GREEN SYNTHESIS OF SILVER NANOPARTICLES USING THE LEAF EXTRACT OF PUTRANJIVA ROXBURGHII WALL. AND THEIR ANTIMICROBIAL ACTIVITY.

 Objective: This study deals with the synthesis of silver nanoparticles (AgNP's) from the extract of the leaves of the plant Putranjiva roxburghii wall.Using biological method, i.e., green synthesis.Methods: The extract from the leaves acts as a reducing and stabilizing agent for the AgNP's. Further characterization was done using varioustechniques like ultraviolet (UV)-visible spectrophotometry, which shows surface plasmon resonance, Fourier transform infra-red spectroscopyanalysis shows formation of various bonds, scanning electron microscope (SEM) and transmission electron microscope (TEM) analysis depictsthe distribution and average size of nanoparticles. The antimicrobial activity was also checked against various bacteria and fungi using minimuminhibitory concentration and well diffusion assay.Result: UV analysis shows strong plasmon resonance between 420 and 480 nm SEM analysis shows the distribution of synthesized nanoparticles,whereas TEM analysis shows the average particle size to be near about 5 nm and well diffusion assay proved that these nanoparticles are effectiveagainst different microorganisms.Conclusion: P. roxburghii wall. shows strong potential for the reduction of silver from Ag+ to Ag0 and nanoparticles so formed are strongly activeagainst various microorganism.Keywords: Putranjiva roxburghii, Fourier transform infra-red, Scanning electron microscope, Transmission electron microscope

Fuzzy logic based cluster head election led energy efficiency in history assisted cognitive radio networks

The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by the energy consumption of the power-constrained CR nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual CR nodes before it is being forwarded to the base station (BS). In this article, an energy-efficient fuzzy logic-based clustering (EEFC) algorithm is proposed, which uses a novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic-based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing the Mamdani method for fuzzification and the Centroid method for defuzzification. It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as low-energy adaptive clustering hierarchy (LEACH), CH election using fuzzy logic (CHEF), energy-aware unequal clustering using fuzzy logic (EAUCF), and fuzzy logic-based energy-efficient clustering hierarchy (FLECH), our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in the existing history-assisted energy efficient infrastructure CR network to analyze and demonstrate the overall augmented energy efficiency of the system

Newer Horizon of Mesenchymal Stem Cell-Based Therapy in the Management of SARS-CoV-2-Associated Mucormycosis: A Safe Hope for Future Medicine

SARS-CoV-2-infected patients are reported to show immunocompromised behavior that gives rise to a wide variety of complications due to impaired innate immune response, cytokine storm, and thrombo-inflammation. Prolonged use of steroids, diabetes mellitus, and diabetic ketoacidosis (DKA) are some of the factors responsible for the growth of Mucorales in such immunocompromised patients and, thus, can lead to a life-threatening condition referred to as mucormycosis. Therefore, an early diagnosis and cell-based management cosis is the need of the hour to help affected patients overcome this severe condition. In addition, extended exposure to antifungal drugs/therapeutics is found to initiate hormonal and neurological complications. More recently, mesenchymal stem cells (MSCs) have been used to exhibit immunomodulatory function and proven to be beneficial in a clinical cell-based regenerative approach. The immunomodulation ability of MSCs in mucormycosis patient boosts the immunity by the release of chemotactic proteins. MSC-based therapy in mucormycosis along with the combination of short-term antifungal drugs can be utilized as a prospective approach for mucormycosis treatment with promising outcomes. However, preclinical and in mucormyIn mucormycosis, the hyphae of clinical trials are needed to establish the precise mechanism of MSCs in mucormycosis treatment

Effect of once-a-day milk feeding on behavior and growth performance of pre-weaning calves

Objective The objectives of the present study were to evaluate the effects of once-a-day milk feeding on growth performance and routine behavior of preweaning dairy calves. Methods At 22nd day of age, twenty-four Holstein calves were randomly assigned to one of two treatment groups (n = 12/treatment) based on milk feeding frequency (MF): i) 3 L of milk feeding two times a day; ii) 6 L of milk feeding once a day. The milk feeding amount was reduced to half for all calves between 56 and 60 days of age and weaning was done at 60 days of age. To determine the increase in weight and structural measurements, each calf was weighed and measured at 3 weeks of age and then at weaning. The daily behavioral activity of each calf was assessed from the 22nd day of age till weaning (60th day of age) through Nederlandsche Apparatenfabriek (NEDAP) software providing real-time data through a logger fitted on the calf’s foot. Results There was no interaction (p≥0.17) between MF and sex of the calves for routine behavioral parameters, body weight and structural measurements. Similarly, there was no effect of MF on routine behavioral parameters, body weight and structural measurements. However, the sex of the calves affected body weight gain in calves. Male calves had 27% greater total body weight and average daily gain than female calves. There was no effect of the sex of the calves on behavioral measurements. Collectively, in the current study, no negative effects of a once-a-day milk feeding regimen were found on routine behavioral and growth parameters of preweaning calves in group housing. Conclusion Once-a-day milk feeding can be safely adopted in preweaning calves from 22nd day of age

Antibacterial and antibiofilm activity of Abroma augusta stabilized silver (Ag) nanoparticles against drug-resistant clinical pathogens

Infectious diseases remain among the most pressing concerns for human health. This issue has grown even more complex with the emergence of multidrug-resistant (MDR) bacteria. To address bacterial infections, nanoparticles have emerged as a promising avenue, offering the potential to target bacteria at multiple levels and effectively eliminate them. In this study, silver nanoparticles (AA-AgNPs) were synthesized using the leaf extract of a medicinal plant, Abroma augusta. The synthesis method is straightforward, safe, cost-effective, and environment friendly, utilizing the leaf extract of this Ayurvedic herb. The UV-vis absorbance peak at 424 nm indicated the formation of AA-AgNPs, with the involvement of numerous functional groups in the synthesis and stabilization of the particles. AA-AgNPs exhibited robust antibacterial and antibiofilm activities against methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). The MIC values of AA-AgNPs ranged from 8 to 32 μg/mL. Electron microscopic examination of the interaction of AA-AgNPs with the test bacterial pathogens showed a deleterious impact on bacterial morphology, resulting from membrane rupture and leakage of intracellular components. AA-AgNPs also demonstrated a dose-dependent effect in curtailing biofilm formation below inhibitory doses. Overall, this study highlights the potential of AA-AgNPs in the successful inhibition of both the growth and biofilms of MRSA and VRE bacteria. Following studies on toxicity and dose optimization, such AgNPs could be developed into effective medical remedies against infections

A novel insertion mutation in the cartilage-derived morphogenetic protein-1 (CDMP1) gene underlies Grebe-type chondrodysplasia in a consanguineous Pakistani family

<p>Abstract</p> <p>Background</p> <p>Grebe-type chondrodysplasia (GCD) is a rare autosomal recessive syndrome characterized by severe acromesomelic limb shortness with non-functional knob like fingers resembling toes. Mutations in the cartilage-derived morphogenetic protein 1 (<it>CDMP1</it>) gene cause Grebe-type chondrodysplasia.</p> <p>Methods</p> <p>Genotyping of six members of a Pakistani family with Grebe-type chondrodysplasia, including two affected and four unaffected individuals, was carried out by using polymorphic microsatellite markers, which are closely linked to <it>CDMP1 </it>locus on chromosome 20q11.22. To screen for a mutation in <it>CDMP1 </it>gene, all of its coding exons and splice junction sites were PCR amplified from genomic DNA of affected and unaffected individuals of the family and sequenced directly in an ABI Prism 310 automated DNA sequencer.</p> <p>Results</p> <p>Genotyping results showed linkage of the family to <it>CDMP1 </it>locus. Sequence analysis of the <it>CDMP1 </it>gene identified a novel four bases insertion mutation (1114insGAGT) in exon 2 of the gene causing frameshift and premature termination of the polypeptide.</p> <p>Conclusion</p> <p>We describe a 4 bp novel insertion mutation in <it>CDMP1 </it>gene in a Pakistani family with Grebe-type chondrodysplasia. Our findings extend the body of evidence that supports the importance of <it>CDMP1 </it>in the development of limbs.</p

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

On the usage of history for energy efficient spectrum sensing

Spectrum sensing is one of the most challenging issues in cognitive radio networks. It provides protection to primary users (PUs) from interference and also creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce number of false alarms and missed detection. At the same time, spectrum sensing should be energy efficient to ensure the longevity of cognitive radio devices. This work presents a novel scheme which investigates the usage of history for energy efficient spectrum sensing in infrastructure cognitive radio networks. The presented scheme employs an iteratively developed history processing database. It is shown that usage of history helps predicting PU activity and results into reduced spectrum scanning by SUs thereby improving the sensing related energy consumption

History-assisted energy-efficient spectrum sensing for infrastructure-based cognitive radio networks

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art

Dynamic adjustment of weighting and safety factors in playout buffers for enhancing VoIP quality

The quality of Voice over Internet Protocol (VoIP) calls is highly influenced by transmission impairments such as delay, packet loss and jitter, with jitter being manifested as one of the deleterious effects affecting its quality. A jitter buffer is usually employed at the receiver side to mitigate its effects by adapting its parameters in a trade-off between delay and packet loss. This paper proposes a novel de-jitter algorithm that adaptively changes the size of the playout buffer depending on the network states, in order to effectively handle the packet loss and delay, whereas E-model is used to quantify speech quality. Based on the statistics of the received packets, the adaptive playout buffer algorithm dynamically adjusts the weighting factor (α) and the safety factor (β) for regulating the delay and trade-off loss, thus maximizing the quality for VoIP