Spray-freeze-drying as emerging and substantial quality enhancement technique in food industry.

Spray freeze drying is an emerging technology in the food industry with numerous applications. Its ability to preserve food quality, maintain nutritional value, and reduce bulk make it an attractive option to food manufacturers. Spray freeze drying can be used to reduce the water content of foods while preserving the shelf life and nutritional value. Spray freeze-drying of food products is a process that involves atomizing food into small droplets and then flash-freezing them. The frozen droplets are then placed in a vacuum chamber and heated, causing the liquid to evaporate and the solid particles to become a dry powder. Spray freeze drying has become a valuable tool for the food industry through its ability to process a wide range of food products. This review's prime focus is understanding spray freeze-dried approaches and emphasizing their applicability in various products.

Ultrasound-assisted extraction of phytochemicals from green coconut shell: Optimization by integrated artificial neural network and particle swarm technique.

This study employs artificial neural network (ANN) and particle swarm optimization (PSO) to maximize antioxidant and antimicrobial activity from green coconut shells. Phytochemical analysis was carried out on the extract obtained from ultrasound-assisted extraction performed at different combinations of time (10, 20, and 30 min), temperature (30, 35, and 40 °C), and the ratio of solid-solvent (1:10, 1:20, and 1:30 g/ml). The presence of these bioactive compounds exhibits antimicrobial and antioxidant activities. Quantitative analysis showed that the total phenolic compounds ranged from 7.08 to 33.46 mg GAE/g, flavonoids ranged from 2.09 to 28.46 mg QE/g, tannins ranged from 70.5 to 141.09 mg TAE/g, and antioxidant activity of 49.98-66.1 %. The FTIR analysis detected the presence of C[bond, double bond]O, O-H, and C-H bonds. The optimized condition of ultrasound-assisted extraction (UAE) was compared with the optimized condition of the microwave. The result of ultrasound-assisted extraction was observed to be better than microwave-assisted extraction.

A retrospective study of environmental predictors of dengue in Delhi from 2015 to 2018 using the generalized linear model.

Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015-2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015-18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated ß coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August-November).

Temporal variation and geospatial clustering of dengue in Delhi, India 2015-2018.

OBJECTIVES: The study was focused on geographical mapping of dengue cases and also to identify the hotspots or high-risk areas of dengue in Delhi. DESIGN: A retrospective spatial-temporal (ecological) study. Descriptive analysis was used to know the distribution of dengue cases by age, sex, seasons and districts of Delhi. The spatiotemporal analysis was performed using inverse distance weighting and Getis-Ord Gi* statistic to know the geographical distribution and identify the hotspot areas. SETTINGS: All the confirmed and diagnosed dengue cases (IgM +ve or NS1 Antigen +ve ELISA) recorded by the Municipal Corporation of Delhi for the last 4 years (2015-2018) were collected with their local address. The location of all the dengue cases was geocoded using their address to prepare the spatiotemporal dengue database. PARTICIPANTS: Record of all the dengue cases (4179) reported for treatment in the hospitals during the past 4 years were extracted and included in the study. Data were not collected directly from dengue patients. RESULTS: Seasonal occurrence of dengue cases (4179) shows that the cases start emerging in July, peaked in September-October and declined in December. The proportions of dengue cases were recorded high among the males 57.3% compared with females 42.6%, and differences were also recorded in all the age groups with more cases in age groups <15 and 16-30 years. Mapping of the cases reflects the spatial heterogeneity in the geographical distribution. The geomapping of cases indicates the presence of a significantly high number of cases in West, Southwest, South and Southeast districts of Delhi. High-risk areas or hotspots were also identified in this region. CONCLUSION: Dengue occurrence shows significant association with age, sex and seasons. The spatial analysis identified the high-risk areas, which can aid health administrators to take necessary action for prevention and better disease management.

Genome-wide identification and characterization of Lectin receptor-like kinase (LecRLK) genes in mungbean (Vigna radiata L. Wilczek).

Lectins are a diverse group of proteins found throughout plant species. Numerous lectins are involved in many important processes such as organogenesis, defense mechanism, signaling, and stress response. Although the mungbean whole genome sequence has been published, distribution, diversification, and gene structure of lectin genes in mungbean are still unknown. A total of 73 putative lectin genes with kinase domain have been identified through BLAST and HMM profiling. Furthermore, these sequences could be classified into three families, such as G-type, L-type, and C-type VrLecRLKs. 59 out of 73 VrLecRLKs were distributed on to 11 chromosomes, whereas rest could not be anchored onto any specific chromosome. Gene structure analysis revealed a varying number of exons in 73 VrLecRLK genes. Gene ontology annotations were grouped into three categories like biological processes, cellular components and molecular functions, which were associated with signaling pathways, defense responses, transferase activity, binding activity, and kinase activity. The comprehensive and systematic studies of LecRLK genes family provides a reference and foundation for further functional analysis of VrLecRLK genes in mungbean.

Effective heart disease prediction system using data mining techniques.

The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, an effective heart disease prediction system (EHDPS) is developed using neural network for predicting the risk level of heart disease. The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The EHDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge, eg, relationships between medical factors related to heart disease and patterns, to be established. We have employed the multilayer perceptron neural network with backpropagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict the risk level of heart diseases.

Palladium(ii) complexes of N,N-diphenylacetamide based thio/selenoethers and flower shaped Pd16S7 and prismatic Pd17Se15 nano-particles tailored as catalysts for C-C and C-O coupling.

2-Bromo-N,N-diphenylacetamide (P1) prepared by reacting diphenylamine with α-bromoacetyl bromide, on treatment with Na2S and Na2Se generated in situ, has resulted in thio and selenoether ligands, L1 ((Ph2NCOCH2)2S) and L2 ((Ph2NCOCH2)2Se), respectively. Reacting these ligands with Na2PdCl4 in ethanol at room temperature resulted in their complexes [Pd(L1/L2)2Cl2] (C1/C2). P1, L1, L2, C1 and C2 were characterized by 1H, 13C{1H} and 77Se{1H} NMR spectroscopy, IR spectroscopy and High resolution mass spectrometry (HR-MS). Single crystal structures of all the five compounds were determined by X-ray diffraction. In both C1 and C2 the geometry of Pd is nearly square planar. Flower shaped Pd16S7 (size 26-50 nm) and prismatic Pd17Se15 NPs (size 20-55 nm) were synthesized by a single source precursor route (thermolysis at ∼280 °C in trioctylphosphine) from air and moisture insensitive C1 and C2, respectively. These shapes of the two nanophases were unknown hitherto. They were characterized by powder X-ray diffraction (PXRD), SEM-EDX and HR-TEM and found to show good catalytic activity for Suzuki-Miyaura (Pd loading 0.5 mol%; yield up to 96%) and C-O (Pd loading 0.5 mol%; yield up to 96%) coupling reactions (at 100 °C) of aryl bromides with phenylboronic acid and phenol, respectively. The complexes C1 and C2 were found very efficient for Suzuki-Miyaura and C-O coupling as revealed by their optimum loading of 0.0001-0.01 and 0.1 mol% of Pd, respectively, for the two reactions. The reuse of the complexes or NPs as a catalyst is demonstrated.

Pharmacological Drug Delivery Strategies for Improved Therapeutic Effects: Recent Advances.

The latest pharmacologic research has resulted number of new molecules with the potential to modernize the prevention or treatment of different complex diseases, including cancer. The therapeutics generally include moieties such as proteins, drugs and genes, etc. Current activities in the pharmacological field include the development of novel drug-delivery systems to overcome pharmacokinetic glitches such as limited bioavailability, unwanted distribution, drug resistant, and stability, etc. Therefore, to address these issues various biotechnological and pharmacological techniques has been introduced. However, effective drug delivery with improved efficacy remains challenging. This review is focused towards different strategies such as physical and biological methods for efficacious delivery at desired tissues and even sub-cellular targeting. Emphasis is also given about nanotechnology based drug or gene delivery strategies and co-delivery of drug-drug; gene-gene or combinations of drug-gene, etc. are the current cutting-edge methods, which are under clinical or pre-clinical stage of research. Uses of biodegradable materials, such as liposomes and polymeric particles are another class of drug delivery vehicles, which have shown tremendous success, are also discussed. Towards the end, future directions of pharmacological drug delivery methods have also been summarized.