Pathogenesis related (PR) proteins are one of the major sources of plant derived allergens. These proteins are induced by the plants as a defense response system in stress conditions like microbial and insect infections, wounding, exposure to harsh chemicals, and atmospheric conditions. However, some plant tissues that are more exposed to environmental conditions like UV irradiation and insect or fungal attacks express these proteins constitutively. These proteins are mostly resistant to proteases and most of them show considerable stability at low pH. Many of these plant pathogenesis related proteins are found to act as food allergens, latex allergens, and pollen allergens. Proteins having similar amino acid sequences among the members of PR proteins may be responsible for cross-reactivity among allergens from diverse plants. This review analyzes the different pathogenesis related protein families that have been reported as allergens. Proteins of these families have been characterized in regard to their biological functions, amino acid sequence, and cross-reactivity. The three-dimensional structures of some of these allergens have also been evaluated to elucidate the antigenic determinants of these molecules and to explain the cross-reactivity among the various allergens.

Lactoferrin is a multifunctional, iron-binding glycoprotein which displays a wide array of modes of action to execute its primary antimicrobial function. It contains various antimicrobial peptides which are released upon its hydrolysis by proteases. These peptides display a similarity with the antimicrobial cationic peptides found in nature. In the current scenario of increasing resistance to antibiotics, there is a need for the discovery of novel antimicrobial drugs. In this context, the structural and functional perspectives on some of the antimicrobial peptides found in N-lobe of lactoferrin have been reviewed. This paper provides the comparison of lactoferrin peptides with other antimicrobial peptides found in nature as well as interspecies comparison of the structural properties of these peptides within the native lactoferrin.

Structural polymorphism of DNA has constantly been evolving from the time of illustration of the double helical model of DNA by Watson and Crick. A variety of non-canonical DNA structures have constantly been documented across the globe. DNA attracted worldwide attention as a carrier of genetic information. In addition to the classical Watson–Crick duplex, DNA can actually adopt diverse structures during its active participation in cellular processes like replication, transcription, recombination and repair. Structures like hairpin, cruciform, triplex, G-triplex, quadruplex, i-motif and other alternative non-canonical DNA structures have been studied at length and have also shown their in vivo occurrence. This review mainly focuses on non-canonical structures adopted by DNA oligonucleotides which have certain prerequisites for their formation in terms of sequence, its length, number and orientation of strands along with varied solution conditions. This conformational polymorphism of DNA might be the basis of different functional properties of a specific set of DNA sequences, further giving some insights for various extremely complicated biological phenomena. Many of these structures have already shown their linkages with diseases like cancer and genetic disorders, hence making them an extremely striking target for structure-specific drug designing and therapeutic applications.

Sepsis is a condition caused by infection followed by unregulated inflammatory response which may lead to the organ dysfunction. During such condition, over-production of oxidants is one of the factors which contribute cellular toxicity and ultimately organ failure and mortality. Antioxidants having free radicals scavenging activity exert protective role in various diseases. This study has been designed to evaluate the levels of oxidative and antioxidative activity in sepsis patients and their correlation with the severity of the sepsis. A total of 100 sepsis patients and 50 healthy controls subjects were enrolled in this study from the period October 2016 to June 2017. The investigation included measurements of oxidative enzyme, myeloperoxidase (MPO), antioxidant enzymes including superoxide dismutase activity (SOD) and catalase activity (CAT) and cytokines (TNF-α, IL-8 and IFN-γ). Furthermore, the level of these activities was correlated with severity of sepsis. Augmented levels of oxidants were found in sepsis as demonstrated by DMPO nitrone adduct formation and plasma MPO level activity (1.37 ± 0.51 in sepsis vs 0.405 ± 0.16 in control subjects). Cytokines were also found to be increased in sepsis patients. However, plasma SOD and CAT activities were significantly attenuated (P < .001) in the sepsis patients compared with controls subjects. Moreover, inverse relation between antioxidant enzymes (SOD and CAT) and organ failure assessment (SOFA), physiological score (APACHE II), organ toxicity specific markers have been observed as demonstrated by Pearson's correlation coefficient. This study suggests that imbalance between oxidant and antioxidant plays key role in the severity of sepsis.

The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic. Using the concept of attention, we develop a two-phase solution. In the first phase, we classify subjectivity -whether a given tweet is neutral or subjective with respect to the given topic. In the second phase, we classify sentiment of the subjective tweets (ignoring the neutral tweets) -whether a given subjective tweet has a FAVOR or AGAINST stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep neural network for each phase, and embed attention at each of the phases. On the SemEval 2016 stance detection Twitter task dataset [7], we obtain a best-case macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the existing deep learning based solutions. Our framework, T-PAN, is the first in the topical stance detection literature, that uses deep learning within a two-phase architecture.