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"Kumar, Naveen"
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Oral feed-based administration of phage cocktail protects rohu fish (Labeo rohita) against Aeromonas hydrophila infection
Aeromonas hydrophila is one of the major freshwater fish pathogens. In the current study, a cocktail of D6 and CF7 phages was given orally to Labeo rohita to assess phage survival in fish organs as well as to determine the therapeutic efficacy of phage treatment against fish mortality caused by A. hydrophila. In the phage-coated feed, prepared by simple spraying method, phage counts were quite stable for up to 2 months with a decline of ≤ 0.23 log10 and ≤ 1.66 log10 PFU/g feed during 4 oC and room temperature storage. Throughout the experimental period of 7 days, both phages could be detected in the gut of fish fed with phage-coated feed. Besides, both CF7 and D6 phages were also detected in fish kidneys indicating the ability of both the phage to cross the intestinal barrier. During challenge studies with LD50 dose of A. hydrophila, phage cocktail doses of 1 × 106 – 1 × 108 PFU/g feed prevented the mortality in L. rohita with relative percentage survival (RPS) of 8.7–65.2. When challenged with LD90 dose of A. hydrophila, an RPS value of 28.6 was obtained at a phage cocktail dose of 1 × 108 PFU/g feed. The RPS data showed that orally-fed phage cocktail protected the fish against the mortality caused by A. hydrophila in a dose-dependent manner. Simple practical approaches for phage cocktail development, medicated feed preparation and oral administration along with phage survival and protection data make the current study useful for farmer-level application.
Journal Article
Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose
Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.
Journal Article
Prevalence and antimicrobial resistance of food safety related Vibrio species in inland saline water shrimp culture farms
This study evaluated the potential pathogenicity and antimicrobial resistance (AMR) of
Vibrio
species isolated from inland saline shrimp culture farms. Out of 200
Vibrio
isolates obtained from 166 shrimp/water samples, 105 isolates were identified as
V. parahaemolyticus
and 31 isolates were identified as
V. alginolyticus
and
V. cholerae
, respectively. During PCR screening of virulence-associated genes, the presence of
the tlh
gene was confirmed in 70 and 19 isolates of
V. parahaemolyticus
and
V. alginolyticus
, respectively. Besides, 10 isolates of
V. parahaemolyticus
were also found positive for
trh
gene. During antibiotic susceptibility testing (AST), very high resistance to cefotaxime (93.0%), amoxiclav (90.3%), ampicillin (88.2%), and ceftazidime (73.7%) was observed in all
Vibrio species
. Multiple antibiotic resistance (MAR) index values of
Vibrio
isolates ranged from 0.00 to 0.75, with 90.1% of isolates showing resistance to ≥ 3 antibiotics. The AST and MAR patterns did not significantly vary sample-wise or
Vibrio
species-wise. During the minimum inhibitory concentration (MIC) testing of various antibiotics against
Vibrio
isolates, the highest MIC values were recorded for amoxiclav followed by kanamycin. These results indicated that multi-drug resistant
Vibrio
species could act as the reservoirs of antibiotic resistance genes in the shrimp culture environment. The limited host range of 12 previously isolated
V. parahaemolyticus
phages against
V. parahaemolyticus
isolates from this study indicated that multiple strains of
V. parahaemolyticus
were prevalent in inland saline shrimp culture farms. The findings of the current study emphasize that routine monitoring of emerging aquaculture areas is critical for AMR pathogen risk assessment.
Journal Article
AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices
Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness mechanical energy while enabling self‐sustaining sensing for self‐powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low‐power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI‐enhanced TENG solutions. Triboelectric nanogenerators (TENGs) enable sustainable energy harvesting and self‐powered sensing but face challenges in material optimization, fabrication, and stability. Integrating artificial intelligence (AI) enhances TENG performance through machine learning, improving energy output, adaptability, and predictive maintenance. This review explores AI‐driven TENG advancements, key challenges, and future research directions for practical applications.
Journal Article
Multi-environment evaluation of rice genotypes: impact of weather and culm biochemical parameters against sheath blight infection
IntroductionSheath blight caused by Rhizoctonia solani is one of the major diseases of rice, causing widespread crop losses. The use of semi-dwarf rice varieties in the ongoing nutrient-intensive rice cultivation system has further accentuated the incidence of the disease. An ideal solution to this problem would be identifying a stable sheath blight-tolerant genotype.Material and methodsA multi-environment evaluation of 32 rice genotypes against sheath blight infection was conducted over six seasons across two locations (Agricultural Research Farm, Institute of Agricultural Sciences, Banaras Hindu University (28.18° N, 38.03° E, and 75.5 masl), for four years during the wet seasons ( kharif ) from 2015 to 2018 and two seasons at the National Rice Research Institute (20°27’09” N, 85°55’57” E, 26 masl), Cuttack, Odisha, during the dry season ( rabi ) of 2019 and the kharif of 2019, including susceptible and resistant check. Percent disease index data were collected over 4 weeks (on the 7th, 14th, 21st, and 28th day after infection), along with data on other morphological and physiological traits.Result and discussionThe resistant genotypes across seasons were the ones with a higher hemicellulose content (13.93-14.64) and lower nitrogen content (1.10- 1.31) compared with the susceptible check Tapaswini (G32) (hemicellulose 12.96, nitrogen 1.38), which might explain the resistant reaction. Three different stability models—additive main effect and multiplicative interaction (AMMI), genotype + genotype x environment (GGE) biplot, and multi-trait stability index (MTSI)—were then used to identify the stable resistant genotypes across six seasons. The results obtained with all three models had common genotypes highlighted as stable and having a low area under the disease progress curve (AUDPC) values. The ideal stable genotypes with low disease incidence were IC 283139 (G19), Tetep (G28), IC 260917 (G4), and IC 277274 (G10), with AUDPC values of 658.91, 607.46, 479.69, and 547.94, respectively. Weather parameters such as temperature, rainfall, sunshine hours, and relative humidity were also noted daily. Relative humidity was positively correlated with the percent disease index.
Journal Article
Epidural volume extension technique in high risk obstetric patients - Case series
ABSTRACT
Epidural volume extension involves injection of normal saline into the epidural space soon after an intrathecal injection, with the aim of augmenting the sensory block height. It has significant dose-sparing effect, providing adequate level of anaesthesia and analgesia with minimal haemodynamic disturbances. We present this case series that shows the successful use of this technique in high risk cardiac patients coming for elective lower segment caesarean section.
Journal Article
Modified microplate method for rapid and efficient estimation of siderophore produced by bacteria
In this study, siderophore production by various bacteria amongst the plant-growth-promoting rhizobacteria was quantified by a rapid and efficient method. In total, 23 siderophore-producing bacterial isolates/strains were taken to estimate their siderophore-producing ability by the standard method (chrome azurol sulphonate assay) as well as 96 well microplate method. Production of siderophore was estimated in percent siderophore unit by both the methods. It was observed that data obtained by both methods correlated positively with each other proving the correctness of microplate method. By the modified microplate method, siderophore production by several bacterial strains can be estimated both qualitatively and quantitatively at one go, saving time, chemicals, making it very less tedious, and also being cheaper in comparison with the method currently in use. The modified microtiter plate method as proposed here makes it far easier to screen the plant-growth-promoting character of plant-associated bacteria.
Journal Article
Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors
We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH
4
+
ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH
4
+
ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH
4
+
concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH
4
+
ion levels. The proposed NH
4
+
sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.
Journal Article