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Background: Autonomic function tests play a crucial role in the diagnosis and prognosis of clinical conditions. Although there are multiple autonomic function tests available, there is still a need for rapid and less expensive tests for the benefit of the general population. The pupil-to-limbus diameter (PLD) ratio is one such test that is simple and cost-effective. It uses the principle of pupil dilation and constriction due to the influence of the autonomic neurons’ activity. Aims and Objectives: The present study aimed to observe a correlation between PLD ratio with blood pressure and pulse rate in young adults. Materials and Methods: The present study was conducted at the Department of Physiology, Jhalawar Medical College, Jhalawar. The present study was an observational study conducted among 1st-year medical students in the age group of 18–24 years. PLD ratio was measured by the two-box method as described in the literature. After providing 3–5 min of rest, blood pressure and pulse were recorded in the right arm in the sitting position by a digital blood pressure monitor. Results: Positive correlation was observed between the PLD ratio of right eye and pulse rate (r=0.9696). There is a weak negative correlation exists between the PLD ratio of right eye and systolic blood pressure (r value=−0.3802). Conclusion: The study concludes that the PLD ratio correlates with autonomic activity. However, there is a need for a study with large population to confirm the correlation. Furthermore, there is a study to assess the autonomic responses in female participants in different phases of menstrual cycle.

Wheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced computer vision is crucial for safeguarding wheat quality. However, the literature relied on inadequate feature selection acquired from computationally expensive backbones followed by shallow layered networks. It is resultantly limiting their capacity to recognize and prioritize diseased areas effectively. Therefore, this paper introduces an optimal features-assisted lightweight framework that integrates EfficientNet-B3 with a spatial attention (SA) mechanism to capture healthy and unhealthy patterns effectively. The proposed framework harnesses this capability to address the vital regions affected by wheat diseases via an optimized, lightweight, and attentive network. Subsequently, we thoroughly analyzed several backbone features to identify robust hyperparameters conducive to achieving our lightweight objective. Furthermore, we employed SA blocks to fortify the network, directing attention efficiently towards diseased regions. The efficacy of the proposed network is validated through comprehensive evaluations conducted on both our proposed and LWDCD2020 benchmarks. Comparative analyses with existing methods consistently showcase superiority, firmly establishing our proposed approach as a viable network for wheat disease recognition. additionally, we also collected our own dataset, namely the Wheat Disease Five Classes Classification Dataset (WD5CC), and included diverse images.

Nutrition is an important aspect of a healthy lifestyle for all individuals, including adolescents. The objective of this cross-sectional descriptive survey study was to investigate university students’ awareness of immunity enhancing foods, food nutritive values, and eating practices during the COVID-19 pandemic. A total of 839 university students from four different universities in Pakistan participated in the study from October 2021 to January 2022, 397 of which were male and 442 were female. A total of 419 students were studying in medical disciplines while 420 were non-medical students. The students had significant knowledge (p

Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium. A total of 200 dig¬ital microscopic cell images comprising 40 of each of the genera mentioned above were used in this study. Results: The developed technique could identify and distinguish microscopic images of Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium with the highest accuracy of 92.20% for Staphylococcus and the lowest of 77.40% for Salmonella. Among the five epochs, the accuracy rate of bacterial genera identification of Staphylococcus was graded 1, and Streptococcus, Escherichia, Corynebacterium, and Salmonella as 2, 3, 4, and 5, respectively. Conclusion: The experimental results suggest using the DL method to predict bacterial genera included in this study. However, further improvement with more bacterial genera, especially of similar morphology, is necessary to make the technique widely used for bacterial genera identification. [J Adv Vet Anim Res 2022; 9(4.000): 573-582]

The analysis and classification of dates is based on their external features: size, appearance, shape, and colour. The process is currently performed manually after harvesting as part of the post-harvesting process. Grading manually is tedious because it usually results in time delays, product quality risks, and it is associated with time and cost delays as well. Although the use of computers and information technology has seen tremendous growth in many small and large sectors, it has been in its infancy in the cultivation of fruit and dates. Using image processing algorithms, we can enhance human vision capabilities through analysis and make images easier to comprehend. A major objective of computer vision-based algorithms for classifying and sorting of dates is to make the procedure fully automated by minimizing the manual component involved in the process. This paper presents an image processing-based algorithm that uses machine learning techniques to extract the characteristics of colour intensity and colour homogeneity, allowing us to grade images in a more timely and automated manner. In order to obtain the results, we extracted the appearance of the date images based on an image processing algorithm. It is used as a validation element for the results that the quality of dates-fruit images can be evaluated through the prior selection process in both separate and in groups. This study has managed to achieve a rate of 95% accuracy in data classification. Doi: 10.28991/ESJ-2022-06-04-03 Full Text: PDF

HIV infection impairs host immunity, leading to progressive disease. An anti-retroviral treatment efficiently controls viremia but cannot completely restore the immune dysfunction in HIV-infected individuals. Both host and viral factors determine the rate of disease progression. Among the host factors, innate immunity plays a critical role; however, the mechanism(s) associated with dysfunctional innate responses are poorly understood among HIV disease progressors, which was investigated here. The gene expression profiles of TLRs and innate cytokines in HIV-infected (LTNPs and progressors) and HIV-uninfected individuals were examined. Since the progressors showed a dysregulated TLR-mediated innate response, we investigated the role of TLR agonists in restoring the innate functions of the progressors. The stimulation of PBMCs with TLR3 agonist-poly:(I:C), TLR7 agonist-GS-9620 and TLR9 agonist-ODN 2216 resulted in an increased expression of IFN-α, IFN-β and IL-6. Interestingly, the expression of IFITM3, BST-2, IFITM-3, IFI-16 was also increased upon stimulation with TLR3 and TLR7 agonists, respectively. To further understand the molecular mechanism involved, the role of miR-155 was explored. Increased miR-155 expression was noted among the progressors. MiR-155 inhibition upregulated the expression of TLR3, NF-κB, IRF-3, TNF-α and the APOBEC-3G, IFITM-3, IFI-16 and BST-2 genes in the PBMCs of the progressors. To conclude, miR-155 negatively regulates TLR-mediated cytokines as wel l as the expression of host restriction factors, which play an important role in mounting anti-HIV responses; hence, targeting miR-155 might be helpful in devising strategic approaches towards alleviating HIV disease progression.

HPV, or Human Papilloma Virus, has been the primary causative agent of genital warts and cervical cancer worldwide. It is a sexually transmitted infection mainly affecting women of reproductive age group, also infecting men and high-risk group individuals globally, resulting in high mortality. In recent years, HPV has also been found to be the major culprit behind anogenital cancers in both gender and oropharyngeal and colorectal cancers. Few studies have reported the incidence of HPV in breast cancers as well. For a few decades, the burden of HPV-associated malignancies has been increasing at an alarming rate due to a lack of adequate awareness, famine vaccine coverage and hesitancy. The effectiveness of currently available vaccines has been limited to prophylactic efficacy and does not prevent malignancies associated with post-exposure persistent infection. This review focuses on the current burden of HPV-associated malignancies, their causes and strategies to combat the growing prevalence of the cancers. With the advent of new technologies associated with treatment pertaining to therapeutic interventions and employing effective vaccine coverage, the burden of this disease may be reduced in the population.

TSH receptor antibodies (TRAbs) are the pathological hallmark of Graves’ disease, present in nearly all patients with the disease. Euthyroid Graves’ ophthalmopathy (EGO) is a well-recognized clinical entity, but its occurrence in patients with negative TRAbs is a potential source of diagnostic confusion. A 66-year-old female presented to our endocrinology clinic with right eye pain and diplopia in the absence of thyroid dysfunction. TRAbs were negative, as measured with a highly sensitive third-generation thyrotropin-binding inhibitory immunoglobulin (TBII) ELISA assay. CT and MRI scans of the orbit showed asymmetrical thickening of the inferior rectus muscles but no other inflammatory or malignant orbital pathology. Graves’ ophthalmopathy (GO) was diagnosed on the basis of the clinical and radiological features, and she underwent surgical recession of the inferior rectus muscle with complete resolution of the diplopia and orbital pain. She remained euthyroid over the course of follow-up but ultimately developed overt clinical and biochemical hyperthyroidism, 24 months after the initial presentation. By this time, she had developed positive TRAb as well as thyroid peroxidase antibodies. She responded to treatment with thionamides and remains euthyroid. This case highlights the potential for negative thyroid-specific autoantibodies in the presentation of EGO and underscores the variable temporal relationship between the clinical expression of thyroid dysfunction and orbital disease in the natural evolution of Graves’ disease.

Fungi are a prospective resource of bioactive compounds, but conventional methods of drug discovery are not effective enough to fully explore their metabolic potential. This study aimed to develop an easily attainable method to elicit the metabolic potential of fungi using Aspergillus nidulans laeA as a transcription regulation tool. In this study, functional analysis of Aspergillus nidulans laeA (AnLaeA) and Aspergillus sp. Z5 laeA (Az5LaeA) was done in the fungus Aspergillus sp. Z5. Heterologous AnLaeA-and native Az5LaeA-overexpression exhibited similar phenotypic effects and caused an increase in production of a bioactive compound diorcinol in Aspergillus sp. Z5, which proved the conserved function of this global regulator. In particular, heteroexpression of AnLaeA showed a significant impact on the expression of velvet complex genes, diorcinol synthesis-related genes, and different transcription factors (TFs). Moreover, heteroexpression of AnLaeA influenced the whole genome gene expression of Aspergillus sp. Z5 and triggered the upregulation of many genes. Overall, these findings suggest that heteroexpression of AnLaeA in fungi serves as a simple and easy method to explore their metabolic potential. In relation to this, AnLaeA was overexpressed in the fungus Penicillium sp. LC1-4, which resulted in increased production of quinolactacin A.

Carbon (C) and nitrogen (N) are two essential elements that influence plant growth and development. The C and N metabolic pathways influence each other to affect gene expression, but little is known about which genes are regulated by interaction between C and N or the mechanisms by which the pathways interact. In the present investigation, proteome analysis of N-efficient and N-inefficient Indian mustard, grown under varied combinations of low-N, sufficient-N, ambient [CO2] and elevated [CO2] was carried out to identify proteins and the encoding genes of the interactions between C and N. Two-dimensional gel electrophoresis (2-DE) revealed 158 candidate protein spots. Among these, 72 spots were identified by matrix-assisted laser desorption ionization-time of flight/time of flight mass spectrometry (MALDI-TOF/TOF). The identified proteins are related to various molecular processes including photosynthesis, energy metabolism, protein synthesis, transport and degradation, signal transduction, nitrogen metabolism and defense to oxidative, water and heat stresses. Identification of proteins like PII-like protein, cyclophilin, elongation factor-TU, oxygen-evolving enhancer protein and rubisco activase offers a peculiar overview of changes elicited by elevated [CO2], providing clues about how N-efficient cultivar of Indian mustard adapt to low N supply under elevated [CO2] conditions. This study provides new insights and novel information for a better understanding of adaptive responses to elevated [CO2] under N deficiency in Indian mustard.

Nowadays, various drugs on the market are becoming more and more resistant to numerous diseases, thus declining their efficacy for treatment purposes in human beings. Antibiotic resistance is one among the top listed threat around the world which eventually urged the discovery of new potent drugs followed by an increase in the number of deaths caused by cancer due to chemotherapy resistance as well. Accordingly, marine cyanobacteria, being the oldest prokaryotic microorganisms belonging to a monophyletic group, have proven themselves as being able to generate pharmaceutically important natural products. They have long been known to produce distinct and structurally complex secondary metabolites including peptides, polyketides, alkaloids, lipids, and terpenes with potent biological properties and applications. As such, this review will focus on recently published novel compounds isolated from marine cyanobacteria along with their potential bioactivities such as antibacterial, antifungal, anticancer, anti-tuberculosis, immunosuppressive and anti-inflammatory capacities. Moreover, various structural classes, as well as their technological uses will also be discussed.

BACKGROUND: Nitrogen [N] is a critical limiting nutrient for plants and has to be exogenously supplied to many crops, to achieve high yield with significant economic and environmental costs, specifically for rice. Development of low-input nitrogen sustainable crop is necessary for sustainable agriculture. Identification of regulatory elements associated with low-N tolerance is imperative for formulating innovative approaches for developing low-N tolerant crop plants, using gene manipulation. MicroRNAs (miRNAs) are known to play crucial roles in the modulation of gene expression in plants under various environmental conditions. METHODOLOGY/PRINCIPAL FINDINGS: MiRNAs associated with low-N tolerance have not been identified so far. In this study, we investigated microarray-based miRNA expression in low-N tolerant and low-N sensitive rice genotypes under low N condition. Expressions of 32 miRNAs differed significantly in the two genotypes. Of these 32 miRNAs, expressions of nine miRNAs were further validated experimentally in leaves as well as in roots. Of these differentially expressed miRNAs, six miRNAs (miR156, miR164, miR528, miR820, miR821 and miR1318) were reported in leaves and four (miR164, miR167, miR168 and miR528) in roots. Target genes of all the 32 miRNAs were predicted, which encode transcription factors, and proteins associated with metabolic processes or stress responses. Expression levels of some of the corresponding miRNA targets were analysed and found to be significantly higher in low N-tolerant genotype than low-N sensitive genotype. These findings suggested that miRNAs played an important role in low-N tolerance in rice. CONCLUSIONS/SIGNIFICANCE: Genome-wide differences in expression of miRNA in low N-tolerant and low N-sensitive rice genotypes were reported. This provides a platform for selection as well as manipulation of genotypes for better N utilization efficiency.