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Abstract Conventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype–phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties.

Background Itolizumab downregulates the synthesis of proinflammatory cytokines and adhesion molecules by inhibiting CD6 leading to lower levels of interferon-γ, interleukin-6, and tumor necrotic factor-α and reduced T-cell infiltration at inflammatory sites. This study aims to compare the effects of tocilizumab and itolizumab in the management of severe coronavirus disease 2019 (COVID-19). Methods The study population was adults (>18 years) with severe COVID-19 pneumonia admitted to the intensive care unit receiving either tocilizumab or itolizumab during their stay. The primary outcome was clinical improvement (CI), defined as a two-point reduction on a seven-point ordinal scale in the status of the patient from initiating the drug or live discharge. The secondary outcomes were time until CI, improvement in PO2/FiO2 ratio, best PO2/FiO2 ratio, need for mechanical ventilation after administration of study drugs, time to discharge, and survival days. Results Of the 126 patients included in the study, 92 received tocilizumab and 34 received itolizumab. CI was seen in 46.7% and 61.7% of the patients in the tocilizumab and itolizumab groups, respectively and was not statistically significant (P=0.134). The PO2/FiO2 ratio was significantly better with itolizumab compared to tocilizumab (median [interquartile range]: 315 [200–380] vs. 250 [150–350], P=0.043). The incidence of serious adverse events due to the study drugs was significantly higher with itolizumab compared to tocilizumab (14.7% vs. 3.3%, P=0.032). Conclusions The CI with itolizumab is similar to tocilizumab. Better oxygenation can be achieved with itolizumab and it can be a substitute for tocilizumab in managing severe COVID-19.

Genes are often selected for molecular taxonomy citing their agreement to taxonomically derived species trees. These results are then used to define and edit existing taxonomic hierarchies of species. We counter the validity of this practice experimentally using mitochondrial genes routinely employed for the purpose. We constructed trees from complete mitochondrial DNA sequence (except D-loop) and its thirteen protein-coding genes namely nd1, nd2, nd3, nd4, nd4L, nd5, nd6, atp6, atp8, COI, COII, COIII, and cytB of 255 species of Cypriniformes fish using the maximum likelihood and Bayesian methods. While nd4, nd5, COI, COII, COIII, and cytB formed trees at par with the species tree, no gene produced complete concurrence. Similarly, while few genes formed similar branching patterns at family (COI and COIII) or broad group (nd1, nd2, and nd3; nd5 and COII) levels, no two gene trees fully agreed with each other. Tajima's D estimates of all studied genes indicated non-random evolution, violating the basic assumption of molecular phylogenetics. Bootstrap or Posterior probability values were also poor for several taxa indicating low robustness. Our findings reiterate that tree concurrence noted in studies involving smaller sample sizes is not a reflection of the evolutionary signature of the species.

Background and Aims: Breast cancer surgery can be carried out as day-care procedure to increase patient turnover, decrease disease progression and financial burden. The present study was carried out to assess the role of dexmedetomidine in breast cancer surgery as a day-care procedure. Methods: This prospective randomised, double-blind study was carried out on 100 patients screened for day-care breast cancer surgery. They were divided into two groups of 50 each; who received either normal saline (Group NS) or 0.6 μg/kg/h dexmedetomidine (Group D) infusion from 10 min before induction until skin closure. All patients were given general anaesthesia. The incidence of discharge, post-operative pain (POP), average rescue analgesia (fentanyl) required and side effects were noted. Statistical analysis was performed using Student's t-test and Chi-square test. Results: Incidence of discharge in group NS was 60% compared to 88% in Group D (P = 0.001). Average rescue analgesia requirement by group NS was 136.07 ± 43.06 μg, whereas it was 77.5 ± 29.86 μg in Group D (P = 0.01). The incidence of POP in 6 h and within 2 h of expected discharge time in Group NS was 56% and 28%, respectively, and in Group D, it was 8% in both the periods (P < 0.001 and 0.01). Side effects such as post-operative nausea, vomiting and bleeding were encountered in eight and two patients, respectively, in Group NS and two and one patients, respectively, in Group D. Conclusion: Dexmedetomidine as an anaesthetic adjuvant makes breast cancer surgery feasible on day-care basis.

Background: Typhoid fever is classically caused by Salmonella enterica serotype typhi.Recently the frequency of isolation of S. paratyphi A (SPA) has been increased in comparison to S. typhi in Indian scenario. Aim: To observe the rate of isolation and antimicrobial susceptibility pattern of SPA from suspected enteric fever cases attending tertiary care centres of Eastern Orissa. Settings and Design: Retrospective study Materials and Methods: 1488 blood samples were collected during different duration of fever and cultured in BACTEC blood culture system and bottles showing signal for growth were subcultured and identified as Salmonella spp. by standard procedure and mini API (Biomeriux) and antimicrobial susceptibility by disc diffusion method. Statistical Analysis: Chi square test. Results: 167 Salmonella spp. were isolated including 83.8% Salmonella paratyphi A and 16.6% S. typhi. Among them 102 were males and 65 were females with mean age of 22.7 yrs. S. paratyphi A was the predominant spp. each year but during 2008 – 2011, there was a dramatic rise (significant P value- 0.034). Multidrug resistance was noticed in 10.2% of the isolates. 98% of S. paratyphi A were resistant to nalidixic acid and 41% to ciprofloxacin, but the MIC of ciprofloxacin was raised between 1-2 μgm/dl showing the relation between nalidixic acid resistance and raised MIC of ciprofloxacin. Conclusion: Nalidixic acid should be tested along with ciprofloxacin disc while testing for susceptibility and MIC of ciprofloxacin is mandatory before advocating therapy to prevent treatment failure.

Identification of a reference gene unaffected by the experimental conditions is obligatory for accurate measurement of gene expression through relative quantification. Most existing methods directly analyze variability in crossing point (Cp) values of reference genes and fail to account for template-independent factors that affect Cp values in their estimates. We describe the use of three simple statistical methods namely analysis of variance (ANOVA), normal quantile-quantile correlation (NQQC) and effective expression support (EES), on pooled expression ratios of reference genes in a panel to overcome this issue. The pooling of expression ratios across the genes in the panel nullify the sample specific effects uniformly affecting all genes that are falsely reflected as instability. Our methods also offer the flexibility to include sample specific PCR efficiencies in estimations, when available, for improved accuracy. Additionally, we describe a correction factor from the ANOVA method to correct the relative fold change of a target gene if no truly stable reference gene could be found in the analyzed panel. The analysis is described on a synthetic data set to simplify the explanation of the statistical treatment of data.

Patients in the perioperative period and intensive care unit are commonly exposed to blood transfusion (BT). They are at increased risk of transfusion transmitted bacterial, viral and protozoal diseases. The risk of viral transmission has decreased steadily, but the risk of bacterial transmission remains same. Bacterial contamination is more in platelet concentrates than in red cells and least in plasma. The chances of sepsis, morbidity and mortality depend on the number of transfusions and underlying condition of the patient. Challenges to safe BT continue due to new emerging pathogens and various management problems. Strategies to restrict BT, optimal surgical and anaesthetic techniques to reduce blood loss and efforts to develop transfusion alternatives should be made. Literature search was performed using search words/phrases blood transfusion, transfusion, transfusion transmitted diseases, transfusion transmitted bacterial diseases, transfusion transmitted viral diseases, transfusion transmitted protozoal diseases or combinations, on PubMed and Google Scholar from 1990 to 2014.

Recurrence of pterygium is one of the main concerns in pterygium management. Recurrent pterygium is defined as the regrowth of fibrovascular tissue over the previously excised primary pterygium area, extending beyond the limbus and advancing towards the cornea. In most cases, it is characterised by increased conjunctival inflammation, fibroblast proliferation, and a faster progression towards corneal involvement compared to primary pterygium. This rapid regrowth can lead to irregular astigmatism, symblepharon, corneal fibrosis, severe vision impairment, and rarely, blindness. Incomplete removal of the primary pterygium, excessive suturing, the bare sclera technique, smaller conjunctival grafts, thicker conjunctival grafts with Tenon’s capsule, and poor control of postoperative inflammation are some of the most common surgical factors contributing to recurrence. Among all surgical procedures, the bare sclera technique has the highest recurrence rate. In the present case series, the author presented three cases of atypical recurrent pterygium and their successful management. The first case involved recurrent double-headed pterygium with severe symblepharon, resulting in lower fornix obliteration, shortening, diplopia, and corneal neovascularisation. The second case featured Grade-IV pterygium covering the pupillary area, and the third case involved a kissing type of doubleheaded pterygium. These cases were complications of the bare sclera technique, leading to blindness. Successful treatment of recurrent pterygium can improve visual and cosmetic outcomes. This case series aimed to highlight the burden and consequences of recurrent pterygium as a cause of avoidable blindness.

This paper establishes a hybridized intelligent machine learning based currency exchange forecasting model using Extreme Learning Machines (ELMs) and the Jaya optimization technique. This model can very well forecast the exchange price of USD (US Dollar) to INR (Indian Rupee) and USD to EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. The proposed ELM-Jaya model has been compared with existing optimized Neural Network and Functional Link Artificial Neural Network based predictive models. Finally, the model has been validated using various performance measures such as; MAPE, Theil's U, ARV and MAE. The comparison of different features demonstrates that the technical indicators outperform both the statistical measures and a combination of statistical measures and technical indicators in ELM-Jaya forecasting model. Keywords: Currency exchange prediction, Extreme Learning Machine (ELM), Jaya, Neural Network (NN), Functional Link Artificial Neural Network (FLANN)