which in the successive years were 1.2%, 1.4%, 0.8%, 2.4% and 1.3%, respectively (p=0.019). Compared with the baseline year, deaths were slightly lower in the first COVID-19-year (age-sex adjusted OR 0.68, 95% CI 0.31 to 1.47) but significantly more in the second COVID-19-year (OR 1.97, 95% CI 1.10 to 3.54). This variation attenuated following adjustment for clinical presentation, extent of CAD, in-hospital treatment and duration of hospitalisation. CONCLUSIONS: In-hospital mortality among patients with CAD undergoing PCI was significantly higher in the second year of the COVID-19 pandemic in India and could be one of the reasons for excess deaths in the country. These patients had more severe CAD, lower LVEF, and more vasopressor and mechanical support and duration of hospitalisation.

An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time.

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.